modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-28 18:27:46
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 534
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-07-28 18:26:08
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
NbAiLab/nb-t5-base-v3
|
NbAiLab
| 2021-12-22T12:12:07Z | 17 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
language: no
license: cc-by-4.0
tags:
- seq2seq
datasets:
- Norwegian Nynorsk/Bokmål
---
# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8.
This model is currently training. It will finish in January 2022. Please do not use yet..
```
|
hrdipto/wav2vec2-base-timit-demo-colab
|
hrdipto
| 2021-12-22T08:25:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
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: 0.4241
- Wer: 0.3381
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7749 | 4.0 | 500 | 2.0639 | 1.0018 |
| 0.9252 | 8.0 | 1000 | 0.4853 | 0.4821 |
| 0.3076 | 12.0 | 1500 | 0.4507 | 0.4044 |
| 0.1732 | 16.0 | 2000 | 0.4315 | 0.3688 |
| 0.1269 | 20.0 | 2500 | 0.4481 | 0.3559 |
| 0.1087 | 24.0 | 3000 | 0.4354 | 0.3464 |
| 0.0832 | 28.0 | 3500 | 0.4241 | 0.3381 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
dpasch01/finetune-clm-employment
|
dpasch01
| 2021-12-22T07:59:51Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetune-clm-employment
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. -->
# finetune-clm-employment
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: 1.8445
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3283 | 1.0 | 3989 | 1.9578 |
| 2.0824 | 2.0 | 7978 | 1.9013 |
| 1.9936 | 3.0 | 11967 | 1.8625 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
hrdipto/wav2vec2-xls-r-timit-tokenizer-base
|
hrdipto
| 2021-12-22T07:19:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-timit-tokenizer-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-timit-tokenizer-base
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: 3.0828
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.3134 | 4.03 | 500 | 3.0814 | 1.0 |
| 2.9668 | 8.06 | 1000 | 3.0437 | 1.0 |
| 2.9604 | 12.1 | 1500 | 3.0337 | 1.0 |
| 2.9619 | 16.13 | 2000 | 3.0487 | 1.0 |
| 2.9588 | 20.16 | 2500 | 3.0859 | 1.0 |
| 2.957 | 24.19 | 3000 | 3.0921 | 1.0 |
| 2.9555 | 28.22 | 3500 | 3.0828 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17
|
csukuangfj
| 2021-12-22T04:24:10Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17
cd icefall-asr-librispeech-transducer-bpe-500-2021-12-17
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `cb04c8a7509425ab45fae888b0ca71bbbd23f0de`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout cb04c8a7509425ab45fae888b0ca71bbbd23f0de
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/cb04c8a7509425ab45fae888b0ca71bbbd23f0de/egs/librispeech/ASR/transducer/train.py#L196>
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer, plus a 4-layer LSTM with hidden size 512.
-----
## Description
This repo provides pre-trained RNN-T Conformer model for the librispeech dataset
using [icefall][icefall].
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir transducer/exp-lr-2.5-full \
--full-libri 1 \
--max-duration 250 \
--lr-factor 2.5
```
The command for decoding is:
```
epoch=26
avg=12
./transducer/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer/exp-lr-2.5-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
```
You can find the decoding log for the above command in this
repo: [log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04](log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04).
The best WER using greedy search is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 3.16 | 7.71 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```
./transducer/export.py \
--epoch 26 \
--avg 12 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer/exp-lr-2.5-full
```
**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-bpe-500-2021-12-17/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
huggingtweets/_luisinhobr-beckvencido
|
huggingtweets
| 2021-12-22T02:57:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/_luisinhobr-beckvencido/1640141850327/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/1470914400764715012/YO9XqA0n_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/1390224220643278850/LcIZLss-_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">agrummgit ag😜 & luisfer nando</div>
<div style="text-align: center; font-size: 14px;">@_luisinhobr-beckvencido</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 agrummgit ag😜 & luisfer nando.
| Data | agrummgit ag😜 | luisfer nando |
| --- | --- | --- |
| Tweets downloaded | 3226 | 2366 |
| Retweets | 379 | 367 |
| Short tweets | 672 | 503 |
| Tweets kept | 2175 | 1496 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34idoh6o/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 @_luisinhobr-beckvencido's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1w6ipjqa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1w6ipjqa/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/_luisinhobr-beckvencido')
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)
|
Jeska/BertjeWDialDataALL04
|
Jeska
| 2021-12-22T02:47:07Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: BertjeWDialDataALL04
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. -->
# BertjeWDialDataALL04
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9717
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2954 | 1.0 | 1542 | 2.0372 |
| 2.2015 | 2.0 | 3084 | 2.0104 |
| 2.1661 | 3.0 | 4626 | 2.0372 |
| 2.1186 | 4.0 | 6168 | 1.9549 |
| 2.0939 | 5.0 | 7710 | 1.9438 |
| 2.0867 | 6.0 | 9252 | 1.9648 |
| 2.0462 | 7.0 | 10794 | 1.9465 |
| 2.0315 | 8.0 | 12336 | 1.9412 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
huggingtweets/offalgirl
|
huggingtweets
| 2021-12-21T22:49:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/offalgirl/1640126966800/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/1386871257958952961/WOAsSOo4_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">jeans «маруся»</div>
<div style="text-align: center; font-size: 14px;">@offalgirl</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 jeans «маруся».
| Data | jeans «маруся» |
| --- | --- |
| Tweets downloaded | 1785 |
| Retweets | 271 |
| Short tweets | 143 |
| Tweets kept | 1371 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/whe99549/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 @offalgirl's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ogp97c2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ogp97c2/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/offalgirl')
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)
|
tingtingyuli/wav2vec2-base-timit-demo-colab
|
tingtingyuli
| 2021-12-21T22:26:02Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
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: 0.4371
- Wer: 0.3402
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6515 | 4.0 | 500 | 1.9481 | 0.9825 |
| 0.8007 | 8.0 | 1000 | 0.4364 | 0.4424 |
| 0.2559 | 12.0 | 1500 | 0.4188 | 0.3848 |
| 0.1483 | 16.0 | 2000 | 0.4466 | 0.3524 |
| 0.1151 | 20.0 | 2500 | 0.4492 | 0.3519 |
| 0.0971 | 24.0 | 3000 | 0.4568 | 0.3453 |
| 0.0765 | 28.0 | 3500 | 0.4371 | 0.3402 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
enelpol/czywiesz-question
|
enelpol
| 2021-12-21T21:24:34Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"pl",
"dataset:enelpol/czywiesz",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: pl
datasets:
- enelpol/czywiesz
task_categories:
- question_answering
task_ids:
- open-domain-qa
multilinguality:
- monolingual
size_categories:
- 1k<n<10K
---
## Model description
This is the question encoder for the Polish DPR question answering model. The full model consists of two encoders.
Please read [context encoder documentation](https://huggingface.co/enelpol/czywiesz-context) to get the details of the model.
|
Ayham/albert_gpt2_summarization_xsum
|
Ayham
| 2021-12-21T21:20:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: albert_gpt2_summarization_xsum
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. -->
# albert_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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: 8
- 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: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
akashsivanandan/wav2vec2-large-xls-r-300m-tamil-colab
|
akashsivanandan
| 2021-12-21T18:26:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-tamil-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-large-xls-r-300m-tamil-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8072
- Wer: 0.6531
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 11.0967 | 1.0 | 118 | 4.6437 | 1.0 |
| 3.4973 | 2.0 | 236 | 3.2588 | 1.0 |
| 3.1305 | 3.0 | 354 | 2.6566 | 1.0 |
| 1.2931 | 4.0 | 472 | 0.9156 | 0.9944 |
| 0.6851 | 5.0 | 590 | 0.7474 | 0.8598 |
| 0.525 | 6.0 | 708 | 0.6649 | 0.7995 |
| 0.4325 | 7.0 | 826 | 0.6740 | 0.7752 |
| 0.3766 | 8.0 | 944 | 0.6220 | 0.7628 |
| 0.3256 | 9.0 | 1062 | 0.6316 | 0.7322 |
| 0.2802 | 10.0 | 1180 | 0.6442 | 0.7305 |
| 0.2575 | 11.0 | 1298 | 0.6885 | 0.7280 |
| 0.2248 | 12.0 | 1416 | 0.6702 | 0.7197 |
| 0.2089 | 13.0 | 1534 | 0.6781 | 0.7173 |
| 0.1893 | 14.0 | 1652 | 0.6981 | 0.7049 |
| 0.1652 | 15.0 | 1770 | 0.7154 | 0.7436 |
| 0.1643 | 16.0 | 1888 | 0.6798 | 0.7023 |
| 0.1472 | 17.0 | 2006 | 0.7381 | 0.6947 |
| 0.1372 | 18.0 | 2124 | 0.7240 | 0.7065 |
| 0.1318 | 19.0 | 2242 | 0.7305 | 0.6714 |
| 0.1211 | 20.0 | 2360 | 0.7288 | 0.6597 |
| 0.1178 | 21.0 | 2478 | 0.7417 | 0.6699 |
| 0.1118 | 22.0 | 2596 | 0.7476 | 0.6753 |
| 0.1016 | 23.0 | 2714 | 0.7973 | 0.6647 |
| 0.0998 | 24.0 | 2832 | 0.8027 | 0.6633 |
| 0.0917 | 25.0 | 2950 | 0.8045 | 0.6680 |
| 0.0907 | 26.0 | 3068 | 0.7884 | 0.6565 |
| 0.0835 | 27.0 | 3186 | 0.8009 | 0.6622 |
| 0.0749 | 28.0 | 3304 | 0.8123 | 0.6536 |
| 0.0755 | 29.0 | 3422 | 0.8006 | 0.6555 |
| 0.074 | 30.0 | 3540 | 0.8072 | 0.6531 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
ken11/albert-base-japanese-v1
|
ken11
| 2021-12-21T18:04:30Z | 946 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"japanese",
"ja",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
tags:
- fill-mask
- japanese
- albert
language:
- ja
license: mit
widget:
- text: "2022年の[MASK]概要"
---
## albert-base-japanese-v1
日本語事前学習済みALBERTモデルです
## How to use
### ファインチューニング
このモデルはPreTrainedモデルです
基本的には各種タスク用にファインチューニングして使用されることを想定しています
### Fill-Mask
このモデルではTokenizerにSentencepieceを利用しています
そのままでは`[MASK]`トークンのあとに[余計なトークンが混入する問題](https://ken11.jp/blog/sentencepiece-tokenizer-bug)があるので、利用する際には以下のようにする必要があります
#### for PyTorch
```py
from transformers import (
AlbertForMaskedLM, AlbertTokenizerFast
)
import torch
tokenizer = AlbertTokenizerFast.from_pretrained("ken11/albert-base-japanese-v1")
model = AlbertForMaskedLM.from_pretrained("ken11/albert-base-japanese-v1")
text = "大学で[MASK]の研究をしています"
tokenized_text = tokenizer.tokenize(text)
del tokenized_text[tokenized_text.index(tokenizer.mask_token) + 1]
input_ids = [tokenizer.cls_token_id]
input_ids.extend(tokenizer.convert_tokens_to_ids(tokenized_text))
input_ids.append(tokenizer.sep_token_id)
inputs = {"input_ids": [input_ids], "token_type_ids": [[0]*len(input_ids)], "attention_mask": [[1]*len(input_ids)]}
batch = {k: torch.tensor(v, dtype=torch.int64) for k, v in inputs.items()}
output = model(**batch)[0]
_, result = output[0, input_ids.index(tokenizer.mask_token_id)].topk(5)
print(tokenizer.convert_ids_to_tokens(result.tolist()))
# ['英語', '心理学', '数学', '医学', '日本語']
```
#### for TensorFlow
```py
from transformers import (
TFAlbertForMaskedLM, AlbertTokenizerFast
)
import tensorflow as tf
tokenizer = AlbertTokenizerFast.from_pretrained("ken11/albert-base-japanese-v1")
model = TFAlbertForMaskedLM.from_pretrained("ken11/albert-base-japanese-v1")
text = "大学で[MASK]の研究をしています"
tokenized_text = tokenizer.tokenize(text)
del tokenized_text[tokenized_text.index(tokenizer.mask_token) + 1]
input_ids = [tokenizer.cls_token_id]
input_ids.extend(tokenizer.convert_tokens_to_ids(tokenized_text))
input_ids.append(tokenizer.sep_token_id)
inputs = {"input_ids": [input_ids], "token_type_ids": [[0]*len(input_ids)], "attention_mask": [[1]*len(input_ids)]}
batch = {k: tf.convert_to_tensor(v, dtype=tf.int32) for k, v in inputs.items()}
output = model(**batch)[0]
result = tf.math.top_k(output[0, input_ids.index(tokenizer.mask_token_id)], k=5)
print(tokenizer.convert_ids_to_tokens(result.indices.numpy()))
# ['英語', '心理学', '数学', '医学', '日本語']
```
## Training Data
学習には
- [日本語Wikipediaの全文](https://ja.wikipedia.org/wiki/Wikipedia:%E3%83%87%E3%83%BC%E3%82%BF%E3%83%99%E3%83%BC%E3%82%B9%E3%83%80%E3%82%A6%E3%83%B3%E3%83%AD%E3%83%BC%E3%83%89)
- [livedoorニュースコーパス](https://www.rondhuit.com/download.html#ldcc)
を利用しています
## Tokenizer
トークナイザーは[Sentencepiece](https://github.com/google/sentencepiece)を利用しています
こちらも学習データは同様です
## Licenese
[The MIT license](https://opensource.org/licenses/MIT)
|
LACAI/gpt2-xl-dialog-narrative-persuasion
|
LACAI
| 2021-12-21T17:22:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
Base model: [gpt2-xl](https://huggingface.co/gpt2-xl)
Domain-adapted for dialogue response and narrative generation on a [narrative-aligned variant](https://github.com/AbrahamSanders/gutenberg-dialog#download-narrative-aligned-datasets) of the [Gutenberg Dialogue Dataset (Csaky & Recski, 2021)](https://aclanthology.org/2021.eacl-main.11.pdf)
Fine-tuned for dialogue response generation on [Persuasion For Good (Wang et al., 2019)](https://aclanthology.org/P19-1566.pdf) ([dataset](https://gitlab.com/ucdavisnlp/persuasionforgood))
|
davanstrien/book-genre-classification
|
davanstrien
| 2021-12-21T16:05:46Z | 6 | 2 |
adapter-transformers
|
[
"adapter-transformers",
"bert",
"adapterhub:text-classification",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- bert
- adapterhub:text-classification
- adapter-transformers
---
# Adapter `davanstrien/book-genre-classification` for bert-base-cased
An [adapter](https://adapterhub.ml) for the `bert-base-cased` model that was trained on the [text-classification](https://adapterhub.ml/explore/text-classification/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-cased")
adapter_name = model.load_adapter("davanstrien/book-genre-classification", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer
|
espnet
| 2021-12-21T15:59:04Z | 3 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:yolo_mixtec",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- yolo_mixtec
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer`
This model was trained by ftshijt using yolo_mixtec recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/yolo_mixtec/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Nov 10 02:59:39 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
- Commit date: ``
## asr_train_asr_transformer_specaug_raw_bpe500
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|81348|84.1|11.8|4.1|2.5|18.3|82.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|626187|93.4|2.2|4.4|2.4|9.0|82.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|325684|90.7|5.2|4.1|2.2|11.5|82.5|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_transformer_specaug.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_specaug_raw_bpe500
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe500/train/speech_shape
- exp/asr_stats_raw_bpe500/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe500/valid/speech_shape
- exp/asr_stats_raw_bpe500/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/train/wav.scp
- speech
- kaldi_ark
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/dev/wav.scp
- speech
- kaldi_ark
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- '4'
- '3'
- '1'
- '2'
- A
- ▁NDI
- '''4'
- '''1'
- U
- ▁BA
- O
- ▁I
- E
- 4=
- ▁KU
- ▁TAN
- ▁KA
- '''3'
- NI
- ▁YA
- RA
- 3=
- 2=
- IN
- NA
- ▁TA
- AN
- ▁KAN
- ▁NI
- ▁NDA
- ▁NA
- ▁JI
- KAN
- CHI
- (3)=
- I
- UN
- 1-
- ▁SA
- (4)=
- ▁JA
- XI
- ▁KO
- ▁TI
- TA
- KU
- BI
- ▁YU
- ▁KWA
- KA
- XA
- 1=
- ▁YO
- RI
- NDO
- ▁XA
- TU
- ▁TU
- ▁ÑA
- ▁KI
- ▁XI
- YO
- NDU
- NDA
- ▁CHI
- (2)=
- ▁BI
- ▁NU
- KI
- (1)=
- YU
- 3-
- ▁MI
- 'ON'
- ▁A
- BA
- 4-
- KO
- ▁NDU
- ▁ÑU
- ▁NDO
- NU
- ÑU
- '143'
- ▁SI
- ▁SO
- 13-
- NDI
- ▁AN
- ▁SU
- TIN
- SA
- ▁BE
- TO
- RUN
- KWA
- KWI
- ▁NDE
- ▁KWI
- XIN
- ▁U
- SI
- SO
- ▁TUN
- EN
- ▁KWE
- YA
- (4)=2
- NDE
- TI
- TUN
- ▁TIN
- MA
- ▁SE
- ▁XU
- SU
- ▁LU
- ▁KE
- ▁
- MI
- ▁RAN
- (3)=2
- 14-
- ▁MA
- KUN
- LU
- N
- ▁O
- KE
- NGA
- ▁IS
- ▁JU
- '='
- ▁LA
- ÑA
- JA
- CHUN
- R
- TAN
- PU
- ▁TIEM
- LI
- LA
- CHIU
- ▁PA
- M
- ▁REY
- ▁BAN
- JI
- L
- SUN
- ▁SEÑOR
- ▁JO
- ▁TIO
- KWE
- CHU
- S
- ▁YE
- KIN
- XU
- BE
- ▁CUENTA
- ▁SAN
- RRU
- ▁¿
- CHA
- ▁TO
- RRA
- LO
- TE
- ▁AMIGU
- PA
- XAN
- ▁C
- C
- ▁CHA
- ▁TE
- ▁HIJO
- ▁MB
- ▁PI
- G
- ▁ÁNIMA
- ▁CHE
- ▁P
- B
- NDIO
- SE
- ▁SANTU
- MU
- ▁PADRE
- D
- JU
- Z
- ▁TORO
- ▁PO
- LE
- ▁LI
- RO
- ▁LO
- ▁MESA
- CA
- ▁CHIU
- DO
- ▁BU
- ▁BUTA
- JO
- T
- TRU
- RU
- ▁MBO
- ▁JUAN
- ▁MM
- ▁CA
- ▁M
- ▁MAS
- ▁DE
- V
- ▁MAÑA
- ▁UTA
- DA
- ▁MULA
- ▁YOLOXÓCHITL
- ▁CONSEJU
- ▁Y
- ▁LE
- ÓN
- ▁MISA
- TIU
- ▁CANDELA
- ▁PATRÓN
- ▁PADRINU
- ▁MARCU
- ▁V
- ▁G
- Í
- ▁XE
- ▁MU
- ▁XO
- NGUI
- ▁CO
- ▁HOMBRE
- ▁PESU
- ▁PE
- ▁D
- ▁MACHITI
- CO
- REN
- ▁RANCHU
- ▁MIS
- ▁MACHU
- J
- ▁PAN
- CHO
- H
- ▁CHU
- Y
- ▁TON
- GA
- X
- ▁VI
- ▁FE
- ▁TARRAYA
- ▁SANTÍSIMA
- ▁N
- ▁MAYÓ
- ▁CARRU
- ▁F
- ▁PAPÁ
- ▁PALOMA
- ▁MARÍA
- ▁PEDRU
- ▁CAFÉ
- ▁COMISARIO
- ▁PANELA
- ▁PELÓN
- É
- ▁POZO
- ▁CABRÓN
- ▁GUACHU
- ▁S
- RES
- ▁COSTUMBRE
- ▁SEÑA
- QUI
- ▁ORO
- CH
- ▁MAR
- SIN
- SAN
- ▁COSTA
- ▁MAMÁ
- ▁CINCUENTA
- ▁CHO
- ▁PEDR
- ▁JUNTA
- MÚ
- ▁TIENDA
- ▁JOSÉ
- NC
- ▁ES
- ▁SUERTE
- ▁FAMILIA
- ▁ZAPATU
- NTE
- ▁PASTO
- ▁CON
- Ñ
- ▁BOTE
- CIÓN
- ▁RE
- ▁BOLSA
- ▁MANGO
- ▁JWE
- ▁GASTU
- ▁T
- ▁B
- ▁KW
- ÍN
- ▁HIJA
- ▁CUARENT
- ▁VAQUERU
- ▁NECHITO
- ▁NOVIA
- ▁NOVIO
- JWE
- ▁PUENTE
- ▁SANDÍA
- ▁MALA
- Ó
- ▁ABONO
- ▁JESÚS
- ▁CUARTO
- ▁EFE
- ▁REINA
- ▁COMANDANTE
- ▁ESCUELA
- ▁MANZANA
- ▁MÁQUINA
- LLA
- ▁COR
- ▁JERÓNIMO
- ▁PISTOLA
- NGI
- CIO
- ▁FRANCISCU
- ▁TEODORO
- CER
- ▁SALUBI
- ▁MEZA
- ▁MÚSIC
- ▁RU
- ▁CONSTANTINO
- ▁GARCÍA
- ▁FRENU
- ▁ROSA
- ▁CERVEZA
- ▁CIGARRU
- ▁COMISIÓN
- ▁CUNIJO
- ▁FRANCISCO
- ▁HÍJOLE
- ▁NUEVE
- ▁MUL
- ▁PANTALÓN
- ▁CAMISA
- ▁CHINGADA
- ▁SEMANA
- ▁COM
- GAR
- ▁MARTÍN
- ▁SÁBADO
- ▁TRABAJO
- ▁CINCO
- ▁DIE
- ▁EST
- NDWA
- ▁LECHIN
- ▁COCO
- ILLU
- ▁CORRE
- ▁MADR
- ▁REC
- ▁BAUTISTA
- ▁VENTANA
- ▁CUÑAD
- ▁ANTONIU
- ▁COPALA
- LÍN
- ▁SECUND
- ▁COHETE
- ▁HISTORIA
- ▁POLICÍA
- ENCIA
- ▁CAD
- ▁LUIS
- ▁DOCTOR
- ▁GONZÁLEZ
- ▁JUEVE
- ▁LIBRU
- ▁QUESU
- ▁VIAJE
- ▁CART
- ▁LOCO
- ▁BOL
- ▁COMPADRE
- ▁JWI
- ▁METRU
- ▁BUENO
- ▁TRE
- ▁CASTILLO
- ▁COMITÉ
- ▁ETERNO
- ▁LÍQUIDO
- ▁MOLE
- ▁CAPULCU
- ▁DOMING
- ▁ROMA
- ▁CARAJU
- ▁RIATA
- ▁TRATU
- ▁SEIS
- ▁ADÁN
- ▁JUANCITO
- ▁HOR
- ''''
- ▁ARRÓ
- ▁COCINA
- ▁PALACIO
- ▁RÓMULO
- K
- ▁ALFONSO
- ▁BARTOLO
- ▁FELIPE
- ▁HERRER
- ▁PAULINO
- ▁YEGUA
- ▁LISTA
- Ú
- ▁ABRIL
- ▁CUATRO
- ▁DICIEMBRE
- ▁MARGARITO
- ▁MOJONERA
- ▁SOLEDAD
- ▁VESTIDO
- ▁PELOTA
- RRET
- ▁CAPITÁN
- ▁COMUNIÓN
- ▁CUCHARA
- ▁FERNANDO
- ▁GUADALUPE
- ▁MIGUEL
- ▁PELÚN
- ▁SECRETARIU
- ▁LENCHU
- ▁EVA
- ▁SEGUND
- ▁CANTOR
- ▁CHILPANCINGO
- ▁GABRIEL
- ▁QUINIENTO
- ▁RAÚL
- ▁SEVERIAN
- ▁TUMBADA
- ▁MALINCHI
- ▁PRIMU
- ▁MORAL
- ▁AGOSTO
- ▁CENTÍMETRO
- ▁FIRMA
- ▁HUEHUETÁN
- ▁MANGUERA
- ▁MEDI
- ▁MUERT
- ▁SALAZAR
- ▁VIERNI
- LILL
- ▁LL
- '-'
- ▁CAMPESINO
- ▁CIVIL
- ▁COMISARIADO
- )
- (
- Ã
- ‘
- ¿
- Ü
- ¡
- Q
- F
- Á
- P
- Ÿ
- W
- Ý
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_bpe500/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
input_layer: conv2d
num_blocks: 12
linear_units: 2048
dropout_rate: 0.1
output_size: 512
attention_heads: 4
attention_dropout_rate: 0.0
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
num_blocks: 6
linear_units: 2048
dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
huggingtweets/_luisinhobr-nomesdegato-nomesdj
|
huggingtweets
| 2021-12-21T14:04:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/_luisinhobr-nomesdegato-nomesdj/1640095484918/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/1390224220643278850/LcIZLss-_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/1175884636624510976/KtBI_1GE_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/1245550936807874560/j_zCtKSJ_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">luisfer nando & nomes foda de dj & nomes de gato</div>
<div style="text-align: center; font-size: 14px;">@_luisinhobr-nomesdegato-nomesdj</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 luisfer nando & nomes foda de dj & nomes de gato.
| Data | luisfer nando | nomes foda de dj | nomes de gato |
| --- | --- | --- | --- |
| Tweets downloaded | 2357 | 3250 | 3211 |
| Retweets | 365 | 6 | 69 |
| Short tweets | 503 | 632 | 1710 |
| Tweets kept | 1489 | 2612 | 1432 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1mwm543c/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 @_luisinhobr-nomesdegato-nomesdj's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3nbxg8c7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3nbxg8c7/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/_luisinhobr-nomesdegato-nomesdj')
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)
|
hrdipto/wav2vec2-xls-r-timit-tokenizer
|
hrdipto
| 2021-12-21T11:49:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-timit-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. -->
# wav2vec2-xls-r-timit-tokenizer
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4285
- Wer: 0.3662
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1571 | 4.03 | 500 | 0.5235 | 0.5098 |
| 0.2001 | 8.06 | 1000 | 0.4172 | 0.4375 |
| 0.0968 | 12.1 | 1500 | 0.4562 | 0.4016 |
| 0.0607 | 16.13 | 2000 | 0.4640 | 0.4050 |
| 0.0409 | 20.16 | 2500 | 0.4688 | 0.3914 |
| 0.0273 | 24.19 | 3000 | 0.4414 | 0.3763 |
| 0.0181 | 28.22 | 3500 | 0.4285 | 0.3662 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
bhavikardeshna/multilingual-bert-base-cased-vietnamese
|
bhavikardeshna
| 2021-12-21T11:44:14Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/multilingual-bert-base-cased-spanish
|
bhavikardeshna
| 2021-12-21T11:43:55Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/multilingual-bert-base-cased-hindi
|
bhavikardeshna
| 2021-12-21T11:43:34Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/multilingual-bert-base-cased-german
|
bhavikardeshna
| 2021-12-21T11:43:10Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/multilingual-bert-base-cased-english
|
bhavikardeshna
| 2021-12-21T11:42:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/multilingual-bert-base-cased-chinese
|
bhavikardeshna
| 2021-12-21T11:41:47Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/multilingual-bert-base-cased-arabic
|
bhavikardeshna
| 2021-12-21T11:41:30Z | 27 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/xlm-roberta-base-arabic
|
bhavikardeshna
| 2021-12-21T11:41:04Z | 27 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/xlm-roberta-base-chinese
|
bhavikardeshna
| 2021-12-21T11:40:50Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
patrickvonplaten/xls-r-300m-tr-phoneme
|
patrickvonplaten
| 2021-12-21T11:13:30Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_3_0",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_3_0
- generated_from_trainer
model-index:
- name: xls-r-300m-tr-phoneme
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. -->
# xls-r-300m-tr-phoneme
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the mozilla-foundation/common_voice_3_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4378
- Wer: 0.09936
## 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: 0.000075
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 150
- mixed_precision_training: Native AMP
### Training results
See Training Metrics Tab.
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
jiho0304/curseELECTRA
|
jiho0304
| 2021-12-21T08:51:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
ElectraBERT tuned with korean-bad-speeches
|
kwang1993/wav2vec2-base-timit-demo
|
kwang1993
| 2021-12-21T04:54:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
https://huggingface.co/blog/fine-tune-wav2vec2-english
Use the processor from https://huggingface.co/facebook/wav2vec2-base
|
vuiseng9/pegasus-billsum
|
vuiseng9
| 2021-12-21T01:41:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 41eeb07
# installation, set summarization dependency
# . . .
```
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
NEPOCH=10
RUNID=pegasus-billsum-${NEPOCH}eph-run1
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus/${RUNID}
mkdir -p $OUTDIR
nohup python run_summarization.py \
--model_name_or_path google/pegasus-large \
--dataset_name billsum \
--do_train \
--adafactor \
--learning_rate 2e-4 \
--label_smoothing_factor 0.1 \
--num_train_epochs $NEPOCH \
--per_device_train_batch_size 2 \
--do_eval \
--per_device_eval_batch_size 2 \
--num_beams 8 \
--max_source_length 1024 \
--max_target_length 256 \
--evaluation_strategy steps \
--eval_steps 1000 \
--save_strategy steps \
--save_steps 2000 \
--logging_steps 1 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
```
# Eval
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=3
DT=$(date +%F_%H-%M)
RUNID=pegasus-billsum-${DT}
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus-test/${RUNID}
mkdir -p $OUTDIR
nohup python run_summarization.py \
--model_name_or_path vuiseng9/pegasus-billsum \
--dataset_name billsum \
--max_source_length 1024 \
--max_target_length 256 \
--do_predict \
--per_device_eval_batch_size 8 \
--predict_with_generate \
--num_beams 8 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
```
Although fine-tuning is carried out for 10 epochs, this model is the checkpoint (@12000 steps, 6.6epoch, 210mins) with lowest eval loss during training. Test/predict with this checkpoint should give results below.
```
***** predict metrics *****
predict_gen_len = 179.7363
predict_loss = 1.2452
predict_rouge1 = 56.8657
predict_rouge2 = 38.6531
predict_rougeL = 44.8399
predict_rougeLsum = 51.6266
predict_runtime = 1:19:28.20
predict_samples = 3269
predict_samples_per_second = 0.686
predict_steps_per_second = 0.086
```
|
adam-chell/tweet-sentiment-analyzer
|
adam-chell
| 2021-12-20T21:30:06Z | 4 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets.
email : [email protected]
|
patrickvonplaten/wavlm-libri-clean-100h-base
|
patrickvonplaten
| 2021-12-20T12:59:09Z | 7,849 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- wavlm_libri_finetune
model-index:
- name: wavlm-libri-clean-100h-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-libri-clean-100h-base
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0829
- Wer: 0.0675
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8805 | 0.34 | 300 | 2.8686 | 1.0 |
| 0.2459 | 0.67 | 600 | 0.1858 | 0.1554 |
| 0.1114 | 1.01 | 900 | 0.1379 | 0.1191 |
| 0.0867 | 1.35 | 1200 | 0.1130 | 0.0961 |
| 0.0698 | 1.68 | 1500 | 0.1032 | 0.0877 |
| 0.0663 | 2.02 | 1800 | 0.0959 | 0.0785 |
| 0.0451 | 2.35 | 2100 | 0.0887 | 0.0748 |
| 0.0392 | 2.69 | 2400 | 0.0859 | 0.0698 |
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/wavlm-libri-clean-100h-base-plus
|
patrickvonplaten
| 2021-12-20T12:59:01Z | 14,635 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- wavlm_libri_finetune
model-index:
- name: wavlm-libri-clean-100h-base-plus
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. -->
# wavlm-libri-clean-100h-base-plus
This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0819
- Wer: 0.0683
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8877 | 0.34 | 300 | 2.8649 | 1.0 |
| 0.2852 | 0.67 | 600 | 0.2196 | 0.1830 |
| 0.1198 | 1.01 | 900 | 0.1438 | 0.1273 |
| 0.0906 | 1.35 | 1200 | 0.1145 | 0.1035 |
| 0.0729 | 1.68 | 1500 | 0.1055 | 0.0955 |
| 0.0605 | 2.02 | 1800 | 0.0936 | 0.0859 |
| 0.0402 | 2.35 | 2100 | 0.0885 | 0.0746 |
| 0.0421 | 2.69 | 2400 | 0.0848 | 0.0700 |
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-common_voice-tr-demo-dist
|
patrickvonplaten
| 2021-12-20T12:54:17Z | 13 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- tr
license: apache-2.0
tags:
- speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-tr-demo
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-common_voice-tr-demo-dist
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3856
- Wer: 0.3581
- Cer: 0.0805
## 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: 4
- num_gpus: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- 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: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7391 | 0.92 | 100 | 3.5760 | 1.0 |
| 2.927 | 1.83 | 200 | 3.0796 | 0.9999 |
| 0.9009 | 2.75 | 300 | 0.9278 | 0.8226 |
| 0.6529 | 3.67 | 400 | 0.5926 | 0.6367 |
| 0.3623 | 4.59 | 500 | 0.5372 | 0.5692 |
| 0.2888 | 5.5 | 600 | 0.4407 | 0.4838 |
| 0.285 | 6.42 | 700 | 0.4341 | 0.4694 |
| 0.0842 | 7.34 | 800 | 0.4153 | 0.4302 |
| 0.1415 | 8.26 | 900 | 0.4317 | 0.4136 |
| 0.1552 | 9.17 | 1000 | 0.4145 | 0.4013 |
| 0.1184 | 10.09 | 1100 | 0.4115 | 0.3844 |
| 0.0556 | 11.01 | 1200 | 0.4182 | 0.3862 |
| 0.0851 | 11.93 | 1300 | 0.3985 | 0.3688 |
| 0.0961 | 12.84 | 1400 | 0.4030 | 0.3665 |
| 0.0596 | 13.76 | 1500 | 0.3880 | 0.3631 |
| 0.0359 | 14.68 | 1600 | 0.3878 | 0.3589 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
patrickvonplaten/hubert-librispeech-clean-100h-demo-dist
|
patrickvonplaten
| 2021-12-20T12:53:35Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- speech-recognition
- librispeech_asr
- generated_from_trainer
model-index:
- name: hubert-librispeech-clean-100h-demo-dist
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. -->
# hubert-librispeech-clean-100h-demo-dist
This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0984
- Wer: 0.0883
## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 64
- 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 | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9031 | 0.11 | 100 | 2.9220 | 1.0 |
| 2.6437 | 0.22 | 200 | 2.6268 | 1.0 |
| 0.3934 | 0.34 | 300 | 0.4860 | 0.4182 |
| 0.3531 | 0.45 | 400 | 0.3088 | 0.2894 |
| 0.2255 | 0.56 | 500 | 0.2568 | 0.2426 |
| 0.3379 | 0.67 | 600 | 0.2073 | 0.2011 |
| 0.2419 | 0.78 | 700 | 0.1849 | 0.1838 |
| 0.2128 | 0.9 | 800 | 0.1662 | 0.1690 |
| 0.1341 | 1.01 | 900 | 0.1600 | 0.1541 |
| 0.0946 | 1.12 | 1000 | 0.1431 | 0.1404 |
| 0.1643 | 1.23 | 1100 | 0.1373 | 0.1304 |
| 0.0663 | 1.35 | 1200 | 0.1293 | 0.1307 |
| 0.162 | 1.46 | 1300 | 0.1247 | 0.1266 |
| 0.1433 | 1.57 | 1400 | 0.1246 | 0.1262 |
| 0.1581 | 1.68 | 1500 | 0.1219 | 0.1154 |
| 0.1036 | 1.79 | 1600 | 0.1127 | 0.1081 |
| 0.1352 | 1.91 | 1700 | 0.1087 | 0.1040 |
| 0.0471 | 2.02 | 1800 | 0.1085 | 0.1005 |
| 0.0945 | 2.13 | 1900 | 0.1066 | 0.0973 |
| 0.0843 | 2.24 | 2000 | 0.1102 | 0.0964 |
| 0.0774 | 2.35 | 2100 | 0.1079 | 0.0940 |
| 0.0952 | 2.47 | 2200 | 0.1056 | 0.0927 |
| 0.0635 | 2.58 | 2300 | 0.1026 | 0.0920 |
| 0.0665 | 2.69 | 2400 | 0.1012 | 0.0905 |
| 0.034 | 2.8 | 2500 | 0.1009 | 0.0900 |
| 0.0251 | 2.91 | 2600 | 0.0993 | 0.0883 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
Alerosae/SocratesGPT-2
|
Alerosae
| 2021-12-20T12:36:38Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"en",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: "en"
tags:
- text-generation
pipeline_tag: text-generation
widget:
- text: "The Gods"
- text: "What is"
---
This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly!
|
abhishek/autonlp-prodigy-10-3362554
|
abhishek
| 2021-12-20T11:11:03Z | 6 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-prodigy-10",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-prodigy-10
co2_eq_emissions: 5.340540212393564
---
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 3362554
- CO2 Emissions (in grams): 5.340540212393564
## Validation Metrics
- Loss: 0.14167872071266174
- Accuracy: 0.9587076867229332
- Precision: 0.7351351351351352
- Recall: 0.7923728813559322
- F1: 0.7626816212082591
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-prodigy-10-3362554
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("abhishek/autonlp-prodigy-10-3362554", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-prodigy-10-3362554", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
juliusco/biobert-base-cased-v1.1-squad-finetuned-covbiobert
|
juliusco
| 2021-12-20T07:58:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
model-index:
- name: biobert-base-cased-v1.1-squad-finetuned-covbiobert
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. -->
# biobert-base-cased-v1.1-squad-finetuned-covbiobert
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1-squad](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1-squad) on the covid_qa_deepset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3959
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 486 | 0.3787 |
| 0.161 | 2.0 | 972 | 0.3959 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
austin/adr-ner
|
austin
| 2021-12-20T06:48:11Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: adr-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# adr-ner
This model is a fine-tuned version of [austin/Austin-MeDeBERTa](https://huggingface.co/austin/Austin-MeDeBERTa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0434
- Precision: 0.7305
- Recall: 0.6934
- F1: 0.7115
- Accuracy: 0.9941
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 107 | 0.0630 | 0.0 | 0.0 | 0.0 | 0.9876 |
| No log | 2.0 | 214 | 0.0308 | 0.4282 | 0.3467 | 0.3832 | 0.9900 |
| No log | 3.0 | 321 | 0.0254 | 0.5544 | 0.5603 | 0.5573 | 0.9920 |
| No log | 4.0 | 428 | 0.0280 | 0.6430 | 0.5751 | 0.6071 | 0.9929 |
| 0.0465 | 5.0 | 535 | 0.0266 | 0.5348 | 0.7146 | 0.6118 | 0.9915 |
| 0.0465 | 6.0 | 642 | 0.0423 | 0.7632 | 0.5793 | 0.6587 | 0.9939 |
| 0.0465 | 7.0 | 749 | 0.0336 | 0.6957 | 0.6765 | 0.6860 | 0.9939 |
| 0.0465 | 8.0 | 856 | 0.0370 | 0.6876 | 0.6702 | 0.6788 | 0.9936 |
| 0.0465 | 9.0 | 963 | 0.0349 | 0.6555 | 0.7040 | 0.6789 | 0.9932 |
| 0.0044 | 10.0 | 1070 | 0.0403 | 0.6910 | 0.6808 | 0.6858 | 0.9938 |
| 0.0044 | 11.0 | 1177 | 0.0415 | 0.7140 | 0.6808 | 0.6970 | 0.9939 |
| 0.0044 | 12.0 | 1284 | 0.0440 | 0.7349 | 0.6681 | 0.6999 | 0.9941 |
| 0.0044 | 13.0 | 1391 | 0.0423 | 0.7097 | 0.6977 | 0.7036 | 0.9941 |
| 0.0044 | 14.0 | 1498 | 0.0435 | 0.7174 | 0.6977 | 0.7074 | 0.9941 |
| 0.0006 | 15.0 | 1605 | 0.0434 | 0.7305 | 0.6934 | 0.7115 | 0.9941 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Amalq/roberta-base-finetuned-schizophreniaReddit2
|
Amalq
| 2021-12-20T05:41:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-schizophreniaReddit2
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. -->
# roberta-base-finetuned-schizophreniaReddit2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7785
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 490 | 1.8093 |
| 1.9343 | 2.0 | 980 | 1.7996 |
| 1.8856 | 3.0 | 1470 | 1.7966 |
| 1.8552 | 4.0 | 1960 | 1.7844 |
| 1.8267 | 5.0 | 2450 | 1.7839 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
addy88/wav2vec2-bhojpuri-stt
|
addy88
| 2021-12-19T16:48:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-bhojpuri-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-bhojpuri-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
addy88/wav2vec2-malayalam-stt
|
addy88
| 2021-12-19T16:36:31Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-malayalam-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-malayalam-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
addy88/wav2vec2-marathi-stt
|
addy88
| 2021-12-19T16:31:22Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-marathi-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-marathi-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
addy88/wav2vec2-rajsthani-stt
|
addy88
| 2021-12-19T15:52:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-rajsthani-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-rajsthani-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
addy88/wav2vec2-telugu-stt
|
addy88
| 2021-12-19T15:39:58Z | 1,020 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-telugu-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-telugu-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
addy88/wav2vec2-nepali-stt
|
addy88
| 2021-12-19T15:36:06Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-nepali-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-nepali-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
kco4776/soongsil-bert-wellness
|
kco4776
| 2021-12-19T15:23:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## References
- [Soongsil-BERT](https://github.com/jason9693/Soongsil-BERT)
|
addy88/wav2vec2-gujarati-stt
|
addy88
| 2021-12-19T15:14:38Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-gujarati-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-gujarati-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
nguyenvulebinh/envibert
|
nguyenvulebinh
| 2021-12-19T14:20:51Z | 26 | 5 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"exbert",
"vi",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: vi
tags:
- exbert
license: cc-by-nc-4.0
---
# RoBERTa for Vietnamese and English (envibert)
This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named ***envibert***. The model architecture is custom for production so it only contains 70M parameters.
## Usages
```python
from transformers import RobertaModel
from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
import os
cache_dir='./cache'
model_name='nguyenvulebinh/envibert'
def download_tokenizer_files():
resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model']
for item in resources:
if not os.path.exists(os.path.join(cache_dir, item)):
tmp_file = hf_bucket_url(model_name, filename=item)
tmp_file = cached_path(tmp_file,cache_dir=cache_dir)
os.rename(tmp_file, os.path.join(cache_dir, item))
download_tokenizer_files()
tokenizer = SourceFileLoader("envibert.tokenizer", os.path.join(cache_dir,'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir)
model = RobertaModel.from_pretrained(model_name,cache_dir=cache_dir)
# Encode text
text_input = 'Đại học Bách Khoa Hà Nội .'
text_ids = tokenizer(text_input, return_tensors='pt').input_ids
# tensor([[ 0, 705, 131, 8751, 2878, 347, 477, 5, 2]])
# Extract features
text_features = model(text_ids)
text_features['last_hidden_state'].shape
# torch.Size([1, 9, 768])
len(text_features['hidden_states'])
# 7
```
### Citation
```text
@inproceedings{nguyen20d_interspeech,
author={Thai Binh Nguyen and Quang Minh Nguyen and Thi Thu Hien Nguyen and Quoc Truong Do and Chi Mai Luong},
title={{Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models}},
year=2020,
booktitle={Proc. Interspeech 2020},
pages={4263--4267},
doi={10.21437/Interspeech.2020-1896}
}
```
**Please CITE** our repo when it is used to help produce published results or is incorporated into other software.
# Contact
[email protected]
[](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
|
microsoft/unispeech-1350-en-90-it-ft-1h
|
microsoft
| 2021-12-19T13:19:29Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"audio",
"it",
"dataset:common_voice",
"arxiv:2101.07597",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- it
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
---
# UniSpeech-Large-plus ITALIAN
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Italian phonemes.
When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes.
[Paper: UniSpeech: Unified Speech Representation Learning
with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597)
Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang
**Abstract**
*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.*
The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech.
# Usage
This is an speech model that has been fine-tuned on phoneme classification.
## Inference
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "microsoft/unispeech-1350-en-90-it-ft-1h"
sample = next(iter(load_dataset("common_voice", "it", split="test", streaming=True)))
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
prediction_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(prediction_ids)
# => 'm ɪ a n n o f a tː o ʊ n o f f ɛ r t a k e n o n p o t e v o p r ɔ p r i o r i f j ʊ t a r e'
# for "Mi hanno fatto un\'offerta che non potevo proprio rifiutare."
```
## Evaluation
```python
from datasets import load_dataset, load_metric
import datasets
import torch
from transformers import AutoModelForCTC, AutoProcessor
model_id = "microsoft/unispeech-1350-en-90-it-ft-1h"
ds = load_dataset("mozilla-foundation/common_voice_3_0", "it", split="train+validation+test+other")
wer = load_metric("wer")
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
# taken from
# https://github.com/microsoft/UniSpeech/blob/main/UniSpeech/examples/unispeech/data/it/phonesMatches_reduced.json
with open("./testSeqs_uniform_new_version.text", "r") as f:
lines = f.readlines()
# retrieve ids model is evaluated on
ids = [x.split("\t")[0] for x in lines]
ds = ds.filter(lambda p: p.split("/")[-1].split(".")[0] in ids, input_columns=["path"])
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
def decode(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", sampling_rate=16_000)
logits = model(input_values).logits
pred_ids = torch.argmax(logits, axis=-1)
batch["prediction"] = processor.batch_decode(pred_ids)
batch["target"] = processor.tokenizer.phonemize(batch["sentence"])
return batch
out = ds.map(decode, remove_columns=ds.column_names)
per = wer.compute(predictions=out["prediction"], references=out["target"])
print("per", per)
# -> should give per 0.06685252146070828 - compare to results below
```
# Contribution
The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
# License
The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
# Official Results
See *UniSpeeech-L^{+}* - *it*:
|
new5558/simcse-model-wangchanberta-base-att-spm-uncased
|
new5558
| 2021-12-19T13:01:31Z | 80 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"camembert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# new5558/simcse-model-wangchanberta-base-att-spm-uncased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('new5558/simcse-model-wangchanberta-base-att-spm-uncased')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('new5558/simcse-model-wangchanberta-base-att-spm-uncased')
model = AutoModel.from_pretrained('new5558/simcse-model-wangchanberta-base-att-spm-uncased')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=new5558/simcse-model-wangchanberta-base-att-spm-uncased)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5125 with parameters:
```
{'batch_size': 256, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: CamembertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
rlagusrlagus123/XTC20000
|
rlagusrlagus123
| 2021-12-19T11:00:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- conversational
---
---
#12 epochs, each batch size 2, gradient accumulation steps 2, tail 20000
|
princebansal42/distilbert-base-uncased-finetuned-squad
|
princebansal42
| 2021-12-19T10:27:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
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_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6623
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3993 | 1.0 | 2051 | 1.8058 |
| 1.0467 | 2.0 | 4102 | 1.9564 |
| 0.8304 | 3.0 | 6153 | 2.6623 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/distilbert_gpt2_summarization_cnndm
|
Ayham
| 2021-12-19T06:43:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: distilbert_gpt2_summarization_cnndm
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_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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: 8
- 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: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/roberta_gpt2_summarization_xsum
|
Ayham
| 2021-12-19T06:35:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: roberta_gpt2_summarization_xsum
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. -->
# roberta_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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: 8
- 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: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/bert_gpt2_summarization_cnndm
|
Ayham
| 2021-12-19T06:32:54Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: bert_gpt2_summarization_cnndm
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_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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: 8
- 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: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
zaccharieramzi/UNet-fastmri
|
zaccharieramzi
| 2021-12-19T02:05:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# UNet-fastmri
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4.
It cannot be used on multi-coil data.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
from fastmri_recon.models.functional_models.unet import unet
model = unet(n_layers=4, layers_n_channels=[16, 32, 64, 128], layers_n_non_lins=2,)
model.load_weights('UNet-fastmri/model_weights.h5')
```
Using the model is then as simple as:
```python
model(zero_filled_recon)
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data.
This section is WIP.
## Evaluation results
This model was evaluated using the [fastMRI dataset](https://fastmri.org/dataset/).
| Contrast | PD | PDFS |
|----------|-------|--------|
| PSNR | 33.64 | 29.89 |
| SSIM | 0.807 | 0.6334 |
## Bibtex entry
```
@article{ramzi2020benchmarking,
title={Benchmarking MRI reconstruction neural networks on large public datasets},
author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
journal={Applied Sciences},
volume={10},
number={5},
pages={1816},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}
```
|
zaccharieramzi/PDNet-fastmri
|
zaccharieramzi
| 2021-12-19T01:32:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# PDNet-fastmri
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://www.mdpi.com/2076-3417/10/5/1816.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4.
It cannot be used on multi-coil data.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
from fastmri_recon.models.functional_models.pdnet import pdnet
model = pdnet()
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
model([
kspace, # shape: [n_slices, n_rows, n_cols, 1]
mask, # shape: [n_slices, n_rows, n_cols]
])
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://www.mdpi.com/2076-3417/10/5/1816 for brain data.
This section is WIP.
## Evaluation results
This model was evaluated using the [fastMRI dataset](https://fastmri.org/dataset/).
| Contrast | PD | PDFS |
|----------|-------|--------|
| PSNR | 34.2 | 30.06 |
| SSIM | 0.818 | 0.9554 |
## Bibtex entry
```
@article{ramzi2020benchmarking,
title={Benchmarking MRI reconstruction neural networks on large public datasets},
author={Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
journal={Applied Sciences},
volume={10},
number={5},
pages={1816},
year={2020},
publisher={Multidisciplinary Digital Publishing Institute}
}
```
|
zaccharieramzi/NCPDNet-multicoil-spiral
|
zaccharieramzi
| 2021-12-19T01:01:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# NCPDNet-multicoil-spiral
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian multicoil MRI reconstruction model for spiral trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal.inria.fr/hal-03188997.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct multicoil knee data from Siemens scanner at acceleration factor 4 in a spiral acquisition setting.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
import tensorflow as tf
from fastmri_recon.models.subclassed_models.ncpdnet import NCPDNet
model = NCPDNet(
multicoil=True,
im_size=(640, 400),
dcomp=True,
refine_smaps=True,
)
kspace_shape = 1
inputs = [
tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64),
tf.zeros([1, 2, kspace_shape], dtype=tf.float32),
tf.zeros([1, 1, 640, 320], dtype=tf.complex64),
(tf.constant([320]), tf.ones([1, kspace_shape], dtype=tf.float32)),
]
model(inputs)
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
model([
kspace, # shape: [n_slices, n_coils, n_kspace_samples, 1]
traj, # shape: [n_slices, n_coils, 2, n_kspace_samples]
smaps, # shape: [n_slices, n_coils, n_kspace_samples, n_coils]
(
output_shape, # shape: [n_slices, 1]
dcomp, # shape: [n_slices, n_kspace_samples]
)
])
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://hal.inria.fr/hal-03188997.
This section is WIP.
## Evaluation results
On the fastMRI validation dataset:
- PSNR: 40.68
- SSIM: 0.9255
## Bibtex entry
```
@unpublished{ramzi:hal-03188997,
TITLE = {{NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction}},
AUTHOR = {Ramzi, Zaccharie and G R, Chaithya and Starck, Jean-Luc and Ciuciu, Philippe},
YEAR = {2021},
MONTH = Sep,
}
```
|
zaccharieramzi/NCPDNet-singlecoil-spiral
|
zaccharieramzi
| 2021-12-19T00:47:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# NCPDNet-singlecoil-spiral
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This is a non-Cartesian MRI reconstruction model for spiral trajectories at acceleration factor 4.
The model uses 10 iterations and a small vanilla CNN.
## Model description
For more details, see https://hal.inria.fr/hal-03188997.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct knee data from Siemens scanner at acceleration factor 4 in a spiral acquisition setting.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
import tensorflow as tf
from fastmri_recon.models.subclassed_models.ncpdnet import NCPDNet
model = NCPDNet(
im_size=(640, 400),
dcomp=True,
)
kspace_shape = 1
inputs = [
tf.zeros([1, 1, kspace_shape, 1], dtype=tf.complex64),
tf.zeros([1, 2, kspace_shape], dtype=tf.float32),
(tf.constant([320]), tf.ones([1, kspace_shape], dtype=tf.float32)),
]
model(inputs)
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
model([
kspace, # shape: [n_slices, 1, n_kspace_samples, 1]
traj, # shape: [n_slices, 1, 2, n_kspace_samples]
(
output_shape, # shape: [n_slices, 1]
dcomp, # shape: [n_slices, n_kspace_samples]
)
])
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://hal.inria.fr/hal-03188997.
This section is WIP.
## Evaluation results
On the fastMRI validation dataset:
- PSNR: 33.08
- SSIM: 0.7534
## Bibtex entry
```
@unpublished{ramzi:hal-03188997,
TITLE = {{NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction}},
AUTHOR = {Ramzi, Zaccharie and G R, Chaithya and Starck, Jean-Luc and Ciuciu, Philippe},
YEAR = {2021},
MONTH = Sep,
}
```
|
s3h/mt5-small-finetuned-src-to-trg
|
s3h
| 2021-12-18T20:34:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-src-to-trg
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-src-to-trg
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 40 | nan | 0.1737 | 3.1818 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.6.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
tasosk/bert-base-uncased-airlines
|
tasosk
| 2021-12-18T20:20:24Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-airlines
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-airlines
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3458
- Accuracy: 0.9021
- F1: 0.9022
## 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-06
- 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 405 | 0.3230 | 0.8754 | 0.8750 |
| 0.4658 | 2.0 | 810 | 0.2738 | 0.8986 | 0.8985 |
| 0.2473 | 3.0 | 1215 | 0.2944 | 0.9110 | 0.9111 |
| 0.2498 | 4.0 | 1620 | 0.3322 | 0.8950 | 0.8949 |
| 0.2174 | 5.0 | 2025 | 0.3342 | 0.9021 | 0.9021 |
| 0.2174 | 6.0 | 2430 | 0.3526 | 0.8986 | 0.8985 |
| 0.2055 | 7.0 | 2835 | 0.3458 | 0.9021 | 0.9022 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
zaccharieramzi/UPDNet-knee-af8
|
zaccharieramzi
| 2021-12-18T18:08:29Z | 0 | 0 | null |
[
"arxiv:2010.07290",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# UPDNet-knee-af8
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission).
It is a base model for acceleration factor 8.
The model uses 25 iterations and a medium-ca-prelu U-net, and a medium sensitivity maps refiner.
## Model description
For more details, see https://arxiv.org/abs/2010.07290.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct knee data from Siemens scanner at acceleration factor 8.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
import tensorflow as tf
from fastmri_recon.models.subclassed_models.updnet import UPDNet
model = UPDNet(
multicoil=True,
n_dual=1,
primal_only=True,
n_layers=4,
n_iter=25,
channel_attention_kwargs={'dense': True},
refine_smaps=True,
non_linearity='prelu',
layers_n_channels=[16 * 2**i for i in range(4)],
)
kspace_size = [1, 1, 320, 320]
inputs = [
tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace
tf.zeros(kspace_size, dtype=tf.complex64), # mask
tf.zeros(kspace_size, dtype=tf.complex64), # smaps
]
model(inputs)
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
model([
kspace, # shape: [n_slices, n_coils, n_rows, n_cols, 1]
mask, # shape: [n_slices, n_coils, n_rows, n_cols]
smaps, # shape: [n_slices, n_coils, n_rows, n_cols]
])
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://arxiv.org/abs/2010.07290.
This section is WIP.
## Evaluation results
No evaluation available outside the one from the fastMRI leaderboard (id: `updnet_v3`).
## Bibtex entry
```
@inproceedings{Ramzi2020d,
archivePrefix = {arXiv},
arxivId = {2010.07290},
author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
booktitle = {ISMRM},
eprint = {2010.07290},
pages = {1--4},
title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
url = {http://arxiv.org/abs/2010.07290},
year = {2021}
}
```
|
zaccharieramzi/UPDNet-knee-af4
|
zaccharieramzi
| 2021-12-18T18:08:04Z | 0 | 0 | null |
[
"arxiv:2010.07290",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# UPDNet-knee-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 9th highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/) (0.2dB behind the 2nd submission).
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medium-ca-prelu U-net, and a medium sensitivity maps refiner.
## Model description
For more details, see https://arxiv.org/abs/2010.07290.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct knee data from Siemens scanner at acceleration factor 4.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
import tensorflow as tf
from fastmri_recon.models.subclassed_models.updnet import UPDNet
model = UPDNet(
multicoil=True,
n_dual=1,
primal_only=True,
n_layers=4,
n_iter=25,
channel_attention_kwargs={'dense': True},
refine_smaps=True,
non_linearity='prelu',
layers_n_channels=[16 * 2**i for i in range(4)],
)
kspace_size = [1, 1, 320, 320]
inputs = [
tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace
tf.zeros(kspace_size, dtype=tf.complex64), # mask
tf.zeros(kspace_size, dtype=tf.complex64), # smaps
]
model(inputs)
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
model([
kspace, # shape: [n_slices, n_coils, n_rows, n_cols, 1]
mask, # shape: [n_slices, n_coils, n_rows, n_cols]
smaps, # shape: [n_slices, n_coils, n_rows, n_cols]
])
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://arxiv.org/abs/2010.07290.
This section is WIP.
## Evaluation results
No evaluation available outside the one from the fastMRI leaderboard (id: `updnet_v3`).
## Bibtex entry
```
@inproceedings{Ramzi2020d,
archivePrefix = {arXiv},
arxivId = {2010.07290},
author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
booktitle = {ISMRM},
eprint = {2010.07290},
pages = {1--4},
title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
url = {http://arxiv.org/abs/2010.07290},
year = {2021}
}
```
|
zaccharieramzi/UPDNet-knee-singlecoil-af4
|
zaccharieramzi
| 2021-12-18T17:48:37Z | 0 | 0 | null |
[
"arxiv:2010.07290",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# UPDNet-knee-singlecoil-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model can be used to reconstruct single coil fastMRI data with an acceleration factor of 4.
## Model description
For more details, see https://arxiv.org/abs/2010.07290.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct single coil knee data from Siemens scanner at acceleration factor 4.
It cannot be used on multi-coil data.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
import tensorflow as tf
from fastmri_recon.models.subclassed_models.updnet import UPDNet
model = UPDNet(
n_dual=1,
primal_only=True,
layers_n_channels=[16 * 2**i for i in range(3)],
)
kspace_size = [1, 320, 320]
inputs = [
tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace
tf.zeros(kspace_size, dtype=tf.complex64), # mask
]
model(inputs)
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
model([
kspace, # shape: [n_slices, n_rows, n_cols, 1]
mask, # shape: [n_slices, n_rows, n_cols]
])
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://arxiv.org/abs/2010.07290 for brain data.
This section is WIP.
## Evaluation results
Not available
## Bibtex entry
```
@inproceedings{Ramzi2020d,
archivePrefix = {arXiv},
arxivId = {2010.07290},
author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
booktitle = {ISMRM},
eprint = {2010.07290},
pages = {1--4},
title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
url = {http://arxiv.org/abs/2010.07290},
year = {2021}
}
```
|
zaccharieramzi/XPDNet-brain-af4
|
zaccharieramzi
| 2021-12-18T17:10:04Z | 0 | 0 | null |
[
"arxiv:2010.07290",
"arxiv:2106.00753",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# XPDNet-brain-af4
---
tags:
- TensorFlow
- MRI reconstruction
- MRI
datasets:
- fastMRI
---
This model was used to achieve the 3rd highest submission in terms of PSNR on the fastMRI dataset (see https://fastmri.org/leaderboards/).
It is a base model for acceleration factor 4.
The model uses 25 iterations and a medium MWCNN, and a big sensitivity maps refiner.
## Model description
For more details, see https://arxiv.org/abs/2010.07290.
This section is WIP.
## Intended uses and limitations
This model can be used to reconstruct brain data from Siemens scanner at acceleration factor 4.
It was shown [here](https://arxiv.org/abs/2106.00753), that it can generalize well, although further tests are required.
## How to use
This model can be loaded using the following repo: https://github.com/zaccharieramzi/fastmri-reproducible-benchmark.
After cloning the repo, `git clone https://github.com/zaccharieramzi/fastmri-reproducible-benchmark`, you can install the package via `pip install fastmri-reproducible-benchmark`.
The framework is TensorFlow.
You can initialize and load the model weights as follows:
```python
import tensorflow as tf
from fastmri_recon.models.subclassed_models.denoisers.proposed_params import get_model_specs
from fastmri_recon.models.subclassed_models.xpdnet import XPDNet
n_primal = 5
model_fun, model_kwargs, n_scales, res = [
(model_fun, kwargs, n_scales, res)
for m_name, m_size, model_fun, kwargs, _, n_scales, res in get_model_specs(n_primal=n_primal, force_res=False)
if m_name == 'MWCNN' and m_size == 'medium'
][0]
model_kwargs['use_bias'] = False
run_params = dict(
n_primal=n_primal,
multicoil=True,
n_scales=n_scales,
refine_smaps=True,
refine_big=True,
res=res,
output_shape_spec=True,
n_iter=25,
)
model = XPDNet(model_fun, model_kwargs, **run_params)
kspace_size = [1, 1, 320, 320]
inputs = [
tf.zeros(kspace_size + [1], dtype=tf.complex64), # kspace
tf.zeros(kspace_size, dtype=tf.complex64), # mask
tf.zeros(kspace_size, dtype=tf.complex64), # smaps
tf.constant([[320, 320]]), # shape
]
model(inputs)
model.load_weights('model_weights.h5')
```
Using the model is then as simple as:
```python
model([
kspace, # shape: [n_slices, n_coils, n_rows, n_cols, 1]
mask, # shape: [n_slices, n_coils, n_rows, n_cols]
smaps, # shape: [n_slices, n_coils, n_rows, n_cols]
shape, # shape: [n_slices, 2]
])
```
## Limitations and bias
The limitations and bias of this model have not been properly investigated.
## Training data
This model was trained using the [fastMRI dataset](https://fastmri.org/dataset/).
## Training procedure
The training procedure is described in https://arxiv.org/abs/2010.07290.
This section is WIP.
## Evaluation results
On the fastMRI validation dataset, the same model with a smaller sensitivity maps refiner gives the following results for 30 validation volumes per contrast:
| Contrast | T1 | T2 | FLAIR | T1-POST |
|----------|--------|--------|--------|---------|
| PSNR | 41.56 | 40.68 | 39.60 | 42.53 |
| SSIM | 0.9506 | 0.9554 | 0.9321 | 0.9683 |
Further results can be seen on the fastMRI leaderboards for the test and challenge dataset: https://fastmri.org/leaderboards/
## Bibtex entry
```
@inproceedings{Ramzi2020d,
archivePrefix = {arXiv},
arxivId = {2010.07290},
author = {Ramzi, Zaccharie and Ciuciu, Philippe and Starck, Jean-Luc},
booktitle = {ISMRM},
eprint = {2010.07290},
pages = {1--4},
title = {{XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge}},
url = {http://arxiv.org/abs/2010.07290},
year = {2021}
}
```
|
flboehm/reddit-bert-text_20
|
flboehm
| 2021-12-18T12:47:04Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: reddit-bert-text_20
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. -->
# reddit-bert-text_20
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4702
- Perplexity: 11.82
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9383 | 1.0 | 947 | 2.5420 |
| 2.6448 | 2.0 | 1894 | 2.5241 |
| 2.586 | 3.0 | 2841 | 2.4833 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10
|
Harveenchadha
| 2021-12-17T20:14:16Z | 1,400 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pa",
"arxiv:2107.07402",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: pa
#datasets:
#- Interspeech 2021
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: mit
model-index:
- name: Wav2Vec2 Vakyansh Punjabi Model by Harveen Chadha
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice hi
type: common_voice
args: pa
metrics:
- name: Test WER
type: wer
value: 33.17
---
Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz.
**Note: The result from this model is without a language model so you may witness a higher WER in some cases.**
|
microsoft/unispeech-sat-large-sd
|
microsoft
| 2021-12-17T18:42:36Z | 72 | 1 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"audio-frame-classification",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
datasets:
tags:
- speech
---
# UniSpeech-SAT-Large for Speaker Diarization
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875)
- 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909)
- 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390)
[Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752)
Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu
**Abstract**
*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..*
The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT.
# Fine-tuning details
The model is fine-tuned on the [LibriMix dataset](https://github.com/JorisCos/LibriMix) using just a linear layer for mapping the network outputs.
# Usage
## Speaker Diarization
```python
from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForAudioFrameClassification
from datasets import load_dataset
import torch
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-large-sd')
model = UniSpeechSatForAudioFrameClassification.from_pretrained('microsoft/unispeech-sat-large-sd')
# audio file is decoded on the fly
inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt")
logits = model(**inputs).logits
probabilities = torch.sigmoid(logits[0])
# labels is a one-hot array of shape (num_frames, num_speakers)
labels = (probabilities > 0.5).long()
```
# License
The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
|
osanseviero/fastai_cat_vs_dog
|
osanseviero
| 2021-12-17T14:27:39Z | 32 | 4 |
generic
|
[
"generic",
"image-classification",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
library_name: generic
---
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training
## Training
The model was trained as follows
```python
path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_func(
path, get_image_files(path), valid_pct=0.2, seed=42,
label_func=is_cat, item_tfms=Resize(224))
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)
```
|
osanseviero/fastai_cat_vs_dog_fork2
|
osanseviero
| 2021-12-17T14:27:39Z | 33 | 0 |
generic
|
[
"generic",
"image-classification",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
library_name: generic
---
# Dog vs Cat Image Classification with FastAI CNN
Training is based in FastAI [Quick Start](https://docs.fast.ai/quick_start.html). Example training
## Training
The model was trained as follows
```python
path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_func(
path, get_image_files(path), valid_pct=0.2, seed=42,
label_func=is_cat, item_tfms=Resize(224))
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)
```
|
Rocketknight1/gbert-base-germaner
|
Rocketknight1
| 2021-12-17T14:04:59Z | 5 | 1 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Rocketknight1/gbert-base-germaner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rocketknight1/gbert-base-germaner
This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0340
- Validation Loss: 0.0881
- Epoch: 2
## 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:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4176, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1345 | 0.0865 | 0 |
| 0.0550 | 0.0878 | 1 |
| 0.0340 | 0.0881 | 2 |
### Framework versions
- Transformers 4.15.0.dev0
- TensorFlow 2.6.0
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
microsoft/unispeech-sat-base-plus-sv
|
microsoft
| 2021-12-17T13:56:17Z | 1,232 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"audio-xvector",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- speech
---
# UniSpeech-SAT-Base for Speaker Verification
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875)
- 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909)
- 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390)
[Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752)
Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu
**Abstract**
*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..*
The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT.
# Fine-tuning details
The model is fine-tuned on the [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) using an X-Vector head with an Additive Margin Softmax loss
[X-Vectors: Robust DNN Embeddings for Speaker Recognition](https://www.danielpovey.com/files/2018_icassp_xvectors.pdf)
# Usage
## Speaker Verification
```python
from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForXVector
from datasets import load_dataset
import torch
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-base-plus-sv')
model = UniSpeechSatForXVector.from_pretrained('microsoft/unispeech-sat-base-plus-sv')
# audio files are decoded on the fly
inputs = feature_extractor(dataset[:2]["audio"]["array"], return_tensors="pt")
embeddings = model(**inputs).embeddings
embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()
# the resulting embeddings can be used for cosine similarity-based retrieval
cosine_sim = torch.nn.CosineSimilarity(dim=-1)
similarity = cosine_sim(embeddings[0], embeddings[1])
threshold = 0.89 # the optimal threshold is dataset-dependent
if similarity < threshold:
print("Speakers are not the same!")
```
# License
The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
|
patrickvonplaten/wavlm-libri-clean-100h-large
|
patrickvonplaten
| 2021-12-17T13:40:58Z | 10,035 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- wavlm_libri_finetune
model-index:
- name: wavlm-librispeech-clean-100h-dist
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. -->
# wavlm-libri-clean-100h-large
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0601
- Wer: 0.0491
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8069 | 0.34 | 300 | 0.7510 | 0.5809 |
| 0.2483 | 0.67 | 600 | 0.2023 | 0.1929 |
| 0.1033 | 1.01 | 900 | 0.1123 | 0.1028 |
| 0.0742 | 1.35 | 1200 | 0.0858 | 0.0771 |
| 0.057 | 1.68 | 1500 | 0.0722 | 0.0663 |
| 0.0421 | 2.02 | 1800 | 0.0682 | 0.0582 |
| 0.0839 | 2.35 | 2100 | 0.0630 | 0.0534 |
| 0.0307 | 2.69 | 2400 | 0.0603 | 0.0508 |
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
ivanlau/language-detection-fine-tuned-on-xlm-roberta-base
|
ivanlau
| 2021-12-17T10:33:13Z | 13,130 | 16 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:common_language",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: common_language
type: common_language
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.9738386718094919
---
<!-- 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. -->
# language-detection-fine-tuned-on-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [common_language](https://huggingface.co/datasets/common_language) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1886
- Accuracy: 0.9738
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1 | 1.0 | 22194 | 0.1886 | 0.9738 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
### Notebook
[notebook](https://github.com/IvanLauLinTiong/language-detector/blob/main/xlm_roberta_base_commonlanguage_language_detector.ipynb)
|
llange/xlm-roberta-large-spanish-clinical
|
llange
| 2021-12-17T10:27:39Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2112.08754",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# CLIN-X-ES: a pre-trained language model for the Spanish clinical domain
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain" by Lukas Lange, Heike Adel, Jannik Strötgen and Dietrich Klakow.
The paper can be found [here](https://arxiv.org/abs/2112.08754).
In case of questions, please contact the authors as listed on the paper.
Please cite the above paper when reporting, reproducing or extending the results.
@misc{lange-etal-2021-clin-x,
author = {Lukas Lange and
Heike Adel and
Jannik Str{\"{o}}tgen and
Dietrich Klakow},
title = {CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain},
year={2021},
eprint={2112.08754},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2112.08754}
}
## Training details
The model is based on the multilingual XLM-R transformer `(xlm-roberta-large)`, which was trained on 100 languages and showed superior performance in many different tasks across languages and can even outperform monolingual models in certain settings (Conneau et al. 2020).
Even though XLM-R was pre-trained on 53GB of Spanish documents, this was only 2% of the overall training data. To steer this model towards the Spanish clinical domain, we sample documents from the Scielo archive (https://scielo.org/)
and the MeSpEn resources (Villegas et al. 2018). The resulting corpus has a size of 790MB and is highly specific for the clinical domain.
We initialize CLIN-X using the pre-trained XLM-R weights and train masked language modeling (MLM) on the Spanish clinical corpus for 3 epochs which roughly corresponds to 32k steps. This allows researchers and practitioners to address
the Spanish clinical domain with an out-of-the-box tailored model.
## Results for Spanish concept extraction
We apply CLIN-X-ES to five Spanish concept extraction tasks from the clinical domain in a standard sequence labeling architecture similar to Devlin et al. 2019 and compare to a Spanish BERT model called BETO. In addition, we perform experiments with an improved architecture `(+ OurArchitecture)` as described in the paper linked above. The code for our model architecture can be found [here](https://github.com/boschresearch/clin_x).
| | Cantemist | Meddocan | Meddoprof (NER) | Meddoprof (CLASS) | Pharmaconer |
|------------------------------------------|-----------|----------|-----------------|-------------------|-------------|
| BETO (Spanish BERT) | 81.30 | 96.81 | 79.19 | 74.59 | 87.70 |
| CLIN-X (ES) | 83.22 | 97.08 | 79.54 | 76.95 | 90.05 |
| CLIN-X (ES) + OurArchitecture | **88.24** | **98.00** | **81.68** | **80.54** | **92.27** |
### Results for English concept extraction
As the CLIN-X-ES model is based on XLM-R, the model is still multilingual and we demonstrate the positive impact of cross-language domain adaptation by applying this model to five different English sequence labeling tasks from i2b2.
We found that further transfer from related concept extraction is particularly helpful in this cross-language setting. For a detailed description of the transfer process and all other models, we refer to our paper.
| | i2b2 2006 | i2b2 2010 | i2b2 2012 (Concept) | i2b2 2012 (Time) | i2b2 2014 |
|------------------------------------------|-----------|-----------|---------------|---------------|-----------|
| BERT | 94.80 | 85.25 | 76.51 | 75.28 | 94.86 |
| ClinicalBERT | 94.8 | 87.8 | 78.9 | 76.6 | 93.0 |
| CLIN-X (ES) | 95.49 | 87.94 | 79.58 | 77.57 | 96.80 |
| CLIN-X (ES) + OurArchitecture | 98.30 | 89.10 | 80.42 | 78.48 | **97.62** |
| CLIN-X (ES) + OurArchitecture + Transfer | **89.50** | **89.74** | **80.93** | **79.60** | 97.46 |
## Purpose of the project
This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way.
## License
The CLIN-X models are open-sourced under the CC-BY 4.0 license.
See the [LICENSE](LICENSE) file for details.
|
llange/xlm-roberta-large-english-clinical
|
llange
| 2021-12-17T10:27:20Z | 44 | 2 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2112.08754",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# CLIN-X-EN: a pre-trained language model for the English clinical domain
Details on the model, the pre-training corpus and the downstream task performance are given in the paper: "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain" by Lukas Lange, Heike Adel, Jannik Strötgen and Dietrich Klakow.
The paper can be found [here](https://arxiv.org/abs/2112.08754).
In case of questions, please contact the authors as listed on the paper.
Please cite the above paper when reporting, reproducing or extending the results.
@misc{lange-etal-2021-clin-x,
author = {Lukas Lange and
Heike Adel and
Jannik Str{\"{o}}tgen and
Dietrich Klakow},
title = {CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain},
year={2021},
eprint={2112.08754},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2112.08754}
}
## Training details
The model is based on the multilingual XLM-R transformer `(xlm-roberta-large)`, which was trained on 100 languages and showed superior performance in many different tasks across languages and can even outperform monolingual models in certain settings (Conneau et al. 2020).
We train the CLIN-X model on clinical Pubmed abstracts (850MB) filtered
following Haynes et al. (2005). Pubmed is used with the courtesy of the U.S. National Library of Medicine
We initialize CLIN-X using the pre-trained XLM-R weights and train masked language modeling (MLM) on the Spanish clinical corpus for 3 epochs which roughly corresponds to 32k steps. This allows researchers and practitioners to address
the English clinical domain with an out-of-the-box tailored model.
## Results for Spanish concept extraction
We apply CLIN-X-EN to five different English sequence labeling tasks from i2b2 in a standard sequence labeling architecture similar to Devlin et al. 2019 and compare to BERT and ClinicalBERT. In addition, we perform experiments with an improved architecture `(+ OurArchitecture)` as described in the paper linked above. The code for our model architecture can be found [here](https://github.com/boschresearch/clin_x).
| | i2b2 2006 | i2b2 2010 | i2b2 2012 (Concept) | i2b2 2012 (Time) | i2b2 2014 |
|-------------------------------|-----------|-----------|---------------------|------------------|-----------|
| BERT | 94.80 | 82.25 | 76.51 | 75.28 | 94.86 |
| ClinicalBERT | 94.8 | 87.8 | 78.9 | 76.6 | 93.0 |
| CLIN-X (EN) | 96.25 | 88.10 | 79.58 | 77.70 | 96.73 |
| CLIN-X (EN) + OurArchitecture | **98.49** | **89.23** | **80.62** | **78.50** | **97.60** |
## Purpose of the project
This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way.
## License
The CLIN-X models are open-sourced under the CC-BY 4.0 license.
See the [LICENSE](LICENSE) file for details.
|
Souvikcmsa/LogFiBER
|
Souvikcmsa
| 2021-12-17T10:05:05Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2022-03-02T23:29:05Z |
Log FiBER
This model is able to sentence embedding.
|
jamescalam/bert-stsb-gold
|
jamescalam
| 2021-12-17T08:57:06Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Gold-only BERT STSb
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/).
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('bert-stsb-gold')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('bert-stsb-gold')
model = AutoModel.from_pretrained('bert-stsb-gold')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 36,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
|
jamescalam/bert-stsb-cross-encoder
|
jamescalam
| 2021-12-17T08:54:27Z | 1,081 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"text-classification",
"sentence-similarity",
"transformers",
"cross-encoder",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
- cross-encoder
---
# Augmented SBERT STSb
This is a [sentence-transformers](https://www.SBERT.net) cross encoder model.
It is used as a demo model within the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp), for the chapter on [In-domain Data Augmentation with BERT](https://www.pinecone.io/learn/data-augmentation/).
|
tabo/distilbert-base-uncased-finetuned-squad2
|
tabo
| 2021-12-17T07:22:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad2
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-squad2
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: 1.1606
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2306 | 1.0 | 5533 | 1.1557 |
| 0.9535 | 2.0 | 11066 | 1.1260 |
| 0.7629 | 3.0 | 16599 | 1.1606 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
HenryAI/KerasBERTv1
|
HenryAI
| 2021-12-17T03:20:18Z | 6 | 7 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Thanks for checking this out! <br />
This video explains the ideas behind KerasBERT (still very much a work in progress)
https://www.youtube.com/watch?v=J3P8WLAELqk
|
Jeska/BertjeWDialDataALL03
|
Jeska
| 2021-12-16T19:19:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: BertjeWDialDataALL03
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. -->
# BertjeWDialDataALL03
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9459
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 8.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1951 | 1.0 | 1542 | 2.0285 |
| 2.0918 | 2.0 | 3084 | 1.9989 |
| 2.0562 | 3.0 | 4626 | 2.0162 |
| 2.0012 | 4.0 | 6168 | 1.9330 |
| 1.9705 | 5.0 | 7710 | 1.9151 |
| 1.9571 | 6.0 | 9252 | 1.9419 |
| 1.9113 | 7.0 | 10794 | 1.9175 |
| 1.8988 | 8.0 | 12336 | 1.9143 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
airKlizz/mt5-small-wikinewssum-test
|
airKlizz
| 2021-12-16T16:18:08Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-wikinewssum-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-wikinewssum-test
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9354
- Rouge1: 6.8433
- Rouge2: 2.5498
- Rougel: 5.6114
- Rougelsum: 6.353
## 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: 5.6e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 661 | 3.2810 | 6.4161 | 2.403 | 5.3674 | 6.0329 |
| No log | 2.0 | 1322 | 3.1515 | 6.9291 | 2.6826 | 5.6839 | 6.4359 |
| No log | 3.0 | 1983 | 3.0565 | 6.7939 | 2.6113 | 5.6133 | 6.3126 |
| No log | 4.0 | 2644 | 2.9815 | 6.0279 | 2.1637 | 4.9892 | 5.5962 |
| No log | 5.0 | 3305 | 2.9645 | 6.3926 | 2.339 | 5.2716 | 5.9443 |
| 3.9937 | 6.0 | 3966 | 2.9476 | 6.4739 | 2.3615 | 5.3473 | 6.0089 |
| 3.9937 | 7.0 | 4627 | 2.9405 | 6.615 | 2.4309 | 5.4493 | 6.1445 |
| 3.9937 | 8.0 | 5288 | 2.9354 | 6.8433 | 2.5498 | 5.6114 | 6.353 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
mateocolina/xlm-roberta-base-finetuned-marc-en
|
mateocolina
| 2021-12-16T14:39:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
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-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9276
- Mae: 0.5366
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0992 | 1.0 | 235 | 0.9340 | 0.5122 |
| 0.945 | 2.0 | 470 | 0.9276 | 0.5366 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Giannipinelli/xlm-roberta-base-finetuned-marc-en
|
Giannipinelli
| 2021-12-16T14:34:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
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-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9161
- Mae: 0.4634
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1217 | 1.0 | 235 | 0.9396 | 0.4878 |
| 0.9574 | 2.0 | 470 | 0.9161 | 0.4634 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
philschmid/deberta-v3-xsmall-emotion
|
philschmid
| 2021-12-16T12:37:10Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: deberta-v3-xsmall-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.932
---
<!-- 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. -->
# deberta-v3-xsmall-emotion
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1877
- Accuracy: 0.932
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3683 | 1.0 | 500 | 0.8479 | 0.6975 |
| 0.547 | 2.0 | 1000 | 0.2881 | 0.905 |
| 0.2378 | 3.0 | 1500 | 0.2116 | 0.925 |
| 0.1704 | 4.0 | 2000 | 0.1877 | 0.932 |
| 0.1392 | 5.0 | 2500 | 0.1718 | 0.9295 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
rafiulrumy/wav2vec2-large-xlsr-53-demo-colab
|
rafiulrumy
| 2021-12-16T05:09:16Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-53-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-large-xlsr-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7860
- Wer: 1.1067
## 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: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.2273 | 44.42 | 400 | 3.3544 | 1.0 |
| 0.9228 | 88.84 | 800 | 4.7054 | 1.1601 |
| 0.1423 | 133.32 | 1200 | 5.9489 | 1.1578 |
| 0.0751 | 177.74 | 1600 | 5.5939 | 1.1717 |
| 0.0554 | 222.21 | 2000 | 6.1230 | 1.1717 |
| 0.0356 | 266.63 | 2400 | 6.2845 | 1.1613 |
| 0.0288 | 311.11 | 2800 | 6.6109 | 1.2100 |
| 0.0223 | 355.53 | 3200 | 6.5605 | 1.1299 |
| 0.0197 | 399.95 | 3600 | 7.1242 | 1.1682 |
| 0.0171 | 444.42 | 4000 | 7.2452 | 1.1578 |
| 0.0149 | 488.84 | 4400 | 7.4048 | 1.0684 |
| 0.0118 | 533.32 | 4800 | 6.6227 | 1.1172 |
| 0.011 | 577.74 | 5200 | 6.7909 | 1.1566 |
| 0.0095 | 622.21 | 5600 | 6.8088 | 1.1102 |
| 0.0077 | 666.63 | 6000 | 7.4451 | 1.1311 |
| 0.0062 | 711.11 | 6400 | 6.8486 | 1.0777 |
| 0.0051 | 755.53 | 6800 | 6.8812 | 1.1241 |
| 0.0051 | 799.95 | 7200 | 6.9987 | 1.1450 |
| 0.0041 | 844.42 | 7600 | 7.3048 | 1.1323 |
| 0.0044 | 888.84 | 8000 | 6.6644 | 1.1125 |
| 0.0031 | 933.32 | 8400 | 6.6298 | 1.1148 |
| 0.0027 | 977.74 | 8800 | 6.7860 | 1.1067 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
shainahub/covid_qa_distillbert
|
shainahub
| 2021-12-15T19:10:48Z | 20 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
metrics:
- squad_v2 # Example: wer. Use metric id from https://hf.co/metrics
widget:
- text: "What is COVID-19?"
context: "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019.[7] The disease has since spread worldwide, leading to an ongoing pandemic."
- text: "Where was COVID-19 first discovered?"
context: "The first known infections from SARS-CoV-2 were discovered in Wuhan, China. The original source of viral transmission to humans remains unclear, as does whether the virus became pathogenic before or after the spillover event."
- text: "What is Post-COVID syndrome?"
context: "Long COVID, also known as post-COVID-19 syndrome, post-acute sequelae of COVID-19 (PASC), or chronic COVID syndrome (CCS) is a condition characterized by long-term sequelae appearing or persisting after the typical convalescence period of COVID-19. Long COVID can affect nearly every organ system, with sequelae including respiratory system disorders, nervous system and neurocognitive disorders, mental health disorders, metabolic disorders, cardiovascular disorders, gastrointestinal disorders, malaise, fatigue, musculoskeletal pain, and anemia. A wide range of symptoms are commonly reported, including fatigue, headaches, shortness of breath, anosmia (loss of smell), parosmia (distorted smell), muscle weakness, low fever and cognitive dysfunction."
---
<!-- 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 covid_qa_deepset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0976
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2502 | 1.0 | 3880 | 0.1824 |
| 0.2007 | 2.0 | 7760 | 0.1250 |
| 0.1338 | 3.0 | 11640 | 0.0976 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ayham/xlnet_gpt2_summarization_cnn_dailymail
|
Ayham
| 2021-12-15T18:08:27Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: xlnet_gpt2_summarization_cnn_dailymail
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. -->
# xlnet_gpt2_summarization_cnn_dailymail
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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: 8
- 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: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09
|
Jeska
| 2021-12-15T16:50:47Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09
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. -->
# VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly09
This model is a fine-tuned version of [outputDAQonly09/](https://huggingface.co/outputDAQonly09/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4978
- Accuracy: 0.9031
## 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: 1e-05
- 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
- num_epochs: 30.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 330 | 3.9692 | 0.2249 |
| 4.3672 | 2.0 | 660 | 3.1312 | 0.4031 |
| 4.3672 | 3.0 | 990 | 2.5068 | 0.5658 |
| 3.1495 | 4.0 | 1320 | 2.0300 | 0.6600 |
| 2.2491 | 5.0 | 1650 | 1.6517 | 0.7450 |
| 2.2491 | 6.0 | 1980 | 1.3604 | 0.7943 |
| 1.622 | 7.0 | 2310 | 1.1328 | 0.8327 |
| 1.1252 | 8.0 | 2640 | 0.9484 | 0.8611 |
| 1.1252 | 9.0 | 2970 | 0.8212 | 0.8757 |
| 0.7969 | 10.0 | 3300 | 0.7243 | 0.8830 |
| 0.5348 | 11.0 | 3630 | 0.6597 | 0.8867 |
| 0.5348 | 12.0 | 3960 | 0.5983 | 0.8857 |
| 0.3744 | 13.0 | 4290 | 0.5635 | 0.8976 |
| 0.2564 | 14.0 | 4620 | 0.5437 | 0.8985 |
| 0.2564 | 15.0 | 4950 | 0.5124 | 0.9013 |
| 0.1862 | 16.0 | 5280 | 0.5074 | 0.9022 |
| 0.1349 | 17.0 | 5610 | 0.5028 | 0.9049 |
| 0.1349 | 18.0 | 5940 | 0.4876 | 0.9077 |
| 0.0979 | 19.0 | 6270 | 0.4971 | 0.9049 |
| 0.0763 | 20.0 | 6600 | 0.4941 | 0.9022 |
| 0.0763 | 21.0 | 6930 | 0.4957 | 0.9049 |
| 0.0602 | 22.0 | 7260 | 0.4989 | 0.9049 |
| 0.0504 | 23.0 | 7590 | 0.4959 | 0.9040 |
| 0.0504 | 24.0 | 7920 | 0.4944 | 0.9031 |
| 0.0422 | 25.0 | 8250 | 0.4985 | 0.9040 |
| 0.0379 | 26.0 | 8580 | 0.4970 | 0.9049 |
| 0.0379 | 27.0 | 8910 | 0.4949 | 0.9040 |
| 0.0351 | 28.0 | 9240 | 0.4971 | 0.9040 |
| 0.0321 | 29.0 | 9570 | 0.4967 | 0.9031 |
| 0.0321 | 30.0 | 9900 | 0.4978 | 0.9031 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/presentation_sentiment_42
|
aXhyra
| 2021-12-15T13:28:22Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_sentiment_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7175864613336908
---
<!-- 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. -->
# presentation_sentiment_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6491
- F1: 0.7176
## 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: 6.923967812567773e-06
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4391 | 1.0 | 2851 | 0.6591 | 0.6953 |
| 0.6288 | 2.0 | 5702 | 0.6265 | 0.7158 |
| 0.4071 | 3.0 | 8553 | 0.6401 | 0.7179 |
| 0.6532 | 4.0 | 11404 | 0.6491 | 0.7176 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
harshit345/xlsr-53-wav2vec-greek
|
harshit345
| 2021-12-15T13:13:37Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"el",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: el
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: V XLSR Wav2Vec2 Large 53 - greek
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice el
type: common_voice
args: el
metrics:
- name: Test WER
type: wer
value: 18.996669
- name: Test CER
type: cer
value: 5.781874
---
# Wav2Vec2-Large-XLSR-53-greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10 Greek: Single Speaker Speech Dataset](https://www.kaggle.com/bryanpark/greek-single-speaker-speech-dataset).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "el", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
| Reference | Prediction |
| ------------- | ------------- |
| ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ, ΠΟΥ ΜΟΙΆΖΕΙ ΛΕΟΝΤΑΡΆΚΙ ΚΑΙ ΑΕΤΟΥΔΆΚΙ | ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ ΠΟΥ ΜΙΑΣΕ ΛΙΟΝΤΑΡΑΚΉ ΚΑΙ ΑΪΤΟΥΔΆΚΙ |
| ΣΥΝΆΜΑ ΞΕΠΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ, ΔΕΞΙΆ, ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ. | ΣΥΝΆΜΑ ΚΑΙ ΤΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ ΔΕΞΙΆ ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ |
| ΤΑ ΣΥΣΚΕΥΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΥΝΤΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ | ΤΑ ΣΥΣΚΕΦΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΙΔΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ |
| ΑΚΟΛΟΥΘΉΣΕΤΕ ΜΕ! | ΑΚΟΛΟΥΘΉΣΤΕ ΜΕ |
| ΚΑΙ ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΟΝ ΒΡΩ; | Ε ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΙ ΕΒΡΩ |
| ΝΑΙ! ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ | ΝΑΙ ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ |
| ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ. | ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ |
| ΉΛΘΕ ΜΉΝΥΜΑ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΙΛΙΆ; | ΉΛΘΑ ΜΕΊΝΕΙ ΜΕ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΊΛΙΑ |
| ΠΑΡΑΚΆΤΩ, ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ, ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΝΆ ΧΑΜΌΔΕΝΤΡΑ. | ΠΑΡΑΚΆΤΩ ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΡΆ ΧΑΜΌΔΕΝΤΡΑ |
| ΠΡΆΓΜΑΤΙ, ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ | ΠΡΆΓΜΑΤΗ ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ |
## Evaluation
The model can be evaluated as follows on the greek test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "el", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # TODO: adapt this list to include all special characters you removed from the data
normalize_greek_letters = {"ς": "σ"}
# normalize_greek_letters = {"ά": "α", "έ": "ε", "ί": "ι", 'ϊ': "ι", "ύ": "υ", "ς": "σ", "ΐ": "ι", 'ϋ': "υ", "ή": "η", "ώ": "ω", 'ό': "ο"}
remove_chars_greek = {"a": "", "h": "", "n": "", "g": "", "o": "", "v": "", "e": "", "r": "", "t": "", "«": "", "»": "", "m": "", '́': '', "·": "", "’": "", '´': ""}
replacements = {**normalize_greek_letters, **remove_chars_greek}
resampler = {
48_000: torchaudio.transforms.Resample(48_000, 16_000),
44100: torchaudio.transforms.Resample(44100, 16_000),
32000: torchaudio.transforms.Resample(32000, 16_000)
}
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
for key, value in replacements.items():
batch["sentence"] = batch["sentence"].replace(key, value)
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler[sampling_rate](speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]])))
```
**Test Result**: 18.996669 %
## Training
The Common Voice train dataset was used for training. Also all of `CSS10 Greek` was used using the normalized transcripts.
During text preprocessing letter `ς` is normalized to `σ` the reason is that both letters sound the same with `ς` only used as the ending character of words. So, the change can be mapped up to proper dictation easily. I tried removing all accents from letters as well that improved `WER` significantly. The model was reaching `17%` WER easily without having converged. However, the text preprocessing needed to do after to fix transcrtiptions would be more complicated. A language model should fix things easily though. Another thing that could be tried out would be to change all of `ι`, `η` ... etc to a single character since all sound the same. similar for `o` and `ω` these should help the acoustic model part significantly since all these characters map to the same sound. But further text normlization would be needed.
|
raphaelmerx/marian-finetuned-en-map
|
raphaelmerx
| 2021-12-15T12:54:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: marian-finetuned-en-map
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. -->
# marian-finetuned-en-map
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-map](https://huggingface.co/Helsinki-NLP/opus-mt-en-map) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0542
- eval_bleu: 30.0673
- eval_runtime: 870.8596
- eval_samples_per_second: 14.467
- eval_steps_per_second: 0.226
- epoch: 2.29
- step: 17104
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
MMG/bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad
|
MMG
| 2021-12-15T12:03:20Z | 32 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"es",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- squad_es
model-index:
- name: bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad
results: []
language:
- es
---
<!-- 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-spanish-wwm-cased-finetuned-sqac-finetuned-squad
This model is a fine-tuned version of [MMG/bert-base-spanish-wwm-cased-finetuned-sqac](https://huggingface.co/MMG/bert-base-spanish-wwm-cased-finetuned-sqac) on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5325
- {'exact_match': 60.30274361400189, 'f1': 77.01962587890856}
## 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
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/presentation_hate_1234567
|
aXhyra
| 2021-12-15T11:31:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_hate_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7679568806891273
---
<!-- 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. -->
# presentation_hate_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8438
- F1: 0.7680
## 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: 5.436235805743952e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1234567
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6027 | 1.0 | 282 | 0.5186 | 0.7209 |
| 0.3537 | 2.0 | 564 | 0.4989 | 0.7619 |
| 0.0969 | 3.0 | 846 | 0.6405 | 0.7697 |
| 0.0514 | 4.0 | 1128 | 0.8438 | 0.7680 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/presentation_hate_31415
|
aXhyra
| 2021-12-15T11:24:57Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_hate_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7729508817074093
---
<!-- 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. -->
# presentation_hate_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8632
- F1: 0.7730
## 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: 5.436235805743952e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.363 | 1.0 | 282 | 0.4997 | 0.7401 |
| 0.2145 | 2.0 | 564 | 0.5071 | 0.7773 |
| 0.1327 | 3.0 | 846 | 0.7109 | 0.7645 |
| 0.0157 | 4.0 | 1128 | 0.8632 | 0.7730 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/presentation_hate_42
|
aXhyra
| 2021-12-15T11:18:17Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_hate_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7692074096568478
---
<!-- 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. -->
# presentation_hate_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8711
- F1: 0.7692
## 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: 5.436235805743952e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5207 | 1.0 | 282 | 0.4815 | 0.7513 |
| 0.3047 | 2.0 | 564 | 0.5557 | 0.7510 |
| 0.2335 | 3.0 | 846 | 0.6627 | 0.7585 |
| 0.0056 | 4.0 | 1128 | 0.8711 | 0.7692 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/presentation_emotion_31415
|
aXhyra
| 2021-12-15T10:41:54Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: presentation_emotion_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7148501877297316
---
<!-- 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. -->
# presentation_emotion_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1243
- F1: 0.7149
## 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: 5.18796906442746e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.73 | 1.0 | 408 | 0.8206 | 0.6491 |
| 0.3868 | 2.0 | 816 | 0.7733 | 0.7230 |
| 0.0639 | 3.0 | 1224 | 0.9962 | 0.7101 |
| 0.0507 | 4.0 | 1632 | 1.1243 | 0.7149 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.