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huggingtweets/elonmusk-jack | 3edad9deb7a90f81eb1cc3e7c70c8d861423280c | 2022-06-13T04:16:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/elonmusk-jack | 0 | null | transformers | 38,100 | ---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-jack/1655093760817/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/1529956155937759233/Nyn1HZWF_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/1115644092329758721/AFjOr-K8_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI CYBORG π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & jack</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-jack</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 Elon Musk & jack.
| Data | Elon Musk | jack |
| --- | --- | --- |
| Tweets downloaded | 3200 | 3232 |
| Retweets | 147 | 1137 |
| Short tweets | 959 | 832 |
| Tweets kept | 2094 | 1263 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2zwk8y4o/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 @elonmusk-jack's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16z5871k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16z5871k/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/elonmusk-jack')
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)
|
huggingtweets/fbinegotiator | 26007c335e8c3d271bbc26d2371ee7f94997df40 | 2022-06-13T04:22:31.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/fbinegotiator | 0 | null | transformers | 38,101 | ---
language: en
thumbnail: http://www.huggingtweets.com/fbinegotiator/1655094146705/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/1312911855187181568/W1hAKDaA_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Christopher Voss</div>
<div style="text-align: center; font-size: 14px;">@fbinegotiator</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 Christopher Voss.
| Data | Christopher Voss |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 370 |
| Short tweets | 98 |
| Tweets kept | 2767 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uat42o9x/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 @fbinegotiator's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1g9amvgc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1g9amvgc/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/fbinegotiator')
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)
|
nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L5 | f2d9b621c3cedb02cda71537ff256fed8acb4ddd | 2022-06-13T14:19:45.000Z | [
"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___t22027_162754.csv__g_mt5_base_L5 | 0 | null | transformers | 38,102 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L5
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___t22027_162754.csv__g_mt5_base_L5
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: 0.7722
- Rouge2: 0.0701
- Rougel: 0.772
- Rougelsum: 0.7717
- Gen Len: 6.329
## 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 | 131773 | nan | 0.7722 | 0.0701 | 0.772 | 0.7717 | 6.329 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
iaanimashaun/opus-mt-en-sw-finetuned-en-to-sw | e8058ec9903b036ae58ffb8903d2823feac394e5 | 2022-06-16T06:40:29.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | iaanimashaun | null | iaanimashaun/opus-mt-en-sw-finetuned-en-to-sw | 0 | null | transformers | 38,103 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: opus-mt-en-sw-finetuned-en-to-sw
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. -->
# opus-mt-en-sw-finetuned-en-to-sw
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-sw](https://huggingface.co/Helsinki-NLP/opus-mt-en-sw) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 113 | 0.9884 | 50.2226 | 19.0434 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
simecek/humandna_DEBERTA_1epoch | 01ed779246471e336e6937c26e2c9e02e5666c42 | 2022-06-13T07:06:03.000Z | [
"pytorch",
"deberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/humandna_DEBERTA_1epoch | 0 | null | transformers | 38,104 | Entry not found |
huggingtweets/demondicekaren | f4fb47bb69e9288d601fd6f6c6b6c216798c0d33 | 2022-06-13T07:19:24.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/demondicekaren | 0 | null | transformers | 38,105 | ---
language: en
thumbnail: http://www.huggingtweets.com/demondicekaren/1655104759793/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/1488027988075507712/FTIBkQRn_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ππ² || DEMONDICE</div>
<div style="text-align: center; font-size: 14px;">@demondicekaren</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 ππ² || DEMONDICE.
| Data | ππ² || DEMONDICE |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 371 |
| Short tweets | 617 |
| Tweets kept | 2258 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fxxzewl/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 @demondicekaren's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ow01rap) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ow01rap/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/demondicekaren')
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)
|
sangcamap/sangcamaptest | 8a846c2e8b721df6179714595ed7264931ff265f | 2022-06-13T17:20:22.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"license:gpl-3.0",
"autotrain_compatible"
] | question-answering | false | sangcamap | null | sangcamap/sangcamaptest | 0 | null | transformers | 38,106 | ---
license: gpl-3.0
---
|
huggingtweets/ruinsman | 1c067ff2fb97b78f5425601e0ad6de2fc38a4b20 | 2022-06-13T09:33:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/ruinsman | 0 | null | transformers | 38,107 | ---
language: en
thumbnail: http://www.huggingtweets.com/ruinsman/1655112758889/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/1428391928110911499/qWeZuRbL_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ManAmongTheRuins</div>
<div style="text-align: center; font-size: 14px;">@ruinsman</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 ManAmongTheRuins.
| Data | ManAmongTheRuins |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 424 |
| Short tweets | 213 |
| Tweets kept | 2547 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3evn1l2w/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 @ruinsman's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/apc372yb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/apc372yb/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/ruinsman')
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)
|
huggingtweets/salgotrader | 6392dae0ca80d3d3526ff9352fe879451d352f09 | 2022-06-13T14:46:27.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/salgotrader | 0 | null | transformers | 38,108 | ---
language: en
thumbnail: http://www.huggingtweets.com/salgotrader/1655131582645/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/1521075169611112448/S_w82Ewg_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">0xPatrician.eth</div>
<div style="text-align: center; font-size: 14px;">@salgotrader</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 0xPatrician.eth.
| Data | 0xPatrician.eth |
| --- | --- |
| Tweets downloaded | 910 |
| Retweets | 250 |
| Short tweets | 84 |
| Tweets kept | 576 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2f275xqv/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 @salgotrader's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ljt0uhcw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ljt0uhcw/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/salgotrader')
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)
|
ryo0634/bert-base-log_linear-dependency-0 | dd79b8943265b08fb085fee23a5efdd7e8720a18 | 2022-06-13T15:00:44.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ryo0634 | null | ryo0634/bert-base-log_linear-dependency-0 | 0 | null | transformers | 38,109 | Entry not found |
sdugar/cross-en-de-fr-minilm-384d-sentence-transformer | 369c22a4907fd3e63e7b4f58b97e310dbb33a4b1 | 2022-06-13T16:51:13.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | sdugar | null | sdugar/cross-en-de-fr-minilm-384d-sentence-transformer | 0 | null | transformers | 38,110 | ---
license: mit
---
|
kravchenko/uk-mt5-small-gec | 3c7ee9608074378b4225944e5a54423e4235dd1b | 2022-06-13T16:29:10.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | kravchenko | null | kravchenko/uk-mt5-small-gec | 0 | null | transformers | 38,111 | Entry not found |
simecek/DNAMobileBert | ce054e936cccefab743d3437df9d74469323efc6 | 2022-06-14T16:23:31.000Z | [
"pytorch",
"tensorboard",
"mobilebert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simecek | null | simecek/DNAMobileBert | 0 | null | transformers | 38,112 | Entry not found |
kravchenko/uk-mt5-base-gec | b2c55c45d4db1b961faa7b8043d1b175ca7fdee9 | 2022-06-13T16:31:43.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | kravchenko | null | kravchenko/uk-mt5-base-gec | 0 | null | transformers | 38,113 | Entry not found |
kravchenko/uk-mt5-large-gec | fc922cf49a155b489e53288137f23e0526916e82 | 2022-06-13T16:39:46.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | kravchenko | null | kravchenko/uk-mt5-large-gec | 0 | null | transformers | 38,114 | Entry not found |
nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2 | 45b541b07e13f1d4fe92b1f5d2fa8c98395fda4e | 2022-06-14T02:06:07.000Z | [
"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___t22027_162754.csv__g_mt5_base_L2 | 0 | null | transformers | 38,115 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2
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___t22027_162754.csv__g_mt5_base_L2
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: 0.0127
- Rouge2: 0.0
- Rougel: 0.0128
- Rougelsum: 0.0129
- Gen Len: 6.329
## 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 | 131773 | nan | 0.0127 | 0.0 | 0.0128 | 0.0129 | 6.329 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mailenpellegrino/transformerRuperta | baa9279e6e95b66c4e613eeb56f966addd5f3d07 | 2022-06-13T18:19:40.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | mailenpellegrino | null | mailenpellegrino/transformerRuperta | 0 | null | transformers | 38,116 | Entry not found |
mcalcagno/mcd101-finedtuned-beto-xnli | 77d9dc44315d2cab074e3678484a78cffc74a712 | 2022-06-13T18:23:45.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | mcalcagno | null | mcalcagno/mcd101-finedtuned-beto-xnli | 0 | null | transformers | 38,117 | Entry not found |
micamorales/roberta-NLI-simple | ef1e8e206424f19266285517f5b6deb234905671 | 2022-06-13T18:20:55.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | micamorales | null | micamorales/roberta-NLI-simple | 0 | null | transformers | 38,118 | Entry not found |
micamorales/roberta-NLI-simple2 | e791abf9d525beac9895c2ca65d63f3415e24ad0 | 2022-06-13T18:27:40.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | micamorales | null | micamorales/roberta-NLI-simple2 | 0 | null | transformers | 38,119 | Entry not found |
income/jpq-question_encoder-base-msmarco-contriever | 09594e60bc9934fc8ecd0f123c14304495ebe83c | 2022-06-13T21:00:45.000Z | [
"pytorch",
"bert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-question_encoder-base-msmarco-contriever | 0 | null | transformers | 38,120 | ---
license: apache-2.0
---
|
jacklin/DeLADE-CLS-P | 4f82e5ddf097de89f4f866f47c3189245d50ff0a | 2022-06-13T21:42:41.000Z | [
"pytorch",
"arxiv:2112.04666"
] | null | false | jacklin | null | jacklin/DeLADE-CLS-P | 0 | null | null | 38,121 | This model, (DeLADE+[CLS])+, is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone using hard negative mining and knowledge distillation with ColBERT teacher, which is detailed in the below paper.
*[A Dense Representation Framework for Lexical and Semantic Matching](https://arxiv.org/pdf/2112.04666.pdf)* Sheng-Chieh Lin and Jimmy Lin.
You can find the usage of the model in our [DHR repo](https://github.com/jacklin64/DHR): (1) [Inference on MSMARCO Passage Ranking](https://github.com/castorini/DHR/blob/main/docs/msmarco-passage-train-eval.md); (2) [Inference on BEIR datasets](https://github.com/castorini/DHR/blob/main/docs/beir-eval.md).
|
micamorales/roberta-NLI-abs2 | c5135ea951469f365e01c5172500c5174ba3469d | 2022-06-13T21:12:14.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | micamorales | null | micamorales/roberta-NLI-abs2 | 0 | null | transformers | 38,122 | Entry not found |
huggingtweets/honiemun | 15f9f1e720ba63dc0e22aea866d434e8bebf03ce | 2022-06-13T23:11:55.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/honiemun | 0 | null | transformers | 38,123 | ---
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/1509372264424296448/HVPI1lQu_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ππ°π―πͺπ¦ β‘</div>
<div style="text-align: center; font-size: 14px;">@honiemun</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 ππ°π―πͺπ¦ β‘.
| Data | ππ°π―πͺπ¦ β‘ |
| --- | --- |
| Tweets downloaded | 3207 |
| Retweets | 231 |
| Short tweets | 381 |
| Tweets kept | 2595 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/teqt0sk7/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 @honiemun's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bqoay71) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bqoay71/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/honiemun')
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)
|
geronimo/RobertaBNE2 | f5069c098d5c485be25b565ec11f2d934dee8b8e | 2022-06-14T20:35:43.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | geronimo | null | geronimo/RobertaBNE2 | 0 | null | transformers | 38,124 | Entry not found |
huggingtweets/horse_js | b2887f889e51e8124f3efe8b8133913a17170037 | 2022-06-14T05:59:52.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/horse_js | 0 | null | transformers | 38,125 | ---
language: en
thumbnail: http://www.huggingtweets.com/horse_js/1655186387828/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/1844491454/horse-js_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Horse JS</div>
<div style="text-align: center; font-size: 14px;">@horse_js</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 Horse JS.
| Data | Horse JS |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 1 |
| Short tweets | 163 |
| Tweets kept | 3036 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ucaep55/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 @horse_js's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/213qs19z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/213qs19z/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/horse_js')
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)
|
winson/custom-resnet50d | a5b507c389136f5ef50754d20fceff3086dbec1c | 2022-06-14T09:34:53.000Z | [
"pytorch",
"resnet",
"transformers"
] | null | false | winson | null | winson/custom-resnet50d | 0 | null | transformers | 38,126 | Entry not found |
mshoaibsarwar/pegasus-pdm-news | 74fd9c4784ec251ab5ad7210992a8587e1da3df8 | 2022-06-14T14:54:33.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"unk",
"dataset:mshoaibsarwar/autotrain-data-pdm-news",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | mshoaibsarwar | null | mshoaibsarwar/pegasus-pdm-news | 0 | 1 | transformers | 38,127 | |
saiharsha/vit-base-beans | 9718aa042724a7a57613c543e87e69e6613decb6 | 2022-06-14T09:54:53.000Z | [
"pytorch",
"vit",
"image-classification",
"dataset:beans",
"transformers",
"vision",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | saiharsha | null | saiharsha/vit-base-beans | 0 | null | transformers | 38,128 | ---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9699248120300752
---
<!-- 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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1824
- Accuracy: 0.9699
## 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: 24
- eval_batch_size: 24
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.672 | 1.0 | 44 | 0.5672 | 0.9398 |
| 0.411 | 2.0 | 88 | 0.3027 | 0.9699 |
| 0.2542 | 3.0 | 132 | 0.2078 | 0.9699 |
| 0.1886 | 4.0 | 176 | 0.1882 | 0.9699 |
| 0.1931 | 5.0 | 220 | 0.1824 | 0.9699 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Waleed-bin-Qamar/ConvNext-For-Covid-Classification | 41e33d9daf4481ca75aec8a99986c0b3dcd97f43 | 2022-06-14T11:14:27.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers",
"license:afl-3.0"
] | image-classification | false | Waleed-bin-Qamar | null | Waleed-bin-Qamar/ConvNext-For-Covid-Classification | 0 | null | transformers | 38,129 | ---
license: afl-3.0
---
|
sdugar/cross-en-de-fr-xlmr-768d-sentence-transformer | c2c60c8b610be019fff69ce80f5b4f19d0d59bd4 | 2022-06-15T06:40:04.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | sdugar | null | sdugar/cross-en-de-fr-xlmr-768d-sentence-transformer | 0 | null | transformers | 38,130 | ---
license: mit
---
|
lmqg/t5-small-squadshifts-new_wiki | a0b198126757cf64b66231f6227691a9d49cf705 | 2022-06-14T10:33:35.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lmqg | null | lmqg/t5-small-squadshifts-new_wiki | 0 | null | transformers | 38,131 | Entry not found |
lmqg/t5-small-squadshifts-amazon | 998ff2b330fe510b89b261a0a390a844ba7bf2bf | 2022-06-14T10:38:29.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lmqg | null | lmqg/t5-small-squadshifts-amazon | 0 | null | transformers | 38,132 | Entry not found |
sdugar/test | 9d36bbf943618db48ca68cbe2f877a9783cd97e3 | 2022-06-14T10:45:34.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | sdugar | null | sdugar/test | 0 | null | transformers | 38,133 | ---
license: mit
---
|
huggingtweets/iamekagra | 161c2257e78b20d2d4946fc1e562d4576caab581 | 2022-06-14T11:39:21.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/iamekagra | 0 | null | transformers | 38,134 | ---
language: en
thumbnail: http://www.huggingtweets.com/iamekagra/1655206726797/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/1436804952119132162/47MeY1N1_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ekagra Sinha</div>
<div style="text-align: center; font-size: 14px;">@iamekagra</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 Ekagra Sinha.
| Data | Ekagra Sinha |
| --- | --- |
| Tweets downloaded | 487 |
| Retweets | 69 |
| Short tweets | 69 |
| Tweets kept | 349 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ceh71sg/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 @iamekagra's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/rf0li8b0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/rf0li8b0/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/iamekagra')
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)
|
huggingtweets/duckybhai | 47a05932f28c79ee9ecbc4de7fb36b24681be3f7 | 2022-06-14T11:44:57.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/duckybhai | 0 | null | transformers | 38,135 | ---
language: en
thumbnail: http://www.huggingtweets.com/duckybhai/1655207092084/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/1494814887410909195/1_cZ1OGN_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Saad Ur Rehman</div>
<div style="text-align: center; font-size: 14px;">@duckybhai</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 Saad Ur Rehman.
| Data | Saad Ur Rehman |
| --- | --- |
| Tweets downloaded | 2045 |
| Retweets | 158 |
| Short tweets | 233 |
| Tweets kept | 1654 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/e0w83ypv/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 @duckybhai's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2tc4ee4o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2tc4ee4o/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/duckybhai')
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)
|
huggingtweets/imrankhanpti | 8ba96dcb73b6110e22f62440d3a7d89b430efb07 | 2022-06-14T12:28:35.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/imrankhanpti | 0 | null | transformers | 38,136 | ---
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/1526278959746392069/t3sMBz94_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Imran Khan</div>
<div style="text-align: center; font-size: 14px;">@imrankhanpti</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 Imran Khan.
| Data | Imran Khan |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 28 |
| Short tweets | 8 |
| Tweets kept | 3214 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s8u3tpn/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 @imrankhanpti's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g9j8i8kg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g9j8i8kg/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/imrankhanpti')
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)
|
mgtoxd/wav2vec2test | 3331578689a38133a54cc8071249bc25e5979e0f | 2022-06-14T14:48:44.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | mgtoxd | null | mgtoxd/wav2vec2test | 0 | null | transformers | 38,137 | Entry not found |
huggingtweets/lukaesch | 56b24ee7b0e767631d18882d7f8ad42e835ce688 | 2022-06-14T16:33:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/lukaesch | 0 | null | transformers | 38,138 | ---
language: en
thumbnail: http://www.huggingtweets.com/lukaesch/1655224388749/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/635525362471038977/hSfNBIhy_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lukas (SoTrusty.com) π</div>
<div style="text-align: center; font-size: 14px;">@lukaesch</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 Lukas (SoTrusty.com) π.
| Data | Lukas (SoTrusty.com) π |
| --- | --- |
| Tweets downloaded | 1051 |
| Retweets | 326 |
| Short tweets | 60 |
| Tweets kept | 665 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/v5uo1xq4/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 @lukaesch's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31s1ya5a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31s1ya5a/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/lukaesch')
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)
|
ndaheim/cima_joint_model | ad6c1a6e5819c196fd90f65004936456e248c5a0 | 2022-06-14T17:13:04.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ndaheim | null | ndaheim/cima_joint_model | 0 | null | transformers | 38,139 | Entry not found |
mcalcagno/mcd101-finedtuned-roberta-xnli | 349dd648e1717aada151734ee8c90a9c6b6881ac | 2022-06-14T17:41:58.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | mcalcagno | null | mcalcagno/mcd101-finedtuned-roberta-xnli | 0 | null | transformers | 38,140 | Entry not found |
kravchenko/uk-mt5-base-gec-tokenized | 381c5aa038dd1d5903dba8ec77f0f0632d29020a | 2022-06-14T20:31:16.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | kravchenko | null | kravchenko/uk-mt5-base-gec-tokenized | 0 | null | transformers | 38,141 | Entry not found |
nateraw/koala-panda-wombat | 3d16d91577b4cc716027007254bf9bb99f384cd2 | 2022-06-14T20:31:04.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | nateraw | null | nateraw/koala-panda-wombat | 0 | null | transformers | 38,142 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: koala-panda-wombat
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9850746393203735
---
# koala-panda-wombat
Autogenerated by HuggingPicsπ€πΌοΈ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### koala
#### panda
#### wombat
 |
geronimo/RobertaBNE23 | 92fde9cf4a14aecdeed8ed1f606703a5af5d974c | 2022-06-14T20:53:46.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | geronimo | null | geronimo/RobertaBNE23 | 0 | null | transformers | 38,143 | Entry not found |
huggingtweets/rangersfc | f6706468764c53493cc056347049aa39a7aaee7f | 2022-06-14T20:58:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/rangersfc | 0 | null | transformers | 38,144 | ---
language: en
thumbnail: http://www.huggingtweets.com/rangersfc/1655240322192/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/1513529336107839491/OQuphidQ_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rangers Football Club</div>
<div style="text-align: center; font-size: 14px;">@rangersfc</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 Rangers Football Club.
| Data | Rangers Football Club |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 315 |
| Short tweets | 338 |
| Tweets kept | 2597 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3150wqc2/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 @rangersfc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bzvo1hp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bzvo1hp/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/rangersfc')
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)
|
mcalcagno/mcd101-finedtuned-recognaibert-xnli | 8ea8d7667ff2ccbf37498e072aaa09fde25376ce | 2022-06-14T22:14:24.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | mcalcagno | null | mcalcagno/mcd101-finedtuned-recognaibert-xnli | 0 | null | transformers | 38,145 | Entry not found |
lmqg/t5-base-squadshifts-new_wiki | 13c68e3c881f0676a961795cbc5a66ca7a282407 | 2022-06-15T00:00:52.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lmqg | null | lmqg/t5-base-squadshifts-new_wiki | 0 | null | transformers | 38,146 | Entry not found |
lmqg/t5-base-squadshifts-nyt | d79e5f0cf6d7039b2fc7864696a61f63d06e3ceb | 2022-06-15T00:03:05.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lmqg | null | lmqg/t5-base-squadshifts-nyt | 0 | null | transformers | 38,147 | Entry not found |
steven123/Teeth_B | d5e5b89568da3988b303885b94dbbb60534c3177 | 2022-06-15T00:31:50.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | steven123 | null | steven123/Teeth_B | 0 | null | transformers | 38,148 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Teeth_B
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6800000071525574
---
# Teeth_B
Autogenerated by HuggingPicsπ€πΌοΈ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Good Teeth
#### Missing Teeth
#### Rotten Teeth
 |
phunc/t5-small-finetuned-xsum | c84b0833663721b8db58392fee40d5557239329e | 2022-06-15T07:18:50.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | phunc | null | phunc/t5-small-finetuned-xsum | 0 | null | transformers | 38,149 | Entry not found |
mgtoxd/tsttst | e320939176933d28c32d6af787ea32f21fbd7307 | 2022-06-15T13:19:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | mgtoxd | null | mgtoxd/tsttst | 0 | null | transformers | 38,150 | Entry not found |
shurafa16/opus-mt-ar-en-finetuned-ar-to-en | f4e3d1bb1a8662f0e9d73ef14413eb7af5c71403 | 2022-06-18T14:11:53.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:news_commentary",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | shurafa16 | null | shurafa16/opus-mt-ar-en-finetuned-ar-to-en | 0 | null | transformers | 38,151 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- news_commentary
metrics:
- bleu
model-index:
- name: opus-mt-ar-en-finetuned-ar-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: news_commentary
type: news_commentary
args: ar-en
metrics:
- name: Bleu
type: bleu
value: 32.8872
---
<!-- 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-ar-en-finetuned-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on the news_commentary dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6933
- Bleu: 32.8872
- Gen Len: 56.084
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 188 | 0.7407 | 30.7259 | 56.296 |
| No log | 2.0 | 376 | 0.6927 | 32.2038 | 58.602 |
| 0.8066 | 3.0 | 564 | 0.6898 | 33.1091 | 57.72 |
| 0.8066 | 4.0 | 752 | 0.6925 | 33.0842 | 56.574 |
| 0.8066 | 5.0 | 940 | 0.6933 | 32.8872 | 56.084 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
winson/distilbert-base-uncased-finetuned-imdb-accelerate | e671d13fce19a8e976e636af2d76915e8e638bcb | 2022-06-25T13:10:32.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | winson | null | winson/distilbert-base-uncased-finetuned-imdb-accelerate | 0 | null | transformers | 38,152 | Entry not found |
flyswot/test2 | 1a1a1386da9dea81029e16071651cda3abeabb1c | 2022-06-15T15:49:17.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | image-classification | false | flyswot | null | flyswot/test2 | 0 | null | transformers | 38,153 | ---
tags:
- generated_from_trainer
model-index:
- name: test2
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. -->
# test2
This model is a fine-tuned version of [flyswot/convnext-tiny-224_flyswot](https://huggingface.co/flyswot/convnext-tiny-224_flyswot) on the None dataset.
## 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: 0.1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.1 | 23 | 0.1128 | 0.9787 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
|
xin811/dummy-t5-small-finetuned-en-zh | 8f1a83be43f853afc97dee8f3ca39c5b7ac59077 | 2022-06-15T13:47:51.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | xin811 | null | xin811/dummy-t5-small-finetuned-en-zh | 0 | null | transformers | 38,154 | Entry not found |
ouiame/T5_mlsum | 795d06f64dd84bb086ffee897152ab58573a2754 | 2022-06-16T05:31:30.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"fr",
"dataset:ouiame/autotrain-data-trainproject",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | ouiame | null | ouiame/T5_mlsum | 0 | null | transformers | 38,155 | ---
tags: autotrain
language: fr
widget:
- text: "I love AutoTrain π€"
datasets:
- ouiame/autotrain-data-trainproject
co2_eq_emissions: 976.8219757938544
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 985232789
- CO2 Emissions (in grams): 976.8219757938544
## Validation Metrics
- Loss: 1.7047555446624756
- Rouge1: 20.2108
- Rouge2: 7.8633
- RougeL: 16.9554
- RougeLsum: 17.3178
- Gen Len: 18.9874
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ouiame/autotrain-trainproject-985232789
``` |
income/jpq-gpl-dbpedia-entity-document_encoder-base-msmarco-distilbert-tas-b | 71ba1b044acf7b842dfbe2cfd91b971a11a28edd | 2022-06-15T17:08:10.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-dbpedia-entity-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,156 | ---
license: apache-2.0
---
|
income/jpq-gpl-fever-question_encoder-base-msmarco-distilbert-tas-b | e07b38ac708d8146775fa5be19b8d14ad72f85c0 | 2022-06-15T17:08:46.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-fever-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,157 | ---
license: apache-2.0
---
|
income/jpq-gpl-fever-document_encoder-base-msmarco-distilbert-tas-b | 122c12b86bcec5e0ae27977a6e40e0eea55d25b1 | 2022-06-15T17:10:19.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-fever-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,158 | ---
license: apache-2.0
---
|
income/jpq-gpl-hotpotqa-document_encoder-base-msmarco-distilbert-tas-b | a9af1edeea3c5e8ee61edc9d9a0d3754e58073a6 | 2022-06-15T17:18:18.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-hotpotqa-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,159 | ---
license: apache-2.0
---
|
income/jpq-gpl-quora-document_encoder-base-msmarco-distilbert-tas-b | 9764717bcaf6998087032cb7fd6eab8438eca968 | 2022-06-15T17:32:42.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-quora-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,160 | ---
license: apache-2.0
---
|
income/jpq-gpl-robust04-question_encoder-base-msmarco-distilbert-tas-b | 2bc8b96125b944fb76c9a0c03ef632320549ed27 | 2022-06-15T17:33:15.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-robust04-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,161 | ---
license: apache-2.0
---
|
huggingtweets/_mohamads | 656fc6298ab310b3d7aacac55af0f5c5b31da3f9 | 2022-06-15T17:37:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/_mohamads | 0 | null | transformers | 38,162 | ---
language: en
thumbnail: http://www.huggingtweets.com/_mohamads/1655314541919/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/1522920330960027648/Z5piAxnG_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">𧬠Ω
ΨΩ
Ψ― Ψ§ΩΨ²ΩΨ±Ψ§ΩΩ</div>
<div style="text-align: center; font-size: 14px;">@_mohamads</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 𧬠Ω
ΨΩ
Ψ― Ψ§ΩΨ²ΩΨ±Ψ§ΩΩ.
| Data | 𧬠Ω
ΨΩ
Ψ― Ψ§ΩΨ²ΩΨ±Ψ§ΩΩ |
| --- | --- |
| Tweets downloaded | 1108 |
| Retweets | 75 |
| Short tweets | 90 |
| Tweets kept | 943 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y8wg10zm/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 @_mohamads's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jm1spua) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jm1spua/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/_mohamads')
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)
|
income/jpq-gpl-robust04-document_encoder-base-msmarco-distilbert-tas-b | 37ae74036461e78d70a848e1cfeb8f1fe8101986 | 2022-06-15T17:34:22.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-robust04-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,163 | ---
license: apache-2.0
---
|
income/jpq-gpl-scifact-question_encoder-base-msmarco-distilbert-tas-b | 46eb424a137ad025c67fd70f1db63a3660a1db32 | 2022-06-15T17:37:16.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-scifact-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,164 | ---
license: apache-2.0
---
|
income/jpq-gpl-signal1m-question_encoder-base-msmarco-distilbert-tas-b | 2a1b94cf078a357eb92a632fcc63e0e292fed97e | 2022-06-15T17:39:14.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-signal1m-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,165 | ---
license: apache-2.0
---
|
income/jpq-gpl-signal1m-document_encoder-base-msmarco-distilbert-tas-b | 60c4c3e53c8a30907b499fa224be31e3f5de5d0d | 2022-06-15T17:42:28.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-signal1m-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,166 | ---
license: apache-2.0
---
|
income/jpq-gpl-trec-covid-document_encoder-base-msmarco-distilbert-tas-b | c58b63cbb75783de7594fc08909227961a13836f | 2022-06-15T17:43:34.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-trec-covid-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,167 | ---
license: apache-2.0
---
|
lmqg/t5-large-squadshifts-new_wiki | 191bb8eb51e2138b616da80b695c066888351ca3 | 2022-06-16T03:47:40.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | lmqg | null | lmqg/t5-large-squadshifts-new_wiki | 0 | null | transformers | 38,168 | Entry not found |
kcarnold/inquisitive2 | d90e688bfa08040454d6c785b43f75c83a02f7e1 | 2022-06-15T19:55:47.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | kcarnold | null | kcarnold/inquisitive2 | 0 | null | transformers | 38,169 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: inquisitive2
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. -->
# inquisitive2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1760
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0
- Datasets 2.3.0
- Tokenizers 0.12.1
|
liux3790/autotrain-journals-covid-990032813 | 38860543b33056598fc3ee233170c8525c00aa2c | 2022-06-15T19:09:50.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:liux3790/autotrain-data-journals-covid",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | liux3790 | null | liux3790/autotrain-journals-covid-990032813 | 0 | null | transformers | 38,170 | |
huggingtweets/yemeen | 6961464c661d0d1f22c778196c8841934e78f4fe | 2022-06-15T21:27:04.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/yemeen | 0 | null | transformers | 38,171 | ---
language: en
thumbnail: http://www.huggingtweets.com/yemeen/1655328324400/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/1438226079030947845/pwH4SUlU_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ππππππ</div>
<div style="text-align: center; font-size: 14px;">@yemeen</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 ππππππ.
| Data | ππππππ |
| --- | --- |
| Tweets downloaded | 2911 |
| Retweets | 1038 |
| Short tweets | 198 |
| Tweets kept | 1675 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3it77r2s/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 @yemeen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39fvs51l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39fvs51l/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/yemeen')
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)
|
income/jpq-gpl-trec-news-document_encoder-base-msmarco-distilbert-tas-b | cf589b23a8e9e94e3d3e1b67d0db05ef0e307a7e | 2022-06-15T21:53:59.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-trec-news-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,172 | ---
license: apache-2.0
---
|
income/jpq-gpl-webis-touche2020-question_encoder-base-msmarco-distilbert-tas-b | ddbc62a6fa654a98930b40ef94842c93621ca826 | 2022-06-15T21:54:42.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-gpl-webis-touche2020-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,173 | ---
license: apache-2.0
---
|
income/jpq-genq-arguana-document_encoder-base-msmarco-distilbert-tas-b | 858475f3c91184a9c8fa12c301a45a840673350d | 2022-06-15T21:58:21.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-arguana-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,174 | ---
license: apache-2.0
---
|
income/jpq-genq-trec-news-question_encoder-base-msmarco-distilbert-tas-b | c0fe5f6212f26c6018d42a01c11197db31defce7 | 2022-06-15T21:58:49.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-trec-news-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,175 | ---
license: apache-2.0
---
|
income/jpq-genq-trec-news-document_encoder-base-msmarco-distilbert-tas-b | dec697141e0122247c241a4eda5e6627c83f678d | 2022-06-15T21:59:20.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-trec-news-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,176 | ---
license: apache-2.0
---
|
income/jpq-genq-fever-document_encoder-base-msmarco-distilbert-tas-b | 3e1341cd91621512b4b4c8fc3d972316db31ff7c | 2022-06-15T22:05:11.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-fever-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,177 | ---
license: apache-2.0
---
|
income/jpq-genq-fiqa-question_encoder-base-msmarco-distilbert-tas-b | c05f1995f342847e4125f9e9a9974002902ae1b5 | 2022-06-15T22:06:17.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-fiqa-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,178 | ---
license: apache-2.0
---
|
income/jpq-genq-nq-document_encoder-base-msmarco-distilbert-tas-b | ecf3a134aa1a6bf4e764b95d2b5eb520cb95072b | 2022-06-15T22:33:31.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-nq-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,179 | ---
license: apache-2.0
---
|
income/jpq-genq-robust04-question_encoder-base-msmarco-distilbert-tas-b | f3eb85c1793891857737857bac267bd99d5f54ea | 2022-06-15T22:47:31.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-robust04-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,180 | ---
license: apache-2.0
---
|
income/jpq-genq-robust04-document_encoder-base-msmarco-distilbert-tas-b | e4fb83af6aa9d64099ce7258e71721c98d9f1996 | 2022-06-15T22:48:04.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-robust04-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,181 | ---
license: apache-2.0
---
|
income/jpq-genq-scifact-question_encoder-base-msmarco-distilbert-tas-b | 3b94da5682b523e5928d982e7d8667e49f4b0cd2 | 2022-06-15T22:49:37.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-scifact-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,182 | ---
license: apache-2.0
---
|
huggingtweets/hotdogsladies | c82eaea6774869d6b5e611a36fa717254b8504ea | 2022-06-15T23:01:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/hotdogsladies | 0 | null | transformers | 38,183 | ---
language: en
thumbnail: http://www.huggingtweets.com/hotdogsladies/1655334112277/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/1474526156430798849/0Z_zfYqH_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Merlin Mann</div>
<div style="text-align: center; font-size: 14px;">@hotdogsladies</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 Merlin Mann.
| Data | Merlin Mann |
| --- | --- |
| Tweets downloaded | 314 |
| Retweets | 41 |
| Short tweets | 48 |
| Tweets kept | 225 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/epnyc8a1/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 @hotdogsladies's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3bjnvmjn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3bjnvmjn/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/hotdogsladies')
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)
|
huggingtweets/skysports | 253cd8e09402a83c171443967cdeb17a363a1bbd | 2022-06-15T23:05:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/skysports | 0 | null | transformers | 38,184 | ---
language: en
thumbnail: http://www.huggingtweets.com/skysports/1655334298376/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/1483397012657688577/19JEENoX_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sky Sports</div>
<div style="text-align: center; font-size: 14px;">@skysports</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 Sky Sports.
| Data | Sky Sports |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 720 |
| Short tweets | 21 |
| Tweets kept | 2509 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3m4jcaji/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 @skysports's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/4psw7x27) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/4psw7x27/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/skysports')
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)
|
kravchenko/uk-mt5-large-gec-tokenized | ea3448561272e1fcaba7590d084c3f6c7b2760dd | 2022-06-15T23:32:14.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | kravchenko | null | kravchenko/uk-mt5-large-gec-tokenized | 0 | null | transformers | 38,185 | Entry not found |
huggingtweets/pronewchaos | af96dbb4e11e39a7207e173773f60c1f5395ff1a | 2022-06-16T04:13:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/pronewchaos | 0 | null | transformers | 38,186 | ---
language: en
thumbnail: http://www.huggingtweets.com/pronewchaos/1655352793305/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/1519208550865653760/gxiNIWdv_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Saitoshi Nanomoto πβοΈπ₯</div>
<div style="text-align: center; font-size: 14px;">@pronewchaos</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 Saitoshi Nanomoto πβοΈπ₯.
| Data | Saitoshi Nanomoto πβοΈπ₯ |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 18 |
| Short tweets | 617 |
| Tweets kept | 2615 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3b2f6bkt/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 @pronewchaos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1lho9s4n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1lho9s4n/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/pronewchaos')
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)
|
huggingtweets/acai28 | 5bf8bf83d7bc931c8c5fa616841d2c72fb0da05d | 2022-06-16T03:39:49.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/acai28 | 0 | null | transformers | 38,187 | ---
language: en
thumbnail: http://www.huggingtweets.com/acai28/1655350773093/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/1527251112604184576/3dKVjGwK_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">alec</div>
<div style="text-align: center; font-size: 14px;">@acai28</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 alec.
| Data | alec |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 165 |
| Short tweets | 488 |
| Tweets kept | 2592 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/rd31m5h3/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 @acai28's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/w8y3ix5h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/w8y3ix5h/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/acai28')
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)
|
jhliu/ClinicalNoteBERT-base-uncased-NTD-MIMIC-segment | 4ca0f1b96c50615052cc0279ad29f3d7113a5828 | 2022-06-16T04:22:15.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | jhliu | null | jhliu/ClinicalNoteBERT-base-uncased-NTD-MIMIC-segment | 0 | null | transformers | 38,188 | Entry not found |
Rakesh111/hindi_model | 8ef93aec6d2f3077a04317f7bc2b5853f91371b1 | 2022-06-16T07:05:02.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Rakesh111 | null | Rakesh111/hindi_model | 0 | null | transformers | 38,189 | |
sayanmandal/t5-small_6_3-hi_en-en_mix | 2d0a952450dbe8674f65f88501c324ed5dc254ed | 2022-06-16T14:54:24.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sayanmandal | null | sayanmandal/t5-small_6_3-hi_en-en_mix | 0 | null | transformers | 38,190 | Entry not found |
huggingtweets/minusgn | 053884b36a36f6cbc3074522f8a3cd110a93ba1a | 2022-06-16T09:01:01.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/minusgn | 0 | null | transformers | 38,191 | ---
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/1081285419512127488/Mkb9FgN3_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">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Isak Vik</div>
<div style="text-align: center; font-size: 14px;">@minusgn</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 Isak Vik.
| Data | Isak Vik |
| --- | --- |
| Tweets downloaded | 3222 |
| Retweets | 190 |
| Short tweets | 550 |
| Tweets kept | 2482 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1dy32g00/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 @minusgn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3njlvz02) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3njlvz02/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/minusgn')
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)
|
ndaheim/cima_ungrounded_joint_model | 8ad164dd0f6f7712080f614c36469cff0f476895 | 2022-06-16T09:29:01.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ndaheim | null | ndaheim/cima_ungrounded_joint_model | 0 | null | transformers | 38,192 | Entry not found |
anuragiiser/convnext-tiny-finetuned-mri | d5dcdeca0dffcce0ba8e063ffcad59affeb32598 | 2022-06-28T09:53:31.000Z | [
"pytorch",
"convnext",
"image-classification",
"transformers"
] | image-classification | false | anuragiiser | null | anuragiiser/convnext-tiny-finetuned-mri | 0 | null | transformers | 38,193 | |
philmunz/poc_dl | cc4b3d77874b7deee3980227bec6ec977d699018 | 2022-06-16T14:32:21.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | philmunz | null | philmunz/poc_dl | 0 | null | transformers | 38,194 | Entry not found |
huggingtweets/basilhalperin-ben_golub-tylercowen | 9bb4e000b2208d21132bba7379a15f14d32051a3 | 2022-06-16T17:09:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/basilhalperin-ben_golub-tylercowen | 0 | null | transformers | 38,195 | ---
language: en
thumbnail: http://www.huggingtweets.com/basilhalperin-ben_golub-tylercowen/1655399323629/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/1483290763056320512/oILN7yPo_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/1043847779355897857/xyZk8v-m_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/1284936824075550723/ix2eGZd7_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI CYBORG π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">tylercowen & Basil Halperin & Ben Golub πΊπ¦</div>
<div style="text-align: center; font-size: 14px;">@basilhalperin-ben_golub-tylercowen</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 tylercowen & Basil Halperin & Ben Golub πΊπ¦.
| Data | tylercowen | Basil Halperin | Ben Golub πΊπ¦ |
| --- | --- | --- | --- |
| Tweets downloaded | 2642 | 1024 | 3247 |
| Retweets | 2065 | 80 | 1009 |
| Short tweets | 43 | 60 | 390 |
| Tweets kept | 534 | 884 | 1848 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4x0ck2xi/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 @basilhalperin-ben_golub-tylercowen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fuzqv36t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fuzqv36t/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/basilhalperin-ben_golub-tylercowen')
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)
|
income/jpq-genq-signal1m-question_encoder-base-msmarco-distilbert-tas-b | 1d75ae8ae6d83decea0d7407e566a201b78e37fc | 2022-06-16T17:45:36.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-signal1m-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,196 | ---
license: apache-2.0
---
|
income/jpq-genq-signal1m-document_encoder-base-msmarco-distilbert-tas-b | 2c0a9fa150723063b5990ca7fba0ffb4b20950ad | 2022-06-16T17:46:02.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-signal1m-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,197 | ---
license: apache-2.0
---
|
income/jpq-genq-trec-covid-question_encoder-base-msmarco-distilbert-tas-b | 5c2de9edaf2e5a8aedb9c5025892dd964e2a806d | 2022-06-16T17:46:35.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-trec-covid-question_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,198 | ---
license: apache-2.0
---
|
income/jpq-genq-trec-covid-document_encoder-base-msmarco-distilbert-tas-b | 1a4d6d3c793db42d2bc71627d39685d4fd25ec5a | 2022-06-16T17:47:01.000Z | [
"pytorch",
"distilbert",
"transformers",
"license:apache-2.0"
] | null | false | income | null | income/jpq-genq-trec-covid-document_encoder-base-msmarco-distilbert-tas-b | 0 | null | transformers | 38,199 | ---
license: apache-2.0
---
|