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scasutt/wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise
|
5949a4f7ac9fa00d3f0ef3b589a72baeb16ff8c8
|
2022-04-19T19:21:38.000Z
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
| false |
scasutt
| null |
scasutt/wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise
| 0 | null |
transformers
| 37,000 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3471
- Wer: 0.4048
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0574 | 1.68 | 500 | 3.4185 | 0.9954 |
| 1.45 | 3.36 | 1000 | 0.7043 | 0.7171 |
| 0.8285 | 5.03 | 1500 | 0.3874 | 0.5050 |
| 0.668 | 6.71 | 2000 | 0.3321 | 0.4512 |
| 0.5324 | 8.39 | 2500 | 0.3394 | 0.4321 |
| 0.4775 | 10.07 | 3000 | 0.3533 | 0.4231 |
| 0.4421 | 11.74 | 3500 | 0.3487 | 0.4084 |
| 0.441 | 13.42 | 4000 | 0.3471 | 0.4048 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
stevems1/bert-base-uncased-Ganapati
|
dcf3d4abdcd06e1f953c89a19264816061c891d5
|
2022-04-19T13:47:44.000Z
|
[
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
fill-mask
| false |
stevems1
| null |
stevems1/bert-base-uncased-Ganapati
| 0 | null |
transformers
| 37,001 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-Ganapati
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. -->
# bert-base-uncased-Ganapati
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 2273 | 0.0000 |
| 0.0 | 2.0 | 4546 | 0.0000 |
| 0.0 | 3.0 | 6819 | 0.0000 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tuhailong/PairSupCon-roberta-wwm-ext
|
986354bd345575de9f51f4a588b348b883e71bcb
|
2022-04-20T02:44:32.000Z
|
[
"pytorch",
"bert",
"zh",
"dataset:dialogue",
"transformers",
"sbert"
] | null | false |
tuhailong
| null |
tuhailong/PairSupCon-roberta-wwm-ext
| 0 | null |
transformers
| 37,002 |
---
language: zh
tags:
- sbert
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder
model's train code by [PairSupCon](https://github.com/amazon-research/sentence-representations/tree/main/PairSupCon)
### Usage
[test.py](https://github.com/TTurn/sentence-representations/edit/main/PairSupCon/test.py)
#### Code
train code from https://github.com/TTurn/sentence-representations/tree/main/PairSupCon
|
nielsr/segformer-finetuned-sidewalk-trainer
|
0ddc4e6a88cd865b2d026277f182367e5f5dc241
|
2022-04-19T13:28:26.000Z
|
[
"pytorch",
"tensorboard",
"segformer",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | null | false |
nielsr
| null |
nielsr/segformer-finetuned-sidewalk-trainer
| 0 | null |
transformers
| 37,003 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: segformer-finetuned-sidewalk-trainer
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. -->
# segformer-finetuned-sidewalk-trainer
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) 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: 5e-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: 10.0
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
phosseini/glucose-bert-large
|
186c9a6f8ed11bb49ea9a836988cb97206d59405
|
2022-04-19T19:00:21.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
phosseini
| null |
phosseini/glucose-bert-large
| 0 | null |
transformers
| 37,004 |
Entry not found
|
tau/false_large_pmi_para0_sent1_span2_True_multi_masks_7_1024_0.3_epoch1
|
0b5cd8bbe338236c89f31d18dfd428969e6fa7b6
|
2022-04-19T18:59:48.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
| false |
tau
| null |
tau/false_large_pmi_para0_sent1_span2_True_multi_masks_7_1024_0.3_epoch1
| 0 | null |
transformers
| 37,005 |
Entry not found
|
tau/false_large_rouge_para0_sent1_span2_True_multi_masks_7_1024_0.3_epoch1
|
b88869217463c7c2c2c022acdcb9aa1cf49f4644
|
2022-04-19T19:13:52.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
| false |
tau
| null |
tau/false_large_rouge_para0_sent1_span2_True_multi_masks_7_1024_0.3_epoch1
| 0 | null |
transformers
| 37,006 |
Entry not found
|
maveriq/lingbert-mini-1M
|
bf5707aa16b1c2c45358afb2be6285cbe0961ccd
|
2022-04-19T19:20:28.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
maveriq
| null |
maveriq/lingbert-mini-1M
| 0 | null |
transformers
| 37,007 |
Entry not found
|
scasutt/wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise_slow_fast
|
9fab457fefa9d78cb06b3bd1ceca51643113d4ff
|
2022-04-20T04:52:57.000Z
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
| false |
scasutt
| null |
scasutt/wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise_slow_fast
| 0 | null |
transformers
| 37,008 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise_slow_fast
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise_slow_fast
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4007
- Wer: 0.3785
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0535 | 1.2 | 500 | 3.3994 | 0.9954 |
| 1.1495 | 2.4 | 1000 | 0.6490 | 0.7155 |
| 0.7148 | 3.6 | 1500 | 0.3812 | 0.4690 |
| 0.5305 | 4.8 | 2000 | 0.3529 | 0.4373 |
| 0.475 | 6.0 | 2500 | 0.3616 | 0.4123 |
| 0.3772 | 7.19 | 3000 | 0.3823 | 0.4074 |
| 0.3632 | 8.39 | 3500 | 0.3665 | 0.3929 |
| 0.3579 | 9.59 | 4000 | 0.3838 | 0.3917 |
| 0.3386 | 10.79 | 4500 | 0.3888 | 0.3839 |
| 0.3193 | 11.99 | 5000 | 0.3872 | 0.3757 |
| 0.2976 | 13.19 | 5500 | 0.3986 | 0.3785 |
| 0.2915 | 14.39 | 6000 | 0.4007 | 0.3785 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/billgates-kellytclements-xychelsea
|
ef8f0595bacf64533a01c3b1a3ff6c415d6dbdfe
|
2022-04-19T20:11:34.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/billgates-kellytclements-xychelsea
| 0 | null |
transformers
| 37,009 |
---
language: en
thumbnail: http://www.huggingtweets.com/billgates-kellytclements-xychelsea/1650398924367/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/1256728742292074496/96By_wwT_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/1414439092373254147/JdS8yLGI_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/1431338485504430082/zQ6S8nOo_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Chelsea E. Manning & Bill Gates & Kelly T. Clements</div>
<div style="text-align: center; font-size: 14px;">@billgates-kellytclements-xychelsea</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 Chelsea E. Manning & Bill Gates & Kelly T. Clements.
| Data | Chelsea E. Manning | Bill Gates | Kelly T. Clements |
| --- | --- | --- | --- |
| Tweets downloaded | 3248 | 3213 | 1777 |
| Retweets | 15 | 199 | 296 |
| Short tweets | 1219 | 7 | 26 |
| Tweets kept | 2014 | 3007 | 1455 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37pv1ayu/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 @billgates-kellytclements-xychelsea's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e303z5q) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e303z5q/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/billgates-kellytclements-xychelsea')
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)
|
proseph/ctrlv-wav2vec2-tokenizer
|
ad6a9da2c4e759ae266dfb57a3c159b87363eb42
|
2022-04-20T03:40:35.000Z
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
| false |
proseph
| null |
proseph/ctrlv-wav2vec2-tokenizer
| 0 | null |
transformers
| 37,010 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ctrlv-wav2vec2-tokenizer
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. -->
# ctrlv-wav2vec2-tokenizer
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3967
- Wer: 0.3138
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4359 | 3.45 | 500 | 1.3595 | 0.9159 |
| 0.5692 | 6.9 | 1000 | 0.4332 | 0.4036 |
| 0.2198 | 10.34 | 1500 | 0.4074 | 0.3678 |
| 0.1314 | 13.79 | 2000 | 0.3480 | 0.3409 |
| 0.0929 | 17.24 | 2500 | 0.3714 | 0.3346 |
| 0.0692 | 20.69 | 3000 | 0.3977 | 0.3224 |
| 0.0542 | 24.14 | 3500 | 0.4068 | 0.3187 |
| 0.0422 | 27.59 | 4000 | 0.3967 | 0.3138 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
waynehills/Waynehills_mT5_Mulang
|
4534d8bf839325b63d90b905c4b952f76a5ac563
|
2022-04-21T04:12:17.000Z
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
| false |
waynehills
| null |
waynehills/Waynehills_mT5_Mulang
| 0 | null |
transformers
| 37,011 |
Entry not found
|
huggingtweets/elonmusk-iamsrk
|
2d303af1b3a526d47c6ad370a6be47406bdd6def
|
2022-04-20T04:58:07.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/elonmusk-iamsrk
| 0 | null |
transformers
| 37,012 |
---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-iamsrk/1650430682800/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/1503591435324563456/foUrqiEw_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/1318511011117199362/htNsviXp_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Shah Rukh Khan</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-iamsrk</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 & Shah Rukh Khan.
| Data | Elon Musk | Shah Rukh Khan |
| --- | --- | --- |
| Tweets downloaded | 221 | 3212 |
| Retweets | 14 | 56 |
| Short tweets | 69 | 278 |
| Tweets kept | 138 | 2878 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39qg1l4s/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-iamsrk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/840j96ek) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/840j96ek/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-iamsrk')
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)
|
obokkkk/wav2vec2-base-timit-demo-colab
|
5a63ee31a1e6a01b97ce88e6334315d40b2798be
|
2022-04-21T09:23:05.000Z
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
| false |
obokkkk
| null |
obokkkk/wav2vec2-base-timit-demo-colab
| 0 | null |
transformers
| 37,013 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4779
- Wer: 0.3468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4408 | 4.0 | 500 | 1.2302 | 0.9116 |
| 0.561 | 8.0 | 1000 | 0.4809 | 0.4320 |
| 0.2091 | 12.0 | 1500 | 0.4285 | 0.3880 |
| 0.1221 | 16.0 | 2000 | 0.4448 | 0.3665 |
| 0.0858 | 20.0 | 2500 | 0.4622 | 0.3585 |
| 0.0597 | 24.0 | 3000 | 0.4621 | 0.3517 |
| 0.0453 | 28.0 | 3500 | 0.4779 | 0.3468 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
npleshkanov/adapter_labse_intent_classifier
|
3842ffb0110f57adba6dae3cf338cd6ab13c3469
|
2022-04-20T09:52:52.000Z
|
[
"pytorch",
"tensorboard",
"bert",
"transformers"
] | null | false |
npleshkanov
| null |
npleshkanov/adapter_labse_intent_classifier
| 0 | null |
transformers
| 37,014 |
Entry not found
|
masakhane/afrimbart_wol_fr_news
|
19a1183c5029c107962bad3c0284d3d0951583a2
|
2022-04-20T13:52:41.000Z
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afrimbart_wol_fr_news
| 0 | null |
transformers
| 37,015 |
---
license: afl-3.0
---
|
masakhane/afrimbart_fr_wol_news
|
b4e4d676d987465f6e4eb7cbebc7e84ad332c960
|
2022-04-20T13:52:44.000Z
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afrimbart_fr_wol_news
| 0 | null |
transformers
| 37,016 |
---
license: afl-3.0
---
|
masakhane/afrimt5_wol_fr_news
|
700dcb6ea1923af048926f3b4adc2e6726faee70
|
2022-04-20T13:52:48.000Z
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afrimt5_wol_fr_news
| 0 | null |
transformers
| 37,017 |
---
license: afl-3.0
---
|
masakhane/afrimt5_fr_wol_news
|
5afbdb9eb327b7009aca2d65fa2d16065e509f11
|
2022-04-20T13:52:52.000Z
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afrimt5_fr_wol_news
| 0 | null |
transformers
| 37,018 |
---
license: afl-3.0
---
|
masakhane/afribyt5_wol_fr_news
|
c395035a83d563630cdc87fc759ec46e11a8a8d0
|
2022-04-20T15:07:52.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afribyt5_wol_fr_news
| 0 | null |
transformers
| 37,019 |
---
license: afl-3.0
---
|
masakhane/afribyt5_fr_wol_news
|
c26279e4dff10710d4cc30c5de5c9bf400d1ab35
|
2022-04-20T15:08:03.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afribyt5_fr_wol_news
| 0 | null |
transformers
| 37,020 |
---
license: afl-3.0
---
|
masakhane/byt5_wol_fr_news
|
516b1ccf80faee0103751e007bf34a0c11a75728
|
2022-04-20T15:07:55.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/byt5_wol_fr_news
| 0 | null |
transformers
| 37,021 |
---
license: afl-3.0
---
|
masakhane/byt5_fr_wol_news
|
e8ddd544849103ede2d293d8b10ab822343e0164
|
2022-04-20T15:07:59.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/byt5_fr_wol_news
| 0 | null |
transformers
| 37,022 |
---
license: afl-3.0
---
|
masakhane/mt5_fr_wol_news
|
6b0a2c366cc2b803ceff8e8ba1cfab76609758ec
|
2022-04-20T16:19:43.000Z
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/mt5_fr_wol_news
| 0 | null |
transformers
| 37,023 |
---
license: afl-3.0
---
|
masakhane/mt5_wol_fr_news
|
11c810d667fb3f28aa7da7dc10359728e7ac7db2
|
2022-04-20T16:19:28.000Z
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/mt5_wol_fr_news
| 0 | null |
transformers
| 37,024 |
---
license: afl-3.0
---
|
masakhane/mbart50_wol_fr_news
|
7a094fdd844d2313e54a06bf98aef31440884005
|
2022-04-20T16:19:39.000Z
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/mbart50_wol_fr_news
| 0 | null |
transformers
| 37,025 |
---
license: afl-3.0
---
|
masakhane/mbart50_fr_wol_news
|
a7832d14b32b2af8dd818a33105b2994ea263b0c
|
2022-04-20T16:19:22.000Z
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/mbart50_fr_wol_news
| 0 | null |
transformers
| 37,026 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_wol_news
|
6a16428266a82a4babb3485fc5236d11c5f543ed
|
2022-04-20T17:34:38.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_fr_wol_news
| 0 | null |
transformers
| 37,027 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_wol_fr_news
|
6a840a39b5f20167d2abefa6d0ed1fd48acbe26c
|
2022-04-20T17:34:48.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_wol_fr_news
| 0 | null |
transformers
| 37,028 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_wol_fr_rel_news
|
b0a6d312a9d47ae359264f0d5d67918da2280795
|
2022-04-20T17:34:53.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_wol_fr_rel_news
| 0 | null |
transformers
| 37,029 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_wol_rel_news_ft
|
728c510a0119d79e3724538a25d6f1bec7f4d41e
|
2022-04-20T19:20:09.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_fr_wol_rel_news_ft
| 0 | null |
transformers
| 37,030 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_wol_fr_rel_ft
|
73afafaee1f9eac624ebd7ef51a167064de3c6fd
|
2022-04-20T18:36:05.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_wol_fr_rel_ft
| 0 | null |
transformers
| 37,031 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_wol_rel_ft
|
ee6f7c7b7890979631dc57b33801c01433a531e8
|
2022-04-20T18:36:18.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_fr_wol_rel_ft
| 0 | null |
transformers
| 37,032 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_wol_rel
|
b1767f2f89a6f2c55d9cdf7858fc77c16a1f1d8c
|
2022-04-20T19:20:13.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_fr_wol_rel
| 0 | null |
transformers
| 37,033 |
---
license: afl-3.0
---
|
jesperjmb/MergeIntrosNSP
|
13259bae8aba94d0fc206dfc40d02bb34c9f1bd9
|
2022-05-19T08:04:20.000Z
|
[
"pytorch",
"bert",
"next-sentence-prediction",
"transformers"
] | null | false |
jesperjmb
| null |
jesperjmb/MergeIntrosNSP
| 0 | null |
transformers
| 37,034 | |
obokkkk/wav2vec2-base-timit-demo-colab2
|
ff346d67262d9692ad941e0b7588df97ffccedd2
|
2022-04-20T19:01:52.000Z
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
| false |
obokkkk
| null |
obokkkk/wav2vec2-base-timit-demo-colab2
| 0 | null |
transformers
| 37,035 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4805
- Wer: 0.3398
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4737 | 4.0 | 500 | 1.2889 | 0.9293 |
| 0.5838 | 8.0 | 1000 | 0.4751 | 0.4353 |
| 0.2141 | 12.0 | 1500 | 0.4809 | 0.3881 |
| 0.1259 | 16.0 | 2000 | 0.4587 | 0.3683 |
| 0.084 | 20.0 | 2500 | 0.4941 | 0.3601 |
| 0.0582 | 24.0 | 3000 | 0.4811 | 0.3482 |
| 0.0439 | 28.0 | 3500 | 0.4805 | 0.3398 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
huggingtweets/elonmusk-nicolebehnam-punk6529
|
9cc3f6a2028167aa06ed7c6b654ac78b3169f26e
|
2022-04-20T20:38:53.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/elonmusk-nicolebehnam-punk6529
| 0 | null |
transformers
| 37,036 |
---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-nicolebehnam-punk6529/1650487127903/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/1503591435324563456/foUrqiEw_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/1440017111531855879/A4p6F07H_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/1505511419982213126/2XfmKzFp_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & 6529 & nic b</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-nicolebehnam-punk6529</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 & 6529 & nic b.
| Data | Elon Musk | 6529 | nic b |
| --- | --- | --- | --- |
| Tweets downloaded | 640 | 3241 | 3249 |
| Retweets | 34 | 887 | 241 |
| Short tweets | 201 | 390 | 1088 |
| Tweets kept | 405 | 1964 | 1920 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3d9axu9g/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-nicolebehnam-punk6529's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ekidqlxj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ekidqlxj/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-nicolebehnam-punk6529')
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/nicolebehnam
|
554f486b68d25cb2c97a02a24a325cda789e8f7d
|
2022-04-20T21:05:47.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/nicolebehnam
| 0 | null |
transformers
| 37,037 |
---
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/1505511419982213126/2XfmKzFp_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">nic b</div>
<div style="text-align: center; font-size: 14px;">@nicolebehnam</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 nic b.
| Data | nic b |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 241 |
| Short tweets | 1088 |
| Tweets kept | 1920 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/a4rx8y3x/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 @nicolebehnam's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/y6mwoo39) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/y6mwoo39/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/nicolebehnam')
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/torstenvolk
|
1a9e57f5251bbdb763ee2bb40028b83bf303722b
|
2022-04-21T00:16:11.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/torstenvolk
| 0 | null |
transformers
| 37,038 |
---
language: en
thumbnail: http://www.huggingtweets.com/torstenvolk/1650500124030/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/1575782906/110930-ENMA-115240-web_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Torsten Volk</div>
<div style="text-align: center; font-size: 14px;">@torstenvolk</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 Torsten Volk.
| Data | Torsten Volk |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 449 |
| Short tweets | 60 |
| Tweets kept | 2741 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pgfl6jg/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 @torstenvolk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1iccl44p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1iccl44p/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/torstenvolk')
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)
|
nnn/shangpin-pre-training
|
05a58c3c7d69b44d50b4b31a6a4b1aa409ccac65
|
2022-04-21T03:10:08.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
nnn
| null |
nnn/shangpin-pre-training
| 0 | null |
transformers
| 37,039 | |
wojciechkrukar/t5-small-finetuned-xsum
|
1c6694683ac1cc2bf0588118ae00424baac0f3de
|
2022-04-21T07:23:54.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
| false |
wojciechkrukar
| null |
wojciechkrukar/t5-small-finetuned-xsum
| 0 | null |
transformers
| 37,040 |
Entry not found
|
frozenwalker/SciFive_pubmedqa_question_generation_using_NmCo_prompt_entity
|
159ccde11c12e06162d73c6a58920b86e2875fba
|
2022-04-21T06:32:06.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
| false |
frozenwalker
| null |
frozenwalker/SciFive_pubmedqa_question_generation_using_NmCo_prompt_entity
| 0 | null |
transformers
| 37,041 |
Entry not found
|
satpalsr/arbit-test
|
95b394990c327c98ccc8eeb8fbe398097df6af50
|
2022-04-21T08:33:16.000Z
|
[
"pytorch"
] | null | false |
satpalsr
| null |
satpalsr/arbit-test
| 0 | null | null | 37,042 |
Entry not found
|
huggingtweets/route2fi
|
680a2b334d2e528ea01323d3c789beae7eeaa49e
|
2022-04-21T10:07:42.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/route2fi
| 0 | null |
transformers
| 37,043 |
---
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/1469588644088451073/VEu0DKDG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Route 2 FI</div>
<div style="text-align: center; font-size: 14px;">@route2fi</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 Route 2 FI.
| Data | Route 2 FI |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1 |
| Short tweets | 264 |
| Tweets kept | 2985 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gjkyb1x/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 @route2fi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3q0o96ub) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3q0o96ub/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/route2fi')
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)
|
simonnedved/bert-mlm
|
af805bbc8a0f430b0d387c1b9ace8ab178b38931
|
2022-04-21T14:21:11.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
| false |
simonnedved
| null |
simonnedved/bert-mlm
| 0 | null |
transformers
| 37,044 |
---
license: apache-2.0
---
|
orendar/en_he_roberta
|
72ba0f6a6167be1e0576c2da668342deb039e1b0
|
2022-04-21T16:16:09.000Z
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
| false |
orendar
| null |
orendar/en_he_roberta
| 0 | null |
transformers
| 37,045 |
Entry not found
|
surajnair/r3m-50
|
51a1ff994641fd1c6880d34a9ea0664e46d9fbbb
|
2022-04-21T20:32:54.000Z
|
[
"pytorch",
"r3m",
"transformers"
] | null | false |
surajnair
| null |
surajnair/r3m-50
| 0 | null |
transformers
| 37,046 |
This model contains the pre-trained ResNet50 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic learning.
|
julien-c/gpt2-from-colab
|
736cc7d10233a138577d734818b50a0442da59d6
|
2022-04-21T20:12:19.000Z
|
[
"pytorch",
"license:apache-2.0"
] | null | false |
julien-c
| null |
julien-c/gpt2-from-colab
| 0 | null | null | 37,047 |
---
license: apache-2.0
---
\nhello
|
masakhane/afrimbart_ibo_en_news
|
7887e1db10e6872c90571c11826634714ff87ba3
|
2022-04-22T09:40:50.000Z
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afrimbart_ibo_en_news
| 0 | null |
transformers
| 37,048 |
---
license: afl-3.0
---
|
masakhane/afrimbart_en_ibo_news
|
866a42aa51076b11667618711ef7b6f746658105
|
2022-04-22T09:40:47.000Z
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afrimbart_en_ibo_news
| 0 | null |
transformers
| 37,049 |
---
license: afl-3.0
---
|
masakhane/afribyt5_ibo_en_news
|
244cfcc77821f0840e327024c774b70fca78b236
|
2022-04-22T10:50:16.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/afribyt5_ibo_en_news
| 0 | null |
transformers
| 37,050 |
---
license: afl-3.0
---
|
masakhane/mbart50_ibo_en_news
|
2259ca6f3836d22660dd05c8af922c6e0f3c1b22
|
2022-04-22T10:50:22.000Z
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/mbart50_ibo_en_news
| 0 | null |
transformers
| 37,051 |
---
license: afl-3.0
---
|
masakhane/mt5_ibo_en_news
|
5669c0487d9ebb2661c7daaa9b2c918d3dc5271d
|
2022-04-22T11:48:33.000Z
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/mt5_ibo_en_news
| 0 | null |
transformers
| 37,052 |
---
license: afl-3.0
---
|
masakhane/mt5_en_ibo_news
|
56c8c5361ac008e699b15b4ae58d5af4dad5efcc
|
2022-04-22T11:48:39.000Z
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/mt5_en_ibo_news
| 0 | null |
transformers
| 37,053 |
---
license: afl-3.0
---
|
masakhane/byt5_en_ibo_news
|
bfc386981c56e49f87adedde4965b7dfdc17e4c4
|
2022-04-22T11:48:36.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/byt5_en_ibo_news
| 0 | null |
transformers
| 37,054 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_ibo_en_rel_news
|
a141b9bb810908ac0f137d91832cddb41ad9ba8c
|
2022-04-22T12:45:10.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_ibo_en_rel_news
| 0 | null |
transformers
| 37,055 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_en_ibo_rel_news
|
cae876ed98491711623fffb1377d0b085b0a03c6
|
2022-04-22T12:45:12.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_en_ibo_rel_news
| 0 | null |
transformers
| 37,056 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_ibo_en_rel_news_ft
|
71641cb8667ec9a52354b62e0360db546e8deadd
|
2022-04-22T13:49:20.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_ibo_en_rel_news_ft
| 0 | null |
transformers
| 37,057 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_en_ibo_rel_ft
|
5b9518aa11d25fb06de75b1a590e28a267f62ba5
|
2022-04-22T13:49:27.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_en_ibo_rel_ft
| 0 | null |
transformers
| 37,058 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_en_ibo_rel
|
e2b47f707c6f48d7d4ad9cb0dd2afa9982b37417
|
2022-04-22T14:45:22.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_en_ibo_rel
| 0 | null |
transformers
| 37,059 |
---
license: afl-3.0
---
|
masakhane/m2m100_418M_ibo_en_rel
|
8a19c96129df88a7133147f0640b3fc9b264d4ac
|
2022-04-22T14:45:24.000Z
|
[
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] |
text2text-generation
| false |
masakhane
| null |
masakhane/m2m100_418M_ibo_en_rel
| 0 | null |
transformers
| 37,060 |
---
license: afl-3.0
---
|
negfir/bert_uncased_L-8_H-256_A-4wiki103
|
a301777c18ca6d82ab9d2880ebf19f733e17eff8
|
2022-04-21T21:45:44.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
negfir
| null |
negfir/bert_uncased_L-8_H-256_A-4wiki103
| 0 | null |
transformers
| 37,061 |
Entry not found
|
samake/distilbert-base-uncased-finetuned-ner
|
bbd9576993c98cd3a29ce9d8db331dd75c6dba64
|
2022-04-22T06:57:56.000Z
|
[
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
| false |
samake
| null |
samake/distilbert-base-uncased-finetuned-ner
| 0 | null |
transformers
| 37,062 |
Entry not found
|
rajat99/Fine_Tuning_XLSR_300M_on_OpenSLR_model
|
cdf281c8f1bc69c8a5ccfb68456b4c3933096acc
|
2022-04-22T13:11:07.000Z
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
| false |
rajat99
| null |
rajat99/Fine_Tuning_XLSR_300M_on_OpenSLR_model
| 0 | null |
transformers
| 37,063 |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Fine_Tuning_XLSR_300M_on_OpenSLR_model
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. -->
# Fine_Tuning_XLSR_300M_on_OpenSLR_model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2669
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 5.5102 | 23.53 | 400 | 3.2669 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
huggingtweets/plsnobullywaaa
|
85439266a7d1de07ac474c33587c753950fc207a
|
2022-04-22T20:47:21.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/plsnobullywaaa
| 0 | null |
transformers
| 37,064 |
---
language: en
thumbnail: http://www.huggingtweets.com/plsnobullywaaa/1650660437516/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/1511292594214551557/4T_znkpc_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">clementine</div>
<div style="text-align: center; font-size: 14px;">@plsnobullywaaa</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 clementine.
| Data | clementine |
| --- | --- |
| Tweets downloaded | 774 |
| Retweets | 32 |
| Short tweets | 258 |
| Tweets kept | 484 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/125ldexx/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 @plsnobullywaaa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2whc68l3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2whc68l3/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/plsnobullywaaa')
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/proanatwink
|
011ac7a0809f0a1c7e99cbbe2148bb81a6bbbd94
|
2022-04-22T17:26:21.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/proanatwink
| 0 | null |
transformers
| 37,065 |
---
language: en
thumbnail: http://www.huggingtweets.com/proanatwink/1650648376939/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/1509040026625224705/B_S4MCbD_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">God is Love (((they)))/them🪲✊🏼🇺🇦🇮🇱🏳️⚧️</div>
<div style="text-align: center; font-size: 14px;">@proanatwink</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 God is Love (((they)))/them🪲✊🏼🇺🇦🇮🇱🏳️⚧️.
| Data | God is Love (((they)))/them🪲✊🏼🇺🇦🇮🇱🏳️⚧️ |
| --- | --- |
| Tweets downloaded | 613 |
| Retweets | 120 |
| Short tweets | 142 |
| Tweets kept | 351 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/yp8eka3q/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 @proanatwink's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lu2xkr5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lu2xkr5/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/proanatwink')
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/charlottefang77
|
198609d0f7e2510df6a06a51bdae5a2632999324
|
2022-04-23T17:49:26.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/charlottefang77
| 0 | null |
transformers
| 37,066 |
---
language: en
thumbnail: http://www.huggingtweets.com/charlottefang77/1650736161071/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/1509915576566620162/LShNQbfF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">♡ Charlotte Fang 刹利</div>
<div style="text-align: center; font-size: 14px;">@charlottefang77</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 ♡ Charlotte Fang 刹利.
| Data | ♡ Charlotte Fang 刹利 |
| --- | --- |
| Tweets downloaded | 3190 |
| Retweets | 1655 |
| Short tweets | 381 |
| Tweets kept | 1154 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2lq9iqf9/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 @charlottefang77's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39i3lnlw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39i3lnlw/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/charlottefang77')
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/miyarepostbot
|
27bc5a8575e63fff98e14ec9f90314ee242e5e98
|
2022-04-22T18:13:23.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/miyarepostbot
| 0 | null |
transformers
| 37,067 |
---
language: en
thumbnail: http://www.huggingtweets.com/miyarepostbot/1650651175106/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/1400304659688878088/Lbb8zMZE_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Miya</div>
<div style="text-align: center; font-size: 14px;">@miyarepostbot</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 Miya.
| Data | Miya |
| --- | --- |
| Tweets downloaded | 1840 |
| Retweets | 23 |
| Short tweets | 214 |
| Tweets kept | 1603 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2lftgxb7/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 @miyarepostbot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1b87ps3a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1b87ps3a/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/miyarepostbot')
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/mimpathy
|
94bff4994fdbde1d4e85d463df7f2aea3f2d4a04
|
2022-04-22T18:39:10.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/mimpathy
| 0 | null |
transformers
| 37,068 |
---
language: en
thumbnail: http://www.huggingtweets.com/mimpathy/1650652745938/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/1269411300624363520/-xYW6d_6_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">𝓗𝓸𝓷𝓸𝓻</div>
<div style="text-align: center; font-size: 14px;">@mimpathy</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 𝓗𝓸𝓷𝓸𝓻.
| Data | 𝓗𝓸𝓷𝓸𝓻 |
| --- | --- |
| Tweets downloaded | 2299 |
| Retweets | 211 |
| Short tweets | 331 |
| Tweets kept | 1757 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17w4ucd3/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 @mimpathy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qr7mqkc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qr7mqkc/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/mimpathy')
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/unbridled_id
|
752044ed1f24421489853fc55ae2ab574ce14596
|
2022-04-29T20:24:49.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/unbridled_id
| 0 | null |
transformers
| 37,069 |
---
language: en
thumbnail: http://www.huggingtweets.com/unbridled_id/1651263884816/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/1376263696389914629/_FzhUcTW_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sierra Armor 𝔼𝕣𝕚𝕤</div>
<div style="text-align: center; font-size: 14px;">@unbridled_id</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 Sierra Armor 𝔼𝕣𝕚𝕤.
| Data | Sierra Armor 𝔼𝕣𝕚𝕤 |
| --- | --- |
| Tweets downloaded | 3146 |
| Retweets | 551 |
| Short tweets | 413 |
| Tweets kept | 2182 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bhxlbvg/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 @unbridled_id's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/n3ccyzg2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/n3ccyzg2/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/unbridled_id')
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/propertyexile
|
e47b57bf68df6f7cd179b84ea0577f6bf4f63e66
|
2022-05-09T05:28:39.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/propertyexile
| 0 | null |
transformers
| 37,070 |
---
language: en
thumbnail: http://www.huggingtweets.com/propertyexile/1652074114021/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/1523442545153519616/mYJEJtEL_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Primo</div>
<div style="text-align: center; font-size: 14px;">@propertyexile</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 Primo.
| Data | Primo |
| --- | --- |
| Tweets downloaded | 304 |
| Retweets | 37 |
| Short tweets | 26 |
| Tweets kept | 241 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1q8zni52/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 @propertyexile's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1f85w6fy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1f85w6fy/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/propertyexile')
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)
|
negfir/bert_uncased_L-6_H-512_A-8wiki103
|
4d05b6492b51c8cf31750468fdfc399481a8c88b
|
2022-04-22T20:28:38.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
negfir
| null |
negfir/bert_uncased_L-6_H-512_A-8wiki103
| 0 | null |
transformers
| 37,071 |
Entry not found
|
shahriarg/pretrained_kyw_e1
|
af08d81214035c4c7a628683a9e884070a6f2f7a
|
2022-04-22T20:51:32.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
shahriarg
| null |
shahriarg/pretrained_kyw_e1
| 0 | null |
transformers
| 37,072 |
Entry not found
|
huggingtweets/newscollected
|
54b2074ec6d530f6c186f494af94b6cc47cd6091
|
2022-05-14T14:14:25.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/newscollected
| 0 | null |
transformers
| 37,073 |
---
language: en
thumbnail: http://www.huggingtweets.com/newscollected/1652537660752/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/1522032150358511616/83U7w6rG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">del co</div>
<div style="text-align: center; font-size: 14px;">@newscollected</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 del co.
| Data | del co |
| --- | --- |
| Tweets downloaded | 370 |
| Retweets | 30 |
| Short tweets | 68 |
| Tweets kept | 272 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sfc2k02/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 @newscollected's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zsagze5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zsagze5/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/newscollected')
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/angelicism010-propertyexile-wretched_worm
|
ab7040c642e4a10f39846a1caacff3c76c3c60a3
|
2022-04-23T01:52:59.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/angelicism010-propertyexile-wretched_worm
| 0 | null |
transformers
| 37,074 |
---
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/1517583783020666881/mmUj6mkI_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/1383763210314997773/aIIDR23G_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/1517290992361422848/E5jRRDlu_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Primo & offlineism010 & wretched worm</div>
<div style="text-align: center; font-size: 14px;">@angelicism010-propertyexile-wretched_worm</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 Primo & offlineism010 & wretched worm.
| Data | Primo | offlineism010 | wretched worm |
| --- | --- | --- | --- |
| Tweets downloaded | 200 | 278 | 3234 |
| Retweets | 32 | 4 | 320 |
| Short tweets | 17 | 28 | 549 |
| Tweets kept | 151 | 246 | 2365 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3o7b93qp/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 @angelicism010-propertyexile-wretched_worm's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30uxuf66) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30uxuf66/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/angelicism010-propertyexile-wretched_worm')
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/h0uldin
|
88ea59201dc0719e6ed2d6c54af377569f0c6b1e
|
2022-06-07T17:23:20.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/h0uldin
| 0 | null |
transformers
| 37,075 |
---
language: en
thumbnail: http://www.huggingtweets.com/h0uldin/1654622595098/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/1532159785692549122/Vt4uxT07_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">H</div>
<div style="text-align: center; font-size: 14px;">@h0uldin</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 H.
| Data | H |
| --- | --- |
| Tweets downloaded | 723 |
| Retweets | 166 |
| Short tweets | 116 |
| Tweets kept | 441 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/22nta9wb/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 @h0uldin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jd5cs4g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jd5cs4g/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/h0uldin')
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/angelicism010
|
a54df49013c7b147df51b78599d2217cf812e2f5
|
2022-04-23T23:32:13.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/angelicism010
| 0 | null |
transformers
| 37,076 |
---
language: en
thumbnail: http://www.huggingtweets.com/angelicism010/1650756728850/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/1383763210314997773/aIIDR23G_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">offlineism010</div>
<div style="text-align: center; font-size: 14px;">@angelicism010</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 offlineism010.
| Data | offlineism010 |
| --- | --- |
| Tweets downloaded | 278 |
| Retweets | 4 |
| Short tweets | 28 |
| Tweets kept | 246 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2luo02mm/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 @angelicism010's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3v3jaemf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3v3jaemf/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/angelicism010')
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)
|
negfir/bert_uncased_L-12_H-768_A-12wiki103
|
1e877188e1b61b94f09c68f8634a817725bbec75
|
2022-04-23T03:02:15.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
negfir
| null |
negfir/bert_uncased_L-12_H-768_A-12wiki103
| 0 | null |
transformers
| 37,077 |
Entry not found
|
negfir/bert_uncased_L-12_H-512_A-8wiki103
|
b565745f15a8033e21ab9299e6ebf56c005a949b
|
2022-04-23T06:53:21.000Z
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
| false |
negfir
| null |
negfir/bert_uncased_L-12_H-512_A-8wiki103
| 0 | null |
transformers
| 37,078 |
Entry not found
|
ywan/unite-up
|
970bfcf85d82315f6f3522a1fedf76b44e0252ad
|
2022-04-24T03:35:43.000Z
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"metric",
"quality estimation",
"translation evaluation",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
| false |
ywan
| null |
ywan/unite-up
| 0 | null |
transformers
| 37,079 |
---
license: apache-2.0
tags:
- metric
- quality estimation
- translation evaluation
---
This model is the English-targeted version of "UniTE: Unified Translation Evaluation".
|
ywan/unite-mup
|
540eddef1fbf341a861c9afccb59ece5ade118b7
|
2022-04-24T04:06:41.000Z
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"metric",
"quality estimation",
"translation evaluation",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
| false |
ywan
| null |
ywan/unite-mup
| 0 | null |
transformers
| 37,080 |
---
license: apache-2.0
tags:
- metric
- quality estimation
- translation evaluation
---
This model is the multilingual version of "UniTE: Unified Translation Evaluation".
|
huggingtweets/newscollected-nickmullensgf
|
66f4c7e076a68a86a37e62851942f11c05422ac4
|
2022-05-12T13:41:10.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/newscollected-nickmullensgf
| 0 | null |
transformers
| 37,081 |
---
language: en
thumbnail: http://www.huggingtweets.com/newscollected-nickmullensgf/1652362865457/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/1522032150358511616/83U7w6rG_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/1469950344918671364/-037cCwh_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">del co & kayla</div>
<div style="text-align: center; font-size: 14px;">@newscollected-nickmullensgf</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 del co & kayla.
| Data | del co | kayla |
| --- | --- | --- |
| Tweets downloaded | 366 | 3215 |
| Retweets | 30 | 946 |
| Short tweets | 67 | 362 |
| Tweets kept | 269 | 1907 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nqg16qms/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 @newscollected-nickmullensgf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3jf63jpr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3jf63jpr/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/newscollected-nickmullensgf')
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/dnlklr
|
b1441d7c8fe10cdf41bb5a1ed580ee0be9c9f432
|
2022-04-23T18:02:48.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/dnlklr
| 0 | null |
transformers
| 37,082 |
---
language: en
thumbnail: http://www.huggingtweets.com/dnlklr/1650736963681/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/1485855322895880192/6tnb9u8H_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Daniel Keller</div>
<div style="text-align: center; font-size: 14px;">@dnlklr</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 Daniel Keller.
| Data | Daniel Keller |
| --- | --- |
| Tweets downloaded | 3229 |
| Retweets | 85 |
| Short tweets | 555 |
| Tweets kept | 2589 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gzfhywi9/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 @dnlklr's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pz5v2py) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pz5v2py/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/dnlklr')
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/c8ohe2cqqe092cq
|
32a2b1bebba00e2f2fc4bea85027dbf1dc1a368b
|
2022-05-24T21:29:49.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/c8ohe2cqqe092cq
| 0 | null |
transformers
| 37,083 |
---
language: en
thumbnail: http://www.huggingtweets.com/c8ohe2cqqe092cq/1653427783549/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/1519998754425872385/VoEOP0Xg_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">A kind Face</div>
<div style="text-align: center; font-size: 14px;">@c8ohe2cqqe092cq</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from A kind Face.
| Data | A kind Face |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 189 |
| Short tweets | 1151 |
| Tweets kept | 1902 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1czak1k4/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 @c8ohe2cqqe092cq's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/35wrrmdp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/35wrrmdp/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/c8ohe2cqqe092cq')
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)
|
smeoni/nbme-deberta-v2-xlarge
|
f3e4943743c58dfb9d41088b328ea959343ea591
|
2022-04-24T17:56:24.000Z
|
[
"pytorch",
"tensorboard",
"deberta-v2",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] |
fill-mask
| false |
smeoni
| null |
smeoni/nbme-deberta-v2-xlarge
| 0 | null |
transformers
| 37,084 |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: nbme-deberta-v2-xlarge
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. -->
# nbme-deberta-v2-xlarge
This model is a fine-tuned version of [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5986
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5771 | 1.0 | 1847 | 6.6380 |
| 6.4068 | 2.0 | 3694 | 6.6034 |
| 6.3597 | 3.0 | 5541 | 6.5986 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jackh1995/base
|
fbf461152ffe35223eeb10ff3ae7bef7ebe6cf04
|
2022-04-24T07:51:13.000Z
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
| false |
jackh1995
| null |
jackh1995/base
| 0 | null |
transformers
| 37,085 |
Entry not found
|
jackh1995/albert-base
|
6da75bb41440f982a9324cc4c0529d5d9769a709
|
2022-04-24T07:57:41.000Z
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
| false |
jackh1995
| null |
jackh1995/albert-base
| 0 | null |
transformers
| 37,086 |
Entry not found
|
smeoni/nbme-electra-large-generator
|
1de42bb009358a9c3485e88e04ac4e6fe078e027
|
2022-04-24T11:08:43.000Z
|
[
"pytorch",
"tensorboard",
"electra",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-generation
| false |
smeoni
| null |
smeoni/nbme-electra-large-generator
| 0 | null |
transformers
| 37,087 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: nbme-electra-large-generator
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. -->
# nbme-electra-large-generator
This model is a fine-tuned version of [google/electra-large-generator](https://huggingface.co/google/electra-large-generator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0122
- Accuracy: 0.9977
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 195 | 0.1125 | 0.9789 |
| No log | 2.0 | 390 | 0.0141 | 0.9973 |
| 0.6233 | 3.0 | 585 | 0.0122 | 0.9977 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
scasutt/wav2vec2-base_toy_train_double_data
|
d8b94d96a67103d942d0077ef5762dfe0478f889
|
2022-04-24T15:55:54.000Z
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] |
automatic-speech-recognition
| false |
scasutt
| null |
scasutt/wav2vec2-base_toy_train_double_data
| 0 | null |
transformers
| 37,088 |
Entry not found
|
macavaney/monot5-base-msmarco-sim5
|
677bc221b85059e54b7f9b443ab63e182c825d4f
|
2022-04-24T15:29:15.000Z
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
| false |
macavaney
| null |
macavaney/monot5-base-msmarco-sim5
| 0 | null |
transformers
| 37,089 |
Entry not found
|
huggingtweets/plasma_node
|
fab7b1e5eda3cc023d815181a57d5becf9525cbd
|
2022-06-09T09:49:38.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/plasma_node
| 0 | null |
transformers
| 37,090 |
---
language: en
thumbnail: http://www.huggingtweets.com/plasma_node/1654768173539/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/1448820786395975694/619AxWvJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Plasmanode</div>
<div style="text-align: center; font-size: 14px;">@plasma_node</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 Plasmanode.
| Data | Plasmanode |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 573 |
| Short tweets | 339 |
| Tweets kept | 2330 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21cfw258/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 @plasma_node's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/s5kag6o2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/s5kag6o2/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/plasma_node')
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)
|
ridhamrudhar/wav2vec2-common_voice-pa-In-demo
|
956e9c9892d3720fee8e83954c05d329e57665a7
|
2022-04-26T08:16:23.000Z
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] |
automatic-speech-recognition
| false |
ridhamrudhar
| null |
ridhamrudhar/wav2vec2-common_voice-pa-In-demo
| 0 | null |
transformers
| 37,091 |
Entry not found
|
dbmdz/flair-hipe-2022-ajmc-en
|
5aa03394b9fb7a325b74e6197b1e1a9e906e8a2b
|
2022-04-28T14:31:08.000Z
|
[
"pytorch",
"license:mit"
] | null | false |
dbmdz
| null |
dbmdz/flair-hipe-2022-ajmc-en
| 0 | null | null | 37,092 |
---
license: mit
---
|
dbmdz/flair-hipe-2022-ajmc-fr
|
540b6d70a0e48fe14e7b0ae9c492d5549fad65c6
|
2022-04-28T14:33:13.000Z
|
[
"pytorch",
"license:mit"
] | null | false |
dbmdz
| null |
dbmdz/flair-hipe-2022-ajmc-fr
| 0 | null | null | 37,093 |
---
license: mit
---
|
huggingtweets/jstoone
|
f7061ded65c46bd0b2b11309007a30625abaedec
|
2022-04-25T13:31:37.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/jstoone
| 0 | null |
transformers
| 37,094 |
---
language: en
thumbnail: http://www.huggingtweets.com/jstoone/1650893492572/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/1233003191538790400/3OxNooXT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Jakob Steinn</div>
<div style="text-align: center; font-size: 14px;">@jstoone</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 Jakob Steinn.
| Data | Jakob Steinn |
| --- | --- |
| Tweets downloaded | 3204 |
| Retweets | 713 |
| Short tweets | 177 |
| Tweets kept | 2314 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1j98493p/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 @jstoone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3vtqate8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vtqate8/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/jstoone')
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)
|
sanchit-gandhi/xtreme_s_xlsr_2_bart_covost2_fr_en
|
0b6bcac3d8047ccae6ba3746584c5cb96fc5a3c4
|
2022-05-06T12:38:45.000Z
|
[
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"dataset:xtreme_s",
"transformers",
"generated_from_trainer",
"model-index"
] |
automatic-speech-recognition
| false |
sanchit-gandhi
| null |
sanchit-gandhi/xtreme_s_xlsr_2_bart_covost2_fr_en
| 0 | null |
transformers
| 37,095 |
---
tags:
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- bleu
model-index:
- name: ''
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. -->
#
This model was trained from scratch on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1356
- Bleu: 0.0000
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6458 | 0.31 | 500 | 4.3754 | 0.0 |
| 2.3505 | 0.62 | 1000 | 4.3071 | 0.0 |
| 2.2152 | 0.93 | 1500 | 3.9444 | 0.0 |
| 2.79 | 1.23 | 2000 | 3.2046 | 0.0000 |
| 2.569 | 1.54 | 2500 | 2.6812 | 0.0000 |
| 2.322 | 1.85 | 3000 | 2.4081 | 0.0000 |
| 2.3435 | 2.16 | 3500 | 2.2696 | 0.0000 |
| 2.2063 | 2.47 | 4000 | 2.2452 | 0.0000 |
| 2.1087 | 2.78 | 4500 | 2.1356 | 0.0000 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 2.1.1.dev0
- Tokenizers 0.11.0
|
xiaoGato/DialoGPT-small-villanelle
|
4a683fc14c8af4bb385273a5c5d1d5b33020b754
|
2022-04-25T17:29:43.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
| false |
xiaoGato
| null |
xiaoGato/DialoGPT-small-villanelle
| 0 | null |
transformers
| 37,096 |
---
tags:
- conversational
---
# Killing Eve DialoGPT Model
|
jhonparra18/wav2vec2-large-xls-r-300m-guarani-small-wb
|
c6be00acfd794225602073c322762bbcf1e0a798
|
2022-04-27T16:40:31.000Z
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
| false |
jhonparra18
| null |
jhonparra18/wav2vec2-large-xls-r-300m-guarani-small-wb
| 0 | null |
transformers
| 37,097 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-guarani-small-wb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-guarani-small-wb
This model is a fine-tuned version of [glob-asr/wav2vec2-large-xls-r-300m-guarani-small](https://huggingface.co/glob-asr/wav2vec2-large-xls-r-300m-guarani-small) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1622
- Wer: 0.2446
- Cer: 0.0368
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.1818 | 0.32 | 10 | 0.1196 | 0.2146 | 0.0305 |
| 0.2953 | 0.65 | 20 | 0.1801 | 0.3090 | 0.0426 |
| 0.2941 | 0.97 | 30 | 0.1935 | 0.3090 | 0.0420 |
| 0.2786 | 1.29 | 40 | 0.1899 | 0.3305 | 0.0483 |
| 0.2665 | 1.61 | 50 | 0.1716 | 0.3176 | 0.0454 |
| 0.2752 | 1.94 | 60 | 0.1895 | 0.3948 | 0.0564 |
| 0.2482 | 2.26 | 70 | 0.1753 | 0.3176 | 0.0449 |
| 0.2486 | 2.58 | 80 | 0.1501 | 0.2747 | 0.0403 |
| 0.2878 | 2.9 | 90 | 0.1890 | 0.3348 | 0.0529 |
| 0.2539 | 3.23 | 100 | 0.2076 | 0.4635 | 0.0610 |
| 0.2069 | 3.55 | 110 | 0.1711 | 0.3476 | 0.0466 |
| 0.2262 | 3.87 | 120 | 0.1839 | 0.3605 | 0.0500 |
| 0.2032 | 4.19 | 130 | 0.1724 | 0.3391 | 0.0489 |
| 0.1997 | 4.52 | 140 | 0.1498 | 0.2704 | 0.0414 |
| 0.2216 | 4.84 | 150 | 0.1531 | 0.3047 | 0.0472 |
| 0.2294 | 5.16 | 160 | 0.1882 | 0.3176 | 0.0500 |
| 0.2305 | 5.48 | 170 | 0.1799 | 0.3176 | 0.0483 |
| 0.2052 | 5.81 | 180 | 0.1645 | 0.3262 | 0.0477 |
| 0.2192 | 6.13 | 190 | 0.1439 | 0.2060 | 0.0339 |
| 0.1844 | 6.45 | 200 | 0.1557 | 0.2918 | 0.0403 |
| 0.1803 | 6.77 | 210 | 0.1664 | 0.3004 | 0.0426 |
| 0.1831 | 7.1 | 220 | 0.1780 | 0.3176 | 0.0477 |
| 0.1618 | 7.42 | 230 | 0.1671 | 0.2661 | 0.0437 |
| 0.1528 | 7.74 | 240 | 0.2108 | 0.3176 | 0.0506 |
| 0.1335 | 8.06 | 250 | 0.1677 | 0.2575 | 0.0408 |
| 0.1736 | 8.39 | 260 | 0.1581 | 0.3004 | 0.0460 |
| 0.1607 | 8.71 | 270 | 0.1529 | 0.3047 | 0.0403 |
| 0.1451 | 9.03 | 280 | 0.1666 | 0.2747 | 0.0408 |
| 0.1534 | 9.35 | 290 | 0.1722 | 0.2833 | 0.0437 |
| 0.1567 | 9.68 | 300 | 0.1747 | 0.2918 | 0.0397 |
| 0.1356 | 10.0 | 310 | 0.1659 | 0.2961 | 0.0443 |
| 0.1248 | 10.32 | 320 | 0.1752 | 0.3348 | 0.0449 |
| 0.149 | 10.65 | 330 | 0.1792 | 0.3348 | 0.0449 |
| 0.1471 | 10.97 | 340 | 0.1843 | 0.3391 | 0.0460 |
| 0.1564 | 11.29 | 350 | 0.2015 | 0.3433 | 0.0460 |
| 0.1597 | 11.61 | 360 | 0.1798 | 0.2618 | 0.0380 |
| 0.161 | 11.94 | 370 | 0.1716 | 0.2747 | 0.0374 |
| 0.1481 | 12.26 | 380 | 0.1776 | 0.2747 | 0.0397 |
| 0.1168 | 12.58 | 390 | 0.1900 | 0.2961 | 0.0454 |
| 0.1173 | 12.9 | 400 | 0.1987 | 0.3090 | 0.0454 |
| 0.1245 | 13.23 | 410 | 0.1710 | 0.2918 | 0.0408 |
| 0.1118 | 13.55 | 420 | 0.1808 | 0.3047 | 0.0431 |
| 0.1111 | 13.87 | 430 | 0.1893 | 0.2747 | 0.0403 |
| 0.1041 | 14.19 | 440 | 0.1876 | 0.2918 | 0.0431 |
| 0.1152 | 14.52 | 450 | 0.1800 | 0.2790 | 0.0408 |
| 0.107 | 14.84 | 460 | 0.1717 | 0.2747 | 0.0385 |
| 0.1139 | 15.16 | 470 | 0.1652 | 0.2704 | 0.0391 |
| 0.0922 | 15.48 | 480 | 0.1659 | 0.2618 | 0.0391 |
| 0.101 | 15.81 | 490 | 0.1610 | 0.2489 | 0.0362 |
| 0.0835 | 16.13 | 500 | 0.1584 | 0.2403 | 0.0362 |
| 0.1251 | 16.45 | 510 | 0.1601 | 0.2575 | 0.0380 |
| 0.0888 | 16.77 | 520 | 0.1632 | 0.2661 | 0.0380 |
| 0.0968 | 17.1 | 530 | 0.1674 | 0.2661 | 0.0385 |
| 0.1105 | 17.42 | 540 | 0.1629 | 0.2833 | 0.0391 |
| 0.0914 | 17.74 | 550 | 0.1623 | 0.3090 | 0.0408 |
| 0.0843 | 18.06 | 560 | 0.1611 | 0.3004 | 0.0408 |
| 0.0861 | 18.39 | 570 | 0.1583 | 0.2661 | 0.0385 |
| 0.0861 | 18.71 | 580 | 0.1579 | 0.2618 | 0.0385 |
| 0.0678 | 19.03 | 590 | 0.1585 | 0.2661 | 0.0374 |
| 0.0934 | 19.35 | 600 | 0.1613 | 0.2489 | 0.0368 |
| 0.0976 | 19.68 | 610 | 0.1617 | 0.2446 | 0.0368 |
| 0.0799 | 20.0 | 620 | 0.1622 | 0.2446 | 0.0368 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/gerardoalone
|
e7d20371deebb8e0173bd9bf7b07ae30e39ddd79
|
2022-04-26T03:31:54.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/gerardoalone
| 0 | null |
transformers
| 37,098 |
---
language: en
thumbnail: http://www.huggingtweets.com/gerardoalone/1650943909493/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/1513716426795855876/jWAK0lo4_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">gay wedding technology</div>
<div style="text-align: center; font-size: 14px;">@gerardoalone</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 gay wedding technology.
| Data | gay wedding technology |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 406 |
| Short tweets | 737 |
| Tweets kept | 2096 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1p260sem/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 @gerardoalone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p1683gy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p1683gy/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/gerardoalone')
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/femboi_canis
|
4d528adb868e648a3360a8014aa313e0c0913fd9
|
2022-04-26T00:26:30.000Z
|
[
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] |
text-generation
| false |
huggingtweets
| null |
huggingtweets/femboi_canis
| 0 | null |
transformers
| 37,099 |
---
language: en
thumbnail: http://www.huggingtweets.com/femboi_canis/1650932783971/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/1479992104306843648/e2XQNywk_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🌻 Ole Grim | Femboi | Cane | It/Its | Hy/Hym 🔞</div>
<div style="text-align: center; font-size: 14px;">@femboi_canis</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 🌻 Ole Grim | Femboi | Cane | It/Its | Hy/Hym 🔞.
| Data | 🌻 Ole Grim | Femboi | Cane | It/Its | Hy/Hym 🔞 |
| --- | --- |
| Tweets downloaded | 3207 |
| Retweets | 412 |
| Short tweets | 206 |
| Tweets kept | 2589 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/27g3w5y2/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 @femboi_canis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jv8wsew4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jv8wsew4/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/femboi_canis')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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