modelId
stringlengths
4
112
sha
stringlengths
40
40
lastModified
stringlengths
24
24
tags
list
pipeline_tag
stringclasses
29 values
private
bool
1 class
author
stringlengths
2
38
config
null
id
stringlengths
4
112
downloads
float64
0
36.8M
likes
float64
0
712
library_name
stringclasses
17 values
__index_level_0__
int64
0
38.5k
readme
stringlengths
0
186k
huggingtweets/curlyjunglejake
fe0d1fde24aebb902b0ccf3f2965a982396a25f0
2021-05-21T23:51:27.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/curlyjunglejake
9
null
transformers
12,300
--- language: en thumbnail: https://www.huggingtweets.com/curlyjunglejake/1611588649017/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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/866006337255227393/jLbqeyn3_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Dr. Jacob Glanville 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@curlyjunglejake 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 [@curlyjunglejake's tweets](https://twitter.com/curlyjunglejake). <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'>2193</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'>94</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'>194</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1905</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wpg429u/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 @curlyjunglejake's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2u5lcs29) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2u5lcs29/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/curlyjunglejake'</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)
huggingtweets/danielgedda
1a36f0104c5c4ee508fc18af5fa6d84997a943a1
2021-05-22T00:34:52.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/danielgedda
9
null
transformers
12,301
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <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/1267943406304743424/QS6bXLq-_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Daniel Gedda Nuño 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@danielgedda 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 [@danielgedda's tweets](https://twitter.com/danielgedda). <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'>3124</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'>2715</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'>36</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>373</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/xk4kfjse/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 @danielgedda's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3lyvifcb) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3lyvifcb/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/danielgedda'</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)
huggingtweets/disabledjess
014fe0402ef420e26aac8b481ddf121ccaeb965a
2021-05-22T01:44:27.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/disabledjess
9
null
transformers
12,302
--- language: en thumbnail: https://www.huggingtweets.com/disabledjess/1616670355194/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1336779061025267715/zRfiUbb7_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Jess O'Brien 🤖 AI Bot </div> <div style="font-size: 15px">@disabledjess 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 [@disabledjess's tweets](https://twitter.com/disabledjess). | Data | Quantity | | --- | --- | | Tweets downloaded | 713 | | Retweets | 324 | | Short tweets | 34 | | Tweets kept | 355 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dt08vg5c/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 @disabledjess's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zxrg63ip) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zxrg63ip/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/disabledjess') 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)
huggingtweets/doctor_emmet
ad2d266c99f15d828c9261a27d27af7548091eb3
2021-05-22T01:53:07.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/doctor_emmet
9
null
transformers
12,303
--- language: en thumbnail: https://www.huggingtweets.com/doctor_emmet/1603833315216/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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/1250027548785938432/KHyOaVQY_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Emmet Burke 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@doctor_emmet 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 [@doctor_emmet's tweets](https://twitter.com/doctor_emmet). <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'>2496</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'>204</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'>176</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2116</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/duj1xqx6/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 @doctor_emmet's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/yzdnl9ld) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/yzdnl9ld/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/doctor_emmet'</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 -->
huggingtweets/eiritana
b10e27a71574403423f8445c536e56ccd8ee3382
2021-05-22T02:49:05.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/eiritana
9
null
transformers
12,304
--- language: en thumbnail: https://www.huggingtweets.com/eiritana/1617882396659/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1178700495487164418/kNT2--o-_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Eiritana ᚖ エリタナ - XY Æ SR-71✨🌐 🍓🖤🤍🖤✨ 🤖 AI Bot </div> <div style="font-size: 15px">@eiritana 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 [@eiritana's tweets](https://twitter.com/eiritana). | Data | Quantity | | --- | --- | | Tweets downloaded | 3226 | | Retweets | 1692 | | Short tweets | 567 | | Tweets kept | 967 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nuy0f3c/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 @eiritana's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2puqx075) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2puqx075/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/eiritana') 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)
huggingtweets/etcanada
8568994512a753a86850a6be78ab7e98aa51d72d
2021-05-22T03:32:55.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/etcanada
9
null
transformers
12,305
--- language: en thumbnail: https://www.huggingtweets.com/etcanada/1613324841076/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1159160036125564930/33nAmouA_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">ET Canada 🤖 AI Bot </div> <div style="font-size: 15px">@etcanada 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 [@etcanada's tweets](https://twitter.com/etcanada). | Data | Quantity | | --- | --- | | Tweets downloaded | 3241 | | Retweets | 27 | | Short tweets | 13 | | Tweets kept | 3201 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mkfurkr/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 @etcanada's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37a3w2d0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37a3w2d0/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/etcanada') 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)
huggingtweets/femboympreg
d5023508851141bf4834dc75a7ecb5eef2872590
2021-05-22T04:05:37.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/femboympreg
9
null
transformers
12,306
--- language: en thumbnail: https://www.huggingtweets.com/femboympreg/1617809081812/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1370404573374976005/WyjvD-FA_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Storm | 嵐 🤖 AI Bot </div> <div style="font-size: 15px">@femboympreg 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 [@femboympreg's tweets](https://twitter.com/femboympreg). | Data | Quantity | | --- | --- | | Tweets downloaded | 3212 | | Retweets | 594 | | Short tweets | 969 | | Tweets kept | 1649 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/30bwh0wo/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 @femboympreg's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/8vc73356) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/8vc73356/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/femboympreg') 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)
huggingtweets/gatchabot
3f7bc6397bf85915836024a9730d668de77ac129
2021-05-22T05:04:29.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/gatchabot
9
null
transformers
12,307
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <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)
huggingtweets/girlchrismarker
b85789619b1a7daae89905fbf46b9dc7bc109f65
2021-05-22T05:30:22.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/girlchrismarker
9
null
transformers
12,308
--- language: en thumbnail: https://www.huggingtweets.com/girlchrismarker/1614168569443/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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)
huggingtweets/granblue_en
78ffb654ed40783dd3aa871df9f2516667d4d3be
2021-05-22T06:05:45.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/granblue_en
9
null
transformers
12,309
--- language: en thumbnail: http://www.huggingtweets.com/granblue_en/1600399682930/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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)
huggingtweets/hannesbajohr
8404983d47b97030aaa94da97fdb91ec2c3731e3
2021-05-22T06:34:54.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/hannesbajohr
9
null
transformers
12,310
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/467172766416789504/01jisH73_400x400.png')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Hannes Bajohr 🤖 AI Bot </div> <div style="font-size: 15px">@hannesbajohr 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 [@hannesbajohr's tweets](https://twitter.com/hannesbajohr). | Data | Quantity | | --- | --- | | Tweets downloaded | 3210 | | Retweets | 1663 | | Short tweets | 293 | | Tweets kept | 1254 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/32cptzpn/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 @hannesbajohr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2lxf36v7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2lxf36v7/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/hannesbajohr') 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)
huggingtweets/hardmaru
f86a5444f953121df346551bab75e5fbb82ccc3c
2021-05-22T06:37:39.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/hardmaru
9
null
transformers
12,311
--- language: en thumbnail: https://www.huggingtweets.com/hardmaru/1620671462182/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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(&#39;https://pbs.twimg.com/profile_images/1244133811278852097/rxL5LqpS_400x400.png&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">hardmaru</div> <div style="text-align: center; font-size: 14px;">@hardmaru</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 hardmaru. | Data | hardmaru | | --- | --- | | Tweets downloaded | 3244 | | Retweets | 587 | | Short tweets | 246 | | Tweets kept | 2411 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rlh65t6/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 @hardmaru's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bwhefwe) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bwhefwe/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/hardmaru') 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)
huggingtweets/itemlabel
d0aaf08b0703b2f714f7a281621ff446c38b193c
2021-05-22T08:37:14.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/itemlabel
9
null
transformers
12,312
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1359348009725808641/KyPjQGzk_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">itemLabel 🤖 AI Bot </div> <div style="font-size: 15px">@itemlabel 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 [@itemlabel's tweets](https://twitter.com/itemlabel). | Data | Quantity | | --- | --- | | Tweets downloaded | 3188 | | Retweets | 1796 | | Short tweets | 389 | | Tweets kept | 1003 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10hookja/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 @itemlabel's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1u63m0wj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1u63m0wj/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/itemlabel') 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)
huggingtweets/johannesreck
f6dc23694bc54c5a2e44db0a82bebf1c6b8a2235
2021-05-22T09:55:59.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/johannesreck
9
null
transformers
12,313
--- language: en thumbnail: https://www.huggingtweets.com/johannesreck/1617820959621/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/657647990769872896/fzDbsUop_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Johannes Reck 🤖 AI Bot </div> <div style="font-size: 15px">@johannesreck 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 [@johannesreck's tweets](https://twitter.com/johannesreck). | Data | Quantity | | --- | --- | | Tweets downloaded | 1579 | | Retweets | 335 | | Short tweets | 38 | | Tweets kept | 1206 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d9mk25o/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 @johannesreck's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rjx3zio) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rjx3zio/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/johannesreck') 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)
huggingtweets/johnlimouze
c39c5d8843f0cb252e0740e4ecd4d0c3a4dcb60c
2021-05-22T09:58:04.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/johnlimouze
9
null
transformers
12,314
--- language: en thumbnail: https://www.huggingtweets.com/johnlimouze/1614164967543/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1247836262519771136/IzX0FhAt_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">John Limouze 🤖 AI Bot </div> <div style="font-size: 15px">@johnlimouze 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 [@johnlimouze's tweets](https://twitter.com/johnlimouze). | Data | Quantity | | --- | --- | | Tweets downloaded | 3217 | | Retweets | 402 | | Short tweets | 615 | | Tweets kept | 2200 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3q6a1aqr/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 @johnlimouze's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5916wbk0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5916wbk0/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/johnlimouze') 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)
huggingtweets/jokowi
da03578fa16d4c622de7e16ad4f00b0b16ec52ab
2021-05-22T10:02:24.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/jokowi
9
null
transformers
12,315
--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <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(&#39;https://pbs.twimg.com/profile_images/1299550083097059332/uK26iMOu_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Joko Widodo</div> <div style="text-align: center; font-size: 14px;">@jokowi</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 Joko Widodo. | Data | Joko Widodo | | --- | --- | | Tweets downloaded | 3240 | | Retweets | 1 | | Short tweets | 5 | | Tweets kept | 3234 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/c1qe98am/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 @jokowi's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/gawgg6d1) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gawgg6d1/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/jokowi') 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)
huggingtweets/josephmama666
246dba2d0649f18ed0e8b2948ebfc0bf00abf133
2021-05-22T10:07:11.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/josephmama666
9
null
transformers
12,316
--- language: en thumbnail: https://www.huggingtweets.com/josephmama666/1614134283340/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1337312159324258305/XLP7epZE_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">j 🤖 AI Bot </div> <div style="font-size: 15px">@josephmama666 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 [@josephmama666's tweets](https://twitter.com/josephmama666). | Data | Quantity | | --- | --- | | Tweets downloaded | 3156 | | Retweets | 1809 | | Short tweets | 201 | | Tweets kept | 1146 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/157t36eh/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 @josephmama666's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ahfjdey) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ahfjdey/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/josephmama666') 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)
huggingtweets/l2k
1d8f3d9ddc6bce20fcbeea298b6afe967208ccd5
2021-05-22T11:20:37.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/l2k
9
null
transformers
12,317
--- language: en thumbnail: http://res.cloudinary.com/huggingtweets/image/upload/v1599871089/l2k.jpg tags: - huggingtweets widget: - text: "My dream is" --- <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/573383872/img_0621_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lukas Biewald 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@l2k 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 [@l2k's tweets](https://twitter.com/l2k). <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'>2580</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'>598</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'>88</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1894</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/17e2cw73/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 @l2k's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/10mi5zis) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/10mi5zis/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/l2k'</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)
huggingtweets/michelleobama
865f44e599db26e135fd7dec62f95c939e71a802
2022-06-13T15:21:39.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/michelleobama
9
null
transformers
12,318
--- language: en thumbnail: http://www.huggingtweets.com/michelleobama/1655133694921/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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(&#39;https://pbs.twimg.com/profile_images/1507471015110139906/T9rDVcLd_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Michelle Obama</div> <div style="text-align: center; font-size: 14px;">@michelleobama</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 Michelle Obama. | Data | Michelle Obama | | --- | --- | | Tweets downloaded | 1932 | | Retweets | 439 | | Short tweets | 10 | | Tweets kept | 1483 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2m7f8b6p/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 @michelleobama's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/200pdxti) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/200pdxti/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/michelleobama') 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)
huggingtweets/mikrodystopies
8d0d47ccc5ab9b770bbcd7ce57994f74ae36da52
2021-05-22T14:41:31.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/mikrodystopies
9
null
transformers
12,319
--- language: en thumbnail: https://www.huggingtweets.com/mikrodystopies/1604658435538/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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/1313931951791902720/P5xuzPnM_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Mikrodystopies 🤖 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@mikrodystopies 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 [@mikrodystopies's tweets](https://twitter.com/mikrodystopies). <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'>1353</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'>3</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1336</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3ujepu0f/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 @mikrodystopies's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/6omc5zso) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/6omc5zso/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/mikrodystopies'</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 -->
huggingtweets/mutilumila
114dcb07237ac964d927fe5a47a94d815af35962
2021-05-22T15:35:13.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/mutilumila
9
null
transformers
12,320
--- language: en thumbnail: https://www.huggingtweets.com/mutilumila/1616785118212/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1367580181171470336/VGbeIwgL_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">p a ' u l 🤖 AI Bot </div> <div style="font-size: 15px">@mutilumila 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 [@mutilumila's tweets](https://twitter.com/mutilumila). | Data | Quantity | | --- | --- | | Tweets downloaded | 3227 | | Retweets | 432 | | Short tweets | 618 | | Tweets kept | 2177 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xkgonzr/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 @mutilumila's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2oplbn5a) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2oplbn5a/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/mutilumila') 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)
huggingtweets/noellayoshino
5dd1a4b6216db2b5e0c907eaad0be643656d886c
2021-05-22T16:40:41.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/noellayoshino
9
null
transformers
12,321
--- language: en thumbnail: https://www.huggingtweets.com/noellayoshino/1620681697974/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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(&#39;https://pbs.twimg.com/profile_images/1327039258998304768/RijuiRwR_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Noella Ch. 💜 ENVtuber 💜 maybe pyon musk arc</div> <div style="text-align: center; font-size: 14px;">@noellayoshino</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 Noella Ch. 💜 ENVtuber 💜 maybe pyon musk arc. | Data | Noella Ch. 💜 ENVtuber 💜 maybe pyon musk arc | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 349 | | Short tweets | 1041 | | Tweets kept | 1859 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ho6398t5/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 @noellayoshino's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/r6l29rjm) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/r6l29rjm/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/noellayoshino') 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)
huggingtweets/notanastronomer
b404b8fd315c581b855df49498cdc870d4dfea92
2021-05-22T16:52:36.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/notanastronomer
9
null
transformers
12,322
--- language: en thumbnail: https://www.huggingtweets.com/notanastronomer/1616727503635/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1344888565176487936/SIjKeap6_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lauren Gilbert 🤖 AI Bot </div> <div style="font-size: 15px">@notanastronomer 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 [@notanastronomer's tweets](https://twitter.com/notanastronomer). | Data | Quantity | | --- | --- | | Tweets downloaded | 3221 | | Retweets | 255 | | Short tweets | 349 | | Tweets kept | 2617 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a2lf1xnl/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 @notanastronomer's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kzasb23) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kzasb23/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/notanastronomer') 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)
huggingtweets/peterxinping
18178d36056033e66815a5eb6b325d13c3a71641
2021-05-22T18:31:31.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/peterxinping
9
null
transformers
12,323
--- language: en thumbnail: https://www.huggingtweets.com/peterxinping/1604073988733/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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/1305634622982615040/IfCxeFKW_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Peter 🦍🍌 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@peterxinping 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 [@peterxinping's tweets](https://twitter.com/peterxinping). <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'>3191</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'>145</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'>585</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2461</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/18v07hjh/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 @peterxinping's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/2vg3a37t) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/2vg3a37t/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/peterxinping'</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 -->
huggingtweets/philosoraptor
1f33b669f6f50d7445c66122bca15f57baf86afb
2021-05-22T18:39:54.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/philosoraptor
9
null
transformers
12,324
--- language: en thumbnail: https://www.huggingtweets.com/philosoraptor/1616695417900/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/968909875/symbol_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Real organic pattern 🤖 AI Bot </div> <div style="font-size: 15px">@philosoraptor 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 [@philosoraptor's tweets](https://twitter.com/philosoraptor). | Data | Quantity | | --- | --- | | Tweets downloaded | 3196 | | Retweets | 700 | | Short tweets | 278 | | Tweets kept | 2218 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3k8xlpzy/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 @philosoraptor's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5wwiewx7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5wwiewx7/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/philosoraptor') 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)
huggingtweets/rocio_old
b06174e2de2e6e27c78580191f6d1c3948490f09
2021-05-22T21:18:47.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/rocio_old
9
null
transformers
12,325
--- language: en thumbnail: https://www.huggingtweets.com/rocio_old/1608309167358/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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/1008386786501038085/1GlH4lXi_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Rocio ☀ 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@rocio_old 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 [@rocio_old's tweets](https://twitter.com/rocio_old). <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'>3012</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'>590</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'>491</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1931</td> </tr> </tbody> </table> [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1efigh7w/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 @rocio_old's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3tu41ukw) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3tu41ukw/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/rocio_old'</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)
huggingtweets/scrubphilosophy
f1d6e1c6c678b0c19f473e83f15c220bbdfb5773
2021-05-22T22:16:23.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/scrubphilosophy
9
null
transformers
12,326
--- language: en thumbnail: https://www.huggingtweets.com/scrubphilosophy/1616731281223/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1198090654263283719/Vud98Uvd_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Scrub 🤖 AI Bot </div> <div style="font-size: 15px">@scrubphilosophy 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 [@scrubphilosophy's tweets](https://twitter.com/scrubphilosophy). | Data | Quantity | | --- | --- | | Tweets downloaded | 1923 | | Retweets | 512 | | Short tweets | 467 | | Tweets kept | 944 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39yhwp4h/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 @scrubphilosophy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33gnfi5r) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33gnfi5r/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/scrubphilosophy') 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)
huggingtweets/sellarsrespectr
543833fb179574e0757b926a48995f2533e1838d
2021-05-22T22:25:43.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/sellarsrespectr
9
null
transformers
12,327
--- language: en thumbnail: https://www.huggingtweets.com/sellarsrespectr/1616720155815/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1004831714231742464/zoP72CMZ_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">•Nate• •BLM• 🤖 AI Bot </div> <div style="font-size: 15px">@sellarsrespectr 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 [@sellarsrespectr's tweets](https://twitter.com/sellarsrespectr). | Data | Quantity | | --- | --- | | Tweets downloaded | 3237 | | Retweets | 272 | | Short tweets | 416 | | Tweets kept | 2549 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s51p72h/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 @sellarsrespectr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tus3zndp) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tus3zndp/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/sellarsrespectr') 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)
huggingtweets/sky_obito
d029d9f98678d8597951e37f2dec0bffdcc5be90
2021-05-22T23:00:58.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/sky_obito
9
null
transformers
12,328
--- language: en thumbnail: https://www.huggingtweets.com/sky_obito/1614214046985/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1347274090051117057/3fKG8-pm_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Lenalee (CW: Dragon Prince) 🤖 AI Bot </div> <div style="font-size: 15px">@sky_obito 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 [@sky_obito's tweets](https://twitter.com/sky_obito). | Data | Quantity | | --- | --- | | Tweets downloaded | 3113 | | Retweets | 2349 | | Short tweets | 236 | | Tweets kept | 528 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1z2vftrh/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 @sky_obito's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/396z3s7q) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/396z3s7q/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/sky_obito') 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)
huggingtweets/spknnk
ad9d151cb65abaa95d3b5f5430f5bef2763d69eb
2021-05-22T23:43:17.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/spknnk
9
null
transformers
12,329
--- language: en thumbnail: https://www.huggingtweets.com/spknnk/1616845130596/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1355067555254300673/j96wD3_V_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">я миша 🤖 AI Bot </div> <div style="font-size: 15px">@spknnk 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 [@spknnk's tweets](https://twitter.com/spknnk). | Data | Quantity | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 42 | | Short tweets | 1066 | | Tweets kept | 2142 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/qqeli5b6/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 @spknnk's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hgf21to) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hgf21to/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/spknnk') 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)
huggingtweets/spookymachine
42cfad065b2e8e485d413385d9db29c24483050a
2021-05-22T23:44:41.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/spookymachine
9
null
transformers
12,330
--- language: en thumbnail: https://www.huggingtweets.com/spookymachine/1617758539359/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1379523570473242625/YmJkdku3_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Alea, Conjecture Of Goo 🤖 AI Bot </div> <div style="font-size: 15px">@spookymachine 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 [@spookymachine's tweets](https://twitter.com/spookymachine). | Data | Quantity | | --- | --- | | Tweets downloaded | 3236 | | Retweets | 217 | | Short tweets | 254 | | Tweets kept | 2765 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p3syzv61/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 @spookymachine's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g5tax8a) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g5tax8a/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/spookymachine') 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)
huggingtweets/swedense
cd7b012c61530b2e3b7ffe8935fbd3eadc229338
2021-05-23T00:27:12.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/swedense
9
null
transformers
12,331
--- language: en thumbnail: https://www.huggingtweets.com/swedense/1603209768542/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <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/378800000278977006/4c9e101ebb2a66314de5f74fb4bd7787_400x400.png')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Sweden.se 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@swedense 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 [@swedense's tweets](https://twitter.com/swedense). <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'>3243</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'>438</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'>686</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>2119</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/gn7q9sno/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 @swedense's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/3pxwkwmx) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/3pxwkwmx/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/swedense'</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 -->
huggingtweets/uwusman
c6b1b732896c723da9917dfc40cbec18e141a67c
2021-05-23T03:30:45.000Z
[ "pytorch", "jax", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/uwusman
9
null
transformers
12,332
--- language: en thumbnail: https://www.huggingtweets.com/uwusman/1614213200557/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1362109761894772739/TQjSw0lI_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">UwUsman el Pez | piss arc 🤖 AI Bot </div> <div style="font-size: 15px">@uwusman 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 [@uwusman's tweets](https://twitter.com/uwusman). | Data | Quantity | | --- | --- | | Tweets downloaded | 3241 | | Retweets | 576 | | Short tweets | 629 | | Tweets kept | 2036 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rutezz3k/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 @uwusman's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3i0d4br9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3i0d4br9/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/uwusman') 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)
iarfmoose/wav2vec2-large-xlsr-kyrgyz
6019fb1bd765e42c8ad1cf70944b661cf266d2db
2021-07-06T05:57:02.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "ky", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
iarfmoose
null
iarfmoose/wav2vec2-large-xlsr-kyrgyz
9
null
transformers
12,333
--- language: ky datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Kyrgyz by Adam Montgomerie results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ky type: common_voice args: ky metrics: - name: Test WER type: wer value: 34.71 --- # Wav2Vec2-Large-XLSR-53-Kyrgyz Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Kyrgyz using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ky", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz") model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): \\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\\\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Kyrgyz test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ky", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz") model = Wav2Vec2ForCTC.from_pretrained("iarfmoose/wav2vec2-large-xlsr-kyrgyz") model.to("cuda") chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�\\\\\\\\–\\\\\\\\—\\\\\\\\¬\\\\\\\\⅛]' resampler = torchaudio.transforms.Resample(48_000, 16_000) def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 34.71 % ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Kyrgyz/XLSR_Kyrgyz.ipynb) A notebook of the evaluation script can be found [here](https://github.com/AMontgomerie/wav2vec2-xlsr/blob/main/Kyrgyz/wav2vec2_ky_eval.ipynb)
icelab/spacebert_CR
039c85430ddfcccc7a8e6d1bb7ab78d1af456884
2022-02-16T09:29:17.000Z
[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
icelab
null
icelab/spacebert_CR
9
null
transformers
12,334
--- widget: - text: "The CubeSat RF design shall either have one RF inhibit and a RF power output no greater than 1.5W at the transmitter antenna's RF input OR the CubeSat shall have a minimum of two independent RF inhibits (CDS 3.3.9) (ISO 5.5.6)." --- --- # spacebert_CR ### Model desciption This is a fine-tuned SpaceSciBERT model, for a Concept Recognition task, from the SpaceTransformers model family presented in SpaceTransformers: Language Modeling for Space Systems. The original Git repo is strath-ace/smart-nlp. The [fine-tuning](https://github.com/strath-ace/smart-nlp/blob/master/SpaceTransformers/CR/CR_ECSS_dataset.json) dataset is available for download and consists of 874 unique manual annotated ECSS requirements. The notebookfor fine-tuning can be accessed in Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EGh9bdxq6RqIzbvKuptAWvmIBG2EQJzJ?usp=sharing) ### BibTeX entry and citation info ``` @ARTICLE{ 9548078, author={Berquand, Audrey and Darm, Paul and Riccardi, Annalisa}, journal={IEEE Access}, title={SpaceTransformers: Language Modeling for Space Systems}, year={2021}, volume={9}, number={}, pages={133111-133122}, doi={10.1109/ACCESS.2021.3115659} } ```
ietz/distilroberta-base-finetuned-jira-qt-issue-titles-and-bodies
95f5cebdc126e2242edaf333ae5aef38fbc4d063
2022-01-07T21:26:22.000Z
[ "pytorch", "roberta", "fill-mask", "en", "transformers", "jira", "code", "issue", "development", "license:mit", "autotrain_compatible" ]
fill-mask
false
ietz
null
ietz/distilroberta-base-finetuned-jira-qt-issue-titles-and-bodies
9
null
transformers
12,335
--- language: - en tags: - jira - code - issue - development license: mit --- `distilroberta-base` finetuned for masked language modeling on 247731 mixed issue titles (n=126213) and descriptions (n=121518). Trained for up to 50 epochs.
inergi/wav2vec2-from-scratch-finetune-dummy
d499b1b5a5bbebbae952e52e6b93ed92eeef5cb4
2021-12-15T08:18:49.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "id", "dataset:common_voice", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
inergi
null
inergi/wav2vec2-from-scratch-finetune-dummy
9
null
transformers
12,336
--- language: id datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Indonesian by cahya results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice id type: common_voice args: id metrics: - name: Test WER type: wer value: 25.86 --- Dummy Model New
infinitejoy/wav2vec2-large-xls-r-300m-assamese
3e7f332d83973ff852cdc66c596a731635f68c05
2022-03-24T11:53:47.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "as", "dataset:mozilla-foundation/common_voice_7_0", "transformers", "audio", "speech", "xlsr-fine-tuning", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
infinitejoy
null
infinitejoy/wav2vec2-large-xls-r-300m-assamese
9
1
transformers
12,337
--- license: apache-2.0 language: as tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning - as - robust-speech-event - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: XLS-R-300M - Assamese results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: as metrics: - name: Test WER type: wer value: 72.64 - name: Test CER type: cer value: 27.35 --- # wav2vec2-large-xls-r-300m-assamese This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_7_0 dataset. It achieves the following results on the evaluation set: - WER: 0.7954545454545454 - CER: 0.32341269841269843 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data To compute the evaluation parameters ```bash cd wav2vec2-large-xls-r-300m-assamese; python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config as --split test --log_outputs ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-4 - train_batch_size: 16 - eval_batch_size: 8 - seed: not given - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 400 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------: | | 1.584065 | NA | 400 | 1.584065 | 0.915512 | | 1.658865 | Na | 800 | 1.658865 | 0.805096 | | 1.882352 | NA | 1200 | 1.882352 | 0.820742 | | 1.881240 | NA | 1600 | 1.881240 | 0.810907 | | 2.159748 | NA | 2000 | 2.159748 | 0.804202 | | 1.992871 | NA | 2400 | 1.992871 | 0.803308 | | 2.201436 | NA | 2800 | 2.201436 | 0.802861 | | 2.165218 | NA | 3200 | 2.165218 | 0.793920 | | 2.253643 | NA | 3600 | 2.253643 | 0.796603 | | 2.265880 | NA | 4000 | 2.265880 | 0.790344 | | 2.293935 | NA | 4400 | 2.293935 | 0.797050 | | 2.288851 | NA | 4800 | 2.288851 | 0.784086 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.13.3 - Tokenizers 0.10.3
infinitejoy/wav2vec2-large-xls-r-300m-kurdish
2e2d1551533f1397a25fe742049655d69fd55df5
2022-03-23T18:33:23.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "kmr", "dataset:mozilla-foundation/common_voice_7_0", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
infinitejoy
null
infinitejoy/wav2vec2-large-xls-r-300m-kurdish
9
1
transformers
12,338
--- language: - kmr license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - hf-asr-leaderboard - kmr - model_for_talk - mozilla-foundation/common_voice_7_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: XLS-R-300M - Kurmanji Kurdish results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: kmr metrics: - name: Test WER type: wer value: 102.308 - name: Test CER type: cer value: 538.748 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-kurdish This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KMR dataset. It achieves the following results on the evaluation set: - Loss: 0.2548 - Wer: 0.2688 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.3161 | 12.27 | 2000 | 0.4199 | 0.4797 | | 1.0643 | 24.54 | 4000 | 0.2982 | 0.3721 | | 0.9718 | 36.81 | 6000 | 0.2762 | 0.3333 | | 0.8772 | 49.08 | 8000 | 0.2586 | 0.3051 | | 0.8236 | 61.35 | 10000 | 0.2575 | 0.2865 | | 0.7745 | 73.62 | 12000 | 0.2603 | 0.2816 | | 0.7297 | 85.89 | 14000 | 0.2539 | 0.2727 | | 0.7079 | 98.16 | 16000 | 0.2554 | 0.2681 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
ionite/DialoGPT-medium-Sh0rtiAI
42d0bd0380e451996fa7ab3afbcf872887b418fc
2021-11-16T01:31:47.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
ionite
null
ionite/DialoGPT-medium-Sh0rtiAI
9
1
transformers
12,339
--- tags: - conversational --- # Sh0rtiAI DialoGPT Model
it5/it5-base-repubblica-to-ilgiornale
45eb3ee75c9f147b4e4beec12a93c84eb31322ac
2022-03-09T08:05:15.000Z
[ "pytorch", "tf", "jax", "tensorboard", "t5", "text2text-generation", "it", "dataset:gsarti/change_it", "arxiv:2203.03759", "transformers", "italian", "sequence-to-sequence", "newspaper", "ilgiornale", "repubblica", "style-transfer", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible" ]
text2text-generation
false
it5
null
it5/it5-base-repubblica-to-ilgiornale
9
null
transformers
12,340
--- language: - it license: apache-2.0 datasets: - gsarti/change_it tags: - italian - sequence-to-sequence - newspaper - ilgiornale - repubblica - style-transfer widget: - text: "WASHINGTON - La Corea del Nord torna dopo nove anni nella blacklist Usa degli Stati considerati sponsor del terrorismo. Come Iran, Siria e Sudan. Lo ha deciso Donald Trump , che ha preferito dare l'annuncio non durante il suo recente viaggio in Asia ma ieri, in una riunione del governo alla Casa Bianca. 'Oggi gli Stati Uniti designeranno la Corea del nord come uno stato sponsor del terrorismo', ha tuonato il tycoon, anticipando che sarà formalizzata oggi dal dipartimento di stato e sarà accompagnata da nuove e più severe sanzioni. 'Il livello più alto' mai imposto a Pyongyang, ha promesso. 'Avrebbe dovuto succedere molto tempo fa', ha aggiunto, scaricando per l'ennesima volta la responsabilità dell'attuale crisi sull'amministrazione Obama. Poi si è scagliato contro un 'regime assassino' che 'deve mettere fine allo sviluppo del suo programma illegale nucleare e balistico'. Per giustificare la svolta, Trump ha accusato Pyongyang non solo di 'minacciare il mondo con una devastazione nucleare' ma anche di aver 'ripetutamente sostenuto atti di terrorismo internazionale', compreso omicidi in suolo straniero. Il riferimento è all' uccisione all'aeroporto della capitale malese di Kim Jong Nam , il fratellastro del leader nordcoreano Kim Jong Un , ma non ci sono altri episodi noti. Tanto che alcuni esperti, come pure dirigenti Usa coperti dall'anonimato, dubitano che Pyongyang risponda ai criteri per una tale designazione. La mossa appare altamente simbolica, dato che la Corea del Nord è già pesantemente sanzionata a livello internazionale. Per il segretario di stato Rex Tillerson è solo l'ultima di una serie di passi per rafforzare la pressione su Pyongyang e costringerla a sedersi ad un tavolo perché gli Usa hanno sempre 'speranza nella diplomazia'. Ma nello stesso tempo è un monito per 'fermare e dissuadere' altri Paesi dal sostenere la Corea del Nord, finita nella blacklist 'anche per l'uso di armi chimiche'. Ma la mossa potrebbe anche essere controproducente, provocando una risposta di Kim o minando gli sforzi per sollecitare Pechino ad una maggiore pressione su Pyongyang. In ogni caso non aiuta il dialogo diretto tra Usa e Corea del Nord, che sembrava essere stato avviato in modo riservato. Come non aiutano gli scambi di insulti fra Trump e Kim. Nord Corea, Trump: 'Cerco di essere amico di Kim, sarebbe una bella cosa per il mondo'. Pyongyang era stata messa nella lista Usa degli Stati sponsor del terrorismo per aver fatto esplodere nel 1987 un volo della Korean Air uccidendo tutti i 115 passeggeri a bordo. Ma l'amministrazione di George W. Bush l'aveva rimossa sperando di far avanzare i negoziati sulla denuclearizzazione della penisola coreana. Il governo giapponese sostiene la decisione degli Stati Uniti di inserire la Corea del Nord nella lista degli stati che sponsorizzano il terrorismo, pur riconoscendo che l'annuncio potrebbe provocare una reazione immediata del regime di Pyongyang. Il premier Shinzo Abe ha accolto con consenso il comunicato Usa e ha detto alla stampa che servirà a incrementare la pressione sulla Corea del Nord. Il ministro della Difesa Itsunori Onodera , pur valutando positivamente la notifica, ha spiegato che si attendono azioni provocatorie dallo stato eremita, ribadendo che è vitale rimanere vigili. Secondo la stampa nipponica Abe aveva richiesto al dipartimento di Stato Usa di mettere la Corea del Nord sulla lista durante l'incontro col presidente Usa Donald Trump a Tokyo a inizio mese. L'ultimo lancio di missile balistico condotto da Pyongyang nell'oceano Pacifico, sorvolando il mare del Giappone, risale allo scorso settembre." - text: "ROMA - Una nuova droga killer è stata sequestrata per la prima volta in Europa dagli investigatori del Nas. Si tratta di una nuova \"miscela psicoattiva altamente tossica\" per la prima volta individuata da forze di polizia, simile all'eroina sintetica, ma molto più economica e letale. Tanto che i 20 grammi scoperti sarebbero stati sufficienti per fabbricare ben 20.000 dosi e lo stesso contatto attraverso la pelle può provocare intossicazione. Individuata per la prima volta, la nuova droga presenta una struttura simile al farmaco sedativo Fentanyl ma con effetti molto più devastanti per l'organismo. Proveniva dell'estero ed era contenuta in un plico postale indirizzato in una città del centro Italia: è stata intercettata tramite accertamenti sul web grazie a un'operazione di intelligence che ha visto come protagonisti i militari della Sezione operativa centrale del Comando carabinieri per la Tutela della salute (Nas). Economica e letale, secondo gli investigatori \"in confronto l'eroina è quasi 'acqua fresca', anzi, proprio per la sua economicità, in alcuni casi viene venduta dai pusher a giovani conviti di comprare eroina\". La diffusione di nuove droghe sintetiche che continuamente appaiono sui mercati necessita di un'attività investigativa costante e complessa. Si tratta infatti di sostanze dalla struttura molecolare molto simile a quella del Fentanyl ma ogni volta leggermente diversa. Di qui la difficoltà di individuarle e l'importanza del nuovo sequestro. \"La chiamano impropriamente 'eroina sintetica' - spiega il comandante dei Nas, generale Adelmo Lusi - per il tipo di effetto psicotropo simile, ma dal punto di vista della tossicità è molto peggio: con 25 milligrammi di eroina ci si sballa, con 25mg di simil-fentanyl, come quello appena sequestrato, si muore\". Le indagini sono partite da ricoveri per overdose in ospedale, in cui arrivavano ragazzi che non rispondevano al trattamento disintossicante per l'eroina. La nuova sostanza verrà ora segnalata per l'inserimento tra le tabelle ministeriali degli stupefacenti prevista dal Dpr 309/1990." - text: "Fragile come il burro. Il nostro territorio è precario. Ne sanno qualcosa i comuni che sono stati investititi dal maltempo . Il dissesto idrogeologico imperversa su tutto il territorio. Infatti, oltre 6.600 comuni , pari all’82% del totale, sono in aree ad elevato rischio idrogeologico, pari al 10% della sua superficie. La popolazione potenzialmente esposta è stimata in 5,8 milioni di persone. I dati emergono dalle recenti analisi fatte da Legambiente e Protezione civile, che mettono in evidenza come in 10 anni in Italia sia raddoppiata l’area dei territori colpiti da alluvioni e frane , passando da una media di quattro regioni all’anno a otto regioni. Nella classifica delle regioni a maggior rischio idrogeologico prima è la Calabria con il 100% dei comuni esposti; al 100% ci sono anche la provincia di Trento, il Molise, la Basilicata, l’Umbria, la Valle d’Aosta. Poi Marche, Liguria al 99%; Lazio, Toscana al 98%; Abruzzo (96%), Emilia-Romagna (95%), Campania e Friuli Venezia Giulia al 92%, Piemonte (87%), Sardegna (81%), Puglia (78%), Sicilia (71%), Lombardia (60%), provincia di Bolzano (59%), Veneto (56%). Tra le cause che condizionano ed amplificano il rischio idrogeologico c’è l’azione dell’uomo (abbandono e degrado, cementificazione, consumo di suolo, abusivismo, disboscamento e incendi). Ma anche e soprattutto la mancanza di una seria manutenzione ordinaria e non ad una organica politica di prevenzione." - text: "Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\"." metrics: - rouge - bertscore - headline-headline-consistency-classifier - headline-article-consistency-classifier model-index: - name: it5-base-repubblica-to-ilgiornale results: - task: type: headline-style-transfer-repubblica-to-ilgiornale name: "Headline style transfer (Repubblica to Il Giornale)" dataset: type: gsarti/change_it name: "CHANGE-IT" metrics: - type: rouge1 value: 0.272 name: "Test Rouge1" - type: rouge2 value: 0.089 name: "Test Rouge2" - type: rougeL value: 0.235 name: "Test RougeL" - type: bertscore value: 0.396 name: "Test BERTScore" args: - model_type: "dbmdz/bert-base-italian-xxl-uncased" - lang: "it" - num_layers: 10 - rescale_with_baseline: True - baseline_path: "bertscore_baseline_ita.tsv" - type: headline-headline-consistency-classifier value: 0.883 name: "Test Headline-Headline Consistency Accuracy" - type: headline-article-consistency-classifier value: 0.880 name: "Test Headline-Article Consistency Accuracy" co2_eq_emissions: emissions: "17g" source: "Google Cloud Platform Carbon Footprint" training_type: "fine-tuning" geographical_location: "Eemshaven, Netherlands, Europe" hardware_used: "1 TPU v3-8 VM" thumbnail: https://gsarti.com/publication/it5/featured.png --- # IT5 Base for News Headline Style Transfer (Repubblica to Il Giornale) 🗞️➡️🗞️ 🇮🇹 This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on news headline style transfer in the Repubblica to Il Giornale direction on the Italian CHANGE-IT dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. ## Using the model The model is trained to generate an headline in the style of Il Giornale from the full body of an article written in the style of Repubblica. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines r2g = pipeline("text2text-generation", model='it5/it5-base-repubblica-to-ilgiornale') r2g("Arriva dal Partito nazionalista basco (Pnv) la conferma che i cinque deputati che siedono in parlamento voteranno la sfiducia al governo guidato da Mariano Rajoy. Pochi voti, ma significativi quelli della formazione politica di Aitor Esteban, che interverrà nel pomeriggio. Pur con dimensioni molto ridotte, il partito basco si è trovato a fare da ago della bilancia in aula. E il sostegno alla mozione presentata dai Socialisti potrebbe significare per il primo ministro non trovare quei 176 voti che gli servono per continuare a governare. \" Perché dovrei dimettermi io che per il momento ho la fiducia della Camera e quella che mi è stato data alle urne \", ha detto oggi Rajoy nel suo intervento in aula, mentre procedeva la discussione sulla mozione di sfiducia. Il voto dei baschi ora cambia le carte in tavola e fa crescere ulteriormente la pressione sul premier perché rassegni le sue dimissioni. La sfiducia al premier, o un'eventuale scelta di dimettersi, porterebbe alle estreme conseguenze lo scandalo per corruzione che ha investito il Partito popolare. Ma per ora sembra pensare a tutt'altro. \"Non ha intenzione di dimettersi - ha detto il segretario generale del Partito popolare , María Dolores de Cospedal - Non gioverebbe all'interesse generale o agli interessi del Pp\".") >>> [{"generated_text": "il nazionalista rajoy: 'voteremo la sfiducia'"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-repubblica-to-ilgiornale") model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-repubblica-to-ilgiornale") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ```
it5/mt5-small-question-answering
2db028bf1d12026e25024cf42e777ce509534e4b
2022-03-09T07:57:03.000Z
[ "pytorch", "tf", "jax", "tensorboard", "mt5", "text2text-generation", "it", "dataset:squad_it", "arxiv:2203.03759", "transformers", "italian", "sequence-to-sequence", "squad_it", "text2text-question-answering", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible" ]
text2text-generation
false
it5
null
it5/mt5-small-question-answering
9
null
transformers
12,341
--- language: - it license: apache-2.0 datasets: - squad_it tags: - italian - sequence-to-sequence - squad_it - text2text-question-answering - text2text-generation widget: - text: "In seguito all' evento di estinzione del Cretaceo-Paleogene, l' estinzione dei dinosauri e il clima umido possono aver permesso alla foresta pluviale tropicale di diffondersi in tutto il continente. Dal 66-34 Mya, la foresta pluviale si estendeva fino a sud fino a 45°. Le fluttuazioni climatiche degli ultimi 34 milioni di anni hanno permesso alle regioni della savana di espandersi fino ai tropici. Durante l' Oligocene, ad esempio, la foresta pluviale ha attraversato una banda relativamente stretta. Si espandeva di nuovo durante il Miocene medio, poi si ritrasse ad una formazione prevalentemente interna all' ultimo massimo glaciale. Tuttavia, la foresta pluviale è riuscita ancora a prosperare durante questi periodi glaciali, consentendo la sopravvivenza e l' evoluzione di un' ampia varietà di specie. Domanda: La foresta pluviale amazzonica è diventata per lo più una foresta interna intorno a quale evento globale?" - text: "L' embargo non era uniforme in tutta Europa. Dei nove membri della Comunità Economica Europea (CEE), i Paesi Bassi hanno dovuto affrontare un embargo totale, il Regno Unito e la Francia hanno ricevuto forniture quasi ininterrotte (poichè si sono rifiutati di consentire all' America di utilizzare i loro aerodromi e le armi e forniture embargo sia agli arabi che agli israeliani), mentre gli altri sei hanno dovuto affrontare tagli parziali. Il Regno Unito era tradizionalmente un alleato di Israele, e il governo di Harold Wilson ha sostenuto gli israeliani durante la guerra dei sei giorni. Il suo successore, Ted Heath, ribaltò questa politica nel 1970, chiedendo a Israele di ritirarsi ai suoi confini prima del 1967. Domanda: Il Regno Unito e la Francia non hanno avuto interruzioni dell' approvvigionamento petrolifero in quanto non hanno consentito a quale paese di utilizzare il loro aeroporto?" - text: "Nel 1962, il grafico Paul Rand ridisegna il logo ABC nella sua forma più conosciuta (e attuale) con le lettere minuscole \"abc\" racchiuse in un unico cerchio nero. Il nuovo logo esordisce in onda per le promozioni di ABC all' inizio della stagione 1963-64. Le lettere ricordano fortemente il carattere tipografico Bauhaus disegnato da Herbert Bayer negli anni Venti, ma condividono anche similitudini con diversi altri caratteri, come ITC Avant Garde e Horatio, e lo Chalet più simile. La semplicità del logo ha reso più facile la riprogettazione e la duplicazione, il che ha conferito un beneficio per ABC (soprattutto prima dell' avvento della computer grafica). Domanda: Di quale carattere tipografico ricordano le lettere dell' iconico logo ABC?" - text: "La fotorespirazione può verificarsi quando la concentrazione di ossigeno è troppo elevata. Rubisco non è in grado di distinguere molto bene tra ossigeno e anidride carbonica, quindi può accidentalmente aggiungere O2 invece di CO2 a RuBP. Questo processo riduce l' efficienza della fotosintesi: consuma ATP e ossigeno, rilascia CO2 e non produce zucchero. Può sprecare fino alla metà del carbonio fissato dal ciclo di Calvin. Diversi meccanismi si sono evoluti in diversi lignaggi che aumentano la concentrazione di anidride carbonica rispetto all' ossigeno all' interno del cloroplasto, aumentando l' efficienza della fotosintesi. Questi meccanismi sono chiamati meccanismi di concentrazione dell' anidride carbonica, o CCM. Tra questi figurano il metabolismo degli acidi crassulaceanici, la fissazione del carbonio C4 e i pirenoidi. I cloroplasti negli impianti C4 sono notevoli in quanto presentano un chiaro dimorfismo cloroplastico. Domanda: Che cosa può fare rubisco per errore?" metrics: - f1 - exact-match model-index: - name: mt5-small-question-answering results: - task: type: question-answering name: "Question Answering" dataset: type: squad_it name: "SQuAD-IT" metrics: - type: f1 value: 0.660 name: "Test F1" - type: exact-match value: 0.560 name: "Test Exact Match" co2_eq_emissions: emissions: 17g" source: "Google Cloud Platform Carbon Footprint" training_type: "fine-tuning" geographical_location: "Eemshaven, Netherlands, Europe" hardware_used: "1 TPU v3-8 VM" thumbnail: https://gsarti.com/publication/it5/featured.png --- # mT5 Small for Question Answering ⁉️ 🇮🇹 This repository contains the checkpoint for the [mT5 Small](https://huggingface.co/google/mt5-small) model fine-tuned on extractive question answering on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. ## Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines qa = pipeline("text2text-generation", model='it5/mt5-small-question-answering') qa("In seguito all' evento di estinzione del Cretaceo-Paleogene, l' estinzione dei dinosauri e il clima umido possono aver permesso alla foresta pluviale tropicale di diffondersi in tutto il continente. Dal 66-34 Mya, la foresta pluviale si estendeva fino a sud fino a 45°. Le fluttuazioni climatiche degli ultimi 34 milioni di anni hanno permesso alle regioni della savana di espandersi fino ai tropici. Durante l' Oligocene, ad esempio, la foresta pluviale ha attraversato una banda relativamente stretta. Si espandeva di nuovo durante il Miocene medio, poi si ritrasse ad una formazione prevalentemente interna all' ultimo massimo glaciale. Tuttavia, la foresta pluviale è riuscita ancora a prosperare durante questi periodi glaciali, consentendo la sopravvivenza e l' evoluzione di un' ampia varietà di specie. Domanda: La foresta pluviale amazzonica è diventata per lo più una foresta interna intorno a quale evento globale?") >>> [{"generated_text": "ultimo massimo glaciale"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/mt5-small-question-answering") model = AutoModelForSeq2SeqLM.from_pretrained("it5/mt5-small-question-answering") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ```
jean-paul/KinyaBERT-small
4575f11375376ae29a8b19610f70c07ee04d02eb
2021-08-29T10:24:38.000Z
[ "pytorch", "bert", "fill-mask", "arxiv:1810.04805", "transformers", "autotrain_compatible" ]
fill-mask
false
jean-paul
null
jean-paul/KinyaBERT-small
9
null
transformers
12,342
# Model description A Pretrained model on the Kinyarwanda language dataset using a masked language modeling (MLM) objective. The BERT model was first introduced in [this paper](https://arxiv.org/abs/1810.04805). This KinyaBERT model was pretrained with uncased tokens which means that no difference between for example ikinyarwanda and Ikinyarwanda. # Training parameters #### Dataset The data set used has both sources from the new articles in Rwanda extracted from different new web pages, dumped Wikipedia files, and the books in Kinyarwanda. The sizes of the sources of data are 72 thousand new articles, three thousand dumped Wikipedia articles, and six books with more than a thousand pages. #### Hyperparameters The model was trained with the default configuration of BERT and Trainer from the Huggingface. However, due to some resource computation issues, we kept the number of transformer layers to 6. # How to use: 1) The model can be used directly with the pipeline for masked language modeling as follows: ``` from transformers import pipeline the_mask_pipe = pipeline( "fill-mask", model='jean-paul/KinyaBERT-small', tokenizer='jean-paul/KinyaBERT-small', ) the_mask_pipe("Ejo ndikwiga nagize [MASK] baje kunsura.") [{'sequence': 'ejo ndikwiga nagize ubwoba baje kunsura.', 'score': 0.15674786269664764, 'token': 2387, 'token_str': 'ubwoba'}, {'sequence': 'ejo ndikwiga nagize ngo baje kunsura.', 'score': 0.13958698511123657, 'token': 196, 'token_str': 'ngo'}, {'sequence': 'ejo ndikwiga nagize inyota baje kunsura.', 'score': 0.07670339196920395, 'token': 8797, 'token_str': 'inyota'}, {'sequence': 'ejo ndikwiga nagize amahirwe baje kunsura.', 'score': 0.07234629988670349, 'token': 1501, 'token_str': 'amahirwe'}, {'sequence': 'ejo ndikwiga nagize abana baje kunsura.', 'score': 0.05717536434531212, 'token': 526, 'token_str': 'abana'}] ``` 2) Direct use from the transformer library to get features using AutoModel ``` from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jean-paul/KinyaBERT-small") model = AutoModelForMaskedLM.from_pretrained("jean-paul/KinyaBERT-small") input_text = "Ejo ndikwiga nagize abashyitsi baje kunsura." encoded_input = tokenizer(input_text, return_tensors='pt') output = model(**encoded_input) ``` __Note__: We used the huggingface implementations for pretraining BERT from scratch, both the BERT model and the classes needed to do it.
jeniakim/hedgehog
d3a64a1c24dce72e4a52c63570f7faa744678f55
2022-03-30T09:27:38.000Z
[ "pytorch", "bert", "token-classification", "en", "transformers", "license:mit", "autotrain_compatible" ]
token-classification
false
jeniakim
null
jeniakim/hedgehog
9
1
transformers
12,343
--- language: en license: mit inference: false --- 🦔 HEDGEhog 🦔: BERT-based multi-class uncertainty cues recognition ==================================================================== # Description A fine-tuned multi-class classification model that detects four different types of uncertainty cues (a.k.a hedges) on a token level. # Uncertainty types label | type | description | example ---| ---| ---| --- E | Epistemic | The proposition is possible, but its truth-value cannot be decided at the moment. | She **may** be already asleep. I | Investigation | The proposition is in the process of having its truth-value determined. | She **examined** the role of NF-kappaB in protein activation. D | Doxatic | The proposition expresses beliefs and hypotheses, which may be known as true or false by others. | She **believes** that the Earth is flat. N | Condition | The proposition is true or false based on the truth-value of another proposition. | **If** she gets the job, she will move to Utrecht. C | *certain* | *n/a* | *n/a* # Intended uses and limitations - The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled. # How to use To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library: ``` from simpletransformers.ner import NERModel model = NERModel( 'bert', 'jeniakim/hedgehog', use_cuda=False, labels=["C", "D", "E", "I", "N"], ) example = "As much as I definitely enjoy solitude, I wouldn't mind perhaps spending little time with you (Björk)" predictions, raw_outputs = model.predict([example]) ``` The predictions look like this: ``` [[{'As': 'C'}, {'much': 'C'}, {'as': 'C'}, {'I': 'C'}, {'definitely': 'C'}, {'enjoy': 'C'}, {'solitude,': 'C'}, {'I': 'C'}, {"wouldn't": 'C'}, {'mind': 'C'}, {'perhaps': 'E'}, {'spending': 'C'}, {'little': 'C'}, {'time': 'C'}, {'with': 'C'}, {'you': 'C'}, {'(Björk)': 'C'}]] ``` In other words, the token 'perhaps' is recognized as an **epistemic uncertainty cue** and all the other tokens are not uncertainty cues. # Training Data HEDGEhog is trained and evaluated on the [Szeged Uncertainty Corpus](https://rgai.inf.u-szeged.hu/node/160) (Szarvas et al. 2012<sup>1</sup>). The original sentence-level XML version of this dataset is available [here](https://rgai.inf.u-szeged.hu/node/160). The token-level version that was used for the training can be downloaded from [here](https://1drv.ms/u/s!AvPkt_QxBozXk7BiazucDqZkVxLo6g?e=IisuM6) in a form of pickled pandas DataFrame's. You can download either the split sets (```train.pkl``` 137MB, ```test.pkl``` 17MB, ```dev.pkl``` 17MB) or the full dataset (```szeged_fixed.pkl``` 172MB). Each row in the df contains a token, its features (these are not relevant for HEDGEhog; they were used to train the baseline CRF model, see [here](https://github.com/vanboefer/uncertainty_crf)), its sentence ID, and its label. # Training Procedure The following training parameters were used: - Optimizer: AdamW - Learning rate: 4e-5 - Num train epochs: 1 - Train batch size: 16 # Evaluation Results class | precision | recall | F1-score | support ---|---|---|---|--- Epistemic | 0.90 | 0.85 | 0.88 | 624 Doxatic | 0.88 | 0.92 | 0.90 | 142 Investigation | 0.83 | 0.86 | 0.84 | 111 Condition | 0.85 | 0.87 | 0.86 | 86 Certain | 1.00 | 1.00 | 1.00 | 104,751 **macro average** | **0.89** | **0.90** | **0.89** | 105,714 # References <sup>1</sup> Szarvas, G., Vincze, V., Farkas, R., Móra, G., & Gurevych, I. (2012). Cross-genre and cross-domain detection of semantic uncertainty. *Computational Linguistics, 38*(2), 335-367.
jky594176/recipe_BART2
0c53b41356f0550307b5706a5fb68eeaa1f1286b
2021-05-31T21:04:14.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
jky594176
null
jky594176/recipe_BART2
9
null
transformers
12,344
Entry not found
jpabbuehl/sagemaker-distilbert-emotion
61884dd0430fa1f49fbd0efc1c08de3e489fc805
2021-11-20T14:22:59.000Z
[ "pytorch", "distilbert", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
jpabbuehl
null
jpabbuehl/sagemaker-distilbert-emotion
9
null
transformers
12,345
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: sagemaker-distilbert-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.929 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sagemaker-distilbert-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1446 - Accuracy: 0.929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9345 | 1.0 | 500 | 0.2509 | 0.918 | | 0.1855 | 2.0 | 1000 | 0.1626 | 0.928 | | 0.1036 | 3.0 | 1500 | 0.1446 | 0.929 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
jrbarnard/t5-generate-answer
661789130f1d7bc047db447b3e777ddd96c79892
2021-06-23T12:30:00.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
jrbarnard
null
jrbarnard/t5-generate-answer
9
null
transformers
12,346
Entry not found
juliamendelsohn/framing_narrative
b84f6f08c81954ff16a3f1ed97984e6f5692ea60
2021-05-20T17:28:06.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
juliamendelsohn
null
juliamendelsohn/framing_narrative
9
null
transformers
12,347
Entry not found
keshan/sinhala-gpt2
a3472bef30f6db8d5f6f230cfdedca87ca29999c
2021-07-11T17:53:31.000Z
[ "pytorch", "tf", "jax", "tensorboard", "gpt2", "feature-extraction", "si", "dataset:mc4", "transformers", "Sinhala", "text-generation" ]
feature-extraction
false
keshan
null
keshan/sinhala-gpt2
9
null
transformers
12,348
--- language: si tags: - Sinhala - text-generation - gpt2 datasets: - mc4 --- ### Overview This is a smaller GPT2 model trained on [MC4](https://github.com/allenai/allennlp/discussions/5056) Sinhala dataset. As Sinhala is one of those low resource languages, there are only a handful of models been trained. So, this would be a great place to start training for more downstream tasks. ## Model Specification The model chosen for training is GPT2 with the following specifications: 1. vocab_size=50257 2. n_embd=768 3. n_head=12 4. n_layer=12 5. n_positions=1024 ## How to Use You can use this model directly with a pipeline for casual language modeling: ```py from transformers import pipeline generator = pipeline('text-generation', model='keshan/sinhala-gpt2') generator("මම", max_length=50, num_return_sequences=5) ```
kitaev/tetra-tag-en
a2ba70aec8fd516c02021fda82974dda02f66c78
2021-05-19T21:01:26.000Z
[ "pytorch", "jax", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
kitaev
null
kitaev/tetra-tag-en
9
null
transformers
12,349
Entry not found
kleinay/qanom-seq2seq-model-baseline
417ef6dcec8ea534c7092263aa0d7f53c7dde13a
2022-04-04T11:05:47.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:kleinay/qanom", "transformers", "semantic-role-labeling", "question-answer generation", "autotrain_compatible" ]
text2text-generation
false
kleinay
null
kleinay/qanom-seq2seq-model-baseline
9
null
transformers
12,350
--- language: - en tags: - semantic-role-labeling - question-answer generation - pytorch datasets: - kleinay/qanom --- # A Seq2Seq model for QANom parsing This is a `t5-small` pretrained model, fine-tuned on the task of generating QANom QAs. "QANom" stands for "QASRL for Nominalizations", which is an adaptation of [QASRL (Question-Answer driven Semantic Role Labeling)](https://qasrl.org) for the nominal predicates domain. See the [QANom paper](https://aclanthology.org/2020.coling-main.274/) for details about the task. The QANom Dataset official site is a [Google drive](https://drive.google.com/drive/folders/15PHKVdPm65ysgdkV47z6J_73kETk7_of), but we also wrapped it into a [Huggingface Dataset](https://huggingface.co/datasets/biu-nlp/qanom), which is easier to plug-and-play with (check out our [HF profile](https://huggingface.co/biu-nlp) for other related datasets, such as QASRL, QAMR, QADiscourse, and QA-Align). ## Demo Visit [our demo](https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo) for interactively exploring our model! ## Usage The model and tokenizer can be downloaded as simply as running: ```python import transformers model = transformers.AutoModelForSeq2SeqLM.from_pretrained("kleinay/qanom-seq2seq-model-baseline") tokenizer = transformers.AutoTokenizer.from_pretrained("kleinay/qanom-seq2seq-model-baseline") ``` However, the model fine-tuning procedure involves input preprocessing (marking the predicate in the sentence, T5's "task prefix", incorporating the predicate type and/or the verbal for of the nominalization) and output postprocessing (parsing the sequence into a list of QASRL-formatted QAs). In order to use the model for QANom parsing easily, we suggest downloading the [`pipeline.py`](https://huggingface.co/kleinay/qanom-seq2seq-model-baseline/blob/main/pipeline.py) file from this repository, and then use the `QASRL_Pipeline` class: ```python from pipeline import QASRL_Pipeline pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline") pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal") ``` Which will output: ```json [{'generated_text': 'who _ _ researched something _ _ ?<extra_id_7> Luke', 'QAs': [{'question': 'who researched something ?', 'answers': ['Luke']}]}] ``` You can learn more about using `transformers.pipelines` in the [official docs](https://huggingface.co/docs/transformers/main_classes/pipelines). Notice that you need to specify which word in the sentence is the predicate, about which the question will interrogate. By default, you should precede the predicate with the `<predicate>` symbol, but you can also specify your own predicate marker: ```python pipe("The student was interested in Luke 's <PRED> research about see animals .", verb_form="research", predicate_type="nominal", predicate_marker="<PRED>") ``` In addition, you can specify additional kwargs for controling the model's decoding algorithm: ```python pipe("The student was interested in Luke 's <predicate> research about see animals .", verb_form="research", predicate_type="nominal", num_beams=3) ```
kykim/t5-kor-small
29fbf536a797fdcb63c4af4b71b70df615e18d53
2021-06-23T12:31:25.000Z
[ "pytorch", "tf", "jax", "t5", "feature-extraction", "transformers" ]
feature-extraction
false
kykim
null
kykim/t5-kor-small
9
null
transformers
12,351
Entry not found
lgris/sew-tiny-portuguese-cv
b924758fccaff0db4c5a8baa8b699fab25ecf68a
2022-03-23T18:27:49.000Z
[ "pytorch", "sew", "automatic-speech-recognition", "pt", "dataset:common_voice", "transformers", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
lgris
null
lgris/sew-tiny-portuguese-cv
9
null
transformers
12,352
--- language: - pt license: apache-2.0 tags: - generated_from_trainer - hf-asr-leaderboard - pt - robust-speech-event datasets: - common_voice model-index: - name: sew-tiny-portuguese-cv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 6 type: common_voice args: pt metrics: - name: Test WER type: wer value: 30.02 - name: Test CER type: cer value: 10.34 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: sv metrics: - name: Test WER type: wer value: 56.46 - name: Test CER type: cer value: 22.94 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: pt metrics: - name: Test WER type: wer value: 57.17 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: pt metrics: - name: Test WER type: wer value: 61.3 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sew-tiny-portuguese-cv This model is a fine-tuned version of [lgris/sew-tiny-pt](https://huggingface.co/lgris/sew-tiny-pt) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.5110 - Wer: 0.2842 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 40000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | No log | 4.92 | 1000 | 0.8468 | 0.6494 | | 3.4638 | 9.85 | 2000 | 0.4978 | 0.3815 | | 3.4638 | 14.78 | 3000 | 0.4734 | 0.3417 | | 0.9904 | 19.7 | 4000 | 0.4577 | 0.3344 | | 0.9904 | 24.63 | 5000 | 0.4376 | 0.3170 | | 0.8849 | 29.55 | 6000 | 0.4225 | 0.3118 | | 0.8849 | 34.48 | 7000 | 0.4354 | 0.3080 | | 0.819 | 39.41 | 8000 | 0.4434 | 0.3004 | | 0.819 | 44.33 | 9000 | 0.4710 | 0.3132 | | 0.7706 | 49.26 | 10000 | 0.4497 | 0.3064 | | 0.7706 | 54.19 | 11000 | 0.4598 | 0.3100 | | 0.7264 | 59.11 | 12000 | 0.4271 | 0.3013 | | 0.7264 | 64.04 | 13000 | 0.4333 | 0.2959 | | 0.6909 | 68.96 | 14000 | 0.4554 | 0.3019 | | 0.6909 | 73.89 | 15000 | 0.4444 | 0.2888 | | 0.6614 | 78.81 | 16000 | 0.4734 | 0.3081 | | 0.6614 | 83.74 | 17000 | 0.4820 | 0.3058 | | 0.6379 | 88.67 | 18000 | 0.4416 | 0.2950 | | 0.6379 | 93.59 | 19000 | 0.4614 | 0.2974 | | 0.6055 | 98.52 | 20000 | 0.4812 | 0.3018 | | 0.6055 | 103.45 | 21000 | 0.4700 | 0.3018 | | 0.5823 | 108.37 | 22000 | 0.4726 | 0.2999 | | 0.5823 | 113.3 | 23000 | 0.4979 | 0.2887 | | 0.5597 | 118.23 | 24000 | 0.4813 | 0.2980 | | 0.5597 | 123.15 | 25000 | 0.4968 | 0.2972 | | 0.542 | 128.08 | 26000 | 0.5331 | 0.3059 | | 0.542 | 133.0 | 27000 | 0.5046 | 0.2978 | | 0.5185 | 137.93 | 28000 | 0.4882 | 0.2922 | | 0.5185 | 142.85 | 29000 | 0.4945 | 0.2938 | | 0.499 | 147.78 | 30000 | 0.4971 | 0.2913 | | 0.499 | 152.71 | 31000 | 0.4948 | 0.2873 | | 0.4811 | 157.63 | 32000 | 0.4924 | 0.2918 | | 0.4811 | 162.56 | 33000 | 0.5128 | 0.2911 | | 0.4679 | 167.49 | 34000 | 0.5098 | 0.2892 | | 0.4679 | 172.41 | 35000 | 0.4966 | 0.2863 | | 0.456 | 177.34 | 36000 | 0.5033 | 0.2839 | | 0.456 | 182.27 | 37000 | 0.5114 | 0.2875 | | 0.4453 | 187.19 | 38000 | 0.5154 | 0.2859 | | 0.4453 | 192.12 | 39000 | 0.5102 | 0.2847 | | 0.4366 | 197.04 | 40000 | 0.5110 | 0.2842 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
liamliang/hate_speech_content
1dc3ab4825b27fb2a1f6e947185386ac6c9c993b
2021-05-19T21:58:27.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
liamliang
null
liamliang/hate_speech_content
9
null
transformers
12,353
Entry not found
liyijing024/hate_speech_target
edfd3575267df0c9df231861e57abdc7ddbfac95
2021-11-23T18:23:13.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
liyijing024
null
liyijing024/hate_speech_target
9
null
transformers
12,354
Entry not found
luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_1e5_bb_lr_1e5_grad_adam
456b78f0769e43207b0f1f26af35444f68d8a07f
2021-10-29T21:07:50.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
luffycodes
null
luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_1e5_bb_lr_1e5_grad_adam
9
null
transformers
12,355
Entry not found
m3hrdadfi/icelandic-ner-bert
2e9742e6d84b25b312654fb49d9d42fce070c7fe
2021-05-27T17:14:13.000Z
[ "pytorch", "tf", "bert", "token-classification", "is", "transformers", "license:apache-2.0", "autotrain_compatible" ]
token-classification
false
m3hrdadfi
null
m3hrdadfi/icelandic-ner-bert
9
null
transformers
12,356
--- language: is license: apache-2.0 widget: - text: "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ." - text: "Til hvers að kjósa flokk , sem þykist vera Jafnaðarmannaflokkur rétt fyrir kosningar , þegar að það er hægt að kjósa sannnan jafnaðarmannaflokk , sjálfan Jafnaðarmannaflokk Íslands - Samfylkinguna ." - text: "Það sannaðist svo eftirminnilega á plötunni Það þarf fólk eins og þig sem kom út fyrir þremur árum , en á henni hann Fálka úr Keflavík og Gáluna , son sinn , til að útsetja lög hans og spila inn ." - text: "Lögin hafa áður komið út sem aukalög á smáskífum af Hail to the Thief , en á disknum er líka myndband og fleira efni fyrir tölvur ." - text: "Britney gerði honum viðvart og hann ók henni á UCLA-sjúkrahúsið í Santa Monica en það er í nágrenni hljóðversins ." --- # IcelandicNER BERT This model was fine-tuned on the MIM-GOLD-NER dataset for the Icelandic language. The [MIM-GOLD-NER](http://hdl.handle.net/20.500.12537/42) corpus was developed at [Reykjavik University](https://en.ru.is/) in 2018–2020 that covered eight types of entities: - Date - Location - Miscellaneous - Money - Organization - Percent - Person - Time ## Dataset Information | | Records | B-Date | B-Location | B-Miscellaneous | B-Money | B-Organization | B-Percent | B-Person | B-Time | I-Date | I-Location | I-Miscellaneous | I-Money | I-Organization | I-Percent | I-Person | I-Time | |:------|----------:|---------:|-------------:|------------------:|----------:|-----------------:|------------:|-----------:|---------:|---------:|-------------:|------------------:|----------:|-----------------:|------------:|-----------:|---------:| | Train | 39988 | 3409 | 5980 | 4351 | 729 | 5754 | 502 | 11719 | 868 | 2112 | 516 | 3036 | 770 | 2382 | 50 | 5478 | 790 | | Valid | 7063 | 570 | 1034 | 787 | 100 | 1078 | 103 | 2106 | 147 | 409 | 76 | 560 | 104 | 458 | 7 | 998 | 136 | | Test | 8299 | 779 | 1319 | 935 | 153 | 1315 | 108 | 2247 | 172 | 483 | 104 | 660 | 167 | 617 | 10 | 1089 | 158 | ## Evaluation The following tables summarize the scores obtained by model overall and per each class. | entity | precision | recall | f1-score | support | |:-------------:|:---------:|:--------:|:--------:|:-------:| | Date | 0.969466 | 0.978177 | 0.973802 | 779.0 | | Location | 0.955201 | 0.953753 | 0.954476 | 1319.0 | | Miscellaneous | 0.867033 | 0.843850 | 0.855285 | 935.0 | | Money | 0.979730 | 0.947712 | 0.963455 | 153.0 | | Organization | 0.893939 | 0.897338 | 0.895636 | 1315.0 | | Percent | 1.000000 | 1.000000 | 1.000000 | 108.0 | | Person | 0.963028 | 0.973743 | 0.968356 | 2247.0 | | Time | 0.976879 | 0.982558 | 0.979710 | 172.0 | | micro avg | 0.938158 | 0.938958 | 0.938558 | 7028.0 | | macro avg | 0.950659 | 0.947141 | 0.948840 | 7028.0 | | weighted avg | 0.937845 | 0.938958 | 0.938363 | 7028.0 | ## How To Use You use this model with Transformers pipeline for NER. ### Installing requirements ```bash pip install transformers ``` ### How to predict using pipeline ```python from transformers import AutoTokenizer from transformers import AutoModelForTokenClassification # for pytorch from transformers import TFAutoModelForTokenClassification # for tensorflow from transformers import pipeline model_name_or_path = "m3hrdadfi/icelandic-ner-bert" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch # model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Kristin manneskja getur ekki lagt frásagnir af Jesú Kristi á hilluna vegna þess að hún sé búin að lesa þær ." ner_results = nlp(example) print(ner_results) ``` ## Questions? Post a Github issue on the [IcelandicNER Issues](https://github.com/m3hrdadfi/icelandic-ner/issues) repo.
maelfabien/marcel_customer_service_large
19b2639f4a19c624d23c9221dc92983f3f655bc9
2021-04-13T23:23:56.000Z
[ "pytorch", "camembert", "text-generation", "transformers" ]
text-generation
false
maelfabien
null
maelfabien/marcel_customer_service_large
9
null
transformers
12,357
Entry not found
maelfabien/marcel_customer_service_medium
25a1ddb0d0ec9d1665288f9dfa64ab16ad6d501f
2021-04-13T23:42:16.000Z
[ "pytorch", "camembert", "text-generation", "transformers" ]
text-generation
false
maelfabien
null
maelfabien/marcel_customer_service_medium
9
null
transformers
12,358
Entry not found
malay-huggingface/xlnet-large-bahasa-cased
e9ab399fc4e57730b3321e9c4df0581c9ad89545
2021-09-26T12:57:26.000Z
[ "pytorch", "xlnet", "feature-extraction", "ms", "transformers" ]
feature-extraction
false
malay-huggingface
null
malay-huggingface/xlnet-large-bahasa-cased
9
null
transformers
12,359
--- language: ms --- # xlnet-large-bahasa-cased Pretrained XLNET large language model for Malay. ## Pretraining Corpus `xlnet-large-bahasa-cased` model was pretrained on ~1.4 Billion words. Below is list of data we trained on, 1. [cleaned local texts](https://github.com/huseinzol05/malay-dataset/tree/master/dumping/clean). 2. [translated The Pile](https://github.com/huseinzol05/malay-dataset/tree/master/corpus/pile). ## Pretraining details - All steps can reproduce from here, [Malaya/pretrained-model/xlnet](https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/xlnet). ## Load Pretrained Model You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this: ```python from transformers import XLNetModel, XLNetTokenizer model = XLNetModel.from_pretrained('malay-huggingface/xlnet-large-bahasa-cased') tokenizer = XLNetTokenizer.from_pretrained( 'malay-huggingface/xlnet-large-bahasa-cased', do_lower_case = False, ) ```
maxidl/iML-distilbert-base-uncased-predict
2fd10dd30d3f71245a4db3533f5ea718a22e93b0
2021-11-18T21:47:02.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
maxidl
null
maxidl/iML-distilbert-base-uncased-predict
9
null
transformers
12,360
Entry not found
maximedb/paws-x-all-x-en
b89acf3dc80b19aad39ca62eb18d57d188feb7d4
2021-10-20T18:40:36.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
maximedb
null
maximedb/paws-x-all-x-en
9
null
transformers
12,361
Entry not found
mbeukman/xlm-roberta-base-finetuned-ner-wolof
0afdf7ac5fe7e91fe1f2cafc8ec6f995b94b1d2b
2021-11-25T09:04:43.000Z
[ "pytorch", "xlm-roberta", "token-classification", "wo", "dataset:masakhaner", "arxiv:2103.11811", "transformers", "NER", "autotrain_compatible" ]
token-classification
false
mbeukman
null
mbeukman/xlm-roberta-base-finetuned-ner-wolof
9
null
transformers
12,362
--- language: - wo tags: - NER datasets: - masakhaner metrics: - f1 - precision - recall widget: - text: "SAFIYETU BÉEY Céy Koronaa !" --- # xlm-roberta-base-finetuned-ner-wolof This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Wolof part. More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). ## About This model is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages. The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set). This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021. This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). ### Contact & More information For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository. ### Training Resources In the interest of openness, and reporting resources used, we list here how long the training process took, as well as what the minimum resources would be to reproduce this. Fine-tuning each model on the NER dataset took between 10 and 30 minutes, and was performed on a NVIDIA RTX3090 GPU. To use a batch size of 32, at least 14GB of GPU memory was required, although it was just possible to fit these models in around 6.5GB's of VRAM when using a batch size of 1. ## Data The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality. The motivation for the use of this data is that it is the "first large, publicly available, high­ quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811). ## Intended Use This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next. ## Limitations This model was only trained on one (relatively small) dataset, covering one task (NER) in one domain (news articles) and in a set span of time. The results may not generalise, and the model may perform badly, or in an unfair / biased way if used on other tasks. Although the purpose of this project was to investigate transfer learning, the performance on languages that the model was not trained for does suffer. Because this model used xlm-roberta-base as its starting point (potentially with domain adaptive fine-tuning on specific languages), this model's limitations can also apply here. These can include being biased towards the hegemonic viewpoint of most of its training data, being ungrounded and having subpar results on other languages (possibly due to unbalanced training data). As [Adelani et al. (2021)](https://arxiv.org/abs/2103.11811) showed, the models in general struggled with entities that were either longer than 3 words and entities that were not contained in the training data. This could bias the models towards not finding, e.g. names of people that have many words, possibly leading to a misrepresentation in the results. Similarly, names that are uncommon, and may not have been found in the training data (due to e.g. different languages) would also be predicted less often. Additionally, this model has not been verified in practice, and other, more subtle problems may become prevalent if used without any verification that it does what it is supposed to. ### Privacy & Ethical Considerations The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details. No explicit ethical considerations or adjustments were made during fine-tuning of this model. ## Metrics The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories. These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise. We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable. The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes. ## Caveats and Recommendations In general, this model performed worse on the 'date' category compared to others, so if dates are a critical factor, then that might need to be taken into account and addressed, by for example collecting and annotating more data. ## Model Structure Here are some performance details on this specific model, compared to others we trained. All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category. This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)): Abbreviation|Description -|- O|Outside of a named entity B-DATE |Beginning of a DATE entity right after another DATE entity I-DATE |DATE entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location | Model Name | Staring point | Evaluation / Fine-tune Language | F1 | Precision | Recall | F1 (DATE) | F1 (LOC) | F1 (ORG) | F1 (PER) | | -------------------------------------------------- | -------------------- | -------------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | | [xlm-roberta-base-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-wolof) (This model) | [base](https://huggingface.co/xlm-roberta-base) | wol | 66.12 | 69.46 | 63.09 | 30.00 | 84.00 | 54.00 | 59.00 | | [xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | wol | 69.01 | 73.25 | 65.23 | 27.00 | 85.00 | 52.00 | 67.00 | | [xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof) | [wol](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-wolof) | wol | 69.02 | 67.60 | 70.51 | 30.00 | 84.00 | 44.00 | 71.00 | ## Usage To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)): ``` from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline model_name = 'mbeukman/xlm-roberta-base-finetuned-ner-wolof' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForTokenClassification.from_pretrained(model_name) nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "SAFIYETU BÉEY Céy Koronaa !" ner_results = nlp(example) print(ner_results) ```
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili
a239f32f3b22fe5a91bb64db052c63ba46ebd5ff
2021-11-25T09:05:03.000Z
[ "pytorch", "xlm-roberta", "token-classification", "sw", "dataset:masakhaner", "arxiv:2103.11811", "transformers", "NER", "autotrain_compatible" ]
token-classification
false
mbeukman
null
mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili
9
null
transformers
12,363
--- language: - sw tags: - NER datasets: - masakhaner metrics: - f1 - precision - recall widget: - text: "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ." --- # xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili part. More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). ## About This model is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages. The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set). This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021. This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). ### Contact & More information For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository. ### Training Resources In the interest of openness, and reporting resources used, we list here how long the training process took, as well as what the minimum resources would be to reproduce this. Fine-tuning each model on the NER dataset took between 10 and 30 minutes, and was performed on a NVIDIA RTX3090 GPU. To use a batch size of 32, at least 14GB of GPU memory was required, although it was just possible to fit these models in around 6.5GB's of VRAM when using a batch size of 1. ## Data The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality. The motivation for the use of this data is that it is the "first large, publicly available, high­ quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811). ## Intended Use This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next. ## Limitations This model was only trained on one (relatively small) dataset, covering one task (NER) in one domain (news articles) and in a set span of time. The results may not generalise, and the model may perform badly, or in an unfair / biased way if used on other tasks. Although the purpose of this project was to investigate transfer learning, the performance on languages that the model was not trained for does suffer. Because this model used xlm-roberta-base as its starting point (potentially with domain adaptive fine-tuning on specific languages), this model's limitations can also apply here. These can include being biased towards the hegemonic viewpoint of most of its training data, being ungrounded and having subpar results on other languages (possibly due to unbalanced training data). As [Adelani et al. (2021)](https://arxiv.org/abs/2103.11811) showed, the models in general struggled with entities that were either longer than 3 words and entities that were not contained in the training data. This could bias the models towards not finding, e.g. names of people that have many words, possibly leading to a misrepresentation in the results. Similarly, names that are uncommon, and may not have been found in the training data (due to e.g. different languages) would also be predicted less often. Additionally, this model has not been verified in practice, and other, more subtle problems may become prevalent if used without any verification that it does what it is supposed to. ### Privacy & Ethical Considerations The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details. No explicit ethical considerations or adjustments were made during fine-tuning of this model. ## Metrics The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories. These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise. We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable. The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes. ## Caveats and Recommendations In general, this model performed worse on the 'date' category compared to others, so if dates are a critical factor, then that might need to be taken into account and addressed, by for example collecting and annotating more data. ## Model Structure Here are some performance details on this specific model, compared to others we trained. All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category. This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)): Abbreviation|Description -|- O|Outside of a named entity B-DATE |Beginning of a DATE entity right after another DATE entity I-DATE |DATE entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location | Model Name | Staring point | Evaluation / Fine-tune Language | F1 | Precision | Recall | F1 (DATE) | F1 (LOC) | F1 (ORG) | F1 (PER) | | -------------------------------------------------- | -------------------- | -------------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | | [xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili) (This model) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | swa | 90.36 | 88.59 | 92.20 | 86.00 | 93.00 | 79.00 | 96.00 | | [xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili) | [hau](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-hausa) | swa | 88.36 | 86.95 | 89.82 | 86.00 | 91.00 | 77.00 | 94.00 | | [xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili) | [ibo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-igbo) | swa | 87.75 | 86.55 | 88.97 | 85.00 | 92.00 | 77.00 | 91.00 | | [xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili) | [kin](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-kinyarwanda) | swa | 87.26 | 85.15 | 89.48 | 83.00 | 91.00 | 75.00 | 93.00 | | [xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili) | [lug](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luganda) | swa | 88.93 | 87.64 | 90.25 | 83.00 | 92.00 | 79.00 | 95.00 | | [xlm-roberta-base-finetuned-luo-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-swahili) | [luo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luo) | swa | 87.93 | 86.91 | 88.97 | 83.00 | 91.00 | 76.00 | 94.00 | | [xlm-roberta-base-finetuned-naija-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-naija-finetuned-ner-swahili) | [pcm](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-naija) | swa | 87.26 | 85.15 | 89.48 | 83.00 | 91.00 | 75.00 | 93.00 | | [xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili) | [wol](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-wolof) | swa | 87.80 | 86.50 | 89.14 | 86.00 | 90.00 | 78.00 | 93.00 | | [xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili) | [yor](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-yoruba) | swa | 87.73 | 86.67 | 88.80 | 85.00 | 91.00 | 75.00 | 93.00 | | [xlm-roberta-base-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-swahili) | [base](https://huggingface.co/xlm-roberta-base) | swa | 88.71 | 86.84 | 90.67 | 83.00 | 91.00 | 79.00 | 95.00 | ## Usage To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)): ``` from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline model_name = 'mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForTokenClassification.from_pretrained(model_name) nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ." ner_results = nlp(example) print(ner_results) ```
michaelrglass/rag-token-nq-kgi0-zsre
823f8804a0e07cc0e55c67aaf0ed549e462ba96e
2021-04-20T12:53:18.000Z
[ "pytorch", "rag", "transformers" ]
null
false
michaelrglass
null
michaelrglass/rag-token-nq-kgi0-zsre
9
null
transformers
12,364
Entry not found
mnaylor/base-bert-finetuned-mtsamples
855d6362b3fc8e2a0b05213c7a719d594560ca6e
2021-07-19T15:53:35.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
mnaylor
null
mnaylor/base-bert-finetuned-mtsamples
9
null
transformers
12,365
# BERT Base Fine-tuned on MTSamples This model is [BERT-base](https://huggingface.co/bert-base-uncased) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
monologg/kocharelectra-base-kmounlp-ner
faa4a5584d173ceda8dc9ffb14f9a8fe150535d1
2020-12-02T15:28:07.000Z
[ "pytorch", "electra", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
monologg
null
monologg/kocharelectra-base-kmounlp-ner
9
null
transformers
12,366
Entry not found
mrm8488/RoBERTinha
389c1984d098683d415cd070d970f1a6dc841060
2021-05-20T18:03:32.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "gl", "transformers", "autotrain_compatible" ]
fill-mask
false
mrm8488
null
mrm8488/RoBERTinha
9
null
transformers
12,367
--- language: gl widget: - text: "Galicia é unha <mask> autónoma española." - text: "A lingua oficial de Galicia é o <mask>." --- # RoBERTinha: RoBERTa-like Language model trained on OSCAR Galician corpus
mrm8488/bert-tiny-3-finetuned-squadv2
6ad3c80fdd0769c6ded488accfd81ad1902f5fba
2021-05-20T00:39:15.000Z
[ "pytorch", "jax", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
mrm8488
null
mrm8488/bert-tiny-3-finetuned-squadv2
9
null
transformers
12,368
Entry not found
mrm8488/bert2bert-mini_shared-question-generation
cda01c12dbe172f223daf3d88dc18cd4eb4b8396
2020-12-26T12:52:16.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
mrm8488
null
mrm8488/bert2bert-mini_shared-question-generation
9
null
transformers
12,369
Entry not found
mrm8488/bert2bert_shared-italian-question-generation
5260aa5037a7fab18c8986b1ba2980f9d2d43017
2020-12-11T14:33:07.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
mrm8488
null
mrm8488/bert2bert_shared-italian-question-generation
9
null
transformers
12,370
Entry not found
mrm8488/bioclinical-roberta-es-finenuned-clinical-ner
55a5299ef9cfe626a5fd7b34b27913f978b4a4e4
2022-01-24T16:04:07.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
mrm8488
null
mrm8488/bioclinical-roberta-es-finenuned-clinical-ner
9
null
transformers
12,371
Entry not found
mrm8488/codebert2codebert-finetuned-code-refinement-small
d74879d65ed6e886c22de0c62688dbfe52073d8d
2021-06-11T14:36:00.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
mrm8488
null
mrm8488/codebert2codebert-finetuned-code-refinement-small
9
null
transformers
12,372
Entry not found
mrm8488/distilroberta-finetuned-rotten_tomatoes-sentiment-analysis
e385ca4b241e411e8de0a7de6e36afc5548254d0
2021-08-26T15:28:40.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
mrm8488
null
mrm8488/distilroberta-finetuned-rotten_tomatoes-sentiment-analysis
9
null
transformers
12,373
Entry not found
mrm8488/electricidad-base-finetuned-medical-diagnostics
e448e764f4eec528e58ad5091ae3cefdc2b94e16
2021-10-04T17:03:07.000Z
[ "pytorch", "tensorboard", "electra", "text-classification", "transformers" ]
text-classification
false
mrm8488
null
mrm8488/electricidad-base-finetuned-medical-diagnostics
9
null
transformers
12,374
--- lang: 'es' widget: - text: "TUMOR DE COMPORTAMIENTO INCIERTO O DESCONOCIDO DEL HNGADO, DE LA VESNCULA BILIAR Y DEL CONDUCTO BILIAR - DiagnNstico Principal - Z01.8 OTROS EXNMENES ESPECIALES ESPECIFICADOS" --- # Electricidad (base) fine-tuned medical diagnostics
mrm8488/mobilebert-uncased-finetuned-squadv1
4611efec8432379bfb867f2f48fe5b55d62d3f10
2020-12-11T21:54:41.000Z
[ "pytorch", "mobilebert", "question-answering", "en", "dataset:squad", "arxiv:2004.02984", "transformers", "autotrain_compatible" ]
question-answering
false
mrm8488
null
mrm8488/mobilebert-uncased-finetuned-squadv1
9
null
transformers
12,375
--- language: en datasets: - squad --- # MobileBERT + SQuAD (v1.1) 📱❓ [mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task. ## Details of the downstream task (Q&A) - Model 🧠 **MobileBERT** is a thin version of *BERT_LARGE*, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint. More about the model [here](https://arxiv.org/abs/2004.02984) ## Details of the downstream task (Q&A) - Dataset 📚 **S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles. ## Model training 🏋️‍ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: ```bash python transformers/examples/question-answering/run_squad.py \ --model_type bert \ --model_name_or_path 'google/mobilebert-uncased' \ --do_eval \ --do_train \ --do_lower_case \ --train_file '/content/dataset/train-v1.1.json' \ --predict_file '/content/dataset/dev-v1.1.json' \ --per_gpu_train_batch_size 16 \ --learning_rate 3e-5 \ --num_train_epochs 5 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir '/content/output' \ --overwrite_output_dir \ --save_steps 1000 ``` It is important to say that this models converges much faster than other ones. So, it is also cheap to fine-tune. ## Test set Results 🧾 | Metric | # Value | | ------ | --------- | | **EM** | **82.33** | | **F1** | **89.64** | | **Size**| **94 MB** | ### Model in action 🚀 Fast usage with **pipelines**: ```python from transformers import pipeline QnA_pipeline = pipeline('question-answering', model='mrm8488/mobilebert-uncased-finetuned-squadv1') QnA_pipeline({ 'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.', 'question': 'Who did identified it ?' }) # Output: {'answer': 'scientists.', 'end': 106, 'score': 0.7885545492172241, 'start': 96} ``` > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) > Made with <span style="color: #e25555;">&hearts;</span> in Spain
mrm8488/t5-small-finetuned-common_gen
ac76a88701e5b6b8af75be9603ee26f4b361fe2f
2021-06-23T13:08:18.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
mrm8488
null
mrm8488/t5-small-finetuned-common_gen
9
null
transformers
12,376
Entry not found
muirkat/tolkien-mythopoeic-gen
6d4d2b3fd8c70619fc0311436b9bef512dda5691
2021-09-17T21:28:53.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-generation
false
muirkat
null
muirkat/tolkien-mythopoeic-gen
9
null
transformers
12,377
--- license: mit tags: - generated_from_trainer model-index: - name: tolkien-mythopoeic-gen results: - task: name: Causal Language Modeling type: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tolkien-mythopoeic-gen This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on Tolkien's mythopoeic works, namely The Silmarillion and Unfinished Tales of Numenor and Middle Earth. It achieves the following results on the evaluation set: - Loss: 3.5110 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5732 | 1.0 | 145 | 3.5110 | | 3.5713 | 2.0 | 290 | 3.5110 | | 3.5718 | 3.0 | 435 | 3.5110 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Tokenizers 0.10.3
nalini2799/CDAC_hindispeechrecognition
97980c233b96b7d854441ff86a2d628f0b678490
2021-12-10T20:20:58.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "hi", "dataset:Interspeech 2021", "transformers", "audio", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
nalini2799
null
nalini2799/CDAC_hindispeechrecognition
9
1
transformers
12,378
--- language: hi datasets: - Interspeech 2021 metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Hindi by Nalini Kumari results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice hi type: common_voice args: hi metrics: - name: Test WER type: wer value: 72.73 --- # Hindi-Speech to Text Model </br> The primary objective of this project is to develop a speech recognition system for the Hindi language. As there are very few systems available for speech to text in the Hindi language. Therefore it is an attempt to develop a system where a language model is developed using Machine learning libraries for speech-to-text conversion in the Hindi language.
napsternxg/scibert_scivocab_uncased_ft_tv_SDU21_AI
879754cce3d67126c42d1f15e70e34e94aa663df
2021-05-20T01:11:49.000Z
[ "pytorch", "jax", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
napsternxg
null
napsternxg/scibert_scivocab_uncased_ft_tv_SDU21_AI
9
null
transformers
12,379
scibert_scivocab_uncased_ft_tv MLM pretrained on SDU21 Task 1 + 2
napsternxg/scibert_scivocab_uncased_tv_SDU21_AI
dfd3d064269982b49d9a74759ca2764955be9c61
2021-05-20T01:12:46.000Z
[ "pytorch", "jax", "bert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
napsternxg
null
napsternxg/scibert_scivocab_uncased_tv_SDU21_AI
9
null
transformers
12,380
scibert_scivocab_uncased_tv submission for SDU21 Task 1 AI
neuropark/sahajBERT-NCC
0225852b736f10da3ef073b276ff6d9004473fed
2021-06-15T12:40:08.000Z
[ "pytorch", "albert", "text-classification", "bn", "dataset:IndicGlue", "transformers", "collaborative", "bengali", "SequenceClassification", "license:apache-2.0" ]
text-classification
false
neuropark
null
neuropark/sahajBERT-NCC
9
2
transformers
12,381
--- language: bn tags: - collaborative - bengali - SequenceClassification license: apache-2.0 datasets: IndicGlue metrics: - Loss - Accuracy - Precision - Recall widget: - text: "এশিয়ায় প্রথম দৃষ্টিহীন ব্যক্তির মাউন্ট এভারেস্ট জয়|" --- # sahajBERT News Article Classification ## Model description [sahajBERT](https://huggingface.co/neuropark/sahajBERT) fine-tuned for news article classification using the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). The model is trained for classifying articles into 5 different classes: | Label id | Label | |:--------:|:----:| |0 | kolkata| |1 | state| |2 | national| |3 | sports| |4 | entertainment| |5 | international| ## Intended uses & limitations #### How to use You can use this model directly with a pipeline for Sequence Classification: ```python from transformers import AlbertForSequenceClassification, TextClassificationPipeline, PreTrainedTokenizerFast # Initialize tokenizer tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NCC") # Initialize model model = AlbertForSequenceClassification.from_pretrained("neuropark/sahajBERT-NCC") # Initialize pipeline pipeline = TextClassificationPipeline(tokenizer=tokenizer, model=model) raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me output = pipeline(raw_text) ``` #### Limitations and bias <!-- Provide examples of latent issues and potential remediations. --> WIP ## Training data The model was initialized with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT) at step 19519 and trained on the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). ## Training procedure Coming soon! <!-- ```bibtex @inproceedings{..., year={2020} } ``` --> ## Eval results Loss: 0.2477145493030548 Accuracy: 0.926293408929837 Macro F1: 0.9079785326650756 Recall: 0.926293408929837 Weighted F1: 0.9266428029354202 Macro Precision: 0.9109938492260489 Micro Precision: 0.926293408929837 Weighted Precision: 0.9288535478995414 Macro Recall: 0.9069095007692186 Micro Recall: 0.926293408929837 Weighted Recall: 0.926293408929837 ### BibTeX entry and citation info Coming soon! <!-- ```bibtex @inproceedings{..., year={2020} } ``` -->
nlokam/books_to_bots_v.00
04ebf8703f36cb5c64775e231b83664475d1eecd
2021-12-02T21:51:33.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
nlokam
null
nlokam/books_to_bots_v.00
9
null
transformers
12,382
--- tags: - conversational --- # Books to Bots V.00
nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Base
cc69d2110175a18be978f1d52fb45b407576e797
2021-06-20T19:02:31.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nreimers
null
nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Base
9
null
transformers
12,383
# MiniLMv2 This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm)
nyu-mll/roberta-base-100M-1
cc1cedeecf92d5877c2c9a84e4a61d499e86c813
2021-05-20T18:53:55.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
nyu-mll
null
nyu-mll/roberta-base-100M-1
9
null
transformers
12,384
# RoBERTa Pretrained on Smaller Datasets We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1. ### Hyperparameters and Validation Perplexity The hyperparameters and validation perplexities corresponding to each model are as follows: | Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity | |--------------------------|---------------|------------|-----------|------------|-----------------------| | [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 | | [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 | | [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 | | [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 | | [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 | | [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 | | [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 | | [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 | | [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 | | [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 | | [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 | | [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 | The hyperparameters corresponding to model sizes mentioned above are as follows: | Model Size | L | AH | HS | FFN | P | |------------|----|----|-----|------|------| | BASE | 12 | 12 | 768 | 3072 | 125M | | MED-SMALL | 6 | 8 | 512 | 2048 | 45M | (AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.) For other hyperparameters, we select: - Peak Learning rate: 5e-4 - Warmup Steps: 6% of max steps - Dropout: 0.1 [link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1 [link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2 [link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3 [link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1 [link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2 [link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3 [link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1 [link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2 [link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3 [link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1 [link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2 [link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
o2poi/sst2-eda-bert
1127bb45d99ffba3f0f4516d9756e3f54b5a9480
2021-06-11T13:00:53.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
o2poi
null
o2poi/sst2-eda-bert
9
null
transformers
12,385
Entry not found
oandreae/financial_sentiment_model
2c1f91cf2b66d4670cb807d473e0635d042170c6
2022-01-20T20:00:01.000Z
[ "pytorch", "tensorboard", "perceiver", "text-classification", "dataset:financial_phrasebank", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
oandreae
null
oandreae/financial_sentiment_model
9
1
transformers
12,386
--- license: apache-2.0 tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - recall - accuracy - precision model-index: - name: financial_sentiment_model results: - task: name: Text Classification type: text-classification dataset: name: financial_phrasebank type: financial_phrasebank args: sentences_50agree metrics: - name: Recall type: recall value: 0.8839956357328868 - name: Accuracy type: accuracy value: 0.8804123711340206 - name: Precision type: precision value: 0.8604175202419276 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # financial_sentiment_model This model is a fine-tuned version of [deepmind/language-perceiver](https://huggingface.co/deepmind/language-perceiver) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.3467 - Recall: 0.8840 - Accuracy: 0.8804 - Precision: 0.8604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Recall | Accuracy | Precision | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:| | 0.4481 | 1.0 | 273 | 0.4035 | 0.8526 | 0.8433 | 0.7955 | | 0.4069 | 2.0 | 546 | 0.4478 | 0.8683 | 0.8289 | 0.8123 | | 0.2225 | 3.0 | 819 | 0.3167 | 0.8747 | 0.8680 | 0.8387 | | 0.1245 | 4.0 | 1092 | 0.3467 | 0.8840 | 0.8804 | 0.8604 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.0+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16
33828469971906e457488ace2957ff18182a4bb1
2020-12-11T21:59:26.000Z
[ "pytorch", "encoder_decoder", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
patrickvonplaten
null
patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16
9
null
transformers
12,387
# Shared Roberta2Roberta Summarization with 🤗 EncoderDecoder Framework This model is a shared Roberta2Roberta model, meaning that the encoder and decoder weights are tied, fine-tuned on summarization. Roberta2Roberta is a `EncoderDecoderModel`, meaning that both the encoder and the decoder are `roberta-base` RoBERTa models. In this setup the encoder and decoder weights are tied. Leveraging the [EncoderDecoderFramework](https://huggingface.co/transformers/model_doc/encoderdecoder.html#encoder-decoder-models), the two pretrained models can simply be loaded into the framework via: ```python roberta2roberta = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base", tie_encoder_decoder=True) ``` The decoder of an `EncoderDecoder` model needs cross-attention layers and usually makes use of causal masking for auto-regressiv generation. Thus, ``roberta2roberta`` is consequently fined-tuned on the `CNN/Daily Mail`dataset and the resulting model `roberta2roberta-share-cnn_dailymail-fp16` is uploaded here. ## Example The model is by no means a state-of-the-art model, but nevertheless produces reasonable summarization results. It was mainly fine-tuned as a proof-of-concept for the 🤗 EncoderDecoder Framework. The model can be used as follows: ```python from transformers import RobertaTokenizer, EncoderDecoderModel model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16") tokenizer = RobertaTokenizer.from_pretrained("roberta-base") article = """(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David B oren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 185 6, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which "369 went to war for the Confede rate States and seven for the Union Army," the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 "colonies" seeking fu ll membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on t he fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for "health and safety incidents" since 2010. At least 30 of the incidents involved hazing, and dozens more invol ved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members al legedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a frat ernity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. "The media has labeled us as the 'nation's deadliest fraternity, ' " Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. "As a result, we are paying Lloy d's of London the highest insurance rates in the Greek-letter world," Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing in cidents.""" input_ids = tokenizer(article, return_tensors="pt").input_ids output_ids = model.generate(input_ids) print(tokenizer.decode(output_ids[0], skip_special_tokens=True)) # should produce # SAE's national chapter suspended after video shows party-bound fraternity members singing racist chant. University of Oklahoma president says university's affiliation with fraternity is permanently done. # SAE has had to close 12 chapters since 2010 after members were killed in hazing. The fraternity has had more than 130 chapters in 18 months. ``` ## Training script: **IMPORTANT**: In order for this code to work, make sure you checkout to the branch [more_general_trainer_metric](https://github.com/huggingface/transformers/tree/more_general_trainer_metric), which slightly adapts the `Trainer` for `EncoderDecoderModels` according to this PR: https://github.com/huggingface/transformers/pull/5840. The following code shows the complete training script that was used to fine-tune `roberta2roberta-cnn_dailymail-fp16 ` for reproducability. The training last ~9h on a standard GPU. ```python #!/usr/bin/env python3 import nlp import logging from transformers import RobertaTokenizer, EncoderDecoderModel, Trainer, TrainingArguments logging.basicConfig(level=logging.INFO) model = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base", tie_encoder_decoder=True) tokenizer = RobertaTokenizer.from_pretrained("roberta-base") # load train and validation data train_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="train") val_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="validation[:5%]") # load rouge for validation rouge = nlp.load_metric("rouge", experiment_id=0) # set decoding params model.config.decoder_start_token_id = tokenizer.bos_token_id model.config.eos_token_id = tokenizer.eos_token_id model.config.max_length = 142 model.config.min_length = 56 model.config.no_repeat_ngram_size = 3 model.early_stopping = True model.length_penalty = 2.0 model.num_beams = 4 encoder_length = 512 decoder_length = 128 batch_size = 16 # map data correctly def map_to_encoder_decoder_inputs(batch): # Tokenizer will automatically set [BOS] <text> [EOS] # cut off at Longformer at 2048 inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length) # force summarization <= 256 outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=decoder_length) batch["input_ids"] = inputs.input_ids batch["attention_mask"] = inputs.attention_mask batch["decoder_input_ids"] = outputs.input_ids batch["labels"] = outputs.input_ids.copy() # mask loss for padding batch["labels"] = [ [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"] ] batch["decoder_attention_mask"] = outputs.attention_mask assert all([len(x) == encoder_length for x in inputs.input_ids]) assert all([len(x) == decoder_length for x in outputs.input_ids]) return batch def compute_metrics(pred): labels_ids = pred.label_ids pred_ids = pred.predictions # all unnecessary tokens are removed pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True) labels_ids[labels_ids == -100] = tokenizer.eos_token_id label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True) rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid return { "rouge2_precision": round(rouge_output.precision, 4), "rouge2_recall": round(rouge_output.recall, 4), "rouge2_fmeasure": round(rouge_output.fmeasure, 4), } # make train dataset ready train_dataset = train_dataset.map( map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"], ) train_dataset.set_format( type="torch", columns=["input_ids", "attention_mask", "decoder_attention_mask", "decoder_input_ids", "labels"], ) # same for validation dataset val_dataset = val_dataset.map( map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"], ) val_dataset.set_format( type="torch", columns=["input_ids", "decoder_attention_mask", "attention_mask", "decoder_input_ids", "labels"], ) # set training arguments - these params are not really tuned, feel free to change training_args = TrainingArguments( output_dir="./", per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, predict_from_generate=True, evaluate_during_training=True, do_train=True, do_eval=True, logging_steps=1000, save_steps=1000, eval_steps=1000, overwrite_output_dir=True, warmup_steps=2000, save_total_limit=3, fp16=True, ) # instantiate trainer trainer = Trainer( model=model, args=training_args, compute_metrics=compute_metrics, train_dataset=train_dataset, eval_dataset=val_dataset, ) # start training trainer.train() ``` ## Evaluation The following script evaluates the model on the test set of CNN/Daily Mail. ```python #!/usr/bin/env python3 import nlp from transformers import RobertaTokenizer, EncoderDecoderModel tokenizer = RobertaTokenizer.from_pretrained("roberta-base") model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16") model.to("cuda") test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test") batch_size = 128 # map data correctly def generate_summary(batch): # Tokenizer will automatically set [BOS] <text> [EOS] # cut off at BERT max length 512 inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512, return_tensors="pt") input_ids = inputs.input_ids.to("cuda") attention_mask = inputs.attention_mask.to("cuda") outputs = model.generate(input_ids, attention_mask=attention_mask) # all special tokens including will be removed output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True) batch["pred"] = output_str return batch results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"]) # load rouge for validation rouge = nlp.load_metric("rouge") pred_str = results["pred"] label_str = results["highlights"] rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid print(rouge_output) ``` The obtained results should be: | - | Rouge2 - mid -precision | Rouge2 - mid - recall | Rouge2 - mid - fmeasure | |----------|:-------------:|:------:|:------:| | **CNN/Daily Mail** | 15.6 | 18.79 | **16.59** |
pchanda/pretrained-smiles-pubchem10m
3f5c603606a89f07c080f8dd564419f8814a161e
2021-05-20T13:01:15.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
pchanda
null
pchanda/pretrained-smiles-pubchem10m
9
null
transformers
12,388
model pretrained on 10m smiles from pubchem.
philschmid/MiniLMv2-L6-H384-emotion
96350ab4dfcc93b17a7759e1ab53dd73db2a5589
2021-12-06T19:59:04.000Z
[ "pytorch", "roberta", "text-classification", "dataset:emotion", "transformers", "generated_from_trainer", "model-index" ]
text-classification
false
philschmid
null
philschmid/MiniLMv2-L6-H384-emotion
9
null
transformers
12,389
--- tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: MiniLMv2-L6-H384-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9215 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MiniLMv2-L6-H384-emotion This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2140 - Accuracy: 0.9215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.432 | 1.0 | 500 | 0.9992 | 0.6805 | | 0.8073 | 2.0 | 1000 | 0.5437 | 0.846 | | 0.4483 | 3.0 | 1500 | 0.3018 | 0.909 | | 0.2833 | 4.0 | 2000 | 0.2412 | 0.915 | | 0.2169 | 5.0 | 2500 | 0.2140 | 0.9215 | | 0.1821 | 6.0 | 3000 | 0.2159 | 0.917 | | 0.154 | 7.0 | 3500 | 0.2084 | 0.919 | | 0.1461 | 8.0 | 4000 | 0.2047 | 0.92 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
pierric/autonlp-my-own-imdb-sentiment-analysis-2131817
fd725976967405236012b2804c686927764ac889
2021-06-29T22:08:35.000Z
[ "pytorch", "roberta", "text-classification", "en", "dataset:pierric/autonlp-data-my-own-imdb-sentiment-analysis", "transformers", "autonlp" ]
text-classification
false
pierric
null
pierric/autonlp-my-own-imdb-sentiment-analysis-2131817
9
null
transformers
12,390
--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - pierric/autonlp-data-my-own-imdb-sentiment-analysis --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 2131817 ## Validation Metrics - Loss: 0.24430708587169647 - Accuracy: 0.9452 - Precision: 0.9303944315545244 - Recall: 0.9624 - AUC: 0.9793824287999999 - F1: 0.946126622099882 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/pierric/autonlp-my-own-imdb-sentiment-analysis-2131817 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("pierric/autonlp-my-own-imdb-sentiment-analysis-2131817", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("pierric/autonlp-my-own-imdb-sentiment-analysis-2131817", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
pitehu/T5_NER_CONLL_ENTITYREPLACE
13fff880ad3a63581e6bebc7d962ef0907dd4d84
2022-01-28T11:05:16.000Z
[ "pytorch", "t5", "text2text-generation", "en", "dataset:CoNLL-2003", "arxiv:2111.10952", "arxiv:1810.04805", "transformers", "license:apache-2.0", "autotrain_compatible" ]
text2text-generation
false
pitehu
null
pitehu/T5_NER_CONLL_ENTITYREPLACE
9
null
transformers
12,391
--- language: - en license: "apache-2.0" datasets: - CoNLL-2003 metrics: - F1 --- This is a T5 small model finetuned on CoNLL-2003 dataset for named entity recognition (NER). Example Input and Output: “Recognize all the named entities in this sequence (replace named entities with one of [PER], [ORG], [LOC], [MISC]): When Alice visited New York” → “When PER visited LOC LOC" Evaluation Result: % of match (for comparison with ExT5: https://arxiv.org/pdf/2111.10952.pdf): | Model| ExT5_{Base} | This Model | T5_NER_CONLL_OUTPUTLIST | :---: | :---: | :---: | :---: | | % of Complete Match| 86.53 | 79.03 | TBA| There are some outputs (212/3453 or 6.14% that does not have the same length as the input) F1 score on testing set of those with matching length : | Model | This Model | T5_NER_CONLL_OUTPUTLIST | BERTbase | :---: | :---: | :---: | :---: | | F1| 0.8901 | 0.8691| 0.9240 **Caveat: The testing set of these aren't the same, due to matching length issue... T5_NER_CONLL_OUTPUTLIST only has 27/3453 missing length (only 0.78%); The BERT number is directly from their paper (https://arxiv.org/pdf/1810.04805.pdf)
psychicautomaton/bert-base-uncased-finetuned-suicide
56962716fd988bc02459a08ad6ce0065da998fcc
2021-12-21T23:48:01.000Z
[ "pytorch", "bert", "text-classification", "transformers" ]
text-classification
false
psychicautomaton
null
psychicautomaton/bert-base-uncased-finetuned-suicide
9
null
transformers
12,392
Entry not found
rafagudinov/en_rent_estate_ads
f5bbec1774ddc1b0f2625b82312b407b331e2355
2021-11-29T17:24:49.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
rafagudinov
null
rafagudinov/en_rent_estate_ads
9
null
transformers
12,393
Entry not found
ramybaly/ner_nerd
a988f226e22e9b17176de5a384bab85a778eb12b
2021-08-07T04:20:30.000Z
[ "pytorch", "tensorboard", "bert", "token-classification", "dataset:nerd", "transformers", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible" ]
token-classification
false
ramybaly
null
ramybaly/ner_nerd
9
null
transformers
12,394
--- license: apache-2.0 tags: - generated_from_trainer datasets: - nerd metrics: - precision - recall - f1 - accuracy model_index: - name: ner_nerd results: - task: name: Token Classification type: token-classification dataset: name: nerd type: nerd args: nerd metric: name: Accuracy type: accuracy value: 0.9391592461061087 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ner_nerd This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset. It achieves the following results on the evaluation set: - Loss: 0.2245 - Precision: 0.7466 - Recall: 0.7873 - F1: 0.7664 - Accuracy: 0.9392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2843 | 1.0 | 8235 | 0.1951 | 0.7352 | 0.7824 | 0.7580 | 0.9375 | | 0.1655 | 2.0 | 16470 | 0.1928 | 0.7519 | 0.7827 | 0.7670 | 0.9398 | | 0.1216 | 3.0 | 24705 | 0.2119 | 0.75 | 0.7876 | 0.7684 | 0.9396 | | 0.0881 | 4.0 | 32940 | 0.2258 | 0.7515 | 0.7896 | 0.7701 | 0.9392 | | 0.0652 | 5.0 | 41175 | 0.2564 | 0.7518 | 0.7875 | 0.7692 | 0.9387 | ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.2
rsvp-AI-ca/segabert-uncased-base-50k
a29f7c6d6a08c418a6d191a03533b848e2073930
2020-12-13T03:04:25.000Z
[ "pytorch", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
rsvp-AI-ca
null
rsvp-AI-ca/segabert-uncased-base-50k
9
null
transformers
12,395
Entry not found
ruiqi-zhong/roberta-large-meta-tuning-test
ce6b194ba8ecd09a8a7cd757d7eb14c7799ca366
2021-09-14T03:24:59.000Z
[ "pytorch", "roberta", "text-classification", "transformers" ]
text-classification
false
ruiqi-zhong
null
ruiqi-zhong/roberta-large-meta-tuning-test
9
null
transformers
12,396
Entry not found
saattrupdan/xlmr-base-texas-squad-da
f69bdb83d3537d0404f471f8cd7b23757b6ba3b1
2022-02-05T15:55:52.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "question-answering", "da", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
saattrupdan
null
saattrupdan/xlmr-base-texas-squad-da
9
null
transformers
12,397
--- language: - da license: mit tags: - generated_from_trainer model-index: - name: xlmr-base-texas-squad-da results: [] widget: - text: "Hvem handler artiklen om?" context: "Forfatter og musiker Flemming Quist Møller er død i en alder af 79 år. Den folkekære kunstner faldt om ved morgenbordet med en blodprop i hjertet i mandags. Det kunne forfatterens søn, Carl Quist-Møller, bekræfte over for TV 2 Lorry.- Han faldt om i det hus i Taarbæk, hvor han er vokset op og også har boet de sidste år af sit liv. Han blev lagt i koma på Rigshospitalet. Her har vi siddet omkring ham i en uge, siger Carl Quist-Møller til mediet.MindeordI mange år var Flemming Quist Møller en del af bandet Bazaar sammen med Peter Bastian, Anders Koppel og Mehmet Ozan.Anders Koppel er tydeligt rørt over vennens død, da Ekstra Bladet rækker ud til ham mandag aften.- Det er en stor del af mit liv, der er forsvundet med Flemmings liv, det er klart. Vi har spillet sammen i 37 år, siger han og fortsætter:- Jeg vil mest huske ham for hans ukonventionelle tilgang til alting. Flemming havde et meget stærkt blik for det autentiske og ærlige. Han var ikke bundet af normer -tværtimod, hvis han så en norm, hvor noget skulle gøres på en bestemt måde, så flygtede han eller prøvede at springe det i stumper og stykker.Ifølge den danske musiker og komponist er netop følgende ord rammende for Flemming Quist Møller: Original, vidende, kompromisløs og humoristisk." --- # TExAS-SQuAD-da This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-da dataset. It achieves the following results on the evaluation set: - Exact match: 63.96% - F1-score: 68.40% In comparison, the `jacobshein/danish-bert-botxo-qa-squad` model achieves 30.37% EM and 37.15% F1. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6438 | 1.0 | 4183 | 1.4711 | | 1.4079 | 2.0 | 8366 | 1.4356 | | 1.2532 | 3.0 | 12549 | 1.4509 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.8.1+cu101 - Datasets 1.12.1 - Tokenizers 0.10.3
saraks/cuad-distil-parties-dates-law-08-25-clean-context1
8c69a44d54e301866cb26adb52fad70b662881af
2021-08-25T10:00:44.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
saraks
null
saraks/cuad-distil-parties-dates-law-08-25-clean-context1
9
null
transformers
12,398
Entry not found
savasy/bert-turkish-uncased-qnli
e72cb2ce635b33c5f1ca4b1cf1c18d925f3f3074
2021-05-20T04:57:50.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
false
savasy
null
savasy/bert-turkish-uncased-qnli
9
null
transformers
12,399
# Turkish QNLI Model I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD https://huggingface.co/dbmdz/bert-base-turkish-uncased # Data: TQuAD I used following TQuAD data set https://github.com/TQuad/turkish-nlp-qa-dataset I convert the dataset into transformers glue data format of QNLI by the following script SQuAD -> QNLI ``` import argparse import collections import json import numpy as np import os import re import string import sys ff="dev-v0.1.json" ff="train-v0.1.json" dataset=json.load(open(ff)) i=0 for article in dataset['data']: title= article['title'] for p in article['paragraphs']: context= p['context'] for qa in p['qas']: answer= qa['answers'][0]['text'] all_other_answers= list(set([e['answers'][0]['text'] for e in p['qas']])) all_other_answers.remove(answer) i=i+1 print(i,qa['question'].replace(";",":") , answer.replace(";",":"),"entailment", sep="\t") for other in all_other_answers: i=i+1 print(i,qa['question'].replace(";",":") , other.replace(";",":"),"not_entailment" ,sep="\t") ``` Under QNLI folder there are dev and test test Training data looks like > 613 II.Friedrich’in bilginler arasındaki en önemli şahsiyet olarak belirttiği kişi kimdir? filozof, kimyacı, astrolog ve çevirmen not_entailment > 614 II.Friedrich’in bilginler arasındaki en önemli şahsiyet olarak belirttiği kişi kimdir? kişisel eğilimi ve özel temaslar nedeniyle not_entailment > 615 Michael Scotus’un mesleği nedir? filozof, kimyacı, astrolog ve çevirmen entailment > 616 Michael Scotus’un mesleği nedir? Palermo’ya not_entailment # Training Training the model with following environment ``` export GLUE_DIR=./glue/glue_dataTR/QNLI export TASK_NAME=QNLI ``` ``` python3 run_glue.py \ --model_type bert \ --model_name_or_path dbmdz/bert-base-turkish-uncased\ --task_name $TASK_NAME \ --do_train \ --do_eval \ --data_dir $GLUE_DIR \ --max_seq_length 128 \ --per_gpu_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir /tmp/$TASK_NAME/ ``` # Evaluation Results == | acc | 0.9124060613527165 | loss| 0.21582801340189717 == > See all my model > https://huggingface.co/savasy