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doraemon1998/opus-mt-en-ro-finetuned-en-to-ro | a40c0c9761083fcc419f572600d1beaf236e4ac1 | 2022-07-15T00:44:03.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | doraemon1998 | null | doraemon1998/opus-mt-en-ro-finetuned-en-to-ro | 2 | null | transformers | 27,400 | Entry not found |
MarLac/wav2vec2-base-timit-demo-google-colab | 329495bcd0653e49e5460ffbe695205765e3c159 | 2022-07-12T15:41:51.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | MarLac | null | MarLac/wav2vec2-base-timit-demo-google-colab | 2 | null | transformers | 27,401 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5816
- Wer: 0.3533
## 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: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.243 | 0.5 | 500 | 1.0798 | 0.7752 |
| 0.834 | 1.01 | 1000 | 0.6206 | 0.5955 |
| 0.5503 | 1.51 | 1500 | 0.5387 | 0.5155 |
| 0.4548 | 2.01 | 2000 | 0.4660 | 0.4763 |
| 0.3412 | 2.51 | 2500 | 0.8381 | 0.4836 |
| 0.3128 | 3.02 | 3000 | 0.4818 | 0.4519 |
| 0.2547 | 3.52 | 3500 | 0.4415 | 0.4230 |
| 0.2529 | 4.02 | 4000 | 0.4624 | 0.4219 |
| 0.2103 | 4.52 | 4500 | 0.4714 | 0.4096 |
| 0.2102 | 5.03 | 5000 | 0.4968 | 0.4087 |
| 0.1838 | 5.53 | 5500 | 0.4643 | 0.4131 |
| 0.1721 | 6.03 | 6000 | 0.4676 | 0.3979 |
| 0.1548 | 6.53 | 6500 | 0.4765 | 0.4085 |
| 0.1595 | 7.04 | 7000 | 0.4797 | 0.3941 |
| 0.1399 | 7.54 | 7500 | 0.4753 | 0.3902 |
| 0.1368 | 8.04 | 8000 | 0.4697 | 0.3945 |
| 0.1276 | 8.54 | 8500 | 0.5438 | 0.3869 |
| 0.1255 | 9.05 | 9000 | 0.5660 | 0.3841 |
| 0.1077 | 9.55 | 9500 | 0.4964 | 0.3947 |
| 0.1197 | 10.05 | 10000 | 0.5349 | 0.3849 |
| 0.1014 | 10.55 | 10500 | 0.5558 | 0.3883 |
| 0.0949 | 11.06 | 11000 | 0.5673 | 0.3785 |
| 0.0882 | 11.56 | 11500 | 0.5589 | 0.3955 |
| 0.0906 | 12.06 | 12000 | 0.5752 | 0.4120 |
| 0.1064 | 12.56 | 12500 | 0.5080 | 0.3727 |
| 0.0854 | 13.07 | 13000 | 0.5398 | 0.3798 |
| 0.0754 | 13.57 | 13500 | 0.5237 | 0.3816 |
| 0.0791 | 14.07 | 14000 | 0.4967 | 0.3725 |
| 0.0731 | 14.57 | 14500 | 0.5287 | 0.3744 |
| 0.0719 | 15.08 | 15000 | 0.5633 | 0.3596 |
| 0.062 | 15.58 | 15500 | 0.5399 | 0.3752 |
| 0.0681 | 16.08 | 16000 | 0.5151 | 0.3759 |
| 0.0559 | 16.58 | 16500 | 0.5564 | 0.3709 |
| 0.0533 | 17.09 | 17000 | 0.5933 | 0.3743 |
| 0.0563 | 17.59 | 17500 | 0.5381 | 0.3670 |
| 0.0527 | 18.09 | 18000 | 0.5685 | 0.3731 |
| 0.0492 | 18.59 | 18500 | 0.5728 | 0.3725 |
| 0.0509 | 19.1 | 19000 | 0.6074 | 0.3807 |
| 0.0436 | 19.6 | 19500 | 0.5762 | 0.3628 |
| 0.0434 | 20.1 | 20000 | 0.6721 | 0.3729 |
| 0.0416 | 20.6 | 20500 | 0.5842 | 0.3700 |
| 0.0431 | 21.11 | 21000 | 0.5374 | 0.3607 |
| 0.037 | 21.61 | 21500 | 0.5556 | 0.3667 |
| 0.036 | 22.11 | 22000 | 0.5608 | 0.3592 |
| 0.04 | 22.61 | 22500 | 0.5272 | 0.3637 |
| 0.047 | 23.12 | 23000 | 0.5234 | 0.3625 |
| 0.0506 | 23.62 | 23500 | 0.5427 | 0.3629 |
| 0.0418 | 24.12 | 24000 | 0.5590 | 0.3626 |
| 0.037 | 24.62 | 24500 | 0.5615 | 0.3555 |
| 0.0429 | 25.13 | 25000 | 0.5806 | 0.3616 |
| 0.045 | 25.63 | 25500 | 0.5777 | 0.3639 |
| 0.0283 | 26.13 | 26000 | 0.5987 | 0.3617 |
| 0.0253 | 26.63 | 26500 | 0.5671 | 0.3551 |
| 0.032 | 27.14 | 27000 | 0.5464 | 0.3582 |
| 0.0321 | 27.64 | 27500 | 0.5634 | 0.3573 |
| 0.0274 | 28.14 | 28000 | 0.5513 | 0.3575 |
| 0.0245 | 28.64 | 28500 | 0.5745 | 0.3537 |
| 0.0251 | 29.15 | 29000 | 0.5759 | 0.3547 |
| 0.0222 | 29.65 | 29500 | 0.5816 | 0.3533 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
rajat99/Fine_Tuning_XLSR_300M_testing_model | 8d719b0d276783c3c1c98d8aa4e33eecde2d4072 | 2022-07-12T12:00:41.000Z | [
"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_testing_model | 2 | null | transformers | 27,402 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Fine_Tuning_XLSR_300M_testing_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_testing_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: 3.2861
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 5.5178 | 23.53 | 400 | 3.2861 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ghadeermobasher/Modified_BiomedNLP-PubMedBERT-base-uncased-abstract_BioRED-Dis-512-5-30 | a7757c3fbf34a0e17eab4c91cd3daee884e632a8 | 2022-07-13T11:19:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified_BiomedNLP-PubMedBERT-base-uncased-abstract_BioRED-Dis-512-5-30 | 2 | null | transformers | 27,403 | |
ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED_Dis-320-8-10 | e9eed7ef97bbfb12969d3571dda92068909069b8 | 2022-07-12T14:44:53.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED_Dis-320-8-10 | 2 | null | transformers | 27,404 | Entry not found |
ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Dis-320-8-10 | 3766b27fab2f054430c12fede4242769f66f9464 | 2022-07-12T14:50:58.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-Dis-320-8-10 | 2 | null | transformers | 27,405 | Entry not found |
andreaschandra/xlm-roberta-base-finetuned-panx-de-fr | 9cde38ac5b1a310054ea4339df05f6627dc876ce | 2022-07-12T15:05:50.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | andreaschandra | null | andreaschandra/xlm-roberta-base-finetuned-panx-de-fr | 2 | null | transformers | 27,406 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1619
- F1: 0.8599
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2851 | 1.0 | 715 | 0.1792 | 0.8239 |
| 0.149 | 2.0 | 1430 | 0.1675 | 0.8401 |
| 0.0955 | 3.0 | 2145 | 0.1619 | 0.8599 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-320-8-10 | 05c8d91042e35d598cf7bc0c409d11bd432145b6 | 2022-07-12T15:23:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-320-8-10 | 2 | null | transformers | 27,407 | Entry not found |
ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-320-8-10 | d9ae3a9edc03b897918254e7ba72cc21ac3afb40 | 2022-07-12T15:37:22.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-320-8-10 | 2 | null | transformers | 27,408 | Entry not found |
andreaschandra/xlm-roberta-base-finetuned-panx-fr | b1f381b7abf4e16732067d5f599c680daac9b91f | 2022-07-12T15:30:15.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | andreaschandra | null | andreaschandra/xlm-roberta-base-finetuned-panx-fr | 2 | null | transformers | 27,409 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.9275221167113059
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1059
- F1: 0.9275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5416 | 1.0 | 191 | 0.2322 | 0.8378 |
| 0.2614 | 2.0 | 382 | 0.1544 | 0.8866 |
| 0.1758 | 3.0 | 573 | 0.1059 | 0.9275 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/masonhaggerty | eabfd05506e6af632c90cd12270323ed4d7042ea | 2022-07-12T17:17:06.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/masonhaggerty | 2 | null | transformers | 27,410 | ---
language: en
thumbnail: http://www.huggingtweets.com/masonhaggerty/1657646221015/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/1410026132121047041/LiYev7vQ_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">Mason Haggerty</div>
<div style="text-align: center; font-size: 14px;">@masonhaggerty</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 Mason Haggerty.
| Data | Mason Haggerty |
| --- | --- |
| Tweets downloaded | 785 |
| Retweets | 71 |
| Short tweets | 82 |
| Tweets kept | 632 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jpav9nmg/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 @masonhaggerty's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bs6k2tzz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bs6k2tzz/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/masonhaggerty')
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)
|
Li-Tang/rare-puppers | 0ace0043db173cf1394d5e8faa2739f3e09ddbfd | 2022-07-12T16:57:55.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | Li-Tang | null | Li-Tang/rare-puppers | 2 | null | transformers | 27,411 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9701492786407471
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
 |
huggingtweets/ydouright | 5251cbea6fa152b18a1adb0f3ceff5b12f02bb08 | 2022-07-12T20:15:17.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/ydouright | 2 | null | transformers | 27,412 | ---
language: en
thumbnail: http://www.huggingtweets.com/ydouright/1657656913047/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/1506510453286924293/NXf3sNMH_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">ethans.data</div>
<div style="text-align: center; font-size: 14px;">@ydouright</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 ethans.data.
| Data | ethans.data |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 119 |
| Short tweets | 572 |
| Tweets kept | 2554 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vfnsep8/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 @ydouright's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f5l1flk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f5l1flk/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/ydouright')
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/dylanfromsf | a72b30ea80ee8a48647f5a7ffff8b0da195968c5 | 2022-07-12T20:29:49.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/dylanfromsf | 2 | null | transformers | 27,413 | ---
language: en
thumbnail: http://www.huggingtweets.com/dylanfromsf/1657657784578/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/1384643526772678657/O7Sz_ZxW_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">dylan</div>
<div style="text-align: center; font-size: 14px;">@dylanfromsf</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 dylan.
| Data | dylan |
| --- | --- |
| Tweets downloaded | 1288 |
| Retweets | 116 |
| Short tweets | 420 |
| Tweets kept | 752 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2526mmm1/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 @dylanfromsf's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ds3020w) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ds3020w/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/dylanfromsf')
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)
|
rvignav/biobert-finetuned-prior-rmv | c0911ad2552712b8c867361d634bac7867d5aa72 | 2022-07-12T23:17:51.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | rvignav | null | rvignav/biobert-finetuned-prior-rmv | 2 | null | transformers | 27,414 | Entry not found |
Team-PIXEL/pixel-base-finetuned-pos-ud-japanese-gsd | f2d45d9f2798b0f6ab6d693d89fceb08dacec2b1 | 2022-07-13T01:14:04.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-japanese-gsd | 2 | null | transformers | 27,415 | Entry not found |
Team-PIXEL/pixel-base-finetuned-pos-ud-tamil-ttb | 02852385055f84f71b7bc1e2a127334568f81097 | 2022-07-13T01:26:57.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-tamil-ttb | 2 | null | transformers | 27,416 | Entry not found |
Team-PIXEL/pixel-base-finetuned-parsing-ud-arabic-padt | 9b90ea609a05f4ec5405a2031525b674faf3b4ef | 2022-07-13T01:45:53.000Z | [
"pytorch",
"pixel",
"transformers"
] | null | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-parsing-ud-arabic-padt | 2 | null | transformers | 27,417 | Entry not found |
dafraile/Clini-dialog-sum-BART | b7b7fc5f6f00040b42ffc3ae39ea790a85d66f62 | 2022-07-19T05:12:30.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | dafraile | null | dafraile/Clini-dialog-sum-BART | 2 | null | transformers | 27,418 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: tst-summarization
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. -->
# tst-summarization
This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.4
- Tokenizers 0.11.6
|
huggingtweets/burdeevt | bee2119429e4eee2668ba0dd5978867e8d6a50eb | 2022-07-13T04:15:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/burdeevt | 2 | null | transformers | 27,419 | ---
language: en
thumbnail: http://www.huggingtweets.com/burdeevt/1657685656540/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/1542316332972228608/Hs2WAuIA_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">Burdee 🐣💖</div>
<div style="text-align: center; font-size: 14px;">@burdeevt</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 Burdee 🐣💖.
| Data | Burdee 🐣💖 |
| --- | --- |
| Tweets downloaded | 2715 |
| Retweets | 1903 |
| Short tweets | 252 |
| Tweets kept | 560 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37eoz4i5/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 @burdeevt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2t35juo3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2t35juo3/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/burdeevt')
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)
|
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2 | 9a8addc7b0adafb39fa3d3b9321adcbdd1e6ec4c | 2022-07-13T10:11:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | nawta | null | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2 | 2 | null | transformers | 27,420 | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2
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-onomatopoeia-finetune_smalldata_ESC50pretrained_2
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6235
- Cer: 0.8973
## 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: 64
- eval_batch_size: 16
- 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 | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0097 | 23.81 | 500 | 2.6235 | 0.8973 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-Dis-320-8-10 | ea644d841d3eb7fd9a424924f4216ba3161568aa | 2022-07-13T11:59:29.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-Dis-320-8-10 | 2 | null | transformers | 27,421 | Entry not found |
ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-CD-320-8-10 | 8d34ea65a212b2510a1312cd2deb80d69deffd33 | 2022-07-13T12:06:48.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-CD-320-8-10 | 2 | null | transformers | 27,422 | Entry not found |
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3 | a5cceff2c986538d1916b64203154a1de52d2115 | 2022-07-13T14:03:36.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | nawta | null | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3 | 2 | null | transformers | 27,423 | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_3
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-onomatopoeia-finetune_smalldata_ESC50pretrained_3
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5350
- Cer: 1.2730
## 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: 64
- eval_batch_size: 16
- 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 | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.4243 | 4.67 | 500 | 2.6901 | 1.1259 |
| 2.4282 | 9.35 | 1000 | 2.7495 | 1.1563 |
| 2.3377 | 14.02 | 1500 | 2.2475 | 0.9617 |
| 2.2434 | 18.69 | 2000 | 2.2765 | 1.1908 |
| 2.2731 | 23.36 | 2500 | 2.2574 | 1.1669 |
| 2.3436 | 28.04 | 3000 | 2.5350 | 1.2730 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-CD-320-8-10 | 32773e25fd018e6a0232483d9ddaa8ab66e53b3a | 2022-07-13T12:21:34.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED-CD-320-8-10 | 2 | null | transformers | 27,424 | Entry not found |
ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-128-32-30 | 98cf6e3d0c07fe2398ac679070f2184a41b589b0 | 2022-07-13T12:27:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-128-32-30 | 2 | null | transformers | 27,425 | Entry not found |
ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-256-16-5 | 192306ec5710678a33d08972470fd69fee7fe1ab | 2022-07-13T12:12:19.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-256-16-5 | 2 | null | transformers | 27,426 | Entry not found |
ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-384-5-20 | 5ae3e1e0c4d523c7642b9920eb9460444b1a7d77 | 2022-07-13T13:16:29.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-scibert_scivocab_cased-BioRED-CD-384-5-20 | 2 | null | transformers | 27,427 | Entry not found |
jordyvl/udpos28-sm-all-POS | 6d40f37e3bde41367174b6b7b69fd5ae0056c902 | 2022-07-13T12:23:52.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:udpos28",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | jordyvl | null | jordyvl/udpos28-sm-all-POS | 2 | null | transformers | 27,428 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- udpos28
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: udpos28-sm-all-POS
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: udpos28
type: udpos28
args: en
metrics:
- name: Precision
type: precision
value: 0.9586517032792105
- name: Recall
type: recall
value: 0.9588997472284696
- name: F1
type: f1
value: 0.9587757092110369
- name: Accuracy
type: accuracy
value: 0.964820639556654
---
<!-- 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. -->
# udpos28-sm-all-POS
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos28 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1479
- Precision: 0.9587
- Recall: 0.9589
- F1: 0.9588
- Accuracy: 0.9648
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1261 | 1.0 | 4978 | 0.1358 | 0.9513 | 0.9510 | 0.9512 | 0.9581 |
| 0.0788 | 2.0 | 9956 | 0.1326 | 0.9578 | 0.9578 | 0.9578 | 0.9642 |
| 0.0424 | 3.0 | 14934 | 0.1479 | 0.9587 | 0.9589 | 0.9588 | 0.9648 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-128-32-30 | 71ecbd6af8c7ac1a343e37cc168c0691609c4afb | 2022-07-13T12:42:18.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-128-32-30 | 2 | null | transformers | 27,429 | Entry not found |
ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-256-16-5 | ac9ddca8a471304a21c11027a6b5962c63d94161 | 2022-07-13T12:26:52.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-256-16-5 | 2 | null | transformers | 27,430 | Entry not found |
ghadeermobasher/Original-biobert-v1.1-BioRED-CD-384-5-20 | f5ae27633a21b7fd99c95d07d2c61e505364501f | 2022-07-13T13:31:32.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-biobert-v1.1-BioRED-CD-384-5-20 | 2 | null | transformers | 27,431 | Entry not found |
ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-384-5-20 | ab60c0de23806b3d38de9b32fb40cd5eb075dff3 | 2022-07-13T13:33:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-scibert_scivocab_cased-BioRED-CD-384-5-20 | 2 | null | transformers | 27,432 | Entry not found |
ghadeermobasher/Modified-biobert-v1.1-BioRED-CD-384-5-20 | e717e5b5851461eef677c1046e74ea2e83863f18 | 2022-07-13T13:49:04.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-biobert-v1.1-BioRED-CD-384-5-20 | 2 | null | transformers | 27,433 | Entry not found |
ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-256-16-5 | e71cc4f0b67e28ef72fa3cc3a382c56237949e68 | 2022-07-13T13:11:50.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-256-16-5 | 2 | null | transformers | 27,434 | Entry not found |
ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-256-16-5 | ba0fa12b62e4f97d6a223375bc766e06e2375ffb | 2022-07-13T13:12:08.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-256-16-5 | 2 | null | transformers | 27,435 | Entry not found |
ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-128-32-30 | d2bf6765b7492c4e2499861ce962a70e854f8fe5 | 2022-07-13T13:08:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-128-32-30 | 2 | null | transformers | 27,436 | Entry not found |
ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-128-32-30 | f1a1a8b011f9ce97504c1c1a3539f4d0ad716eb1 | 2022-07-13T13:31:54.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-128-32-30 | 2 | null | transformers | 27,437 | Entry not found |
ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-384-8-10 | 3898a063feee67480a7f50c8b026fc793a1b5961 | 2022-07-13T13:40:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-384-8-10 | 2 | null | transformers | 27,438 | Entry not found |
ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-384-8-10 | ebe97c36431ba3fcee27f2696c8155f40dadd1ae | 2022-07-13T13:40:44.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-CD-384-8-10 | 2 | null | transformers | 27,439 | Entry not found |
ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED_Dis-320-8-10 | f792738046856699ca85b9e0a6871b1638bc28a4 | 2022-07-13T13:55:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED_Dis-320-8-10 | 2 | null | transformers | 27,440 | Entry not found |
ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED_Dis-256-16-5 | 812b5021ab0b683c4cb0def0e779aec81dddd637 | 2022-07-13T13:38:03.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED_Dis-256-16-5 | 2 | null | transformers | 27,441 | Entry not found |
ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-Dis-256-16-5 | cd7a2a65a6acb6b7191379ff9db82f5302130316 | 2022-07-13T13:38:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-Dis-256-16-5 | 2 | null | transformers | 27,442 | Entry not found |
ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED_Dis-384-8-10 | 2afc34c8f047cd9d11e0e62d489239ef31d5b603 | 2022-07-13T14:03:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BiomedNLP-PubMedBERT-base-uncased-abstract-BioRED_Dis-384-8-10 | 2 | null | transformers | 27,443 | Entry not found |
ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-Dis-384-8-10 | 88dfa6d69d519056c502102505d6f663550f99e5 | 2022-07-13T14:04:21.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Modified-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED-Dis-384-8-10 | 2 | null | transformers | 27,444 | Entry not found |
KeLiu/QETRA_PHP | 0058cd958b387eaea66771b09189a5fdfb4c9c0c | 2022-07-13T13:38:52.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | KeLiu | null | KeLiu/QETRA_PHP | 2 | null | transformers | 27,445 | Entry not found |
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5 | caa30638c48325cc3825facdaa7b5c46d60958b3 | 2022-07-13T14:43:29.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | nawta | null | nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5 | 2 | null | transformers | 27,446 | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_5
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-onomatopoeia-finetune_smalldata_ESC50pretrained_5
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 16
- 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
Team-PIXEL/pixel-base-finetuned-parsing-ud-english-ewt | 479ff0c69592d092b18f4a46f4b1b38c51a89c55 | 2022-07-13T15:01:13.000Z | [
"pytorch",
"pixel",
"transformers"
] | null | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-parsing-ud-english-ewt | 2 | null | transformers | 27,447 | Entry not found |
Team-PIXEL/pixel-base-finetuned-parsing-ud-japanese-gsd | 0465819e183aa17c95e6220ceacba18f0ffdd58d | 2022-07-13T15:16:46.000Z | [
"pytorch",
"pixel",
"transformers"
] | null | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-parsing-ud-japanese-gsd | 2 | null | transformers | 27,448 | Entry not found |
Team-PIXEL/pixel-base-finetuned-parsing-ud-korean-gsd | 02ab5042071b0daae583853c291748c7dc86a7eb | 2022-07-13T15:24:00.000Z | [
"pytorch",
"pixel",
"transformers"
] | null | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-parsing-ud-korean-gsd | 2 | null | transformers | 27,449 | Entry not found |
ghadeermobasher/Original-BiomedNLP-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED_Dis-320-8-10 | 2d9616284397c002115158871fec9e3777f5988d | 2022-07-13T17:05:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/Original-BiomedNLP-bluebert_pubmed_uncased_L-12_H-768_A-12-BioRED_Dis-320-8-10 | 2 | null | transformers | 27,450 | Entry not found |
Bistolero/mt5_32b_DP_1ep | 49a7edcbeed44ddc87298921a97456a02eea58a2 | 2022-07-13T17:06:36.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Bistolero | null | Bistolero/mt5_32b_DP_1ep | 2 | null | transformers | 27,451 | Entry not found |
nloc2578/2 | 4cff7efd32696f70e35a5b9ce67d00e3c767229f | 2022-07-13T20:39:00.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | nloc2578 | null | nloc2578/2 | 2 | null | transformers | 27,452 | ---
tags:
- generated_from_trainer
model-index:
- name: '2'
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. -->
# 2
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.0015
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7584 | 0.33 | 1500 | 2.7788 |
| 3.3283 | 0.67 | 3000 | 3.1709 |
| 3.365 | 1.0 | 4500 | 3.1651 |
| 3.1237 | 1.34 | 6000 | nan |
| 0.0 | 1.67 | 7500 | nan |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
PontifexMaximus/opus-mt-ur-en-finetuned-ur-to-en | 2c56d158c7ca3790132de601e888204a7c95ab77 | 2022-07-14T05:13:12.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PontifexMaximus | null | PontifexMaximus/opus-mt-ur-en-finetuned-ur-to-en | 2 | null | transformers | 27,453 | Entry not found |
liyijing024/swin-base-patch4-window7-224-in22k-finetuned | 89a4847ce7065b6fa8bbffba2b877b7384a3b41d | 2022-07-14T06:53:34.000Z | [
"pytorch",
"tensorboard",
"swin",
"image-classification",
"dataset:imagefolder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | liyijing024 | null | liyijing024/swin-base-patch4-window7-224-in22k-finetuned | 2 | null | transformers | 27,454 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9993279702725674
---
<!-- 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. -->
# swin-base-patch4-window7-224-in22k-finetuned
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0021
- Accuracy: 0.9993
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0253 | 1.0 | 889 | 0.0060 | 0.9980 |
| 0.0134 | 2.0 | 1778 | 0.0031 | 0.9989 |
| 0.0118 | 3.0 | 2667 | 0.0021 | 0.9993 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.8.0+cu111
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
zeehen/dummy-model | a6a004586025572a2280fffc397e25258d1f5589 | 2022-07-14T05:45:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | zeehen | null | zeehen/dummy-model | 2 | null | transformers | 27,455 | Entry not found |
rajat99/Fine_Tuning_XLSR_300M_testing_4_model | 8cbfd1c0d69bf6a6b5d0847c4e5adb3e8eecb082 | 2022-07-14T06:15:09.000Z | [
"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_testing_4_model | 2 | null | transformers | 27,456 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Fine_Tuning_XLSR_300M_testing_4_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_testing_4_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.
## 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: 0.1
- 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: 5
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
NinaXiao/distilroberta-base-wiki-mark | be725281106c5f1b0250aeae98c08e4ad4617f66 | 2022-07-14T09:05:03.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | NinaXiao | null | NinaXiao/distilroberta-base-wiki-mark | 2 | null | transformers | 27,457 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-wiki-mark
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. -->
# distilroberta-base-wiki-mark
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0062
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2841 | 1.0 | 1265 | 2.0553 |
| 2.1536 | 2.0 | 2530 | 1.9840 |
| 2.1067 | 3.0 | 3795 | 1.9731 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
big-kek/large-korzh | 6d1de491c2ea2941caab065b9e8a64105ad69d4f | 2022-07-14T17:04:16.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | big-kek | null | big-kek/large-korzh | 2 | null | transformers | 27,458 | Entry not found |
JoonJoon/t5-small-finetuned-xsum | f68e2a51f86812a5e7931f297c2bd6b4942d7495 | 2022-07-14T19:28:50.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | JoonJoon | null | JoonJoon/t5-small-finetuned-xsum | 2 | null | transformers | 27,459 | Entry not found |
natnova/xlm-roberta-base-finetuned-panx-de | 60f372135d71a16c09f7df5e80361df1455d2bd5 | 2022-07-14T13:29:37.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | natnova | null | natnova/xlm-roberta-base-finetuned-panx-de | 2 | null | transformers | 27,460 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- F1: 0.8649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
JoonJoon/swin-tiny-patch4-window7-224-finetuned-eurosat | 2989fa339e3412ea251adb650defd9c38dfe67e7 | 2022-07-14T14:58:59.000Z | [
"pytorch",
"swin",
"image-classification",
"dataset:imagefolder",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | JoonJoon | null | JoonJoon/swin-tiny-patch4-window7-224-finetuned-eurosat | 2 | null | transformers | 27,461 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9725925925925926
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0814
- Accuracy: 0.9726
## 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: 96
- eval_batch_size: 96
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 384
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3216 | 0.99 | 63 | 0.1349 | 0.9589 |
| 0.2 | 1.99 | 126 | 0.0873 | 0.9704 |
| 0.1664 | 2.99 | 189 | 0.0814 | 0.9726 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 2.3.2
- Tokenizers 0.11.6
|
ericklerouge123/xlm-roberta-base-finetuned-panx-de-fr | 0f4cde7007f5eba0ec445b8359a4e07f9fb12445 | 2022-07-14T16:17:52.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ericklerouge123 | null | ericklerouge123/xlm-roberta-base-finetuned-panx-de-fr | 2 | null | transformers | 27,462 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6886160714285715
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4043
- F1: 0.6886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1347 | 1.0 | 50 | 0.5771 | 0.4880 |
| 0.5066 | 2.0 | 100 | 0.4209 | 0.6582 |
| 0.3631 | 3.0 | 150 | 0.4043 | 0.6886 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Team-PIXEL/pixel-base-finetuned-jaquad | 07d9e058954644c7f8a31823a272da86bc8d578b | 2022-07-14T16:07:40.000Z | [
"pytorch",
"pixel",
"question-answering",
"dataset:SkelterLabsInc/JaQuAD",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-jaquad | 2 | null | transformers | 27,463 | ---
tags:
- generated_from_trainer
datasets:
- SkelterLabsInc/JaQuAD
model-index:
- name: pixel-base-finetuned-jaquad
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. -->
# pixel-base-finetuned-jaquad
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-base) on the SkelterLabsInc/JaQuAD 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 45
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 20000
- mixed_precision_training: Apex, opt level O1
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.12.1
|
ericklerouge123/xlm-roberta-base-finetuned-panx-all | a1be08195605d07bb714a020e4797286ab9e3add | 2022-07-14T16:46:09.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ericklerouge123 | null | ericklerouge123/xlm-roberta-base-finetuned-panx-all | 2 | null | transformers | 27,464 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1348
- F1: 0.8844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3055 | 1.0 | 835 | 0.1755 | 0.8272 |
| 0.1561 | 2.0 | 1670 | 0.1441 | 0.8727 |
| 0.1016 | 3.0 | 2505 | 0.1348 | 0.8844 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
JoonJoon/gpt2-wikitext2 | 211c4a191fce333b1abc3c96f138c87083160328 | 2022-07-14T20:23:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | JoonJoon | null | JoonJoon/gpt2-wikitext2 | 2 | null | transformers | 27,465 | Entry not found |
JoonJoon/bert-base-cased-wikitext2 | 62af6ecc6cb27a918ff57c3261ed0ae7a295c2c8 | 2022-07-14T20:57:50.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | fill-mask | false | JoonJoon | null | JoonJoon/bert-base-cased-wikitext2 | 2 | null | transformers | 27,466 | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-wikitext2
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-cased-wikitext2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.9846
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 7.7422 | 1.0 | 782 | 7.1373 |
| 7.0302 | 2.0 | 1564 | 6.9972 |
| 6.9788 | 3.0 | 2346 | 7.0087 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu102
- Datasets 1.14.0
- Tokenizers 0.10.3
|
johanna-k/small-pw-test | c4538e3c2ccfd79437678511bc6374ae38979fc0 | 2022-07-14T21:31:25.000Z | [
"pytorch",
"canine",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | johanna-k | null | johanna-k/small-pw-test | 2 | null | transformers | 27,467 | Entry not found |
doraemon1998/t5-small-finetuned-en-to-ro | ef4eb7b6bcc13a3387d83edabd078c972d68498a | 2022-07-15T00:46:48.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | doraemon1998 | null | doraemon1998/t5-small-finetuned-en-to-ro | 2 | null | transformers | 27,468 | Entry not found |
doraemon1998/t5-small-finetuned-labels-to-caption | fa69d502357a3efd1b9b25c795c48e8fad806c10 | 2022-07-15T09:53:07.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | doraemon1998 | null | doraemon1998/t5-small-finetuned-labels-to-caption | 2 | null | transformers | 27,469 | Entry not found |
Team-PIXEL/pixel-base-finetuned-masakhaner-hau | e2df9dbb7b5ec2c2fe1ec81c60b677e2ce3c4073 | 2022-07-15T03:14:32.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-hau | 2 | null | transformers | 27,470 | Entry not found |
Team-PIXEL/pixel-base-finetuned-masakhaner-ibo | 6380a0912a836059dc3c831f4522f98e42d73ca9 | 2022-07-15T03:18:06.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-ibo | 2 | null | transformers | 27,471 | Entry not found |
Team-PIXEL/pixel-base-finetuned-masakhaner-kin | 8dc9689cad7f91bfccc02023ea96099c96811aae | 2022-07-15T03:20:40.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-kin | 2 | null | transformers | 27,472 | Entry not found |
Team-PIXEL/pixel-base-finetuned-masakhaner-lug | 6e33be4b6469c73cf1ec8126d8d520a9908553cd | 2022-07-15T03:23:15.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-lug | 2 | null | transformers | 27,473 | Entry not found |
Team-PIXEL/pixel-base-finetuned-masakhaner-luo | 99af8628d7e6a762035fce0c118a679e79bd8e9e | 2022-07-15T03:24:58.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-luo | 2 | null | transformers | 27,474 | Entry not found |
Team-PIXEL/pixel-base-finetuned-masakhaner-swa | 3ca6ee1a2084a8891a9fffb9606147ee8a083bcf | 2022-07-15T03:29:45.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-swa | 2 | null | transformers | 27,475 | Entry not found |
Team-PIXEL/pixel-base-finetuned-masakhaner-wol | 4be5be9e60e0b0a96c91d8474ff40f6570ffd0f4 | 2022-07-15T03:31:50.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-wol | 2 | null | transformers | 27,476 | Entry not found |
WENGSYX/CPMT | 4c802109fd35a08b1997349f733b87002db901be | 2022-07-15T07:29:56.000Z | [
"pytorch",
"bart",
"transformers",
"license:mit"
] | null | false | WENGSYX | null | WENGSYX/CPMT | 2 | null | transformers | 27,477 | ---
license: mit
---
现有的少数民族语言预训练模型仍然较为稀缺，尽管国内少数民族语言模型CINO具有较强的理解能力，但仍然缺乏面向生成与翻译领域的研究。
CMPT (Chinese Minority Pre-Trained Language Model) 是在BART的基础上，加入DeepNorm预训练的超深层生成模型。其最大具有128+128层。其在超过10G的汉英维藏蒙语料中进行受限预训练。其具有较强的理解与生成性能。
**Github Link:** https://github.com/WENGSYX/CMPT
## Usage
```python
>>> from modeling_cmpt import BartForConditionalGeneration
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained('./CMTP')
>>> model = BartForConditionalGeneration.from_pretrained('./CMTP')
>>> inputs = tokenizer.encode("Hello world, 你好 世界", return_tensors='pt')
>>> pred_ids = model.generate(input_ids, num_beams=4, max_length=20)
>>> print(tokenizer.convert_ids_to_tokens(pred_ids[i]))
```
|
Lyla/bert-base-uncased-finetuned-swag | c59c92d1fe04fe59b2c108a3802a246fbf56522f | 2022-07-15T10:22:29.000Z | [
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"transformers"
] | multiple-choice | false | Lyla | null | Lyla/bert-base-uncased-finetuned-swag | 2 | null | transformers | 27,478 | Entry not found |
karsab/distilbert-base-uncased-finetuned-imdb | 97f36ecd8969a8ffde6954f68631f88c18725cc9 | 2022-07-15T12:21:46.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | karsab | null | karsab/distilbert-base-uncased-finetuned-imdb | 2 | null | transformers | 27,479 | Entry not found |
lucashu/TcmYiAnBERT | 4d3cbdca96e3fb30b392d0a271f5df03200fb195 | 2022-07-22T14:45:52.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | lucashu | null | lucashu/TcmYiAnBERT | 2 | null | transformers | 27,480 | # 概述
基于大规模中医医案数据在BERT中文模型上继续训练300轮得到的预训练模型
# 使用方式
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("lucashu/TcmYiAnBERT")
model = AutoModelForMaskedLM.from_pretrained("lucashu/TcmYiAnBERT")
``` |
mipatov/NeuroSkeptic | 2b461bbe6a2811f53d0f6dc3d5006df7c4ce533b | 2022-07-15T17:20:56.000Z | [
"pytorch",
"opt",
"text-generation",
"transformers"
] | text-generation | false | mipatov | null | mipatov/NeuroSkeptic | 2 | null | transformers | 27,481 | Entry not found |
dspg/distilbert-base-uncased-finetuned-squad | a6945318ab2f4fbc8ecab00b0c60708f4bda33af | 2022-07-15T21:34:13.000Z | [
"pytorch",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | dspg | null | dspg/distilbert-base-uncased-finetuned-squad | 2 | null | transformers | 27,482 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1596
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2265 | 1.0 | 5533 | 1.1572 |
| 0.9548 | 2.0 | 11066 | 1.1278 |
| 0.7396 | 3.0 | 16599 | 1.1596 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jens-simon/xls-r-300m-sv-2 | fb8a3ff80bdf775d462c84d7313b8e4fd8cf283f | 2022-07-16T15:49:39.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | jens-simon | null | jens-simon/xls-r-300m-sv-2 | 2 | null | transformers | 27,483 | Try this. |
Aktsvigun/bart-base_abssum_debate_23419 | bc29b8ac4d4aa51b0612985efd5f5e13ac33001a | 2022-07-16T17:53:02.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_23419 | 2 | null | transformers | 27,484 | Entry not found |
Aktsvigun/bart-base_abssum_debate_705525 | c3444ad517ab547127b22c8e16bfbfd100f4993a | 2022-07-16T18:07:20.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_705525 | 2 | null | transformers | 27,485 | Entry not found |
Aktsvigun/bart-base_abssum_debate_4837 | efb99c544b545f6269bf9b521eb918cdfd3d4405 | 2022-07-16T18:22:11.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_4837 | 2 | null | transformers | 27,486 | Entry not found |
Aktsvigun/bart-base_abssum_debate_42 | a92261765bf74985c4a57b1e98c03712d7cd2133 | 2022-07-16T18:41:54.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_42 | 2 | null | transformers | 27,487 | Entry not found |
Aktsvigun/bart-base_abssum_debate_919213 | e1bdaccd1d2ce6c80a715dfd6a8a37d226d54154 | 2022-07-16T18:56:14.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_919213 | 2 | null | transformers | 27,488 | Entry not found |
Aktsvigun/bart-base_abssum_debate_9467153 | 8a63f1e2a8b43a434c63232d8ce920bea4eda307 | 2022-07-16T19:12:00.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_9467153 | 2 | null | transformers | 27,489 | Entry not found |
Aktsvigun/bart-base_abssum_debate_6585777 | d9f8ce9cb9d7d4924b9b42875c5fbe698193afdb | 2022-07-16T19:26:25.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_6585777 | 2 | null | transformers | 27,490 | Entry not found |
Aktsvigun/bart-base_abssum_debate_3878022 | 7cb747b38e75063e3287277e09ad683e8b0b912d | 2022-07-16T19:40:59.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_3878022 | 2 | null | transformers | 27,491 | Entry not found |
Aktsvigun/bart-base_abssum_debate_5537116 | 3288e9cb3207fb5c660d4768b6217743f55ca484 | 2022-07-16T19:52:47.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_5537116 | 2 | null | transformers | 27,492 | Entry not found |
Aktsvigun/bart-base_abssum_debate_5893459 | ebb346056fae13067228655ca3fe1071024f4bfe | 2022-07-16T20:06:55.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_5893459 | 2 | null | transformers | 27,493 | Entry not found |
Aktsvigun/bart-base_abssum_debate_8653685 | 397596a24513f5d28ff6cbc69831474f6c3d5aa3 | 2022-07-16T20:20:43.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_8653685 | 2 | null | transformers | 27,494 | Entry not found |
Aktsvigun/bart-base_abssum_debate_6880281 | 5966243dd07c2c8b65d4904b7f7ad63dae110138 | 2022-07-16T20:34:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_6880281 | 2 | null | transformers | 27,495 | Entry not found |
Aktsvigun/bart-base_abssum_debate_9478495 | 3c87297653ae521faf3a07e14779c69ca63db43c | 2022-07-16T20:47:12.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_9478495 | 2 | null | transformers | 27,496 | Entry not found |
Aktsvigun/bart-base_abssum_debate_2930982 | 710086792c4da7c8520b12de3db6d15eb8437568 | 2022-07-16T21:03:07.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_2930982 | 2 | null | transformers | 27,497 | Entry not found |
Aktsvigun/bart-base_abssum_debate_7629317 | d48c4ed9e9fac7d553ccd698b078628f2fde06c8 | 2022-07-16T21:17:52.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_7629317 | 2 | null | transformers | 27,498 | Entry not found |
Aktsvigun/bart-base_abssum_debate_4065329 | e47ca4b04474901520628384c9dbac305d572695 | 2022-07-16T21:33:07.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_abssum_debate_4065329 | 2 | null | transformers | 27,499 | Entry not found |
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