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train · 241k rows

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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1339300525007835137/YpAMPovA_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Micky The Weirdo from Taranto 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@furrymicky bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@furrymicky's tweets](https://twitter.com/furrymicky). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>459</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>14</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>91</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>354</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/109l35nl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @furrymicky's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/q3uw2fui) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/q3uw2fui/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/furrymicky'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/furrymicky	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Micky The Weirdo from Taranto AI Bot </div> <div style="font-size: 15px; color: #657786">@furrymicky bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @furrymicky's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>459</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>14</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>91</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>354</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @furrymicky's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/furrymicky'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @furrymicky's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>459</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>14</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>91</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>354</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @furrymicky's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/furrymicky'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @furrymicky's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>459</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>14</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>91</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>354</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @furrymicky's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/furrymicky'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1256573356033273856/4iRYlwTb_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">ansq＠漫画読みすぎ 🤖 AI Bot </div> <div style="font-size: 15px">@fuurawa bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@fuurawa's tweets](https://twitter.com/fuurawa). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 1867 \| \| Retweets \| 1276 \| \| Short tweets \| 102 \| \| Tweets kept \| 489 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2q0sdp5o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @fuurawa's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/24t10y8h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/24t10y8h/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/fuurawa') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/fuurawa/1616936220610/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/fuurawa	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	ansq＠漫画読みすぎ AI Bot @fuurawa bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @fuurawa's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @fuurawa's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1387157870391832578/xWRJkuq__400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gabriel Boric Font</div> <div style="text-align: center; font-size: 14px;">@gabrielboric</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gabriel Boric Font. \| Data \| Gabriel Boric Font \| \| --- \| --- \| \| Tweets downloaded \| 3166 \| \| Retweets \| 1575 \| \| Short tweets \| 261 \| \| Tweets kept \| 1330 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/sgtq44wg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gabrielboric's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wl4b6qky) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wl4b6qky/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gabrielboric') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gabrielboric/1628117067958/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gabrielboric	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Gabriel Boric Font @gabrielboric I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Gabriel Boric Font. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gabrielboric's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1312899140615979008/ulnJKPCT_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">ZOZANZI ♤☆♤ VIRAGO 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@gadgetgreen bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gadgetgreen's tweets](https://twitter.com/gadgetgreen). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3189</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1537</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>215</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1437</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1f29q7ag/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gadgetgreen's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1df6ql9u) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1df6ql9u/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gadgetgreen'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gadgetgreen/1602201219260/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gadgetgreen	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">ZOZANZI VIRAGO AI Bot </div> <div style="font-size: 15px; color: #657786">@gadgetgreen bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @gadgetgreen's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3189</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1537</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>215</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1437</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @gadgetgreen's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gadgetgreen'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gadgetgreen's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3189</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1537</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>215</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1437</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gadgetgreen's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gadgetgreen'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gadgetgreen's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3189</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1537</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>215</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1437</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gadgetgreen's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gadgetgreen'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 61, 34, 431, 75, 9, 167, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.036957841366529465, 0.028439832851290703, -0.0015510531375184655, 0.0522519014775753, 0.09104208648204803, 0.01613340899348259, 0.11610153317451477, 0.05408855527639389, -0.03943737968802452, -0.009537769481539726, 0.22685234248638153, 0.07105769217014313, 0.019403526559472084, 0.20068788528442383, 0.009344891645014286, -0.27727609872817993, 0.023726457729935646, 0.06600657850503922, -0.08643968403339386, 0.15519586205482483, 0.06697577983140945, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1372021004969472002/J07dtn_B_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">GⒶge H Leibman 🤖 AI Bot </div> <div style="font-size: 15px">@gagehleibman bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gagehleibman's tweets](https://twitter.com/gagehleibman). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3117 \| \| Retweets \| 600 \| \| Short tweets \| 486 \| \| Tweets kept \| 2031 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vjxnqnf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gagehleibman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/67jfcjhk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/67jfcjhk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gagehleibman') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gagehleibman/1616696622775/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gagehleibman	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	GⒶge H Leibman AI Bot @gagehleibman bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gagehleibman's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gagehleibman's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1306714515094921217/cH_rXwuk_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gail Simone RED HEADED WOMAN NOT BEAR 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@gailsimone bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gailsimone's tweets](https://twitter.com/gailsimone). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3205</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1400</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>322</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1483</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1u34kgh5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gailsimone's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3krfygi5) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3krfygi5/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gailsimone'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gailsimone/1601276450894/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gailsimone	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gail Simone RED HEADED WOMAN NOT BEAR AI Bot </div> <div style="font-size: 15px; color: #657786">@gailsimone bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @gailsimone's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3205</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1400</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>322</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1483</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @gailsimone's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gailsimone'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gailsimone's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3205</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1400</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>322</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1483</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gailsimone's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gailsimone'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gailsimone's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3205</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1400</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>322</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1483</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gailsimone's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gailsimone'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 57, 34, 431, 76, 9, 168, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.04084593802690506, 0.035596683621406555, -0.0024457171093672514, 0.04662978649139404, 0.10991521924734116, 0.022836215794086456, 0.12812861800193787, 0.0424627922475338, -0.03746044635772705, -0.03597303107380867, 0.22758877277374268, 0.1009177565574646, 0.03089720755815506, 0.17962171137332916, 0.010350672528147697, -0.2703946828842163, 0.015237200073897839, 0.0647135004401207, -0.07720091193914413, 0.15752871334552765, 0.05562684312462807, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1276957687507496962/zy4w13io_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gal Shapira 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@galjudo bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@galjudo's tweets](https://twitter.com/galjudo). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3211</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>420</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>653</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2138</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1iczn33x/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @galjudo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/14zzhtt9) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/14zzhtt9/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/galjudo'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/galjudo/1602233220657/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/galjudo	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gal Shapira AI Bot </div> <div style="font-size: 15px; color: #657786">@galjudo bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @galjudo's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3211</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>420</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>653</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2138</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @galjudo's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/galjudo'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @galjudo's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3211</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>420</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>653</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2138</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @galjudo's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/galjudo'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @galjudo's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3211</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>420</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>653</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2138</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @galjudo's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/galjudo'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 57, 34, 429, 75, 9, 167, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.04084593802690506, 0.035596683621406555, -0.0024457171093672514, 0.04662978649139404, 0.10991521924734116, 0.022836215794086456, 0.12812861800193787, 0.0424627922475338, -0.03746044635772705, -0.03597303107380867, 0.22758877277374268, 0.1009177565574646, 0.03089720755815506, 0.17962171137332916, 0.010350672528147697, -0.2703946828842163, 0.015237200073897839, 0.0647135004401207, -0.07720091193914413, 0.15752871334552765, 0.05562684312462807, -0.049801189452409744, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415310065960198148/w9Yr9mLK_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">gãmbs</div> <div style="text-align: center; font-size: 14px;">@gambsvns</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from gãmbs. \| Data \| gãmbs \| \| --- \| --- \| \| Tweets downloaded \| 3246 \| \| Retweets \| 86 \| \| Short tweets \| 308 \| \| Tweets kept \| 2852 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wahjzcj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gambsvns's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1td3tcaf) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1td3tcaf/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gambsvns') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gambsvns/1626385842515/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gambsvns	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT gãmbs @gambsvns I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from gãmbs. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gambsvns's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/999953713958739968/NQspJe-0_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Repulse \| Iragon is on Kickstarter!</div> <div style="text-align: center; font-size: 14px;">@gamerepulse</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Repulse \| Iragon is on Kickstarter!. \| Data \| Repulse \| Iragon is on Kickstarter! \| \| --- \| --- \| \| Tweets downloaded \| 510 \| \| Retweets \| 166 \| \| Short tweets \| 23 \| \| Tweets kept \| 321 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dqejmdb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gamerepulse's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/czq1aton) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/czq1aton/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gamerepulse') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gamerepulse/1637857655050/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gamerepulse	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800"> AI BOT </div> <div style="text-align: center; font-size: 16px; font-weight: 800">Repulse \| Iragon is on Kickstarter!</div> <div style="text-align: center; font-size: 14px;">@gamerepulse</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on tweets from Repulse \| Iragon is on Kickstarter!. \| Data \| Repulse \| Iragon is on Kickstarter! \| \| --- \| --- \| \| Tweets downloaded \| 510 \| \| Retweets \| 166 \| \| Short tweets \| 23 \| \| Tweets kept \| 321 \| Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @gamerepulse's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ## Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on tweets from Repulse \| Iragon is on Kickstarter!.\n\n\| Data \| Repulse \| Iragon is on Kickstarter! \|\n\| --- \| --- \|\n\| Tweets downloaded \| 510 \|\n\| Retweets \| 166 \|\n\| Short tweets \| 23 \|\n\| Tweets kept \| 321 \|\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gamerepulse's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## How to use\n\nYou can use this model directly with a pipeline for text generation:", "## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n![Follow](URL\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on tweets from Repulse \| Iragon is on Kickstarter!.\n\n\| Data \| Repulse \| Iragon is on Kickstarter! \|\n\| --- \| --- \|\n\| Tweets downloaded \| 510 \|\n\| Retweets \| 166 \|\n\| Short tweets \| 23 \|\n\| Tweets kept \| 321 \|\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gamerepulse's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## How to use\n\nYou can use this model directly with a pipeline for text generation:", "## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n![Follow](URL\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]	[ 54, 34, 116, 76, 18, 47, 38 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.## Training data\n\nThe model was trained on tweets from Repulse \| Iragon is on Kickstarter!.\n\n\| Data \| Repulse \| Iragon is on Kickstarter! \|\n\| --- \| --- \|\n\| Tweets downloaded \| 510 \|\n\| Retweets \| 166 \|\n\| Short tweets \| 23 \|\n\| Tweets kept \| 321 \|\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gamerepulse's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.## How to use\n\nYou can use this model directly with a pipeline for text generation:## Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.## About\n\nBuilt by Boris Dayma\n\n![Follow](URL\n\nFor more details, visit the project repository.\n\n![GitHub stars](URL" ]	[ -0.024981124326586723, 0.09835535287857056, -0.00352578260935843, 0.08300304412841797, 0.1193927749991417, 0.026497071608901024, 0.09022251516580582, 0.13181909918785095, -0.022966725751757622, 0.06854181736707687, 0.09090635925531387, 0.001598890288732946, 0.06924008578062057, 0.2247786819934845, 0.13398964703083038, -0.2718711495399475, 0.011417730711400509, -0.09043730795383453, -0.015923751518130302, 0.14265885949134827, 0.09713321179151535, -0.05590057373046875, 0.07162395119667053, 0.010454749688506126, -0.07862824946641922, 0.02149784006178379, 0.015196614898741245, -0.05011197552084923, 0.0790250226855278, 0.03685562312602997, 0.05274316668510437, -0.0094533646479249, 0.07908118516206741, -0.1773459017276764, 0.036506664007902145, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410165726506274819/4HVcR7Es_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gandalf the White (Thulêan Perspective)</div> <div style="text-align: center; font-size: 14px;">@gandalfthewhi19</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gandalf the White (Thulêan Perspective). \| Data \| Gandalf the White (Thulêan Perspective) \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 431 \| \| Short tweets \| 225 \| \| Tweets kept \| 2588 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1r47j719/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gandalfthewhi19's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/u6nhe6ef) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/u6nhe6ef/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gandalfthewhi19') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gandalfthewhi19/1645099160912/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gandalfthewhi19	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Gandalf the White (Thulêan Perspective) @gandalfthewhi19 I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Gandalf the White (Thulêan Perspective). Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gandalfthewhi19's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1326680694370734082/wjLz-oO4_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gary Short</div> <div style="text-align: center; font-size: 14px;">@garyshort</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gary Short. \| Data \| Gary Short \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 94 \| \| Short tweets \| 321 \| \| Tweets kept \| 2833 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2vtmlhlj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @garyshort's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pfbf1ys) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pfbf1ys/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/garyshort') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/garyshort/1647971079915/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/garyshort	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Gary Short @garyshort I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Gary Short. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @garyshort's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000365450982/53f25bdef0dd40bf20b58df314a94770_400x400.jpeg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gaston Gordillo 🤖 AI Bot </div> <div style="font-size: 15px">@gaston_gordillo bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gaston_gordillo's tweets](https://twitter.com/gaston_gordillo). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 705 \| \| Retweets \| 524 \| \| Short tweets \| 5 \| \| Tweets kept \| 176 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kme4rls/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gaston_gordillo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6zu3yfw0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6zu3yfw0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gaston_gordillo') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gaston_gordillo/1617249460228/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gaston_gordillo	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gaston Gordillo AI Bot @gaston\_gordillo bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gaston\_gordillo's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gaston\_gordillo's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1234322984183226369/3KzZ3P1J_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">gatcha 🤖 AI Bot </div> <div style="font-size: 15px">@gatchabot bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gatchabot's tweets](https://twitter.com/gatchabot). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 2200 \| \| Retweets \| 1728 \| \| Short tweets \| 121 \| \| Tweets kept \| 351 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3qhi9616/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gatchabot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1o3eonr9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1o3eonr9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gatchabot') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gatchabot	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	gatcha AI Bot @gatchabot bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gatchabot's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gatchabot's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1610957642616406016/YPHe6yn-_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ᕙ(‾̀◡‾́)ᕗ g</div> <div style="text-align: center; font-size: 14px;">@gaucheian</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ᕙ(‾̀◡‾́)ᕗ g. \| Data \| ᕙ(‾̀◡‾́)ᕗ g \| \| --- \| --- \| \| Tweets downloaded \| 2213 \| \| Retweets \| 92 \| \| Short tweets \| 279 \| \| Tweets kept \| 1842 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/x1bx2fez/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gaucheian's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/0i3i22al) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/0i3i22al/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gaucheian') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gaucheian	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT ᕙ(‾̀◡‾́)ᕗ g @gaucheian I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from ᕙ(‾̀◡‾́)ᕗ g. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gaucheian's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362440200304041986/nLi9iMVI_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gavi Begtrup</div> <div style="text-align: center; font-size: 14px;">@gavibegtrup</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gavi Begtrup. \| Data \| Gavi Begtrup \| \| --- \| --- \| \| Tweets downloaded \| 990 \| \| Retweets \| 67 \| \| Short tweets \| 49 \| \| Tweets kept \| 874 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1kx48u2r/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gavibegtrup's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1n9nuiku) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1n9nuiku/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gavibegtrup') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gavibegtrup/1622127344791/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gavibegtrup	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Gavi Begtrup @gavibegtrup I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Gavi Begtrup. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gavibegtrup's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1347975597134385152/zABvUQAs_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">sam 🤖 AI Bot </div> <div style="font-size: 15px">@gayandonline bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gayandonline's tweets](https://twitter.com/gayandonline). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3002 \| \| Retweets \| 290 \| \| Short tweets \| 293 \| \| Tweets kept \| 2419 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3963etnb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gayandonline's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/146uc4xj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/146uc4xj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gayandonline') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gayandonline/1617808083660/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gayandonline	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	sam AI Bot @gayandonline bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gayandonline's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gayandonline's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1361159012990013445/rVk0X1DL_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">alphonse⛓️🦇🌹 🤖 AI Bot </div> <div style="font-size: 15px">@gaybats1999 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gaybats1999's tweets](https://twitter.com/gaybats1999). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 2783 \| \| Retweets \| 999 \| \| Short tweets \| 225 \| \| Tweets kept \| 1559 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39y8clnw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gaybats1999's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2mzsqlq3) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mzsqlq3/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gaybats1999') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gaybats1999/1614135497450/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gaybats1999	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	alphonse️ AI Bot @gaybats1999 bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gaybats1999's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gaybats1999's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1354923690631323652/MZgzGX3P_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">lisa 🏳️‍⚧️ 🤖 AI Bot </div> <div style="font-size: 15px">@gaydeerinc bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gaydeerinc's tweets](https://twitter.com/gaydeerinc). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3214 \| \| Retweets \| 1108 \| \| Short tweets \| 310 \| \| Tweets kept \| 1796 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nsi7oic/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gaydeerinc's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gqx2ecq) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gqx2ecq/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gaydeerinc') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gaydeerinc/1614165768951/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gaydeerinc	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	lisa ️‍️ AI Bot @gaydeerinc bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gaydeerinc's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gaydeerinc's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1329364120311828487/0VjzWPsR_400x400.png')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">rokos basilisk construction advocate 🤖 AI Bot </div> <div style="font-size: 15px">@gayguynewsnet bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gayguynewsnet's tweets](https://twitter.com/gayguynewsnet). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 592 \| \| Retweets \| 146 \| \| Short tweets \| 64 \| \| Tweets kept \| 382 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2yoxivok/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gayguynewsnet's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dalf0je) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dalf0je/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gayguynewsnet') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gayguynewsnet/1618199553249/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gayguynewsnet	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	rokos basilisk construction advocate AI Bot @gayguynewsnet bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gayguynewsnet's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gayguynewsnet's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1350934729525178370/VHPqhIcr_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">value bastard 🤖 AI Bot </div> <div style="font-size: 15px">@gaypizzaboy bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gaypizzaboy's tweets](https://twitter.com/gaypizzaboy). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3138 \| \| Retweets \| 1548 \| \| Short tweets \| 147 \| \| Tweets kept \| 1443 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vxwbfva/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gaypizzaboy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t6winba) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t6winba/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gaypizzaboy') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gaypizzaboy/1614169105934/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gaypizzaboy	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	value bastard AI Bot @gaypizzaboy bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gaypizzaboy's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gaypizzaboy's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1428482513417105413/TGlo7HWH_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">العلجوم</div> <div style="text-align: center; font-size: 14px;">@gaytoad2</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from العلجوم. \| Data \| العلجوم \| \| --- \| --- \| \| Tweets downloaded \| 3232 \| \| Retweets \| 379 \| \| Short tweets \| 1023 \| \| Tweets kept \| 1830 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2w8lap6f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gaytoad2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34u34diu) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34u34diu/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gaytoad2') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gaytoad2/1629434767014/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gaytoad2	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT العلجوم @gaytoad2 I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from العلجوم. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gaytoad2's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1349844043291889671/yfQAojJv_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gender Critical Argument Bot 🤖 AI Bot </div> <div style="font-size: 15px">@gcargumentbot bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gcargumentbot's tweets](https://twitter.com/gcargumentbot). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 1 \| \| Short tweets \| 223 \| \| Tweets kept \| 3026 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18f76f7w/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gcargumentbot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2lzgykty) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2lzgykty/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gcargumentbot') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gcargumentbot/1616766934700/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gcargumentbot	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gender Critical Argument Bot AI Bot @gcargumentbot bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gcargumentbot's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gcargumentbot's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1377855294655623169/hlahDP3v_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">craz 🗿 🤖 AI Bot </div> <div style="font-size: 15px">@geckogirl0 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@geckogirl0's tweets](https://twitter.com/geckogirl0). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3132 \| \| Retweets \| 1313 \| \| Short tweets \| 225 \| \| Tweets kept \| 1594 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2qub6zq7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @geckogirl0's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1wc0a99s) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1wc0a99s/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/geckogirl0') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/geckogirl0/1617784269558/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/geckogirl0	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	craz AI Bot @geckogirl0 bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @geckogirl0's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @geckogirl0's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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-0.19213621318340302, 0.03111329674720764, -0.12165344506502151, 0.045619383454322815, -0.1751558482646942, 0.11076030135154724, 0.08046020567417145, 0.01831907220184803, 0.011516088619828224, -0.02576824277639389, 0.08821021765470505, 0.11728470027446747, -0.07783648371696472, -0.08528783172369003 ]
null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1309828363385622529/7xxDa_4j_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">100 gecs hater 🤖 AI Bot </div> <div style="font-size: 15px">@gecshater bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gecshater's tweets](https://twitter.com/gecshater). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3238 \| \| Retweets \| 67 \| \| Short tweets \| 550 \| \| Tweets kept \| 2621 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zp0k65t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gecshater's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/13yufu4u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/13yufu4u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gecshater') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gecshater/1617797159320/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gecshater	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	100 gecs hater AI Bot @gecshater bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gecshater's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gecshater's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/517689241812627456/pyBGyEo__400x400.jpeg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lutz Büch 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@geilehirnbude bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@geilehirnbude's tweets](https://twitter.com/geilehirnbude). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3116</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>2906</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>53</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>157</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/wt8lffrr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @geilehirnbude's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1u8augcw) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1u8augcw/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/geilehirnbude'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/geilehirnbude	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lutz Büch AI Bot </div> <div style="font-size: 15px; color: #657786">@geilehirnbude bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @geilehirnbude's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3116</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>2906</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>53</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>157</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @geilehirnbude's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/geilehirnbude'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @geilehirnbude's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3116</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>2906</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>53</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>157</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @geilehirnbude's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/geilehirnbude'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @geilehirnbude's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3116</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>2906</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>53</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>157</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @geilehirnbude's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/geilehirnbude'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1403413998436032514/QdAbQHYm_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">GEEGA ギガ 🔝</div> <div style="text-align: center; font-size: 14px;">@generalgeega</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from GEEGA ギガ 🔝. \| Data \| GEEGA ギガ 🔝 \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 127 \| \| Short tweets \| 1477 \| \| Tweets kept \| 1646 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2owkgdxf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @generalgeega's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21lavo70) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21lavo70/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/generalgeega') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/generalgeega/1624741487901/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/generalgeega	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT GEEGA ギガ @generalgeega I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from GEEGA ギガ . Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @generalgeega's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1366107880483557377/V58xvEUv_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Genji 🤖 AI Bot </div> <div style="font-size: 15px">@genjitoday bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@genjitoday's tweets](https://twitter.com/genjitoday). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 515 \| \| Retweets \| 30 \| \| Short tweets \| 72 \| \| Tweets kept \| 413 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2t88j5a6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @genjitoday's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uhl7b30) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uhl7b30/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/genjitoday') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/genjitoday/1617772086820/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/genjitoday	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Genji AI Bot @genjitoday bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @genjitoday's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @genjitoday's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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-0.19213621318340302, 0.03111329674720764, -0.12165344506502151, 0.045619383454322815, -0.1751558482646942, 0.11076030135154724, 0.08046020567417145, 0.01831907220184803, 0.011516088619828224, -0.02576824277639389, 0.08821021765470505, 0.11728470027446747, -0.07783648371696472, -0.08528783172369003 ]
null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1357875471199969286/yqSYSz1G_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Fishorse • Big Baldsuya 🤖 AI Bot </div> <div style="font-size: 15px">@gentlefishorse bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gentlefishorse's tweets](https://twitter.com/gentlefishorse). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3142 \| \| Retweets \| 1903 \| \| Short tweets \| 159 \| \| Tweets kept \| 1080 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2h9g07c3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gentlefishorse's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ao0ru7g8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ao0ru7g8/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gentlefishorse') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gentlefishorse/1614214431723/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gentlefishorse	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Fishorse • Big Baldsuya AI Bot @gentlefishorse bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gentlefishorse's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gentlefishorse's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/867463880582340608/b2CozYM-_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Suresh Venkatasubramanian 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@geomblog bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@geomblog's tweets](https://twitter.com/geomblog). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3199</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1301</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>178</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1720</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/5jk973vf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @geomblog's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3edsvd65) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3edsvd65/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/geomblog'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/geomblog/1600332316026/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/geomblog	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Suresh Venkatasubramanian AI Bot </div> <div style="font-size: 15px; color: #657786">@geomblog bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @geomblog's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3199</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1301</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>178</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1720</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @geomblog's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/geomblog'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @geomblog's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3199</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1301</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>178</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1720</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @geomblog's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/geomblog'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @geomblog's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3199</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1301</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>178</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1720</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @geomblog's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/geomblog'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1173474608856608768/vEBnPUdm_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">George</div> <div style="text-align: center; font-size: 14px;">@georgenotfound</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from George. \| Data \| George \| \| --- \| --- \| \| Tweets downloaded \| 848 \| \| Retweets \| 6 \| \| Short tweets \| 310 \| \| Tweets kept \| 532 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2doc1coj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @georgenotfound's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/155sbgzb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/155sbgzb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/georgenotfound') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/georgenotfound/1622013920235/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/georgenotfound	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT George @georgenotfound I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from George. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @georgenotfound's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362683032017244162/vjtrYSK1_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gerard Joling</div> <div style="text-align: center; font-size: 14px;">@gerardjoling</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gerard Joling. \| Data \| Gerard Joling \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 102 \| \| Short tweets \| 33 \| \| Tweets kept \| 3115 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nnhwkwwc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gerardjoling's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hq3zjug) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hq3zjug/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gerardjoling') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gerardjoling/1628602714633/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gerardjoling	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Gerard Joling @gerardjoling I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Gerard Joling. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gerardjoling's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1431241007421665284/qoHnns8I_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ᐸGerardSans/ᐳ🤣🇬🇧</div> <div style="text-align: center; font-size: 14px;">@gerardsans</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ᐸGerardSans/ᐳ🤣🇬🇧. \| Data \| ᐸGerardSans/ᐳ🤣🇬🇧 \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 648 \| \| Short tweets \| 586 \| \| Tweets kept \| 2016 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/115pr1rh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gerardsans's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10heg4by) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10heg4by/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gerardsans') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gerardsans/1634670781074/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gerardsans	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT ᐸGerardSans/ᐳ🇬🇧 @gerardsans I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from ᐸGerardSans/ᐳ🇬🇧. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gerardsans's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/973531638301888512/3_-O_PPD_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">stockhausen by proxy 🤖 AI Bot </div> <div style="font-size: 15px">@gesualdofan666 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gesualdofan666's tweets](https://twitter.com/gesualdofan666). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3179 \| \| Retweets \| 242 \| \| Short tweets \| 715 \| \| Tweets kept \| 2222 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10hehnyy/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gesualdofan666's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g22xwzgd) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g22xwzgd/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gesualdofan666') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gesualdofan666/1614135333322/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gesualdofan666	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	stockhausen by proxy AI Bot @gesualdofan666 bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gesualdofan666's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gesualdofan666's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1164709780885106690/5nqTrvC0_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Don Hughes 🦌 🤖 AI Bot </div> <div style="font-size: 15px">@getfiscal bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@getfiscal's tweets](https://twitter.com/getfiscal). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3221 \| \| Retweets \| 1002 \| \| Short tweets \| 409 \| \| Tweets kept \| 1810 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/d6p1oytn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @getfiscal's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28d4ali8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28d4ali8/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/getfiscal') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/getfiscal/1616662151704/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/getfiscal	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Don Hughes AI Bot @getfiscal bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @getfiscal's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @getfiscal's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1092582027994509312/cpYWuYI9_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Glenn Greenwald</div> <div style="text-align: center; font-size: 14px;">@ggreenwald</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Glenn Greenwald. \| Data \| Glenn Greenwald \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 324 \| \| Short tweets \| 160 \| \| Tweets kept \| 2764 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y433olp5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ggreenwald's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/duljho5y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/duljho5y/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ggreenwald') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/ggreenwald/1643622558420/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/ggreenwald	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Glenn Greenwald @ggreenwald I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Glenn Greenwald. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @ggreenwald's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1378610021978664961/wV1Z8BFh_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Nia Hoshi✝⚸ Starlit flower child💫 🤖 AI Bot </div> <div style="font-size: 15px">@ghoooostie bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@ghoooostie's tweets](https://twitter.com/ghoooostie). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 1014 \| \| Retweets \| 81 \| \| Short tweets \| 294 \| \| Tweets kept \| 639 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29pxu2zi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ghoooostie's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/e3clb6b5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/e3clb6b5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ghoooostie') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/ghoooostie/1617871544860/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/ghoooostie	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Nia Hoshi Starlit flower child AI Bot @ghoooostie bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @ghoooostie's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @ghoooostie's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1399131544665706498/1RGp0i9G_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">jum</div> <div style="text-align: center; font-size: 14px;">@ghostmountainn</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from jum. \| Data \| jum \| \| --- \| --- \| \| Tweets downloaded \| 3240 \| \| Retweets \| 839 \| \| Short tweets \| 609 \| \| Tweets kept \| 1792 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8lx8a815/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ghostmountainn's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gafkpo6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gafkpo6/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ghostmountainn') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/ghostmountainn/1623477690371/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/ghostmountainn	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT jum @ghostmountainn I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from jum. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @ghostmountainn's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1227031157088411648/E3Tx20nH_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gilational 🤖 AI Bot </div> <div style="font-size: 15px">@gilational bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gilational's tweets](https://twitter.com/gilational). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 32 \| \| Retweets \| 0 \| \| Short tweets \| 1 \| \| Tweets kept \| 31 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3b638003/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gilational's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37tpk9wh) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37tpk9wh/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gilational') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gilational/1616731790752/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gilational	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gilational AI Bot @gilational bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gilational's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gilational's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1241598015405621251/RfMwNmc__400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lake Yin 🤖 AI Bot </div> <div style="font-size: 15px">@gimoyin bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gimoyin's tweets](https://twitter.com/gimoyin). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 961 \| \| Retweets \| 636 \| \| Short tweets \| 31 \| \| Tweets kept \| 294 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1leyvbxk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gimoyin's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/y78zi5ra) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/y78zi5ra/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gimoyin') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gimoyin/1614111899984/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gimoyin	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Lake Yin AI Bot @gimoyin bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gimoyin's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gimoyin's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1340620089578594304/xWPtVT2j_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gingerbreadfork 🤖 AI Bot </div> <div style="font-size: 15px">@gingerbreadfork bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gingerbreadfork's tweets](https://twitter.com/gingerbreadfork). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 2680 \| \| Retweets \| 607 \| \| Short tweets \| 441 \| \| Tweets kept \| 1632 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bw0i5b8t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gingerbreadfork's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1eqf0r9u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1eqf0r9u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gingerbreadfork') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gingerbreadfork/1618181065321/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gingerbreadfork	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gingerbreadfork AI Bot @gingerbreadfork bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gingerbreadfork's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gingerbreadfork's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1307921775364378624/yMwFpRpo_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">sátántangó nightcore 🤖 AI Bot </div> <div style="font-size: 15px">@girlchrismarker bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@girlchrismarker's tweets](https://twitter.com/girlchrismarker). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 369 \| \| Retweets \| 67 \| \| Short tweets \| 79 \| \| Tweets kept \| 223 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ex2qo7c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @girlchrismarker's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/e1iq56ka) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/e1iq56ka/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/girlchrismarker') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/girlchrismarker/1614168569443/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/girlchrismarker	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	sátántangó nightcore AI Bot @girlchrismarker bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @girlchrismarker's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @girlchrismarker's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1373592959380242432/Vw_88RqG_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">a scared animal bites 🧷 vtuber 🤖 AI Bot </div> <div style="font-size: 15px">@girlmeat5557 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@girlmeat5557's tweets](https://twitter.com/girlmeat5557). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3242 \| \| Retweets \| 871 \| \| Short tweets \| 489 \| \| Tweets kept \| 1882 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wthiey09/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @girlmeat5557's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/io5hvymh) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/io5hvymh/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/girlmeat5557') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/girlmeat5557/1617790352329/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/girlmeat5557	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	a scared animal bites vtuber AI Bot @girlmeat5557 bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @girlmeat5557's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @girlmeat5557's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1251080403256926208/6-nJSYgZ_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Anomalous Girl 🤖 AI Bot </div> <div style="font-size: 15px">@girlshaped bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@girlshaped's tweets](https://twitter.com/girlshaped). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 304 \| \| Retweets \| 115 \| \| Short tweets \| 19 \| \| Tweets kept \| 170 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/35c6178z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @girlshaped's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2re3ffqt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2re3ffqt/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/girlshaped') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/girlshaped/1617757456002/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/girlshaped	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Anomalous Girl AI Bot @girlshaped bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @girlshaped's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @girlshaped's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/693514895837548545/6XcdRZO1_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1149580808161599488/SdEQ8RS-_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1302973092332023810/K9MureTy_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Lukas Valatka & Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺</div> <div style="text-align: center; font-size: 14px;">@gitanasnauseda-lukasvalatka-maldeikiene</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Lukas Valatka & Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺. \| Data \| Lukas Valatka \| Gitanas Nausėda \| Aušra Maldeikienė MEP 🇱🇹🇪🇺 \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 1155 \| 706 \| 348 \| \| Retweets \| 42 \| 44 \| 67 \| \| Short tweets \| 49 \| 0 \| 6 \| \| Tweets kept \| 1064 \| 662 \| 275 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31ci0ia0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gitanasnauseda-lukasvalatka-maldeikiene's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/62ihbz05) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/62ihbz05/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gitanasnauseda-lukasvalatka-maldeikiene') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gitanasnauseda-lukasvalatka-maldeikiene/1620508369581/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gitanasnauseda-lukasvalatka-maldeikiene	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI CYBORG Lukas Valatka & Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺 @gitanasnauseda-lukasvalatka-maldeikiene I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Lukas Valatka & Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gitanasnauseda-lukasvalatka-maldeikiene's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1149580808161599488/SdEQ8RS-_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1302973092332023810/K9MureTy_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺</div> <div style="text-align: center; font-size: 14px;">@gitanasnauseda-maldeikiene</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺. \| Data \| Gitanas Nausėda \| Aušra Maldeikienė MEP 🇱🇹🇪🇺 \| \| --- \| --- \| --- \| \| Tweets downloaded \| 706 \| 348 \| \| Retweets \| 44 \| 67 \| \| Short tweets \| 0 \| 6 \| \| Tweets kept \| 662 \| 275 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32c03vyj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gitanasnauseda-maldeikiene's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1o9iq34s) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1o9iq34s/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gitanasnauseda-maldeikiene') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gitanasnauseda-maldeikiene/1620507874092/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gitanasnauseda-maldeikiene	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI CYBORG Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺 @gitanasnauseda-maldeikiene I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Gitanas Nausėda & Aušra Maldeikienė MEP 🇱🇹🇪🇺. Data: Tweets downloaded, Gitanas Nausėda: 706, Aušra Maldeikienė MEP 🇱🇹🇪🇺: 348 Data: Retweets, Gitanas Nausėda: 44, Aušra Maldeikienė MEP 🇱🇹🇪🇺: 67 Data: Short tweets, Gitanas Nausėda: 0, Aušra Maldeikienė MEP 🇱🇹🇪🇺: 6 Data: Tweets kept, Gitanas Nausėda: 662, Aušra Maldeikienė MEP 🇱🇹🇪🇺: 275 Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gitanasnauseda-maldeikiene's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1338234899430600708/CGlmDFfZ_400x400.png')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Glacius 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@glacius_gaming bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@glacius_gaming's tweets](https://twitter.com/glacius_gaming). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3197</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>352</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>851</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1994</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3udvez1f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glacius_gaming's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1w01n2c4) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1w01n2c4/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/glacius_gaming'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/glacius_gaming/1609012743315/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glacius_gaming	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Glacius AI Bot </div> <div style="font-size: 15px; color: #657786">@glacius_gaming bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @glacius_gaming's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3197</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>352</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>851</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1994</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @glacius_gaming's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/glacius_gaming'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @glacius_gaming's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3197</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>352</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>851</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1994</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @glacius_gaming's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/glacius_gaming'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @glacius_gaming's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3197</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>352</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>851</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1994</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @glacius_gaming's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/glacius_gaming'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 61, 34, 432, 77, 9, 169, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.036957841366529465, 0.028439832851290703, -0.0015510531375184655, 0.0522519014775753, 0.09104208648204803, 0.01613340899348259, 0.11610153317451477, 0.05408855527639389, -0.03943737968802452, -0.009537769481539726, 0.22685234248638153, 0.07105769217014313, 0.019403526559472084, 0.20068788528442383, 0.009344891645014286, -0.27727609872817993, 0.023726457729935646, 0.06600657850503922, -0.08643968403339386, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1355757309063008257/LSlS9j1B_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">elizabeth holmes’s fetus 🤖 AI Bot </div> <div style="font-size: 15px">@glamdemon2004 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@glamdemon2004's tweets](https://twitter.com/glamdemon2004). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3097 \| \| Retweets \| 550 \| \| Short tweets \| 345 \| \| Tweets kept \| 2202 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2v9xfsja/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glamdemon2004's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nyv7aua) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nyv7aua/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glamdemon2004') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/glamdemon2004/1616682008766/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glamdemon2004	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	elizabeth holmes’s fetus AI Bot @glamdemon2004 bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @glamdemon2004's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @glamdemon2004's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1319042424560361474/4EzvOdbO_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">tender corpse affection 🤖 AI Bot </div> <div style="font-size: 15px">@glasseskin bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@glasseskin's tweets](https://twitter.com/glasseskin). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3213 \| \| Retweets \| 724 \| \| Short tweets \| 354 \| \| Tweets kept \| 2135 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2e8tgnhf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glasseskin's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/198cfuf1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/198cfuf1/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glasseskin') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/glasseskin/1617916620472/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glasseskin	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	tender corpse affection AI Bot @glasseskin bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @glasseskin's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @glasseskin's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1346111858961494016/PR1Ir8lo_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Glennys Egan (she/her) 🤖 AI Bot </div> <div style="font-size: 15px">@gleegz bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gleegz's tweets](https://twitter.com/gleegz). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3215 \| \| Retweets \| 272 \| \| Short tweets \| 386 \| \| Tweets kept \| 2557 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mtxfs6h/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gleegz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1d2xgejt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1d2xgejt/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gleegz') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gleegz/1616717872074/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gleegz	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Glennys Egan (she/her) AI Bot @gleegz bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gleegz's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gleegz's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1375897213671403523/OLi1JNeQ_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Inkling ꩜f Jꙮy 🔅🔎🔥 🤖 AI Bot </div> <div style="font-size: 15px">@glitchesroux bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@glitchesroux's tweets](https://twitter.com/glitchesroux). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3178 \| \| Retweets \| 2579 \| \| Short tweets \| 105 \| \| Tweets kept \| 494 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1h103fds/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glitchesroux's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7rgoifll) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7rgoifll/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glitchesroux') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/glitchesroux/1616902247472/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glitchesroux	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Inkling ꩜f Jꙮy AI Bot @glitchesroux bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @glitchesroux's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @glitchesroux's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1484899984126451716/oY7g67aC_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">💙💗🤍 Mama Ava's House of Fun 💙💗🤍</div> <div style="text-align: center; font-size: 14px;">@glitchy22</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 💙💗🤍 Mama Ava's House of Fun 💙💗🤍. \| Data \| 💙💗🤍 Mama Ava's House of Fun 💙💗🤍 \| \| --- \| --- \| \| Tweets downloaded \| 1690 \| \| Retweets \| 198 \| \| Short tweets \| 387 \| \| Tweets kept \| 1105 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2h5yvnyr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glitchy22's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t3bkiiv) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t3bkiiv/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glitchy22') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/glitchy22/1643317484748/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glitchy22	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Mama Ava's House of Fun @glitchy22 I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Mama Ava's House of Fun . Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @glitchy22's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1375299004414763009/2fzD8QOB_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gabriel 🏳️‍🌈💦😈🌎🔥🥺 🤖 AI Bot </div> <div style="font-size: 15px">@glockmetal bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@glockmetal's tweets](https://twitter.com/glockmetal). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3206 \| \| Retweets \| 290 \| \| Short tweets \| 921 \| \| Tweets kept \| 1995 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dx8iokq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glockmetal's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3s7p5y1r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3s7p5y1r/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glockmetal') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/glockmetal/1617166556495/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glockmetal	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gabriel ️‍ AI Bot @glockmetal bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @glockmetal's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @glockmetal's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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-0.19213621318340302, 0.03111329674720764, -0.12165344506502151, 0.045619383454322815, -0.1751558482646942, 0.11076030135154724, 0.08046020567417145, 0.01831907220184803, 0.011516088619828224, -0.02576824277639389, 0.08821021765470505, 0.11728470027446747, -0.07783648371696472, -0.08528783172369003 ]
null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1382482085307305984/PILbFOb-_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">GlowDonk 🤖 AI Bot </div> <div style="font-size: 15px">@glowdonk bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@glowdonk's tweets](https://twitter.com/glowdonk). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3228 \| \| Retweets \| 190 \| \| Short tweets \| 761 \| \| Tweets kept \| 2277 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/sajyw4x6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glowdonk's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27srcmsx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27srcmsx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glowdonk') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/glowdonk/1620242160895/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glowdonk	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	GlowDonk AI Bot @glowdonk bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @glowdonk's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @glowdonk's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1292227674539208704/uNcnG4c3_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">gl0w</div> <div style="text-align: center; font-size: 14px;">@glownigga</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from gl0w. \| Data \| gl0w \| \| --- \| --- \| \| Tweets downloaded \| 3132 \| \| Retweets \| 157 \| \| Short tweets \| 776 \| \| Tweets kept \| 2199 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3t0rqzrr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @glownigga's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qjksoiw) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qjksoiw/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/glownigga') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/glownigga/1626905715267/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/glownigga	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT gl0w @glownigga I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from gl0w. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @glownigga's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	null	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1145832571214815232/KYNcOP04_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1314997569475547137/4x1-5ejx_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/858198338444836864/OFlImt8f_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Go Ando / PREDUCTS / THE GUILD & Ken McAlinn & V</div> <div style="text-align: center; font-size: 14px;">@goando-kenmcalinn-voluntas</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Go Ando / PREDUCTS / THE GUILD & Ken McAlinn & V. \| Data \| Go Ando / PREDUCTS / THE GUILD \| Ken McAlinn \| V \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3247 \| 3250 \| 3246 \| \| Retweets \| 91 \| 22 \| 1040 \| \| Short tweets \| 1680 \| 2144 \| 698 \| \| Tweets kept \| 1476 \| 1084 \| 1508 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kzei9u5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goando-kenmcalinn-voluntas's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2mdna8jc) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mdna8jc/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goando-kenmcalinn-voluntas') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/goando-kenmcalinn-voluntas/1643509465268/predictions.png", "widget": [{"text": "My dream is"}]}	null	huggingtweets/goando-kenmcalinn-voluntas	[ "huggingtweets", "en", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #huggingtweets #en #region-us	AI CYBORG Go Ando / PREDUCTS / THE GUILD & Ken McAlinn & V @goando-kenmcalinn-voluntas I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Go Ando / PREDUCTS / THE GUILD & Ken McAlinn & V. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goando-kenmcalinn-voluntas's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#huggingtweets #en #region-us \n" ]	[ 13 ]	[ "passage: TAGS\n#huggingtweets #en #region-us \n" ]	[ 0.06862699240446091, -0.10726692527532578, -0.010401245206594467, -0.06321337819099426, 0.08621513843536377, 0.10232581198215485, 0.027164114639163017, 0.0985046774148941, 0.13242372870445251, 0.0191008560359478, 0.14544545114040375, -0.02377847209572792, -0.059302784502506256, 0.05649275705218315, 0.013633874244987965, -0.17248544096946716, 0.03670460358262062, -0.015605193562805653, 0.026972385123372078, 0.09363088756799698, 0.021145451813936234, 0.020441683009266853, 0.0902106985449791, -0.08021455258131027, -0.1398237943649292, 0.10563203692436218, 0.07052353769540787, 0.034161631017923355, 0.12517565488815308, -0.03278198093175888, 0.135564923286438, -0.03551725298166275, -0.11858534812927246, -0.20654892921447754, 0.05404523015022278, 0.03882395476102829, 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null	null	null	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/715665333218979842/fLLzpFee_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1145832571214815232/KYNcOP04_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1281544202627674112/zglo72WL_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch</div> <div style="text-align: center; font-size: 14px;">@goando-tsuchinao83-za09313103</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch. \| Data \| 土屋尚史 / Goodpatch \| Go Ando / PREDUCTS / THE GUILD \| shun nozaki / Goodpatch \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3236 \| 3250 \| 798 \| \| Retweets \| 1577 \| 97 \| 34 \| \| Short tweets \| 914 \| 1729 \| 458 \| \| Tweets kept \| 745 \| 1424 \| 306 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31bsh75f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goando-tsuchinao83-za09313103's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/26i8c30r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/26i8c30r/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goando-tsuchinao83-za09313103') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/goando-tsuchinao83-za09313103/1643622988627/predictions.png", "widget": [{"text": "My dream is"}]}	null	huggingtweets/goando-tsuchinao83-za09313103	[ "huggingtweets", "en", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #huggingtweets #en #region-us	AI CYBORG 土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch @goando-tsuchinao83-za09313103 I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from 土屋尚史 / Goodpatch & Go Ando / PREDUCTS / THE GUILD & shun nozaki / Goodpatch. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goando-tsuchinao83-za09313103's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#huggingtweets #en #region-us \n" ]	[ 13 ]	[ "passage: TAGS\n#huggingtweets #en #region-us \n" ]	[ 0.06862699240446091, -0.10726692527532578, -0.010401245206594467, -0.06321337819099426, 0.08621513843536377, 0.10232581198215485, 0.027164114639163017, 0.0985046774148941, 0.13242372870445251, 0.0191008560359478, 0.14544545114040375, -0.02377847209572792, -0.059302784502506256, 0.05649275705218315, 0.013633874244987965, -0.17248544096946716, 0.03670460358262062, -0.015605193562805653, 0.026972385123372078, 0.09363088756799698, 0.021145451813936234, 0.020441683009266853, 0.0902106985449791, -0.08021455258131027, -0.1398237943649292, 0.10563203692436218, 0.07052353769540787, 0.034161631017923355, 0.12517565488815308, -0.03278198093175888, 0.135564923286438, -0.03551725298166275, -0.11858534812927246, -0.20654892921447754, 0.05404523015022278, 0.03882395476102829, 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null	null	null	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1145832571214815232/KYNcOP04_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Go Ando / PREDUCTS / THE GUILD</div> <div style="text-align: center; font-size: 14px;">@goando</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Go Ando / PREDUCTS / THE GUILD. \| Data \| Go Ando / PREDUCTS / THE GUILD \| \| --- \| --- \| \| Tweets downloaded \| 3247 \| \| Retweets \| 91 \| \| Short tweets \| 1680 \| \| Tweets kept \| 1476 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37h8wmzh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goando's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qeev4eu) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qeev4eu/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goando') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/goando/1643510064373/predictions.png", "widget": [{"text": "My dream is"}]}	null	huggingtweets/goando	[ "huggingtweets", "en", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #huggingtweets #en #region-us	AI BOT Go Ando / PREDUCTS / THE GUILD @goando I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Go Ando / PREDUCTS / THE GUILD. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goando's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#huggingtweets #en #region-us \n" ]	[ 13 ]	[ "passage: TAGS\n#huggingtweets #en #region-us \n" ]	[ 0.06862699240446091, -0.10726692527532578, -0.010401245206594467, -0.06321337819099426, 0.08621513843536377, 0.10232581198215485, 0.027164114639163017, 0.0985046774148941, 0.13242372870445251, 0.0191008560359478, 0.14544545114040375, -0.02377847209572792, -0.059302784502506256, 0.05649275705218315, 0.013633874244987965, -0.17248544096946716, 0.03670460358262062, -0.015605193562805653, 0.026972385123372078, 0.09363088756799698, 0.021145451813936234, 0.020441683009266853, 0.0902106985449791, -0.08021455258131027, -0.1398237943649292, 0.10563203692436218, 0.07052353769540787, 0.034161631017923355, 0.12517565488815308, -0.03278198093175888, 0.135564923286438, -0.03551725298166275, -0.11858534812927246, -0.20654892921447754, 0.05404523015022278, 0.03882395476102829, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406546124605898752/YRmbl1wc_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1389328774085365767/QFuxMWoj_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gay Shawn 🏳️‍🌈 & 🔻L O W R Y 🔻</div> <div style="text-align: center; font-size: 14px;">@goatlich-yagisabi</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gay Shawn 🏳️‍🌈 & 🔻L O W R Y 🔻. \| Data \| Gay Shawn 🏳️‍🌈 \| 🔻L O W R Y 🔻 \| \| --- \| --- \| --- \| \| Tweets downloaded \| 406 \| 3156 \| \| Retweets \| 67 \| 390 \| \| Short tweets \| 50 \| 214 \| \| Tweets kept \| 289 \| 2552 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1wtnxwy1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goatlich-yagisabi's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/qrbyfgtb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/qrbyfgtb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goatlich-yagisabi') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/goatlich-yagisabi/1624475783796/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/goatlich-yagisabi	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI CYBORG Gay Shawn ️‍ & L O W R Y @goatlich-yagisabi I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Gay Shawn ️‍ & L O W R Y . Data: Tweets downloaded, Gay Shawn ️‍: 406, L O W R Y: 3156 Data: Retweets, Gay Shawn ️‍: 67, L O W R Y: 390 Data: Short tweets, Gay Shawn ️‍: 50, L O W R Y: 214 Data: Tweets kept, Gay Shawn ️‍: 289, L O W R Y: 2552 Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goatlich-yagisabi's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1326597959710994434/Mzw1eYU3_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">GoDaddy Pro 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@godaddypro bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@godaddypro's tweets](https://twitter.com/godaddypro). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>654</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>86</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>23</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>545</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1axtg72y/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @godaddypro's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/q9egqu3x) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/q9egqu3x/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/godaddypro'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/godaddypro/1606863861513/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/godaddypro	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">GoDaddy Pro AI Bot </div> <div style="font-size: 15px; color: #657786">@godaddypro bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @godaddypro's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>654</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>86</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>23</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>545</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @godaddypro's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/godaddypro'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @godaddypro's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>654</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>86</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>23</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>545</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @godaddypro's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/godaddypro'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @godaddypro's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>654</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>86</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>23</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>545</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @godaddypro's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/godaddypro'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1189466513113321473/muTPl9sy_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Thomas Godden 🤖 AI Bot </div> <div style="font-size: 15px">@goddenthomas bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@goddenthomas's tweets](https://twitter.com/goddenthomas). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 308 \| \| Retweets \| 29 \| \| Short tweets \| 5 \| \| Tweets kept \| 274 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/i8dnp3td/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goddenthomas's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/34v02f8a) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/34v02f8a/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goddenthomas') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/goddenthomas/1617800973798/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/goddenthomas	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Thomas Godden AI Bot @goddenthomas bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @goddenthomas's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goddenthomas's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1288860183515607041/uHoTEsFz_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">GPT-2 Religion AI</div> <div style="text-align: center; font-size: 14px;">@gods_txt</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from GPT-2 Religion AI. \| Data \| GPT-2 Religion AI \| \| --- \| --- \| \| Tweets downloaded \| 3249 \| \| Retweets \| 66 \| \| Short tweets \| 9 \| \| Tweets kept \| 3174 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/l1h0u8uh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gods_txt's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i75xs06) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i75xs06/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gods_txt') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gods_txt/1623749962893/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gods_txt	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT GPT-2 Religion AI @gods\_txt I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from GPT-2 Religion AI. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gods\_txt's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1432780406777020417/XTrp9MCR_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">LOVER//PARIAH</div> <div style="text-align: center; font-size: 14px;">@godslovepariah</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from LOVER//PARIAH. \| Data \| LOVER//PARIAH \| \| --- \| --- \| \| Tweets downloaded \| 525 \| \| Retweets \| 9 \| \| Short tweets \| 10 \| \| Tweets kept \| 506 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6l5fj9xw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @godslovepariah's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3v0x5r1a) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3v0x5r1a/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/godslovepariah') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/godslovepariah/1642565537762/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/godslovepariah	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT LOVER//PARIAH @godslovepariah I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from LOVER//PARIAH. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @godslovepariah's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/456958582731603969/QZKpv6eI_400x400.jpeg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1324123540556316673/YQjGLFLJ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">im pete online & Grateful King</div> <div style="text-align: center; font-size: 14px;">@gohere4porn-onlinepete</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from im pete online & Grateful King. \| Data \| im pete online \| Grateful King \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3190 \| 2141 \| \| Retweets \| 94 \| 557 \| \| Short tweets \| 1003 \| 217 \| \| Tweets kept \| 2093 \| 1367 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w0274vc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gohere4porn-onlinepete's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rvkp85n) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rvkp85n/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gohere4porn-onlinepete') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gohere4porn-onlinepete/1625638031693/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gohere4porn-onlinepete	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI CYBORG im pete online & Grateful King @gohere4porn-onlinepete I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from im pete online & Grateful King. Data: Tweets downloaded, im pete online: 3190, Grateful King: 2141 Data: Retweets, im pete online: 94, Grateful King: 557 Data: Short tweets, im pete online: 1003, Grateful King: 217 Data: Tweets kept, im pete online: 2093, Grateful King: 1367 Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gohere4porn-onlinepete's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/752977934562959361/V2ZYUvDN_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gordon Mohr ꧁👁👁꧂ 🤖 AI Bot </div> <div style="font-size: 15px">@gojomo bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gojomo's tweets](https://twitter.com/gojomo). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 224 \| \| Short tweets \| 251 \| \| Tweets kept \| 2773 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n28dkpx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gojomo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uusd4gca) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uusd4gca/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gojomo') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gojomo	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gordon Mohr ꧁꧂ AI Bot @gojomo bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gojomo's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gojomo's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/849638997286674433/MP_VFga5_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sasha Goldshtein 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@goldshtn bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@goldshtn's tweets](https://twitter.com/goldshtn). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3228</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>334</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>110</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2784</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/vyukb3ol/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goldshtn's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/26u1d2kp) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/26u1d2kp/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/goldshtn'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/goldshtn	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sasha Goldshtein AI Bot </div> <div style="font-size: 15px; color: #657786">@goldshtn bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @goldshtn's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3228</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>334</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>110</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2784</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @goldshtn's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/goldshtn'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @goldshtn's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3228</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>334</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>110</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2784</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @goldshtn's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/goldshtn'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @goldshtn's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3228</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>334</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>110</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2784</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @goldshtn's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/goldshtn'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1348671093125611522/EPvavP8X_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Seth Goldwasser 🤖 AI Bot </div> <div style="font-size: 15px">@goldwasser_seth bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@goldwasser_seth's tweets](https://twitter.com/goldwasser_seth). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 531 \| \| Retweets \| 8 \| \| Short tweets \| 76 \| \| Tweets kept \| 447 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/e8p1yskc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goldwasser_seth's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mj33xci4) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mj33xci4/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goldwasser_seth') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/goldwasser_seth/1616738324749/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/goldwasser_seth	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Seth Goldwasser AI Bot @goldwasser\_seth bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @goldwasser\_seth's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goldwasser\_seth's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1081520386477514752/YjiJLOS2_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">g head 🤖 AI Bot </div> <div style="font-size: 15px">@gonnhead bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gonnhead's tweets](https://twitter.com/gonnhead). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3209 \| \| Retweets \| 2404 \| \| Short tweets \| 400 \| \| Tweets kept \| 405 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/fzjhi41e/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gonnhead's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36u4rhhk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36u4rhhk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gonnhead') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gonnhead/1617924924473/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gonnhead	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	g head AI Bot @gonnhead bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gonnhead's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gonnhead's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1347063976585293826/11EjcLnX_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Good Tweetman</div> <div style="text-align: center; font-size: 14px;">@goodtweet_man</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Good Tweetman. \| Data \| Good Tweetman \| \| --- \| --- \| \| Tweets downloaded \| 3225 \| \| Retweets \| 734 \| \| Short tweets \| 643 \| \| Tweets kept \| 1848 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2czt5qbq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goodtweet_man's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tanvki3u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tanvki3u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goodtweet_man') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/goodtweet_man/1627279760723/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/goodtweet_man	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Good Tweetman @goodtweet\_man I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Good Tweetman. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goodtweet\_man's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1343584679664873479/Xos3xQfk_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Google 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@google bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@google's tweets](https://twitter.com/google). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3247</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>48</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>3</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>3196</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ulajd1f/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @google's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3hx7jdkp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3hx7jdkp/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/google'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/google/1609714473367/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/google	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Google AI Bot </div> <div style="font-size: 15px; color: #657786">@google bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @google's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3247</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>48</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>3</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>3196</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @google's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/google'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @google's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3247</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>48</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>3</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3196</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @google's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/google'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @google's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3247</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>48</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>3</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3196</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @google's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/google'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/993649592422907904/yD7LkqU2_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Google AI</div> <div style="text-align: center; font-size: 14px;">@googleai</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Google AI. \| Data \| Google AI \| \| --- \| --- \| \| Tweets downloaded \| 1754 \| \| Retweets \| 51 \| \| Short tweets \| 20 \| \| Tweets kept \| 1683 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/176c02iv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @googleai's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3cg366zk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3cg366zk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/googleai') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/googleai/1639129810325/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/googleai	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Google AI @googleai I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Google AI. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @googleai's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1378181197239480324/NV6mZagP_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Goon 🤖 AI Bot </div> <div style="font-size: 15px">@goon_lagoon__ bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@goon_lagoon__'s tweets](https://twitter.com/goon_lagoon__). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 2654 \| \| Retweets \| 1390 \| \| Short tweets \| 186 \| \| Tweets kept \| 1078 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11if3arq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @goon_lagoon__'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1fzipcm4) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1fzipcm4/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/goon_lagoon__') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/goon_lagoon__/1617849869460/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/goon_lagoon__	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Goon AI Bot @goon\_lagoon\_\_ bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @goon\_lagoon\_\_'s tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @goon\_lagoon\_\_'s tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1349755150316040194/VpUCtbH8_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gordon Ramsay 🤖 AI Bot </div> <div style="font-size: 15px">@gordonramsay bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gordonramsay's tweets](https://twitter.com/gordonramsay). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3246 \| \| Retweets \| 269 \| \| Short tweets \| 206 \| \| Tweets kept \| 2771 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/27mcq63k/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gordonramsay's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12n07etn) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12n07etn/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gordonramsay') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gordonramsay/1614174227495/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gordonramsay	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gordon Ramsay AI Bot @gordonramsay bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gordonramsay's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gordonramsay's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1319494401991856128/Rvgatmap_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gotham Sharma 🤖 AI Bot </div> <div style="font-size: 15px">@gothamjsharma bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gothamjsharma's tweets](https://twitter.com/gothamjsharma). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3029 \| \| Retweets \| 1090 \| \| Short tweets \| 288 \| \| Tweets kept \| 1651 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3d2w4exv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gothamjsharma's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17mzwxqx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17mzwxqx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gothamjsharma') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gothamjsharma/1618690355639/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gothamjsharma	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gotham Sharma AI Bot @gothamjsharma bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gothamjsharma's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gothamjsharma's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1382600435056394242/azQoqzIb_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Calum Macleod</div> <div style="text-align: center; font-size: 14px;">@gozusabu</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Calum Macleod. \| Data \| Calum Macleod \| \| --- \| --- \| \| Tweets downloaded \| 1926 \| \| Retweets \| 673 \| \| Short tweets \| 279 \| \| Tweets kept \| 974 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y71yp06o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gozusabu's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/dwp3t07q) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/dwp3t07q/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gozusabu') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gozusabu/1627054557412/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gozusabu	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Calum Macleod @gozusabu I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Calum Macleod. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gozusabu's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/858338118218506240/TpJ4sp1v_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Guillaume Peyronnet 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@gpeyronnet bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gpeyronnet's tweets](https://twitter.com/gpeyronnet). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3212</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>633</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>160</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2419</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1jp5vewz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gpeyronnet's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2dz99sln) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2dz99sln/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gpeyronnet'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gpeyronnet	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Guillaume Peyronnet AI Bot </div> <div style="font-size: 15px; color: #657786">@gpeyronnet bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @gpeyronnet's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3212</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>633</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>160</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2419</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @gpeyronnet's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gpeyronnet'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gpeyronnet's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3212</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>633</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>160</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2419</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gpeyronnet's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gpeyronnet'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gpeyronnet's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3212</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>633</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>160</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2419</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gpeyronnet's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gpeyronnet'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1317293570496266241/skdF2SBu_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">AI Wint Pontifex 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@gpt2drilpapa bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gpt2drilpapa's tweets](https://twitter.com/gpt2drilpapa). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>218</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>22</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>3</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>193</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yjlghvn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gpt2drilpapa's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m5q357m9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m5q357m9/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gpt2drilpapa'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gpt2drilpapa/1611164765660/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gpt2drilpapa	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">AI Wint Pontifex AI Bot </div> <div style="font-size: 15px; color: #657786">@gpt2drilpapa bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @gpt2drilpapa's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>218</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>22</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>3</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>193</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @gpt2drilpapa's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gpt2drilpapa'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gpt2drilpapa's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>218</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>22</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>3</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>193</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gpt2drilpapa's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gpt2drilpapa'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gpt2drilpapa's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>218</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>22</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>3</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>193</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gpt2drilpapa's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gpt2drilpapa'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 57, 34, 432, 78, 9, 170, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.04084593802690506, 0.035596683621406555, -0.0024457171093672514, 0.04662978649139404, 0.10991521924734116, 0.022836215794086456, 0.12812861800193787, 0.0424627922475338, -0.03746044635772705, -0.03597303107380867, 0.22758877277374268, 0.1009177565574646, 0.03089720755815506, 0.17962171137332916, 0.010350672528147697, -0.2703946828842163, 0.015237200073897839, 0.0647135004401207, -0.07720091193914413, 0.15752871334552765, 0.05562684312462807, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1377643512540364800/DkedSo33_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">naomi 🍄 🌙 🤖 AI Bot </div> <div style="font-size: 15px">@gr1my_w41fu bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gr1my_w41fu's tweets](https://twitter.com/gr1my_w41fu). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3228 \| \| Retweets \| 603 \| \| Short tweets \| 619 \| \| Tweets kept \| 2006 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3neoafnn/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gr1my_w41fu's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/yds64f47) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/yds64f47/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gr1my_w41fu') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gr1my_w41fu/1617756086013/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gr1my_w41fu	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	naomi AI Bot @gr1my\_w41fu bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gr1my\_w41fu's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gr1my\_w41fu's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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-0.19213621318340302, 0.03111329674720764, -0.12165344506502151, 0.045619383454322815, -0.1751558482646942, 0.11076030135154724, 0.08046020567417145, 0.01831907220184803, 0.011516088619828224, -0.02576824277639389, 0.08821021765470505, 0.11728470027446747, -0.07783648371696472, -0.08528783172369003 ]
null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362578535244648451/QGyOr2Y6_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">🌈ISⒶ//TED🃏🪳🍋 🤖 AI Bot </div> <div style="font-size: 15px">@gr8ful_ted bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gr8ful_ted's tweets](https://twitter.com/gr8ful_ted). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3192 \| \| Retweets \| 347 \| \| Short tweets \| 657 \| \| Tweets kept \| 2188 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pvs8733/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gr8ful_ted's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/evv8duo0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/evv8duo0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gr8ful_ted') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gr8ful_ted/1614111887321/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gr8ful_ted	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	ISⒶ//TED🃏 AI Bot @gr8ful\_ted bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gr8ful\_ted's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gr8ful\_ted's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1145004233516650501/D9FdtjJ5_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">gracchus strupp 🤖 AI Bot </div> <div style="font-size: 15px">@gracchusstrupp bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gracchusstrupp's tweets](https://twitter.com/gracchusstrupp). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 1189 \| \| Retweets \| 690 \| \| Short tweets \| 56 \| \| Tweets kept \| 443 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1m083rwp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gracchusstrupp's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/153lr6i9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/153lr6i9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gracchusstrupp') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gracchusstrupp/1617828463761/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gracchusstrupp	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	gracchus strupp AI Bot @gracchusstrupp bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gracchusstrupp's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gracchusstrupp's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/1255141505720672257/flNLLFAC_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">グランブルー EN 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@granblue_en bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@granblue_en's tweets](https://twitter.com/granblue_en). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3222</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>252</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>59</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2911</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/2pwcb5ci/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @granblue_en's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2tq5wz9d) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2tq5wz9d/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/granblue_en'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/granblue_en/1600399682930/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/granblue_en	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">グランブルー EN AI Bot </div> <div style="font-size: 15px; color: #657786">@granblue_en bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @granblue_en's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3222</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>252</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>59</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2911</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @granblue_en's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/granblue_en'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @granblue_en's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3222</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>252</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>59</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2911</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @granblue_en's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/granblue_en'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @granblue_en's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3222</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>252</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>59</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2911</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @granblue_en's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/granblue_en'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 57, 34, 431, 76, 9, 168, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.04084593802690506, 0.035596683621406555, -0.0024457171093672514, 0.04662978649139404, 0.10991521924734116, 0.022836215794086456, 0.12812861800193787, 0.0424627922475338, -0.03746044635772705, -0.03597303107380867, 0.22758877277374268, 0.1009177565574646, 0.03089720755815506, 0.17962171137332916, 0.010350672528147697, -0.2703946828842163, 0.015237200073897839, 0.0647135004401207, -0.07720091193914413, 0.15752871334552765, 0.05562684312462807, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1392696284549632008/QOl3l-zh_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">ju1ce💎</div> <div style="text-align: center; font-size: 14px;">@grapefried</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ju1ce💎. \| Data \| ju1ce💎 \| \| --- \| --- \| \| Tweets downloaded \| 2034 \| \| Retweets \| 504 \| \| Short tweets \| 403 \| \| Tweets kept \| 1127 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1actx5cl/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @grapefried's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a1nwhd0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/grapefried') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/grapefried/1626857673378/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/grapefried	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT ju1ce @grapefried I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from ju1ce. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @grapefried's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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-0.02819518744945526, -0.18475614488124847, 0.03248962014913559, -0.12301548570394516, 0.05860345810651779, -0.17328192293643951, 0.11332228779792786, 0.07014153152704239, 0.018578365445137024, 0.01264720968902111, -0.007008096668869257, 0.08073210716247559, 0.11507359892129898, -0.0813264548778534, -0.08199906349182129 ]
null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1383140285329334278/LEYj6vPT_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gray 🤖 AI Bot </div> <div style="font-size: 15px">@grayvtuber bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@grayvtuber's tweets](https://twitter.com/grayvtuber). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 363 \| \| Retweets \| 14 \| \| Short tweets \| 52 \| \| Tweets kept \| 297 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rqb2jnzt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @grayvtuber's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fn16ljs) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fn16ljs/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/grayvtuber') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/grayvtuber/1619622413978/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/grayvtuber	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gray AI Bot @grayvtuber bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @grayvtuber's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @grayvtuber's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000520968918/d38fd96468e9ba14c1f9f022eb0c4e61_400x400.png')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Great Minds Quotes 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@greatestquotes bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@greatestquotes's tweets](https://twitter.com/greatestquotes). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3202</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>0</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>3201</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3unqair1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @greatestquotes's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/368rnmms) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/368rnmms/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/greatestquotes'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/greatestquotes/1603925133471/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/greatestquotes	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Great Minds Quotes AI Bot </div> <div style="font-size: 15px; color: #657786">@greatestquotes bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @greatestquotes's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3202</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>1</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>0</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>3201</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @greatestquotes's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/greatestquotes'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @greatestquotes's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3202</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>0</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3201</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @greatestquotes's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/greatestquotes'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @greatestquotes's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3202</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>1</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>0</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>3201</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @greatestquotes's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/greatestquotes'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 57, 34, 431, 77, 9, 169, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.04084593802690506, 0.035596683621406555, -0.0024457171093672514, 0.04662978649139404, 0.10991521924734116, 0.022836215794086456, 0.12812861800193787, 0.0424627922475338, -0.03746044635772705, -0.03597303107380867, 0.22758877277374268, 0.1009177565574646, 0.03089720755815506, 0.17962171137332916, 0.010350672528147697, -0.2703946828842163, 0.015237200073897839, 0.0647135004401207, -0.07720091193914413, 0.15752871334552765, 0.05562684312462807, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1182443074963857408/PH0SGZfK_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ray Greene 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@greene_ray bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@greene_ray's tweets](https://twitter.com/greene_ray). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3187</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>867</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>334</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1986</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/1njnu788/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @greene_ray's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/1cwalrjv) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/1cwalrjv/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/greene_ray'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/greene_ray/1604420107211/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/greene_ray	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ray Greene AI Bot </div> <div style="font-size: 15px; color: #657786">@greene_ray bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @greene_ray's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3187</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>867</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>334</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1986</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @greene_ray's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/greene_ray'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @greene_ray's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3187</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>867</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>334</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1986</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @greene_ray's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/greene_ray'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @greene_ray's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3187</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>867</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>334</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>1986</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @greene_ray's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/greene_ray'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1344698078671024128/Sk9cAYSu_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Gremlin ☭ 🤖 AI Bot </div> <div style="font-size: 15px">@gremlimbs bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gremlimbs's tweets](https://twitter.com/gremlimbs). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 2223 \| \| Retweets \| 448 \| \| Short tweets \| 324 \| \| Tweets kept \| 1451 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d7pcd3r/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gremlimbs's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1egm6qyj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1egm6qyj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gremlimbs') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gremlimbs/1614107802037/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gremlimbs	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Gremlin AI Bot @gremlimbs bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gremlimbs's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gremlimbs's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1286445572795305990/SPf0WZuT_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">BON</div> <div style="text-align: center; font-size: 14px;">@gresham2x</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from BON. \| Data \| BON \| \| --- \| --- \| \| Tweets downloaded \| 3235 \| \| Retweets \| 172 \| \| Short tweets \| 708 \| \| Tweets kept \| 2355 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mb1dknt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gresham2x's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kgizc73h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kgizc73h/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gresham2x') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gresham2x/1623806625441/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gresham2x	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT BON @gresham2x I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from BON. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gresham2x's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1373428284655042565/PsisqZUi_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">mal shah 🤖 AI Bot </div> <div style="font-size: 15px">@griceposting bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@griceposting's tweets](https://twitter.com/griceposting). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3240 \| \| Retweets \| 247 \| \| Short tweets \| 357 \| \| Tweets kept \| 2636 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25trxjkq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @griceposting's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/q9yoq7u8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/q9yoq7u8/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/griceposting') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/griceposting/1616682203001/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/griceposting	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	mal shah AI Bot @griceposting bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @griceposting's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @griceposting's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1359943297322655748/_3lUePeP_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">🩸𝕮𝖚𝖑𝖙𝖘𝖚𝖑𝖙𝖆𝖓𝖙🩸 🤖 AI Bot </div> <div style="font-size: 15px">@gritcult bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gritcult's tweets](https://twitter.com/gritcult). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 554 \| \| Short tweets \| 558 \| \| Tweets kept \| 2132 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nikyb7z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gritcult's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/13st5rcg) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/13st5rcg/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gritcult') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gritcult/1616928724478/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gritcult	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	𝕮𝖚𝖑𝖙𝖘𝖚𝖑𝖙𝖆𝖓𝖙 AI Bot @gritcult bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gritcult's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gritcult's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/899018577302441984/Tf9qt4q2_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">B.S.E. Guillotine Engineering 🤖 AI Bot </div> <div style="font-size: 15px">@grubadubflub bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@grubadubflub's tweets](https://twitter.com/grubadubflub). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 2543 \| \| Retweets \| 559 \| \| Short tweets \| 143 \| \| Tweets kept \| 1841 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1axfr66g/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @grubadubflub's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3vt3dbdy) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vt3dbdy/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/grubadubflub') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/grubadubflub/1614098423599/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/grubadubflub	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	B.S.E. Guillotine Engineering AI Bot @grubadubflub bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @grubadubflub's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @grubadubflub's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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-0.19213621318340302, 0.03111329674720764, -0.12165344506502151, 0.045619383454322815, -0.1751558482646942, 0.11076030135154724, 0.08046020567417145, 0.01831907220184803, 0.011516088619828224, -0.02576824277639389, 0.08821021765470505, 0.11728470027446747, -0.07783648371696472, -0.08528783172369003 ]
null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1282964688070795264/e5WQYNcH_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">George Siemens 🤖 AI Bot </div> <div style="font-size: 15px">@gsiemens bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gsiemens's tweets](https://twitter.com/gsiemens). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 1291 \| \| Retweets \| 84 \| \| Short tweets \| 79 \| \| Tweets kept \| 1128 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39omc3b3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gsiemens's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ifsl362) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ifsl362/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gsiemens') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gsiemens/1617219776300/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gsiemens	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	George Siemens AI Bot @gsiemens bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gsiemens's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gsiemens's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1329756149629915141/0N5P4E9R_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">gudapoyo 🤖 AI Bot </div> <div style="font-size: 15px">@gudapoyo2 bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gudapoyo2's tweets](https://twitter.com/gudapoyo2). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3207 \| \| Retweets \| 32 \| \| Short tweets \| 468 \| \| Tweets kept \| 2707 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1duxqzag/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gudapoyo2's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/22equxej) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/22equxej/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gudapoyo2') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gudapoyo2/1614096751603/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gudapoyo2	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	gudapoyo AI Bot @gudapoyo2 bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gudapoyo2's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gudapoyo2's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1326674278553583616/T79JYB9C_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jamie Moffatt 🤖 AI Bot </div> <div style="font-size: 15px">@guestyperson bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@guestyperson's tweets](https://twitter.com/guestyperson). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3152 \| \| Retweets \| 1179 \| \| Short tweets \| 192 \| \| Tweets kept \| 1781 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pvm3v6e/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guestyperson's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nuca4qh) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nuca4qh/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/guestyperson') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/guestyperson/1614136556129/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/guestyperson	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Jamie Moffatt AI Bot @guestyperson bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @guestyperson's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @guestyperson's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/976898364901134338/IOR5RTSc_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sylvain Gugger 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@guggersylvain bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@guggersylvain's tweets](https://twitter.com/guggersylvain). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>571</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>202</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>31</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>338</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/32frx4d8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guggersylvain's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/21uu01o9) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/21uu01o9/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/guggersylvain'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/guggersylvain	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sylvain Gugger AI Bot </div> <div style="font-size: 15px; color: #657786">@guggersylvain bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @guggersylvain's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>571</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>202</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>31</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>338</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @guggersylvain's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/guggersylvain'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @guggersylvain's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>571</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>202</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>31</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>338</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @guggersylvain's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/guggersylvain'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @guggersylvain's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>571</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>202</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>31</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>338</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @guggersylvain's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/guggersylvain'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1374400679704363008/amonFoGB_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Guillermo Angeris 🤖 AI Bot </div> <div style="font-size: 15px">@guilleangeris bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@guilleangeris's tweets](https://twitter.com/guilleangeris). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3230 \| \| Retweets \| 273 \| \| Short tweets \| 303 \| \| Tweets kept \| 2654 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tg19y8a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guilleangeris's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2shp18hb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2shp18hb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/guilleangeris') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/guilleangeris/1616612740170/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/guilleangeris	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Guillermo Angeris AI Bot @guilleangeris bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @guilleangeris's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @guilleangeris's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1162445050590183424/WL2lQ7OR_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mayor Guy Fieri</div> <div style="text-align: center; font-size: 14px;">@guyfieri</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Mayor Guy Fieri. \| Data \| Mayor Guy Fieri \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 978 \| \| Short tweets \| 132 \| \| Tweets kept \| 2138 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19tc6yav/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guyfieri's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3nefj2bb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3nefj2bb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/guyfieri') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/guyfieri/1663110657180/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/guyfieri	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT Mayor Guy Fieri @guyfieri I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from Mayor Guy Fieri. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @guyfieri's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1281714306749468672/ksZeHrw5_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">GuyFoxDay 🤖 AI Bot </div> <div style="font-size: 15px">@guyfoxday bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@guyfoxday's tweets](https://twitter.com/guyfoxday). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 561 \| \| Short tweets \| 316 \| \| Tweets kept \| 2367 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/o580jv43/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guyfoxday's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rf94m3w) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rf94m3w/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/guyfoxday') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/guyfoxday/1617809504933/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/guyfoxday	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	GuyFoxDay AI Bot @guyfoxday bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @guyfoxday's tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @guyfoxday's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1332093894218182659/-dCbl61O_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">GuyWithThePie (🎂 in 1 week)</div> <div style="text-align: center; font-size: 14px;">@guywiththepie</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from GuyWithThePie (🎂 in 1 week). \| Data \| GuyWithThePie (🎂 in 1 week) \| \| --- \| --- \| \| Tweets downloaded \| 3204 \| \| Retweets \| 445 \| \| Short tweets \| 422 \| \| Tweets kept \| 2337 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1lir19ia/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @guywiththepie's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ru7uv7v) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ru7uv7v/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/guywiththepie') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/guywiththepie/1627573203188/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/guywiththepie	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	AI BOT GuyWithThePie ( in 1 week) @guywiththepie I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on tweets from GuyWithThePie ( in 1 week). Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @guywiththepie's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0001764487533364445, -0.01891571842133999, -0.0068881697952747345, 0.01242890115827322, 0.16224369406700134, 0.04406825825572014, 0.08452208340167999, 0.14250440895557404, -0.026455026119947433, -0.016114573925733566, 0.17334569990634918, 0.17106501758098602, -0.014037161134183407, 0.08718273043632507, -0.05552244931459427, -0.2646014094352722, 0.044212065637111664, 0.058431971818208694, -0.020032864063978195, 0.14111687242984772, 0.0714879110455513, -0.01828647032380104, 0.10845158249139786, -0.02953636273741722, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/424495004/GuidoAvatar_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Guido van Rossum 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@gvanrossum bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gvanrossum's tweets](https://twitter.com/gvanrossum). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3192</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>166</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>169</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2857</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cyt5kq0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gvanrossum's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rte53sg6) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rte53sg6/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gvanrossum'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gvanrossum/1605218553043/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gvanrossum	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Guido van Rossum AI Bot </div> <div style="font-size: 15px; color: #657786">@gvanrossum bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @gvanrossum's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3192</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>166</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>169</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2857</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @gvanrossum's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/gvanrossum'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gvanrossum's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3192</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>166</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>169</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2857</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gvanrossum's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gvanrossum'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @gvanrossum's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>3192</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>166</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>169</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>2857</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @gvanrossum's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/gvanrossum'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 57, 34, 430, 76, 9, 168, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.04084593802690506, 0.035596683621406555, -0.0024457171093672514, 0.04662978649139404, 0.10991521924734116, 0.022836215794086456, 0.12812861800193787, 0.0424627922475338, -0.03746044635772705, -0.03597303107380867, 0.22758877277374268, 0.1009177565574646, 0.03089720755815506, 0.17962171137332916, 0.010350672528147697, -0.2703946828842163, 0.015237200073897839, 0.0647135004401207, -0.07720091193914413, 0.15752871334552765, 0.05562684312462807, 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null	null	transformers	<div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1364599664016691206/NVK2fuwS_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Anarcho-Gwendolism 🧬 🤖 AI Bot </div> <div style="font-size: 15px">@gwenvara_ bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@gwenvara_'s tweets](https://twitter.com/gwenvara_). \| Data \| Quantity \| \| --- \| --- \| \| Tweets downloaded \| 3069 \| \| Retweets \| 1831 \| \| Short tweets \| 350 \| \| Tweets kept \| 888 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p9ao8jnc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gwenvara_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/l9zed4di) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/l9zed4di/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gwenvara_') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/gwenvara_/1616736053941/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/gwenvara_	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Anarcho-Gwendolism AI Bot @gwenvara\_ bot I was made with huggingtweets. Create your own bot based on your favorite user with the demo! How does it work? ----------------- The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. Training data ------------- The model was trained on @gwenvara\_'s tweets. Explore the data, which is tracked with W&B artifacts at every step of the pipeline. Training procedure ------------------ The model is based on a pre-trained GPT-2 which is fine-tuned on @gwenvara\_'s tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. How to use ---------- You can use this model directly with a pipeline for text generation: Limitations and bias -------------------- The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. About ----- Built by Boris Dayma ![Follow](URL For more details, visit the project repository. ![GitHub stars](URL	[]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 57 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.004547144751995802, -0.006708405911922455, -0.007013476919382811, 0.01947171241044998, 0.15818242728710175, 0.03448796644806862, 0.08709780126810074, 0.15389476716518402, -0.019877297803759575, -0.022431448101997375, 0.18047170341014862, 0.173692986369133, -0.012988686561584473, 0.09047263860702515, -0.05271327868103981, -0.2622397541999817, 0.03682216629385948, 0.05513067543506622, -0.007422737777233124, 0.14252057671546936, 0.07580838352441788, -0.023790201172232628, 0.11380083113908768, -0.02966974675655365, 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null	null	transformers	<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/993273677386059777/TngqqZck_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Frank Soboczenski 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@h21k bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@h21k's tweets](https://twitter.com/h21k). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>204</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>14</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>14</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>176</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3vw58heg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @h21k's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/15xkammd) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/15xkammd/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/h21k'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets) <!--- random size file -->	{"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://www.huggingtweets.com/h21k/1602301931118/predictions.png", "widget": [{"text": "My dream is"}]}	text-generation	huggingtweets/h21k	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<link rel="stylesheet" href="URL <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('URL </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Frank Soboczenski AI Bot </div> <div style="font-size: 15px; color: #657786">@h21k bot</div> </div> I was made with huggingtweets. Create your own bot based on your favorite user with the demo! ## How does it work? The model uses the following pipeline. !pipeline To understand how the model was developed, check the W&B report. ## Training data The model was trained on @h21k's tweets. <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>204</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>14</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>14</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>176</td> </tr> </tbody> </table> Explore the data, which is tracked with W&B artifacts at every step of the pipeline. ## Training procedure The model is based on a pre-trained GPT-2 which is fine-tuned on @h21k's tweets. Hyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility. At the end of training, the final model is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/h21k'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from the same limitations and bias as GPT-2. In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma </section> ![Follow](URL <section class='prose'> For more details, visit the project repository. </section> ![GitHub stars](URL	[ "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @h21k's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>204</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>14</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>14</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>176</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @h21k's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/h21k'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report.", "## Training data\n\nThe model was trained on @h21k's tweets.\n\n<table style='border-width:0'>\n<thead style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>\n<th style='border-width:0'>Data</th>\n<th style='border-width:0'>Quantity</th>\n</tr>\n</thead>\n<tbody style='border-width:0'>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Tweets downloaded</td>\n<td style='border-width:0'>204</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Retweets</td>\n<td style='border-width:0'>14</td>\n</tr>\n<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>\n<td style='border-width:0'>Short tweets</td>\n<td style='border-width:0'>14</td>\n</tr>\n<tr style='border-width:0'>\n<td style='border-width:0'>Tweets kept</td>\n<td style='border-width:0'>176</td>\n</tr>\n</tbody>\n</table>\n\nExplore the data, which is tracked with W&B artifacts at every step of the pipeline.", "## Training procedure\n\nThe model is based on a pre-trained GPT-2 which is fine-tuned on @h21k's tweets.\n\nHyperparameters and metrics are recorded in the W&B training run for full transparency and reproducibility.\n\nAt the end of training, the final model is logged and versioned.", "## Intended uses & limitations", "### How to use\n\nYou can use this model directly with a pipeline for text generation:\n\n<pre><code><span style=\"color:#03A9F4\">from</span> transformers <span style=\"color:#03A9F4\">import</span> pipeline\ngenerator = pipeline(<span style=\"color:#FF9800\">'text-generation'</span>,\n model=<span style=\"color:#FF9800\">'huggingtweets/h21k'</span>)\ngenerator(<span style=\"color:#FF9800\">\"My dream is\"</span>, num_return_sequences=<span style=\"color:#8BC34A\">5</span>)</code></pre>", "### Limitations and bias\n\nThe model suffers from the same limitations and bias as GPT-2.\n\nIn addition, the data present in the user's tweets further affects the text generated by the model.", "## About\n\nBuilt by Boris Dayma\n\n</section>\n\n![Follow](URL\n\n<section class='prose'>\nFor more details, visit the project repository.\n</section>\n\n![GitHub stars](URL" ]	[ 57, 34, 427, 75, 9, 167, 48, 58 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## How does it work?\n\nThe model uses the following pipeline.\n\n!pipeline\n\nTo understand how the model was developed, check the W&B report." ]	[ -0.04084593802690506, 0.035596683621406555, -0.0024457171093672514, 0.04662978649139404, 0.10991521924734116, 0.022836215794086456, 0.12812861800193787, 0.0424627922475338, -0.03746044635772705, -0.03597303107380867, 0.22758877277374268, 0.1009177565574646, 0.03089720755815506, 0.17962171137332916, 0.010350672528147697, -0.2703946828842163, 0.015237200073897839, 0.0647135004401207, -0.07720091193914413, 0.15752871334552765, 0.05562684312462807, -0.049801189452409744, 0.08214939385652542, 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