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lindsayng/t5-base-base-fulltrainingset-bias | 18f4bba1050abeb9947332647a6dc858e7553e5a | 2022-06-08T08:13:42.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lindsayng | null | lindsayng/t5-base-base-fulltrainingset-bias | 0 | null | transformers | Entry not found | [
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bmichele/poetry-generation-firstline-mbart-ws-sv-capitalized | 74019ab5998737e8d8f2faab54fc2dfbde20cc62 | 2022-06-08T08:59:47.000Z | [
"pytorch"
] | null | false | bmichele | null | bmichele/poetry-generation-firstline-mbart-ws-sv-capitalized | 0 | null | null | Entry not found | [
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jppaolim/v62_Large_2E | 4298a27b0b0e922c70afe5e0b6a11f9ccb8d102c | 2022-06-08T11:30:57.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jppaolim | null | jppaolim/v62_Large_2E | 0 | null | transformers | # My Story model
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1}
Arthur goes to the beach. Arthur is in his beach chair. He is walking along the beach when he starts feeling a pain in his back. Arthur rushes to the doctor. The doctor says he needs a special cast. Arthur is so relieved he tears up.
Arthur goes to the beach. Arthur takes his wife to the beach. Arthur's wife has sore muscles. He takes her to the local doctor. The doctor gives her medicine. Arthur and his wife enjoy the beach.
Arthur goes to the beach. Arthur always wished he could go to the beach. He always wanted to go by himself. This time he went with his family. When they arrived the place was busy. Arthur was happy to be able to go to the beach.
Arthur goes to the beach. Arthur has never been to the beach. When he finally gets there it is very hot. He decides to go to the beach. He enjoys his vacation at the beach. Arthur is happy that he has never been to the beach.
Arthur goes to the beach. Arthur goes to the beach. He walks up the beach. He sits on the sand. Arthur lies on the beach. He falls asleep.
{'top_p': 0.9, 'top_k': 50, 'temperature': 1, 'repetition_penalty': 1.05}
Arthur goes to the beach. Arthur wants to get sandals. He buys a sandal and a pair of sandals. He gets in the water and does some stretching. He finds a few nice waves to surf. After surfing, he is able to buy some new sandals.
Arthur goes to the beach. Arthur has been playing in the sand all day long. He decides to go swimming. He spends all afternoon in the water. Finally, Arthur heads home. Arthur finally has a fun beach day!
Arthur goes to the beach. Arthur is very happy on vacation. However, Arthur is not excited to be on the beach. He has no idea how to swim. When he finally gets his board out, Arthur begins to get excited. Arthur swam his first time at the beach!
Arthur goes to the beach. Arthur decided to go to the beach one day. He was very excited for it and got in the water. He was afraid of the waves and never went in. When he finally got out, he saw that he had gotten bit. Arthur still cried the rest of the day and went home.
Arthur goes to the beach. Arthur was going to the beach with his girlfriend. Suddenly, he got a text from his girl that they were getting married. Arthur was so excited and was excited for the big day. When he saw the beach, he saw a beautiful woman. He went on the beach to thank her.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.8, 'repetition_penalty': 1.1}
Arthur goes to the beach. Arthur decides he wants to go swimming. He buys his favorite swimsuit and head for the water. As Arthur is about to enter the water, his friends show up. They tell Arthur that he has to pay for the day's swimwear. Arthur still feels guilty, but he doesn't want to be rude.
Arthur goes to the beach. Arthur wants to go to the beach. He decides he has to get a job. He goes to work and gets hired. The next day he leaves his house. Arthur returns home and is happy.
Arthur goes to the beach. Arthur is out with friends. He decides to go to the beach for a swim. At first he doesn't like the water. Then his friends make fun of him for being so skinny. Arthur finally decides to go swimming after all.
Arthur goes to the beach. Arthur has never been to a beach before. He decides to go anyway. On his first day at the beach he gets seasick. He doesn't get any sun on his first day. Afterwards Arthur decides to not go to the beach for another year.
Arthur goes to the beach. Arthur is going to the beach for a swim. He has never been to the beach before. As he is taking his first swim, a wave hits him in the head. His mother rushes over and tells him that he got hit by a wave. Arthur is glad he didn't go swimming.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.6, 'repetition_penalty': 1.15}
Arthur goes to the beach. It was Arthur's first time going to the beach. He went by himself and didn't know anyone. The water was very cold. After a few minutes, he decided to join a group of people. They had fun at the beach.
Arthur goes to the beach. He decides to go for a swim at the beach. The water is very warm and Arthur feels very comfortable in his bathing suit. Suddenly, he notices something strange on the shore. It turns out that someone has been swimming there all day! Arthur is relieved when he finds out who it was.
Arthur goes to the beach. Arthur is on a vacation with his family. They go to the beach and swim in the ocean. A shark jumps out at Arthur. He throws the water over it and it gets scared. Arthur decides not to go back to the beach for another year.
Arthur goes to the beach. He wants to go for a swim. He doesn't want to get wet. He decides to use a towel. The towel gets soaked and he has to walk home. Arthur is glad he took his time.
Arthur goes to the beach. Arthur is on vacation in Hawaii. He decides he wants to go surfing. Arthur takes a day off of work and heads out for the day. When Arthur gets there, he sees that it's very crowded! Arthur decides not to go back home for another two days.
{'top_p': 0.9, 'top_k': 40, 'temperature': 0.4, 'repetition_penalty': 1.2}
Arthur goes to the beach. Arthur is on a trip with his family. He decides to go for a swim at the ocean. The water is very cold and Arthur feels very hot. His parents take him back home. They tell him that he should have stayed in the house.
Arthur goes to the beach. He decides he wants a vacation. He buys his ticket and flies out of town. When he arrives, he is surprised by how beautiful it was! The weather was perfect for him as well. He enjoyed himself immensely at the beach.
Arthur goes to the beach. He decides he wants a nice day on the sand. The sun is shining and it's very hot. His friends come over to play with him. They all have fun playing in the water. Arthur feels much better after his day of fun.
Arthur goes to the beach. Arthur is on vacation in Florida. He decides he wants to go to the beach. His friends tell him they can't make it for a few days. Arthur agrees and heads out with his friends. They all have fun at the beach.
Arthur goes to the beach. He is going for a swim in the ocean. The water was very cold and Arthur didn't want to go. His friends convinced him to go anyway. When he got there, it was freezing! But he still went because he wanted to be with his friends.
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joshanashakya/codebert_sourcecode_nmt_ja2pn_100E_5e-05LR | b03345f1ab51f109b52e1f16c7260306e1dffbe5 | 2022-06-08T10:47:20.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/codebert_sourcecode_nmt_ja2pn_100E_5e-05LR | 0 | null | transformers | Entry not found | [
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joshanashakya/codebert_sourcecode_nmt_pn2ja_50E_2e-05LR | d9e5eb1ad82b97d52bdbe3e0c2ff8d67d72a13c2 | 2022-06-08T11:52:34.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/codebert_sourcecode_nmt_pn2ja_50E_2e-05LR | 0 | null | transformers | Entry not found | [
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joshanashakya/mini_codebert_sourcecode_nmt_pn2ja_50E_2e-05LR | 54aef5c719b593249d432cfe4a685f07042dacc4 | 2022-06-08T12:52:18.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/mini_codebert_sourcecode_nmt_pn2ja_50E_2e-05LR | 0 | null | transformers | Entry not found | [
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huggingtweets/elukkaj | a9b81bc0cf9935671b1839b4ecf0e39e4127bc31 | 2022-06-08T14:01:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/elukkaj | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/elukkaj/1654696881260/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
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style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/996279759570169856/vqZiiVns_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elukka</div>
<div style="text-align: center; font-size: 14px;">@elukkaj</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.
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 Elukka.
| Data | Elukka |
| --- | --- |
| Tweets downloaded | 1113 |
| Retweets | 1 |
| Short tweets | 22 |
| Tweets kept | 1090 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3de86afj/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 @elukkaj's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/scw34f55) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/scw34f55/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/elukkaj')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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joshanashakya/codebert_sourcecode_nmt_pn2ja_100E_5e-05LR | 5cc10270844f5941c2519db313d4cb6a423405b1 | 2022-06-08T14:40:04.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/codebert_sourcecode_nmt_pn2ja_100E_5e-05LR | 0 | null | transformers | Entry not found | [
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huggingtweets/ripvillage | e7a5cbc6c9cf10e1c135e3837aedb88df28917fc | 2022-06-08T16:38:52.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/ripvillage | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/ripvillage/1654706327179/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('https://pbs.twimg.com/profile_images/378800000120011180/ffb093c084cfb4b60f70488a7e6355d0_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mathurin Village</div>
<div style="text-align: center; font-size: 14px;">@ripvillage</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.
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 Mathurin Village.
| Data | Mathurin Village |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 118 |
| Short tweets | 335 |
| Tweets kept | 2790 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3e20ev2s/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 @ripvillage's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ecq32lhi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ecq32lhi/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/ripvillage')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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renjithks/layoutlmv1-er-ner | c89b838fcc64748bd2e462fb0062f6fed615da00 | 2022-06-08T18:53:25.000Z | [
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | token-classification | false | renjithks | null | renjithks/layoutlmv1-er-ner | 0 | null | transformers | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv1-er-ner
results: []
---
<!-- 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. -->
# layoutlmv1-er-ner
This model is a fine-tuned version of [renjithks/layoutlmv1-cord-ner](https://huggingface.co/renjithks/layoutlmv1-cord-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2092
- Precision: 0.7202
- Recall: 0.7238
- F1: 0.7220
- Accuracy: 0.9639
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 41 | 0.2444 | 0.4045 | 0.3996 | 0.4020 | 0.9226 |
| No log | 2.0 | 82 | 0.1640 | 0.5319 | 0.6098 | 0.5682 | 0.9455 |
| No log | 3.0 | 123 | 0.1531 | 0.6324 | 0.6614 | 0.6466 | 0.9578 |
| No log | 4.0 | 164 | 0.1440 | 0.6927 | 0.6743 | 0.6834 | 0.9620 |
| No log | 5.0 | 205 | 0.1520 | 0.6750 | 0.6958 | 0.6853 | 0.9613 |
| No log | 6.0 | 246 | 0.1597 | 0.6840 | 0.6987 | 0.6913 | 0.9605 |
| No log | 7.0 | 287 | 0.1910 | 0.7002 | 0.6887 | 0.6944 | 0.9605 |
| No log | 8.0 | 328 | 0.1860 | 0.6834 | 0.6923 | 0.6878 | 0.9609 |
| No log | 9.0 | 369 | 0.1665 | 0.6785 | 0.7102 | 0.6940 | 0.9624 |
| No log | 10.0 | 410 | 0.1816 | 0.7016 | 0.7052 | 0.7034 | 0.9624 |
| No log | 11.0 | 451 | 0.1808 | 0.6913 | 0.7166 | 0.7038 | 0.9638 |
| No log | 12.0 | 492 | 0.2165 | 0.712 | 0.7023 | 0.7071 | 0.9628 |
| 0.1014 | 13.0 | 533 | 0.2135 | 0.6979 | 0.7109 | 0.7043 | 0.9613 |
| 0.1014 | 14.0 | 574 | 0.2154 | 0.6906 | 0.7109 | 0.7006 | 0.9612 |
| 0.1014 | 15.0 | 615 | 0.2118 | 0.6902 | 0.7016 | 0.6958 | 0.9615 |
| 0.1014 | 16.0 | 656 | 0.2091 | 0.6985 | 0.7080 | 0.7032 | 0.9623 |
| 0.1014 | 17.0 | 697 | 0.2104 | 0.7118 | 0.7123 | 0.7121 | 0.9630 |
| 0.1014 | 18.0 | 738 | 0.2081 | 0.7129 | 0.7231 | 0.7179 | 0.9638 |
| 0.1014 | 19.0 | 779 | 0.2093 | 0.7205 | 0.7231 | 0.7218 | 0.9638 |
| 0.1014 | 20.0 | 820 | 0.2092 | 0.7202 | 0.7238 | 0.7220 | 0.9639 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| [
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tclong/wav2vec2-base-vios-commonvoice | fe5be39cb00ecd3e487ddd6b829f0442ba6a04d0 | 2022-06-09T17:17:08.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | tclong | null | tclong/wav2vec2-base-vios-commonvoice | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-vios-commonvoice
results: []
---
<!-- 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-base-vios-commonvoice
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3823
- Wer: 0.2401
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.2268 | 0.66 | 500 | 0.8746 | 0.5939 |
| 0.8728 | 1.32 | 1000 | 0.6435 | 0.4554 |
| 0.6899 | 1.99 | 1500 | 0.5655 | 0.3995 |
| 0.5842 | 2.65 | 2000 | 0.5267 | 0.3694 |
| 0.5371 | 3.31 | 2500 | 0.4980 | 0.3431 |
| 0.4921 | 3.97 | 3000 | 0.4781 | 0.3276 |
| 0.4508 | 4.64 | 3500 | 0.4434 | 0.3134 |
| 0.433 | 5.3 | 4000 | 0.4348 | 0.2963 |
| 0.404 | 5.96 | 4500 | 0.4248 | 0.2874 |
| 0.3834 | 6.62 | 5000 | 0.4163 | 0.2775 |
| 0.3784 | 7.28 | 5500 | 0.4104 | 0.2751 |
| 0.3669 | 7.95 | 6000 | 0.4143 | 0.2724 |
| 0.3462 | 8.61 | 6500 | 0.4131 | 0.2699 |
| 0.3364 | 9.27 | 7000 | 0.4070 | 0.2617 |
| 0.3249 | 9.93 | 7500 | 0.4076 | 0.2603 |
| 0.3154 | 10.6 | 8000 | 0.3998 | 0.2577 |
| 0.3117 | 11.26 | 8500 | 0.3930 | 0.2505 |
| 0.3101 | 11.92 | 9000 | 0.4003 | 0.2492 |
| 0.298 | 12.58 | 9500 | 0.3960 | 0.2496 |
| 0.2968 | 13.24 | 10000 | 0.3877 | 0.2469 |
| 0.29 | 13.91 | 10500 | 0.3870 | 0.2456 |
| 0.2921 | 14.57 | 11000 | 0.3823 | 0.2401 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t2981_22026.csv___topic_text_google_mt5_base | 2373a30877b780069e78428740a86d3114976556 | 2022-06-09T03:43:43.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nestoralvaro | null | nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t2981_22026.csv___topic_text_google_mt5_base | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t2981_22026.csv___topic_text_google_mt5_base
results: []
---
<!-- 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. -->
# mt5-base-finetuned-xsum-data_prep_2021_12_26___t2981_22026.csv___topic_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.7181
- Rouge2: 0.1008
- Rougel: 0.7173
- Rougelsum: 0.7187
- Gen Len: 6.2965
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 139904 | nan | 0.7181 | 0.1008 | 0.7173 | 0.7187 | 6.2965 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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huggingtweets/kentcdodds-richardbranson-sikiraamer | 101330cbd791bee6875911826d4fd32d4d8c7172 | 2022-06-08T21:08:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/kentcdodds-richardbranson-sikiraamer | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/kentcdodds-richardbranson-sikiraamer/1654722520391/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('https://pbs.twimg.com/profile_images/1496777835062648833/3Ao6Xb2a_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529905780542959616/Ibwrp7VJ_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410740591483293697/tRbW1XoV_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Amer Sikira & Kent C. Dodds 💿 & Richard Branson</div>
<div style="text-align: center; font-size: 14px;">@kentcdodds-richardbranson-sikiraamer</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.
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 Amer Sikira & Kent C. Dodds 💿 & Richard Branson.
| Data | Amer Sikira | Kent C. Dodds 💿 | Richard Branson |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3249 | 3215 |
| Retweets | 94 | 578 | 234 |
| Short tweets | 214 | 507 | 96 |
| Tweets kept | 2942 | 2164 | 2885 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jtwa65l2/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 @kentcdodds-richardbranson-sikiraamer's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3vt6qlgf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vt6qlgf/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/kentcdodds-richardbranson-sikiraamer')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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huggingtweets/mephytis | 13c41c7673cbca0b4512dffd5829a455bb68113d | 2022-06-08T22:50:52.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/mephytis | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/mephytis/1654728647738/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('https://pbs.twimg.com/profile_images/1516396570639573002/4WWU_e38_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">mephy✨</div>
<div style="text-align: center; font-size: 14px;">@mephytis</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.
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 mephy✨.
| Data | mephy✨ |
| --- | --- |
| Tweets downloaded | 2959 |
| Retweets | 322 |
| Short tweets | 737 |
| Tweets kept | 1900 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6sao13mv/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 @mephytis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29ayegfb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29ayegfb/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/mephytis')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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huggingtweets/oddapt | ba3500c4b2fb86873260273c0d7978963b5fda8b | 2022-06-09T00:08:44.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/oddapt | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/oddapt/1654733319638/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('https://pbs.twimg.com/profile_images/1468077034169458690/gt5Iv_y7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Steve Hoyt</div>
<div style="text-align: center; font-size: 14px;">@oddapt</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.
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 Steve Hoyt.
| Data | Steve Hoyt |
| --- | --- |
| Tweets downloaded | 2861 |
| Retweets | 615 |
| Short tweets | 192 |
| Tweets kept | 2054 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8pfy3hb1/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 @oddapt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fphl051) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fphl051/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/oddapt')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
huggingtweets/killthenoise | 0f6f585399d5a2e7bd5006136f753b2126c114ef | 2022-06-09T03:35:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/killthenoise | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/killthenoise/1654745713334/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('https://pbs.twimg.com/profile_images/1531744995463507968/fPvkX5FS_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ᵏᵗⁿ</div>
<div style="text-align: center; font-size: 14px;">@killthenoise</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.
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 ᵏᵗⁿ.
| Data | ᵏᵗⁿ |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 307 |
| Short tweets | 645 |
| Tweets kept | 2293 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kjftyff/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 @killthenoise's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wij7d8z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wij7d8z/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/killthenoise')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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huggingtweets/itsnovaherev2 | 006aa2e9f03037c2b294fcac45e719fde3821c39 | 2022-06-09T03:53:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/itsnovaherev2 | 0 | null | transformers | ---
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('https://pbs.twimg.com/profile_images/1253734967923798018/FJ7AvxLN_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ItsNovaHere</div>
<div style="text-align: center; font-size: 14px;">@itsnovaherev2</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.
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 ItsNovaHere.
| Data | ItsNovaHere |
| --- | --- |
| Tweets downloaded | 588 |
| Retweets | 409 |
| Short tweets | 67 |
| Tweets kept | 112 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2tz4bf7d/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 @itsnovaherev2's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35es3xf7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35es3xf7/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/itsnovaherev2')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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thaonh/vietnews-summarization | 623eeaa9df028d16e6ee8502f63c989751ce612a | 2022-06-09T04:20:07.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | thaonh | null | thaonh/vietnews-summarization | 0 | null | transformers | Entry not found | [
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huggingtweets/usao926 | c652e9710f854df135b362fe949d6964b0079192 | 2022-06-09T03:57:49.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/usao926 | 0 | null | transformers | ---
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('https://pbs.twimg.com/profile_images/1329004510161694722/DkD9DvBN_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">USAO@山奥</div>
<div style="text-align: center; font-size: 14px;">@usao926</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.
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 USAO@山奥.
| Data | USAO@山奥 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 1041 |
| Short tweets | 1987 |
| Tweets kept | 221 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21po1181/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 @usao926's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jl5e9yl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jl5e9yl/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/usao926')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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joshanashakya/codebert_sourcecode_nmt_ja2pn_50E_2e-05LR_16B | 6ffd725f635b6061ede4b436fbb64e22e7b02396 | 2022-06-09T04:01:10.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/codebert_sourcecode_nmt_ja2pn_50E_2e-05LR_16B | 0 | null | transformers | Entry not found | [
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joshanashakya/codebert_sourcecode_nmt_pn2ja_50E_1e-05LR | 7f4681385e929c8e5826926a5e441820a3d2ddc3 | 2022-06-09T04:06:15.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | joshanashakya | null | joshanashakya/codebert_sourcecode_nmt_pn2ja_50E_1e-05LR | 0 | null | transformers | Entry not found | [
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nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t8_54.csv___topic_text_google_mt5_base | 5e0961c172d3c0d3a7313b99a32ec9e9004a1d42 | 2022-06-09T06:59:53.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nestoralvaro | null | nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t8_54.csv___topic_text_google_mt5_base | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t8_54.csv___topic_text_google_mt5_base
results: []
---
<!-- 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. -->
# mt5-base-finetuned-xsum-data_prep_2021_12_26___t8_54.csv___topic_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 1.4678
- Rouge2: 0.1841
- Rougel: 1.4748
- Rougelsum: 1.4701
- Gen Len: 6.4874
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 10645 | nan | 1.4678 | 0.1841 | 1.4748 | 1.4701 | 6.4874 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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huggingtweets/osanseviero | 185ce00fe89c9172b9739467a89b186cf2d19102 | 2022-06-09T10:20:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/osanseviero | 0 | 1 | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/osanseviero/1654769951427/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('https://pbs.twimg.com/profile_images/1106315906165157889/0Hxb1ESL_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Omar Sanseviero</div>
<div style="text-align: center; font-size: 14px;">@osanseviero</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.
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 Omar Sanseviero.
| Data | Omar Sanseviero |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 1158 |
| Short tweets | 224 |
| Tweets kept | 1862 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29bkab0t/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 @osanseviero's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1s35jikq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1s35jikq/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/osanseviero')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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ghadeermobasher/WLT-BioBERT-BC5CDR-Disease | 97becc535f49250a40b43821f30bc80c761775fe | 2022-06-09T10:52:17.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-BioBERT-BC5CDR-Disease | 0 | null | transformers | Entry not found | [
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ghadeermobasher/WLT-PubMedBERT-BC5CDR-Chemical | 20ccac3af28b15a9acd0243f714a51750a05c1f5 | 2022-06-09T11:50:40.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-PubMedBERT-BC5CDR-Chemical | 0 | null | transformers | Entry not found | [
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huggingtweets/politifact | 465e6564658c622441ba47ef693126254b2b0912 | 2022-06-09T11:14:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/politifact | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/politifact/1654773253130/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('https://pbs.twimg.com/profile_images/1286766140115517441/8rq6ZxZm_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">PolitiFact</div>
<div style="text-align: center; font-size: 14px;">@politifact</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.
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 PolitiFact.
| Data | PolitiFact |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 680 |
| Short tweets | 14 |
| Tweets kept | 2556 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vfo2t7i/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 @politifact's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7h3iptm6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7h3iptm6/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/politifact')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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ghadeermobasher/WLT-PubMedBERT-BC4CHEMD | 3bbbe691d70756081d00c1b8c8f59a0270568a85 | 2022-06-09T12:10:30.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-PubMedBERT-BC4CHEMD | 0 | null | transformers | Entry not found | [
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ghadeermobasher/WLT-SciBERT-BC4CHEMD | 1a734c621b08475665a5cd418ac2357707d596a9 | 2022-06-09T19:17:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-SciBERT-BC4CHEMD | 0 | null | transformers | Entry not found | [
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huggingtweets/bbclaurakt | 19ae31f71e132286ee4ade64f7c6c3d98b24e9c2 | 2022-06-09T12:48:19.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/bbclaurakt | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/bbclaurakt/1654778894531/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('https://pbs.twimg.com/profile_images/1533553176619716608/4klYwjkC_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Laura Kuenssberg Translator</div>
<div style="text-align: center; font-size: 14px;">@bbclaurakt</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.
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 Laura Kuenssberg Translator.
| Data | Laura Kuenssberg Translator |
| --- | --- |
| Tweets downloaded | 2063 |
| Retweets | 23 |
| Short tweets | 135 |
| Tweets kept | 1905 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37mk0av7/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 @bbclaurakt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3a8gt7bb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3a8gt7bb/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/bbclaurakt')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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huggingtweets/zaidalyafeai | 576675856695bd09a84f74e5a73fe6cd81e5e901 | 2022-06-09T13:03:12.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/zaidalyafeai | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/zaidalyafeai/1654779787447/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('https://pbs.twimg.com/profile_images/1521723273922461696/m8_zotM4_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Zaid زيد</div>
<div style="text-align: center; font-size: 14px;">@zaidalyafeai</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.
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 Zaid زيد.
| Data | Zaid زيد |
| --- | --- |
| Tweets downloaded | 2295 |
| Retweets | 74 |
| Short tweets | 217 |
| Tweets kept | 2004 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39e5cxbb/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 @zaidalyafeai's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2uc681wq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2uc681wq/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/zaidalyafeai')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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ghadeermobasher/WLT-BioBERT-BC2GM | 1d5add5ec62d129b5a0a66f32c60725eb8a04321 | 2022-06-09T14:09:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-BioBERT-BC2GM | 0 | null | transformers | Entry not found | [
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ghadeermobasher/WLT-BlueBERT-BC2GM | fe293685908d02c578c48cafb395042055293405 | 2022-06-09T13:58:03.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-BlueBERT-BC2GM | 0 | null | transformers | Entry not found | [
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ghadeermobasher/WLT-BioBERT-Linnaeus | fd03f9e18fd611464c90b5d4cb4359c055b2e5b2 | 2022-06-09T14:54:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-BioBERT-Linnaeus | 0 | null | transformers | Entry not found | [
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ghadeermobasher/WLT-SciBERT-BC5CDR-Chemical-T | fc7c19863e4d2bb4daa79bdd0b05086e5ae9d09b | 2022-06-09T17:47:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-SciBERT-BC5CDR-Chemical-T | 0 | null | transformers | Entry not found | [
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huggingtweets/elrichmc | 207975ee994ecbc2e30a77adf2a08b68557dac0a | 2022-06-09T16:04:04.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/elrichmc | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/elrichmc/1654790629445/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('https://pbs.twimg.com/profile_images/1484686785812832263/Beh-qGPk_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ElRichMC</div>
<div style="text-align: center; font-size: 14px;">@elrichmc</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.
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 ElRichMC.
| Data | ElRichMC |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 203 |
| Short tweets | 618 |
| Tweets kept | 2424 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jeok5aq/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 @elrichmc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28fmqsme) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28fmqsme/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/elrichmc')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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ghadeermobasher/WLT-SciBERT-BC5CDR-Chemical-T1 | d930d294b60d9ec1b40743ef1d6734f08ffe483d | 2022-06-09T17:59:25.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/WLT-SciBERT-BC5CDR-Chemical-T1 | 0 | null | transformers | Entry not found | [
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huggingtweets/sorcehri | ebb335dde24c0b8c4311e8a3ccc29fb9bf7de66e | 2022-06-09T16:22:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/sorcehri | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/sorcehri/1654791699329/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
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style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ehri</div>
<div style="text-align: center; font-size: 14px;">@sorcehri</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.
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 ehri.
| Data | ehri |
| --- | --- |
| Tweets downloaded | 3233 |
| Retweets | 280 |
| Short tweets | 837 |
| Tweets kept | 2116 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gn4h8q0/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 @sorcehri's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7zs978ln) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7zs978ln/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/sorcehri')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
huggingtweets/medscape | 0cc741944ec03b048f2a17248fed7e98ae46976c | 2022-06-09T16:30:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/medscape | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/medscape/1654792218439/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('https://pbs.twimg.com/profile_images/1401919208133378050/l2MKtnC7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Medscape</div>
<div style="text-align: center; font-size: 14px;">@medscape</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.
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 Medscape.
| Data | Medscape |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 16 |
| Short tweets | 2 |
| Tweets kept | 3232 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mn0jpyr0/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 @medscape's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n6qbw51) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n6qbw51/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/medscape')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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flood/xlm-roberta-base-finetuned-panx-de-fr | e934cc629b181398d56af00c2a024e8fefd584fe | 2022-06-22T13:33:30.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | flood | null | flood/xlm-roberta-base-finetuned-panx-de-fr | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1612
- F1: 0.8618
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2874 | 1.0 | 715 | 0.1764 | 0.8343 |
| 0.1475 | 2.0 | 1430 | 0.1561 | 0.8508 |
| 0.0936 | 3.0 | 2145 | 0.1612 | 0.8618 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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flood/xlm-roberta-base-finetuned-panx-fr | 43893747d95e996bf1f836dc7bd5cbbe9d2cf0a4 | 2022-06-22T13:37:27.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | flood | null | flood/xlm-roberta-base-finetuned-panx-fr | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8375924680564896
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2794
- F1: 0.8376
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5774 | 1.0 | 191 | 0.3212 | 0.7894 |
| 0.2661 | 2.0 | 382 | 0.2737 | 0.8292 |
| 0.1756 | 3.0 | 573 | 0.2794 | 0.8376 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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flood/xlm-roberta-base-finetuned-panx-it | df837dd00e779105599b769435b9e643a0c7638a | 2022-06-22T13:40:36.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | flood | null | flood/xlm-roberta-base-finetuned-panx-it | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8085969180859691
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2527
- F1: 0.8086
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8319 | 1.0 | 70 | 0.3179 | 0.7474 |
| 0.2959 | 2.0 | 140 | 0.2695 | 0.7916 |
| 0.2036 | 3.0 | 210 | 0.2527 | 0.8086 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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flood/xlm-roberta-base-finetuned-panx-all | 0b8e10dcd91438e548c21a4cd09fa417c96ab53d | 2022-06-22T13:50:09.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | flood | null | flood/xlm-roberta-base-finetuned-panx-all | 0 | null | transformers | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1739
- F1: 0.8525
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3 | 1.0 | 835 | 0.1894 | 0.8104 |
| 0.1564 | 2.0 | 1670 | 0.1751 | 0.8423 |
| 0.1032 | 3.0 | 2505 | 0.1739 | 0.8525 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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saitishmukhametov/kshn4hobos | 257a9c18ad89751a104c4e84af1072fdd158f8dd | 2022-06-09T20:56:00.000Z | [
"pytorch"
] | null | false | saitishmukhametov | null | saitishmukhametov/kshn4hobos | 0 | null | null | Entry not found | [
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simecek/WormDNADeberta | 42f7eb93cc3993f8f53fab613d20f2f96deb8d76 | 2022-06-09T23:55:17.000Z | [
"pytorch",
"tensorboard",
"deberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/WormDNADeberta | 0 | null | transformers | Entry not found | [
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lak/poem_project_2 | bbf5052f29df936db564975a686e3a0765ed30ed | 2022-06-09T20:43:32.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | lak | null | lak/poem_project_2 | 0 | null | transformers | Entry not found | [
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lak/poem_project_3 | 4926b15700d7fa4309557fdedbd031f795a60894 | 2022-06-09T20:45:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | lak | null | lak/poem_project_3 | 0 | null | transformers | Entry not found | [
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nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base | 5a5aad26bb80c272cf56c432a93c04aac8a48ab8 | 2022-06-10T00:52:35.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nestoralvaro | null | nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base
results: []
---
<!-- 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. -->
# mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 2.8146
- Rouge2: 0.6707
- Rougel: 2.8187
- Rougelsum: 2.8098
- Gen Len: 6.4901
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 3869 | nan | 2.8146 | 0.6707 | 2.8187 | 2.8098 | 6.4901 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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simecek/ArabidopsisDNADeberta | 8d47c3a4af581e378958e34d45dc70d760b840ad | 2022-06-10T04:06:21.000Z | [
"pytorch",
"tensorboard",
"deberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/ArabidopsisDNADeberta | 0 | null | transformers | Entry not found | [
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huggingtweets/artificialbuttr | 689403a8d155d0c7c8e267c76a095a212441e7db | 2022-06-10T01:39:43.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/artificialbuttr | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/artificialbuttr/1654825134207/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('https://pbs.twimg.com/profile_images/1485413658351968256/NUVesGCM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">artificialbutter</div>
<div style="text-align: center; font-size: 14px;">@artificialbuttr</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.
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 artificialbutter.
| Data | artificialbutter |
| --- | --- |
| Tweets downloaded | 785 |
| Retweets | 129 |
| Short tweets | 407 |
| Tweets kept | 249 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ypylns0/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 @artificialbuttr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1phf128l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1phf128l/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/artificialbuttr')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
huggingtweets/burkevillemama | 378f2a3ef15aa6f4d97767a6becb7a544ec3fc22 | 2022-06-10T02:15:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/burkevillemama | 0 | null | transformers | ---
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('https://pbs.twimg.com/profile_images/1367879964733804547/buUeka0V_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bree</div>
<div style="text-align: center; font-size: 14px;">@burkevillemama</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.
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 Bree.
| Data | Bree |
| --- | --- |
| Tweets downloaded | 2994 |
| Retweets | 805 |
| Short tweets | 201 |
| Tweets kept | 1988 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/82nbekwu/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 @burkevillemama's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gdpxbzc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gdpxbzc/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/burkevillemama')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
| [
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huggingtweets/macarena_olona | 63d5214e7e4f913bff6b8d2de275dfdea1f7f481 | 2022-06-10T06:32:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/macarena_olona | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/macarena_olona/1654842717478/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('https://pbs.twimg.com/profile_images/1535020786007916545/po7DO1ln_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Macarena Olona</div>
<div style="text-align: center; font-size: 14px;">@macarena_olona</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.
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 Macarena Olona.
| Data | Macarena Olona |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 1797 |
| Short tweets | 225 |
| Tweets kept | 1223 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yx7hguo/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 @macarena_olona's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i64c9y6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i64c9y6/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/macarena_olona')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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flood/pegasus-samsum | 8cbceb3e593275eb296575d51b0c3e0212a0071f | 2022-06-10T07:00:06.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | flood | null | flood/pegasus-samsum | 0 | null | transformers | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- 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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4814
## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7052 | 0.54 | 500 | 1.4814 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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simecek/humandna_MOBILEBERT_1epoch | 179ad0361a3aa22862f2da902b7f2d8c374c464f | 2022-06-10T07:56:37.000Z | [
"pytorch",
"mobilebert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/humandna_MOBILEBERT_1epoch | 0 | null | transformers | Entry not found | [
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huggingtweets/atrioc | b17f9ab25f183b8588c744fd107f9efd2503209a | 2022-06-10T09:05:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/atrioc | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/atrioc/1654851931751/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('https://pbs.twimg.com/profile_images/1522249702837657603/1jNZf3aB_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Atrioc</div>
<div style="text-align: center; font-size: 14px;">@atrioc</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.
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 Atrioc.
| Data | Atrioc |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 746 |
| Short tweets | 502 |
| Tweets kept | 1957 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2zlbp16x/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 @atrioc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3oldn78j) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3oldn78j/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/atrioc')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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tclong/wav2vec2-base-vios-commonvoice-1 | c7b785c27655e3dde2ac8d1ac8bae3a4a0a920eb | 2022-06-11T03:01:54.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | tclong | null | tclong/wav2vec2-base-vios-commonvoice-1 | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-vios-commonvoice-1
results: []
---
<!-- 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-base-vios-commonvoice-1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8913
- Wer: 0.3621
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4706 | 0.55 | 500 | 3.4725 | 1.0 |
| 3.202 | 1.1 | 1000 | 2.7555 | 1.0008 |
| 1.0507 | 1.66 | 1500 | 1.0481 | 0.6196 |
| 0.7325 | 2.21 | 2000 | 0.8120 | 0.4958 |
| 0.599 | 2.76 | 2500 | 0.7035 | 0.4447 |
| 0.5224 | 3.31 | 3000 | 0.6761 | 0.4078 |
| 0.4844 | 3.86 | 3500 | 0.6688 | 0.4011 |
| 0.4234 | 4.42 | 4000 | 0.6080 | 0.3729 |
| 0.4237 | 4.97 | 4500 | 0.5953 | 0.3556 |
| 0.3986 | 5.52 | 5000 | 0.6054 | 0.3478 |
| 0.3554 | 6.07 | 5500 | 0.6193 | 0.3479 |
| 0.3446 | 6.62 | 6000 | 0.5809 | 0.3302 |
| 0.3104 | 7.17 | 6500 | 0.5713 | 0.3283 |
| 0.3166 | 7.73 | 7000 | 0.5593 | 0.3133 |
| 0.2938 | 8.28 | 7500 | 0.5645 | 0.3081 |
| 0.3061 | 8.83 | 8000 | 0.5508 | 0.3020 |
| 0.2986 | 9.38 | 8500 | 0.5462 | 0.3024 |
| 0.2939 | 9.93 | 9000 | 0.5544 | 0.3028 |
| 0.2633 | 10.49 | 9500 | 0.5496 | 0.3024 |
| 0.2683 | 11.04 | 10000 | 0.5439 | 0.2946 |
| 0.2714 | 11.59 | 10500 | 0.5524 | 0.2947 |
| 0.2354 | 12.14 | 11000 | 0.5267 | 0.2918 |
| 0.2488 | 12.69 | 11500 | 0.5728 | 0.2938 |
| 0.2479 | 13.25 | 12000 | 0.5802 | 0.2951 |
| 0.245 | 13.8 | 12500 | 0.5571 | 0.2890 |
| 0.2422 | 14.35 | 13000 | 0.5531 | 0.2871 |
| 0.2369 | 14.9 | 13500 | 0.5453 | 0.2860 |
| 0.2345 | 15.45 | 14000 | 0.5452 | 0.2847 |
| 0.2507 | 16.0 | 14500 | 0.5536 | 0.2884 |
| 0.2454 | 16.56 | 15000 | 0.5577 | 0.2871 |
| 0.2729 | 17.11 | 15500 | 0.6019 | 0.2931 |
| 0.2743 | 17.66 | 16000 | 0.5619 | 0.2905 |
| 0.3031 | 18.21 | 16500 | 0.6401 | 0.3006 |
| 0.315 | 18.76 | 17000 | 0.6044 | 0.2990 |
| 0.4025 | 19.32 | 17500 | 0.6739 | 0.3304 |
| 0.4915 | 19.87 | 18000 | 0.7267 | 0.3472 |
| 0.5539 | 20.42 | 18500 | 0.8078 | 0.3483 |
| 0.7138 | 20.97 | 19000 | 0.9362 | 0.3765 |
| 0.5766 | 21.52 | 19500 | 0.7921 | 0.3392 |
| 0.688 | 22.08 | 20000 | 0.8833 | 0.3693 |
| 0.6964 | 22.63 | 20500 | 0.9137 | 0.3469 |
| 0.7389 | 23.18 | 21000 | 0.9379 | 0.3460 |
| 0.7851 | 23.73 | 21500 | 1.0438 | 0.3653 |
| 0.7619 | 24.28 | 22000 | 0.9313 | 0.3873 |
| 0.7175 | 24.83 | 22500 | 0.8668 | 0.3789 |
| 0.6842 | 25.39 | 23000 | 0.8243 | 0.3761 |
| 0.6941 | 25.94 | 23500 | 0.8557 | 0.3804 |
| 0.7167 | 26.49 | 24000 | 0.8618 | 0.3875 |
| 0.721 | 27.04 | 24500 | 0.8686 | 0.3764 |
| 0.6949 | 27.59 | 25000 | 0.8773 | 0.3690 |
| 0.727 | 28.15 | 25500 | 0.8769 | 0.3666 |
| 0.7363 | 28.7 | 26000 | 0.8867 | 0.3634 |
| 0.7157 | 29.25 | 26500 | 0.8895 | 0.3626 |
| 0.7385 | 29.8 | 27000 | 0.8913 | 0.3621 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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lindsayng/t5-base-allwnc-4epoch-bias-3292d5c9 | 2a8126863d2eee95a64e70fd49373c60ffa800be | 2022-06-10T11:28:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lindsayng | null | lindsayng/t5-base-allwnc-4epoch-bias-3292d5c9 | 0 | null | transformers | Entry not found | [
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simecek/humandna_PERCEIVER_1epoch | 6a3340182823f3e0e796c4995f3c9868a46b1903 | 2022-06-10T14:02:18.000Z | [
"pytorch",
"perceiver",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/humandna_PERCEIVER_1epoch | 0 | null | transformers | Entry not found | [
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vaibhavagg303/Bart-Fine-Tuned | cc03cc75506e52f3590c24c001e81a30456ab2cf | 2022-06-10T18:19:20.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | vaibhavagg303 | null | vaibhavagg303/Bart-Fine-Tuned | 0 | null | transformers | Entry not found | [
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huggingtweets/malzliebchen | cdd46a0ec305d18b1d075180c7e50d994d456211 | 2022-06-10T18:29:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/malzliebchen | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/malzliebchen/1654885748305/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('https://pbs.twimg.com/profile_images/1521909233024913408/4QsF2YzM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Malzbeard's Severed Head</div>
<div style="text-align: center; font-size: 14px;">@malzliebchen</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.
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 Malzbeard's Severed Head.
| Data | Malzbeard's Severed Head |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 41 |
| Short tweets | 486 |
| Tweets kept | 2720 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/e1wzn1e5/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 @malzliebchen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/38g20s6n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/38g20s6n/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/malzliebchen')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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huggingtweets/smallmutuals | 2732407c6fd0b845d8859d494f89dcfcfca3784e | 2022-06-10T19:13:07.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/smallmutuals | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/smallmutuals/1654888348503/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('https://pbs.twimg.com/profile_images/1433527116948180999/wejtDhFm_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Cool Owl Guy</div>
<div style="text-align: center; font-size: 14px;">@smallmutuals</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.
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 Cool Owl Guy.
| Data | Cool Owl Guy |
| --- | --- |
| Tweets downloaded | 367 |
| Retweets | 45 |
| Short tweets | 25 |
| Tweets kept | 297 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/238iiiu5/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 @smallmutuals's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hl8vi9y) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hl8vi9y/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/smallmutuals')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
huggingtweets/jana_aych_ess | 87e7c8004f1d69c6756a0e7fc380ca9c7f9e9c38 | 2022-06-10T19:22:06.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/jana_aych_ess | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/jana_aych_ess/1654888920998/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('https://pbs.twimg.com/profile_images/1169751139409117185/BU60y7P5_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jana 'All Cops Are Bastards' H-S (they/them)</div>
<div style="text-align: center; font-size: 14px;">@jana_aych_ess</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.
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 Jana 'All Cops Are Bastards' H-S (they/them).
| Data | Jana 'All Cops Are Bastards' H-S (they/them) |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 343 |
| Short tweets | 148 |
| Tweets kept | 2743 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3q5i1d01/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 @jana_aych_ess's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3uy7dmw6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3uy7dmw6/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/jana_aych_ess')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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vaibhavagg303/Bart-Fine-Tuned2 | fbf7f90ab7ac306a9762e9e9d1b419fbc4bb3e52 | 2022-06-11T01:15:21.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | vaibhavagg303 | null | vaibhavagg303/Bart-Fine-Tuned2 | 0 | null | transformers | Entry not found | [
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huggingtweets/boopysaur | 88ee800403b3f1b196ec7ba7d405af9fe9662f04 | 2022-06-10T22:57:09.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/boopysaur | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/boopysaur/1654901824865/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('https://pbs.twimg.com/profile_images/1476816918879297559/2jt_Rt2L_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">boop ♡</div>
<div style="text-align: center; font-size: 14px;">@boopysaur</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.
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 boop ♡.
| Data | boop ♡ |
| --- | --- |
| Tweets downloaded | 920 |
| Retweets | 162 |
| Short tweets | 128 |
| Tweets kept | 630 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/398l195g/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 @boopysaur's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3te0suw6/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/boopysaur')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
huggingtweets/jedwill1999 | ca8dd632a646f8996f77745cefe04365fc542cbd | 2022-06-10T23:10:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/jedwill1999 | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/jedwill1999/1654902604867/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('https://pbs.twimg.com/profile_images/1510152678919135250/lfEmlEGJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">a local</div>
<div style="text-align: center; font-size: 14px;">@jedwill1999</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.
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 a local.
| Data | a local |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 1080 |
| Short tweets | 525 |
| Tweets kept | 1641 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qsnsp6t/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 @jedwill1999's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mjjc73pu/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/jedwill1999')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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huggingtweets/theanything_bot | ecc080d2b67010ee5bdb30e39c186790b95114bd | 2022-06-10T23:19:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/theanything_bot | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/theanything_bot/1654903166604/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('https://pbs.twimg.com/profile_images/1532874424776437760/vSP1qWyF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Anything Bot</div>
<div style="text-align: center; font-size: 14px;">@theanything_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.
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 Anything Bot.
| Data | Anything Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 3250 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/oy5g644b/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 @theanything_bot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rui0vn2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rui0vn2/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/theanything_bot')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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melihelis/udesa-model-aah-es-28k | a1778f1576ef2a2d0d4e056a4769e82dfd14f57d | 2022-06-10T23:44:23.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | melihelis | null | melihelis/udesa-model-aah-es-28k | 0 | null | transformers | Entry not found | [
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] |
nateraw/modelcard-creator-demo | e37dbce515203df944b4198c4e36791c8bb1d6af | 2022-06-10T23:58:39.000Z | [
"en",
"dataset:beans",
"arxiv:1810.03993",
"arxiv:1910.09700",
"pytorch",
"modelcards",
"autogenerated-modelcard",
"license:mit"
] | null | false | nateraw | null | nateraw/modelcard-creator-demo | 0 | null | pytorch | ---
language:
- en
license: mit
library_name: pytorch
tags:
- modelcards
- autogenerated-modelcard
datasets:
- beans
metrics:
- accuracy
---
# modelcard-creator-demo
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out of Scope Use](#misuse-and-out-of-scope-use)
- [Limitations and Biases](#limitations-and-biases)
- [Training](#training)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Evaluation Results](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Model Details
<!-- Give an overview of your model, the relevant research paper, who trained it, etc. -->
This isn't really a model, it's just a test repo to see if the [model card creator](https://huggingface.co/spaces/nateraw/modelcard-creator) works!
- Developed by: Nathan Raw
- Language(s):
- License: modelcard-creator-demo is licensed under the mit license
- Resources for more information:
- [Research Paper](https://arxiv.org/pdf/1810.03993.pdf)
- [GitHub Repo](https://github.com/nateraw/modelcards)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# A nice code snippet here that describes how to use the model...
```
## Uses
#### Direct Use
<!-- Describe what kind of tasks this model can be used for directly or problems it can solve. -->
[More Information Needed]
#### Downstream Use
<!-- Describe how this model could be leveraged by a downstream model (if applicable) -->
[More Information Needed]
#### Misuse and Out-of-scope Use
<!-- Describe ways in which this model ***should not*** be used. -->
[More Information Needed]
## Limitations and Biases
<!-- Describe limitations and biases of this model or models of it's type. -->
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.**
[More Information Needed]
## Training
#### Training Data
<!-- Describe the dataset used to train this model. -->
<!-- Refer to data card if dataset is provided and exists on the hub -->
See the data card for additional information.
#### Training Procedure
<!-- Describe the preprocessing, hardware used, training hyperparameters, etc. -->
[More Information Needed]
## Evaluation Results
<!-- Describe evaluation results of this model across any datasets it was evaluated on. -->
[More Information Needed]
## Environmental Impact
<!-- Provide information to document the environmental impact of this model -->
You can estimate carbon emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700)
- **Hardware Type:**
- **Hours used:**
- **Cloud Provider:**
- **Compute Region:**
- **Carbon Emitted:**
## Citation Information
```bibtex
@inproceedings{Mitchell_2019,
doi = {10.1145/3287560.3287596},
url = {https://doi.org/10.1145%2F3287560.3287596},
year = 2019,
month = {jan},
publisher = {{ACM}
},
author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and Ben Hutchinson and Elena Spitzer and Inioluwa Deborah Raji and Timnit Gebru},
title = {Model Cards for Model Reporting},
booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency}
}
``` | [
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] |
huggingtweets/waffle_64 | 22d93b5a1d7d01e6ba8f7dafaa804f5570983a3c | 2022-06-11T04:39:14.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/waffle_64 | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/waffle_64/1654922313776/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('https://pbs.twimg.com/profile_images/1534033778787639296/a9JUby19_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🧇 Werewaffle🐺LOU NATION🐺</div>
<div style="text-align: center; font-size: 14px;">@waffle_64</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.
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 🧇 Werewaffle🐺LOU NATION🐺.
| Data | 🧇 Werewaffle🐺LOU NATION🐺 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 110 |
| Short tweets | 217 |
| Tweets kept | 2922 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1rq6yndm/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 @waffle_64's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ucwnzfby) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ucwnzfby/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/waffle_64')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
| [
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huggingtweets/gustholomulers | 4e35eff4557d0851d8b8fc92aca55c6a6913f61d | 2022-06-11T07:53:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/gustholomulers | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/gustholomulers/1654934015981/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('https://pbs.twimg.com/profile_images/1535477036353040384/tXI_s1Yi_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">soppy</div>
<div style="text-align: center; font-size: 14px;">@gustholomulers</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.
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 soppy.
| Data | soppy |
| --- | --- |
| Tweets downloaded | 1482 |
| Retweets | 55 |
| Short tweets | 329 |
| Tweets kept | 1098 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nhfbopf/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 @gustholomulers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p5yu4wm/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/gustholomulers')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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ryo0634/bert-base-en-0 | adde08287122d6435fcda18199770d3597606d7b | 2022-06-11T13:34:56.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ryo0634 | null | ryo0634/bert-base-en-0 | 0 | null | transformers | Entry not found | [
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] |
huggingtweets/nosuba_13 | 89853d55310725ec8a2633ce223a277847658465 | 2022-06-11T13:40:57.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/nosuba_13 | 0 | 1 | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/nosuba_13/1654954852706/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('https://pbs.twimg.com/profile_images/1382014203796553732/DFDiOrcz_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Noel</div>
<div style="text-align: center; font-size: 14px;">@nosuba_13</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.
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 Noel.
| Data | Noel |
| --- | --- |
| Tweets downloaded | 3170 |
| Retweets | 859 |
| Short tweets | 369 |
| Tweets kept | 1942 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ui1lp214/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 @nosuba_13's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6sn9tlrz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6sn9tlrz/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/nosuba_13')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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lindsayng/t5-base-fullwnc-epoch-4e91e125 | 0fb829932cd50e6d95bf40c189978b1ed1feab17 | 2022-06-11T13:54:35.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lindsayng | null | lindsayng/t5-base-fullwnc-epoch-4e91e125 | 0 | 1 | transformers | Entry not found | [
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finiteautomata/pepe-5k_diff | 9a402e359c253f0d944a9f55b614dd2afd1d4e6a | 2022-06-11T18:32:45.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | finiteautomata | null | finiteautomata/pepe-5k_diff | 0 | null | transformers | Entry not found | [
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qqqqqqqb/bert-finetuned-medlog | 147035f78d64cd33a8e04357e4a9ee5da2ea0594 | 2022-06-14T08:33:53.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | qqqqqqqb | null | qqqqqqqb/bert-finetuned-medlog | 0 | null | transformers | Entry not found | [
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zoha/wav2vec2-base-librispeech100h-google-colab | 6516054bea883ba8b0c5e6b6497f159e0b5acb83 | 2022-06-13T13:39:58.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | zoha | null | zoha/wav2vec2-base-librispeech100h-google-colab | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-librispeech100h-google-colab
results: []
---
<!-- 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-base-librispeech100h-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1156
- Wer: 0.0756
## 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: 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_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.6033 | 0.9 | 1600 | 0.4802 | 0.2728 |
| 0.1912 | 1.79 | 3200 | 0.1601 | 0.1140 |
| 0.1409 | 2.69 | 4800 | 0.1423 | 0.0932 |
| 0.108 | 3.59 | 6400 | 0.1260 | 0.0806 |
| 0.1045 | 4.48 | 8000 | 0.1156 | 0.0756 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
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sactisudesa/bert_sp | 537eab5ef3cf3a1a1e0cb43d56727c0ec2fc91ce | 2022-06-11T18:35:10.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | sactisudesa | null | sactisudesa/bert_sp | 0 | 1 | transformers | Entry not found | [
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florver/modelo_NLI_kvd_vf_5000 | 705f321a9b0d8a6c5c44555093d7801c97c69450 | 2022-06-11T19:01:08.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | florver | null | florver/modelo_NLI_kvd_vf_5000 | 0 | null | transformers | Entry not found | [
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meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3 | 4d31ebc6ef97f870cca6d496b9715a9ef3179c3f | 2022-06-11T19:25:04.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:opus100",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | meghazisofiane | null | meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3 | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus100
metrics:
- bleu
model-index:
- name: opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus100
type: opus100
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 21.3028
---
<!-- 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. -->
# opus-mt-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize8-11epoch-3
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1421
- Bleu: 21.3028
- Meteor: 0.1285
- Gen Len: 9.975
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
| 1.0508 | 1.0 | 100 | 0.1413 | 27.9009 | 0.1416 | 8.85 |
| 0.1253 | 2.0 | 200 | 0.1372 | 23.11 | 0.1345 | 9.855 |
| 0.1017 | 3.0 | 300 | 0.1390 | 21.7885 | 0.1364 | 9.97 |
| 0.0868 | 4.0 | 400 | 0.1378 | 21.3889 | 0.1314 | 9.835 |
| 0.0754 | 5.0 | 500 | 0.1398 | 22.198 | 0.132 | 9.675 |
| 0.0667 | 6.0 | 600 | 0.1396 | 20.8645 | 0.1308 | 10.055 |
| 0.0604 | 7.0 | 700 | 0.1408 | 20.289 | 0.1303 | 10.53 |
| 0.0553 | 8.0 | 800 | 0.1414 | 21.7023 | 0.1293 | 10.005 |
| 0.0518 | 9.0 | 900 | 0.1421 | 21.3028 | 0.1285 | 9.975 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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sactisudesa/bertin_sp | 5a41e566deb90c4ef46e927fba6d739d8fba4080 | 2022-06-12T15:15:01.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | sactisudesa | null | sactisudesa/bertin_sp | 0 | null | transformers | Entry not found | [
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florver/modelo_NLI_kvd_vf_8000 | 2cccf1db8436db52519ace45e4ac1658dd940a0c | 2022-06-11T20:37:33.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | florver | null | florver/modelo_NLI_kvd_vf_8000 | 0 | null | transformers | Entry not found | [
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florver/modelo_NLI_kvd_2_5000 | f45c1ecb6db3bfdfb9a5368d3987e8480243b5cf | 2022-06-11T21:39:43.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | florver | null | florver/modelo_NLI_kvd_2_5000 | 0 | null | transformers | Entry not found | [
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] |
huggingtweets/tayplaysgaymes | e9bb8ae02287ca62a40ee2c08b53b0b9bdd53acf | 2022-06-12T03:56:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/tayplaysgaymes | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/tayplaysgaymes/1655006196516/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('https://pbs.twimg.com/profile_images/1144053838459969536/lv3yBmoX_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Tay</div>
<div style="text-align: center; font-size: 14px;">@tayplaysgaymes</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.
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 Tay.
| Data | Tay |
| --- | --- |
| Tweets downloaded | 3212 |
| Retweets | 693 |
| Short tweets | 367 |
| Tweets kept | 2152 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hmextiq/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 @tayplaysgaymes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3r0cse8x/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/tayplaysgaymes')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
huggingtweets/bosstjanz | d7498ef06699019d58d7d65560f324355a2000f6 | 2022-06-12T09:27:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/bosstjanz | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/bosstjanz/1655026050127/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('https://pbs.twimg.com/profile_images/1342130927737176064/SiNG_CxQ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Zrimškow</div>
<div style="text-align: center; font-size: 14px;">@bosstjanz</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.
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 Zrimškow.
| Data | Zrimškow |
| --- | --- |
| Tweets downloaded | 3225 |
| Retweets | 368 |
| Short tweets | 279 |
| Tweets kept | 2578 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/23nemiqj/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 @bosstjanz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pjrymzt/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/bosstjanz')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base | df5bcb6852bb1cd7e9bfcdda3320392e4de8bc9c | 2022-06-12T12:25:16.000Z | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nestoralvaro | null | nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base
results: []
---
<!-- 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. -->
# mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t55_403.csv__google_mt5_base
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.9712
- Rouge2: 0.1329
- Rougel: 0.9638
- Rougelsum: 0.9675
- Gen Len: 6.4489
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 36479 | nan | 0.9712 | 0.1329 | 0.9638 | 0.9675 | 6.4489 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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huggingtweets/manfightdragon | 3075e0fb053f4f38b9339ca64eafaa4b91d7f26e | 2022-06-12T10:26:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/manfightdragon | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/manfightdragon/1655029573001/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('https://pbs.twimg.com/profile_images/1184073162520031232/V6DOEeLp_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lance McDonald</div>
<div style="text-align: center; font-size: 14px;">@manfightdragon</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.
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 Lance McDonald.
| Data | Lance McDonald |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 209 |
| Short tweets | 214 |
| Tweets kept | 2826 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3pc794z5/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 @manfightdragon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t8940p5/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/manfightdragon')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
huggingtweets/eitapau | 4743a65503ddd2f025475762ffd4fde7c00f01ce | 2022-06-12T12:49:59.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/eitapau | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/eitapau/1655038194341/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('https://pbs.twimg.com/profile_images/1466644335034839043/woyxmPjG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Eeeeita pau!</div>
<div style="text-align: center; font-size: 14px;">@eitapau</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.
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 Eeeeita pau!.
| Data | Eeeeita pau! |
| --- | --- |
| Tweets downloaded | 2460 |
| Retweets | 322 |
| Short tweets | 274 |
| Tweets kept | 1864 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3gcai042/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 @eitapau's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1r1v0rkr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1r1v0rkr/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/eitapau')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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] |
CVPR/DualStyleGAN | bd1a2373dadf0c45a5278da1f5b1176d5b7d9129 | 2022-06-12T15:52:23.000Z | [
"dataset:cartoon",
"dataset:caricature",
"dataset:anime",
"dataset:pixar",
"dataset:slamdunk",
"dataset:arcane",
"dataset:comic",
"arxiv:2203.13248",
"pytorch",
"style-transfer",
"face-stylization",
"license:mit"
] | null | false | CVPR | null | CVPR/DualStyleGAN | 0 | 5 | pytorch | ---
license: mit
library_name: pytorch
tags:
- style-transfer
- face-stylization
datasets:
- cartoon
- caricature
- anime
- pixar
- slamdunk
- arcane
- comic
---
## Model Details
This system provides a web demo for the following paper:
**Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer (CVPR 2022)**
- Algorithm developed by: Shuai Yang, Liming Jiang, Ziwei Liu and Chen Change Loy
- Web demo developed by: [hysts](https://huggingface.co/hysts)
- Resources for more information:
- [Project Page](https://www.mmlab-ntu.com/project/dualstylegan/)
- [Research Paper](https://arxiv.org/abs/2203.13248)
- [GitHub Repo](https://github.com/williamyang1991/DualStyleGAN)
**Abstract**
> Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control.
## Citation Information
```bibtex
@inproceedings{yang2022Pastiche,
author = {Yang, Shuai and Jiang, Liming and Liu, Ziwei and and Loy, Chen Change},
title = {Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2022}
}
``` | [
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lindsayng/t5-base-fullwnc-5epoch2-2dc8dc72 | ddb53f61cd5f0a17e0cdf46668cfe01cb2a3d4c8 | 2022-06-12T14:25:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lindsayng | null | lindsayng/t5-base-fullwnc-5epoch2-2dc8dc72 | 0 | null | transformers | Entry not found | [
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florver/modelo_NLI_kvd_vf_5000_v2 | 7e9c0e233b8a8024f7b98cc7e2d827fb6d88bd06 | 2022-06-12T15:21:59.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | florver | null | florver/modelo_NLI_kvd_vf_5000_v2 | 0 | 1 | transformers | Entry not found | [
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roshnir/xlmr-finetuned-mlqa-dev-cross_hi-en | 02834138dfce3d8a71ce57b307f4dc7364a9b78f | 2022-06-12T15:54:06.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | roshnir | null | roshnir/xlmr-finetuned-mlqa-dev-cross_hi-en | 0 | null | transformers | Entry not found | [
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kijun/mas-kobart-v2 | bbeb6f9e9600c361c0c44a0568ad80eed6f0afab | 2022-06-12T15:48:22.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | kijun | null | kijun/mas-kobart-v2 | 0 | null | transformers | ---
license: mit
---
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] |
simecek/humandna_ALBERT_1epoch | 169c494fe99eea53a3ea0918945c790276ca8e84 | 2022-06-12T16:07:57.000Z | [
"pytorch",
"albert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/humandna_ALBERT_1epoch | 0 | null | transformers | Entry not found | [
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jvanz/lenerbr-autoencoder | 9ed4d0d603c10d37958ce9590f3807dfab6b9673 | 2022-06-12T20:58:42.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | jvanz | null | jvanz/lenerbr-autoencoder | 0 | null | transformers | Entry not found | [
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meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize2 | 218e3f3c2828a4fbd9fcb1329cca2d09c9cf281d | 2022-06-12T21:21:42.000Z | [
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | meghazisofiane | null | meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize2 | 0 | null | transformers | Entry not found | [
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huggingtweets/pandershirts | 670f597bcabc2290cb7a040b297572636a8ac2d4 | 2022-06-12T20:14:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/pandershirts | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/pandershirts/1655064824816/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('https://pbs.twimg.com/profile_images/1535688698993512449/903NKFWz_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Hellvetika</div>
<div style="text-align: center; font-size: 14px;">@pandershirts</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.
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 Hellvetika.
| Data | Hellvetika |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 119 |
| Short tweets | 360 |
| Tweets kept | 2767 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kyjr0nr8/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 @pandershirts's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/k8rb7z0d) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/k8rb7z0d/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/pandershirts')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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simecek/humandna_BERT_1epoch | bfaebcf93fd768f167b7d53507454fa31576464d | 2022-06-12T21:15:00.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/humandna_BERT_1epoch | 0 | null | transformers | Entry not found | [
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sactisudesa/RobertaBNE | fc2a7af926202badf030243dc8a2e73443511578 | 2022-06-12T23:42:04.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | sactisudesa | null | sactisudesa/RobertaBNE | 0 | 1 | transformers | Entry not found | [
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simecek/humandna_DISTILBERT_1epoch | fb241c6ae7cec57f94d885e416925c04b2c1e5f6 | 2022-06-12T23:50:24.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/humandna_DISTILBERT_1epoch | 0 | null | transformers | Entry not found | [
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huggingtweets/liebdog1224 | b8f2218c242fdf5d7baaaa40ba576084ed354099 | 2022-06-13T01:03:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/liebdog1224 | 0 | null | transformers | ---
language: en
thumbnail: http://www.huggingtweets.com/liebdog1224/1655082177490/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('https://pbs.twimg.com/profile_images/1088468936998432769/YexExPjG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Noah Liebers</div>
<div style="text-align: center; font-size: 14px;">@liebdog1224</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.
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 Noah Liebers.
| Data | Noah Liebers |
| --- | --- |
| Tweets downloaded | 362 |
| Retweets | 210 |
| Short tweets | 30 |
| Tweets kept | 122 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36hyn6h4/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 @liebdog1224's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zpm376e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zpm376e/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/liebdog1224')
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*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
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ryo0634/bert-base-log_linear-0 | 5bbf403d7201bf0afab3b7213499a7134d60b686 | 2022-06-13T03:36:50.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ryo0634 | null | ryo0634/bert-base-log_linear-0 | 0 | null | transformers | Entry not found | [
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Subsets and Splits