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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnn-wei0
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 24.2324
---
<!-- 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. -->
# t5-small-finetuned-cnn-wei0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7149
- Rouge1: 24.2324
- Rouge2: 11.7178
- Rougel: 20.0508
- Rougelsum: 22.8698
- Gen Len: 19.0
## 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: 2e-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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9068 | 1.0 | 4786 | 1.7149 | 24.2324 | 11.7178 | 20.0508 | 22.8698 | 19.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnn-wei1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 41.1796
---
<!-- 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. -->
# t5-small-finetuned-cnn-wei1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6819
- Rouge1: 41.1796
- Rouge2: 18.9426
- Rougel: 29.2338
- Rougelsum: 38.4087
- Gen Len: 72.7607
## 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: 4e-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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8582 | 1.0 | 23927 | 1.6819 | 41.1796 | 18.9426 | 29.2338 | 38.4087 | 72.7607 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei0
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 25.7398
---
<!-- 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. -->
# t5-small-finetuned-xsum-wei0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6289
- Rouge1: 25.7398
- Rouge2: 6.1361
- Rougel: 19.8262
- Rougelsum: 19.8284
- Gen Len: 18.7984
## 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.858 | 1.0 | 1701 | 2.6289 | 25.7398 | 6.1361 | 19.8262 | 19.8284 | 18.7984 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
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} | 0 | null | 20% of the training data
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 27.5875
---
<!-- 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. -->
# t5-small-finetuned-xsum-wei1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5287
- Rouge1: 27.5875
- Rouge2: 7.4083
- Rougel: 21.5654
- Rougelsum: 21.5716
- Gen Len: 18.8205
## 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: 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7677 | 1.0 | 3401 | 2.5441 | 27.4235 | 7.2208 | 21.3535 | 21.3636 | 18.8311 |
| 2.735 | 2.0 | 6802 | 2.5287 | 27.5875 | 7.4083 | 21.5654 | 21.5716 | 18.8205 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 29.2287
---
<!-- 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. -->
# t5-small-finetuned-xsum-wei2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4131
- Rouge1: 29.2287
- Rouge2: 8.4073
- Rougel: 23.0934
- Rougelsum: 23.0954
- Gen Len: 18.8236
## 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: 4e-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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.633 | 1.0 | 17004 | 2.4131 | 29.2287 | 8.4073 | 23.0934 | 23.0954 | 18.8236 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
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} | 0 | null | ---
language: fa
license: apache-2.0
tags:
- farsi
- persian
---
# GPT2-Persian
bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:
1. The context size is reduced from 1024 to 256 sub words in order to make the training affordable
2. Instead of BPE, google sentence piece tokenizor is used for tokenization.
3. The training dataset only include Persian text. All non-persian characters are replaced with especial tokens (e.g [LAT], [URL], [NUM])
Please refer to this [blog post](https://medium.com/@khashei/a-not-so-dangerous-ai-in-the-persian-language-39172a641c84) for further detail.
Also try the model [here](https://huggingface.co/bolbolzaban/gpt2-persian?text=%D8%AF%D8%B1+%DB%8C%DA%A9+%D8%A7%D8%AA%D9%81%D8%A7%D9%82+%D8%B4%DA%AF%D9%81%D8%AA+%D8%A7%D9%86%DA%AF%DB%8C%D8%B2%D8%8C+%D9%BE%DA%98%D9%88%D9%87%D8%B4%DA%AF%D8%B1%D8%A7%D9%86) or on [Bolbolzaban.com](http://www.bolbolzaban.com/text).
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline, AutoTokenizer, GPT2LMHeadModel
tokenizer = AutoTokenizer.from_pretrained('bolbolzaban/gpt2-persian')
model = GPT2LMHeadModel.from_pretrained('bolbolzaban/gpt2-persian')
generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':256})
sample = generator('در یک اتفاق شگفت انگیز، پژوهشگران')
```
If you are using Tensorflow import TFGPT2LMHeadModel instead of GPT2LMHeadModel.
## Fine-tuning
Find a basic fine-tuning example on this [Github Repo](https://github.com/khashei/bolbolzaban-gpt2-persian).
## Special Tokens
gpt-persian is trained for the purpose of research on Persian poetry. Because of that all english words and numbers are replaced with special tokens and only standard Persian alphabet is used as part of input text. Here is one example:
Original text: اگر آیفون یا آیپد شما دارای سیستم عامل iOS 14.3 یا iPadOS 14.3 یا نسخههای جدیدتر باشد
Text used in training: اگر آیفون یا آیپد شما دارای سیستم عامل [LAT] [NUM] یا [LAT] [NUM] یا نسخههای جدیدتر باشد
Please consider normalizing your input text using [Hazm](https://github.com/sobhe/hazm) or similar libraries and ensure only Persian characters are provided as input.
If you want to use classical Persian poetry as input use [BOM] (begining of mesra) at the beginning of each verse (مصرع) followed by [EOS] (end of statement) at the end of each couplet (بیت).
See following links for example:
[[BOM] توانا بود](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF)
[[BOM] توانا بود هر که دانا بود [BOM]](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D)
[[BOM] توانا بود هر که دانا بود [BOM] ز دانش دل پیر](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D+%D8%B2+%D8%AF%D8%A7%D9%86%D8%B4+%D8%AF%D9%84+%D9%BE%DB%8C%D8%B1)
[[BOM] توانا بود هر که دانا بود [BOM] ز دانش دل پیربرنا بود [EOS]](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D+%D8%B2+%D8%AF%D8%A7%D9%86%D8%B4+%D8%AF%D9%84+%D9%BE%DB%8C%D8%B1%D8%A8%D8%B1%D9%86%D8%A7+%D8%A8%D9%88%D8%AF++%5BEOS%5D)
If you like to know about structure of classical Persian poetry refer to these [blog posts](https://medium.com/@khashei).
## Acknowledgment
This project is supported by Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
## Citation and Reference
Please reference "bolbolzaban.com" website if you are using gpt2-persian in your research or commertial application.
## Contacts
Please reachout on [Linkedin](https://www.linkedin.com/in/khashei/) or [Telegram](https://t.me/khasheia) if you have any question or need any help to use the model.
Follow [Bolbolzaban](http://bolbolzaban.com/about) on [Twitter](https://twitter.com/bolbol_zaban), [Telegram](https://t.me/bolbol_zaban) or [Instagram](https://www.instagram.com/bolbolzaban/) |
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
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} | 37 | null | ---
tags:
- conversational
---
# Personal DialoGPT Model |
Dean/summarsiation | [] | null | {
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} | 0 | null | ---
language: en
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distil-wav2vec2-adult-child-cls-37m
results: []
---
# DistilWav2Vec2 Adult/Child Speech Classifier 37M
DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a distilled version of [wav2vec2-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-adult-child-cls) on a private adult/child speech classification dataset.
This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------- | ------- | ----------- | ----------------------------------------- |
| `distil-wav2vec2-adult-child-cls-37m` | 37M | wav2vec 2.0 | Adult/Child Speech Classification Dataset |
## Evaluation Results
The model achieves the following results on evaluation:
| Dataset | Loss | Accuracy | F1 |
| --------------------------------- | ------ | -------- | ------ |
| Adult/Child Speech Classification | 0.1431 | 95.89% | 0.9624 |
## 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
- `gradient_accumulation_steps`: 4
- `total_train_batch_size`: 128
- `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
- `lr_scheduler_type`: linear
- `lr_scheduler_warmup_ratio`: 0.1
- `num_epochs`: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
| 0.2586 | 1.0 | 96 | 0.2257 | 0.9298 | 0.9363 |
| 0.1917 | 2.0 | 192 | 0.1743 | 0.9460 | 0.9500 |
| 0.1568 | 3.0 | 288 | 0.1701 | 0.9511 | 0.9545 |
| 0.0965 | 4.0 | 384 | 0.1501 | 0.9548 | 0.9584 |
| 0.1179 | 5.0 | 480 | 0.1431 | 0.9589 | 0.9624 |
## Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
## Authors
DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/). All computation and development are done on Kaggle.
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3 |
DecafNosebleed/DialoGPT-small-ScaraBot | [
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} | 15 | 2022-02-24T05:56:43Z | ---
language: en
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distil-wav2vec2-adult-child-cls-52m
results: []
---
# DistilWav2Vec2 Adult/Child Speech Classifier 52M
DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a distilled version of [wav2vec2-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-adult-child-cls) on a private adult/child speech classification dataset.
This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------- | ------- | ----------- | ----------------------------------------- |
| `distil-wav2vec2-adult-child-cls-52m` | 52M | wav2vec 2.0 | Adult/Child Speech Classification Dataset |
## Evaluation Results
The model achieves the following results on evaluation:
| Dataset | Loss | Accuracy | F1 |
| --------------------------------- | ------ | -------- | ------ |
| Adult/Child Speech Classification | 0.1301 | 96.03% | 0.9639 |
## 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
- `gradient_accumulation_steps`: 4
- `total_train_batch_size`: 128
- `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
- `lr_scheduler_type`: linear
- `lr_scheduler_warmup_ratio`: 0.1
- `num_epochs`: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
| 0.212 | 1.0 | 96 | 0.1561 | 0.9561 | 0.9596 |
| 0.1523 | 2.0 | 192 | 0.1408 | 0.9575 | 0.9616 |
| 0.0844 | 3.0 | 288 | 0.1301 | 0.9603 | 0.9639 |
## Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
## Authors
DistilWav2Vec2 Adult/Child Speech Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Kaggle.
## Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
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} | 0 | null | ---
language: en
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distil-wav2vec2-xls-r-adult-child-cls-64m
results: []
---
# DistilWav2Vec2 XLS-R Adult/Child Speech Classifier 64M
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a distilled version of [wav2vec2-xls-r-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-xls-r-adult-child-cls) on a private adult/child speech classification dataset.
This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------------- | ------- | ----- | ----------------------------------------- |
| `distil-wav2vec2-xls-r-adult-child-cls-64m` | 64M | XLS-R | Adult/Child Speech Classification Dataset |
## Evaluation Results
The model achieves the following results on evaluation:
| Dataset | Loss | Accuracy | F1 |
| --------------------------------- | ------ | -------- | ------ |
| Adult/Child Speech Classification | 0.2571 | 93.86% | 0.9425 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- `learning_rate`: 3e-05
- `train_batch_size`: 16
- `eval_batch_size`: 16
- `seed`: 42
- `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_ratio`: 0.1
- `num_epochs`: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
| 0.5509 | 1.0 | 191 | 0.3685 | 0.9086 | 0.9131 |
| 0.4543 | 2.0 | 382 | 0.3113 | 0.9247 | 0.9285 |
| 0.409 | 3.0 | 573 | 0.2723 | 0.9372 | 0.9418 |
| 0.3024 | 4.0 | 764 | 0.2786 | 0.9381 | 0.9417 |
| 0.3103 | 5.0 | 955 | 0.2571 | 0.9386 | 0.9425 |
## Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
## Authors
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io/). All computation and development are done on Kaggle.
## Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0 |
DecafNosebleed/scarabot-model | [
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 6 | null | ---
language: en
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distil-wav2vec2-xls-r-adult-child-cls-89m
results: []
---
# DistilWav2Vec2 XLS-R Adult/Child Speech Classifier 89M
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a distilled version of [wav2vec2-xls-r-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-xls-r-adult-child-cls) on a private adult/child speech classification dataset.
This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------------------------- | ------- | ----- | ----------------------------------------- |
| `distil-wav2vec2-xls-r-adult-child-cls-89m` | 89M | XLS-R | Adult/Child Speech Classification Dataset |
## Evaluation Results
The model achieves the following results on evaluation:
| Dataset | Loss | Accuracy | F1 |
| --------------------------------- | ------ | -------- | ------ |
| Adult/Child Speech Classification | 0.3048 | 93.54% | 0.9420 |
## 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
- `gradient_accumulation_steps`: 4
- `total_train_batch_size`: 128
- `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
- `lr_scheduler_type`: linear
- `lr_scheduler_warmup_ratio`: 0.1
- `num_epochs`: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
| :-----------: | :---: | :--: | :-------------: | :------: | :----: |
| 0.7711 | 1.0 | 96 | 0.5413 | 0.9017 | 0.9156 |
| 0.5551 | 2.0 | 192 | 0.4627 | 0.9164 | 0.9272 |
| 0.4166 | 3.0 | 288 | 0.3832 | 0.9261 | 0.9352 |
| 0.3928 | 4.0 | 384 | 0.3242 | 0.9331 | 0.9406 |
| 0.3622 | 5.0 | 480 | 0.3048 | 0.9354 | 0.9420 |
## Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
## Authors
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Kaggle.
## Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Declan/Reuters_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | # [models/cnstd](models/cnstd)
存放 [cnstd](https://github.com/breezedeus/cnstd) 中使用的模型。
# [models/cnocr](models/cnocr)
存放 [cnocr](https://github.com/breezedeus/cnocr) 中使用的模型。
|
Declan/test_model | [] | null | {
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} | 0 | null | ---
tags:
- conversational
---
# RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
|
DeepPavlov/bert-base-multilingual-cased-sentence | [
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"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
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} | 140 | null | ---
language: en
datasets: COCA
---
# docusco-bert
## Model description
**docusco-bert** is a fine-tuned BERT model that is ready to use for **token classification**. The model was trained on data sampled from the Corpus of Contemporary American English ([COCA](https://www.english-corpora.org/coca/)) and classifies tokens and token sequences according to a system developed for the [**DocuScope**](https://www.cmu.edu/dietrich/english/research-and-publications/docuscope.html) dictionary-based tagger. Descriptions of the categories are included in a table below.
## About DocuScope
DocuScope is a dicitonary-based tagger that has been developed at Carnegie Mellon University by **David Kaufer** and **Suguru Ishizaki** since the early 2000s. Its categories are rhetorical in their orientation (as opposed to part-of-speech tags, for example, which are morphosyntactic).
DocuScope has been been used in [a wide variety of studies](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=docuscope&btnG=). Here, for example, is [a short analysis of King Lear](https://graphics.cs.wisc.edu/WP/vep/2017/02/14/guest-post-data-mining-king-lear/), and here is [a published study of Tweets](https://journals.sagepub.com/doi/full/10.1177/2055207619844865).
## Intended uses & limitations
#### How to use
The model was trained on data with tags formatted using [IOB](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)), like those used in common tasks like Named Entity Recogition (NER). Thus, you can use this model with a Transformers NER *pipeline*.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("browndw/docusco-bert")
model = AutoModelForTokenClassification.from_pretrained("browndw/docusco-bert")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Globalization is the process of interaction and integration among people, companies, and governments worldwide."
ds_results = nlp(example)
print(ds_results)
```
#### Limitations and bias
This model is limited by its training dataset of American English texts. Moreover, the current version is trained on only a small subset of the corpus. The goal is to train later versions on more data, which should increase accuracy.
## Training data
This model was fine-tuned on data from the Corpus of Contemporary American English ([COCA](https://www.english-corpora.org/coca/)). The training data contain chunks of text randomly sampled of 5 text-types: Academic, Fiction, Magazine, News, and Spoken.
Typically, BERT models are trained on sentence segments. However, DocuScope tags can span setences. Thus, data were split into chunks that don't split **B + I** sequences and end with sentence-final punctuation marks (i.e., period, quesiton mark or exclamaiton point).
Additionally, the order of the chunks was randomized prior to sampling, and statified sampling was used to provide enough training data for low-frequency caegories. The resulting training data consist of:
* 21,460,177 tokens
* 15,796,305 chunks
The specific counts for each category appear in the following table.
Category|Count
-|-
O|3528038
Syntactic Complexity|2032808
Character|1413771
Description|1224744
Narrative|1159201
Negative|651012
Academic Terms|620932
Interactive|594908
Information Exposition|578228
Positive|463914
Force Stressed|432631
Information Topics|394155
First Person|249744
Metadiscourse Cohesive|240822
Strategic|238255
Public Terms|234213
Reasoning|213775
Information Place|187249
Information States|173146
Information ReportVerbs|119092
Confidence High|112861
Confidence Hedged|110008
Future|96101
Inquiry|94995
Contingent|94860
Information Change|89063
Metadiscourse Interactive|84033
Updates|81424
Citation|71241
Facilitate|50451
Uncertainty|35644
Academic WritingMoves|29352
Information ChangePositive|28475
Responsibility|25362
Citation Authority|22414
Information ChangeNegative|15612
Confidence Low|2876
Citation Hedged|895
-|-
Total|15796305
## Training procedure
This model was trained on a single 2.3 GHz Dual-Core Intel Core i5 with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805).
## Eval results
### Overall
metric|test
-|-
f1 |.927
accuracy |.943
### By category
category|precision|recall|f1-score|support
-|-|-|-|-
AcademicTerms|0.91|0.92|0.92|486399
AcademicWritingMoves|0.76|0.82|0.79|20017
Character|0.94|0.95|0.94|1260272
Citation|0.92|0.94|0.93|50812
CitationAuthority|0.86|0.88|0.87|17798
CitationHedged|0.91|0.94|0.92|632
ConfidenceHedged|0.94|0.96|0.95|90393
ConfidenceHigh|0.92|0.94|0.93|113569
ConfidenceLow|0.79|0.81|0.80|2556
Contingent|0.92|0.94|0.93|81366
Description|0.87|0.89|0.88|1098598
Facilitate|0.87|0.90|0.89|41760
FirstPerson|0.96|0.98|0.97|330658
ForceStressed|0.93|0.94|0.93|436188
Future|0.90|0.93|0.92|93365
InformationChange|0.88|0.91|0.89|72813
InformationChangeNegative|0.83|0.85|0.84|12740
InformationChangePositive|0.82|0.86|0.84|22994
InformationExposition|0.94|0.95|0.95|468078
InformationPlace|0.95|0.96|0.96|147688
InformationReportVerbs|0.91|0.93|0.92|95563
InformationStates|0.95|0.95|0.95|139429
InformationTopics|0.90|0.92|0.91|328152
Inquiry|0.85|0.89|0.87|79030
Interactive|0.95|0.96|0.95|602857
MetadiscourseCohesive|0.97|0.98|0.98|195548
MetadiscourseInteractive|0.92|0.94|0.93|73159
Narrative|0.92|0.94|0.93|1023452
Negative|0.88|0.89|0.88|645810
Positive|0.87|0.89|0.88|409775
PublicTerms|0.91|0.92|0.91|184108
Reasoning|0.93|0.95|0.94|169208
Responsibility|0.83|0.87|0.85|21819
Strategic|0.88|0.90|0.89|193768
SyntacticComplexity|0.95|0.96|0.96|1635918
Uncertainty|0.87|0.91|0.89|33684
Updates|0.91|0.93|0.92|77760
-|-|-|-|-
micro avg|0.92|0.93|0.93|10757736
macro avg|0.90|0.92|0.91|10757736
weighted avg|0.92|0.93|0.93|10757736
## DocuScope Category Descriptions
Category (Cluster)|Description|Examples
-|-|-
Academic Terms|Abstract, rare, specialized, or disciplinary-specific terms that are indicative of informationally dense writing|*market price*, *storage capacity*, *regulatory*, *distribution*
Academic Writing Moves|Phrases and terms that indicate academic writing moves, which are common in research genres and are derived from the work of Swales (1981) and Cotos et al. (2015, 2017)|*in the first section*, *the problem is that*, *payment methodology*, *point of contention*
Character|References multiple dimensions of a character or human being as a social agent, both individual and collective|*Pauline*, *her*, *personnel*, *representatives*
Citation|Language that indicates the attribution of information to, or citation of, another source.|*according to*, *is proposing that*, *quotes from*
Citation Authorized|Referencing the citation of another source that is represented as true and not arguable|*confirm that*, *provide evidence*, *common sense*
Citation Hedged|Referencing the citation of another source that is presented as arguable|*suggest that*, *just one opinion*
Confidence Hedged|Referencing language that presents a claim as uncertain|*tends to get*, *maybe*, *it seems that*
Confidence High|Referencing language that presents a claim with certainty|*most likely*, *ensure that*, *know that*, *obviously*
Confidence Low|Referencing language that presents a claim as extremely unlikely|*unlikely*, *out of the question*, *impossible*
Contingent|Referencing contingency, typically contingency in the world, rather than contingency in one's knowledge|*subject to*, *if possible*, *just in case*, *hypothetically*
Description|Language that evokes sights, sounds, smells, touches and tastes, as well as scenes and objects|*stay quiet*, *gas-fired*, *solar panels*, *soft*, *on my desk*
Facilitate|Language that enables or directs one through specific tasks and actions|*let me*, *worth a try*, *I would suggest*
First Person|This cluster captures first person.|*I*, *as soon as I*, *we have been*
Force Stressed|Language that is forceful and stressed, often using emphatics, comparative forms, or superlative forms|*really good*, *the sooner the better*, *necessary*
Future|Referencing future actions, states, or desires|*will be*, *hope to*, *expected changes*
Information Change|Referencing changes of information, particularly changes that are more neutral|*changes*, *revised*, *growth*, *modification to*
Information Change Negative|Referencing negative change|*going downhill*, *slow erosion*, *get worse*
Information Change Positive|Referencing positive change|*improving*, *accrued interest*, *boost morale*
Information Exposition|Information in the form of expository devices, or language that describes or explains, frequently in regards to quantities and comparisons|*final amount*, *several*, *three*, *compare*, *80%*
Information Place|Language designating places|*the city*, *surrounding areas*, *Houston*, *home*
Information Report Verbs|Informational verbs and verb phrases of reporting|*report*, *posted*, *release*, *point out*
Information States|Referencing information states, or states of being|*is*, *are*, *existing*, *been*
Information Topics|Referencing topics, usually nominal subjects or objects, that indicate the “aboutness” of a text|*time*, *money*, *stock price*, *phone interview*
Inquiry|Referencing inquiry, or language that points to some kind of inquiry or investigation|*find out*, *let me know if you have any questions*, *wondering if*
Interactive|Addresses from the author to the reader or from persons in the text to other persons. The address comes in the language of everyday conversation, colloquy, exchange, questions, attention-getters, feedback, interactive genre markers, and the use of the second person.|*can you*, *thank you for*, *please see*, *sounds good to me*
Metadiscourse Cohesive|The use of words to build cohesive markers that help the reader navigate the text and signal linkages in the text, which are often additive or contrastive|*or*, *but*, *also*, *on the other hand*, *notwithstanding*, *that being said*
Metadiscourse Interactive|The use of words to build cohesive markers that interact with the reader|*I agree*, *let’s talk*, *by the way*
Narrative|Language that involves people, description, and events extending in time|*today*, *tomorrow*, *during the*, *this weekend*
Negative|Referencing dimensions of negativity, including negative acts, emotions, relations, and values|*does not*, *sorry for*, *problems*, *confusion*
Positive|Referencing dimensions of positivity, including actions, emotions, relations, and values|*thanks*, *approval*, *agreement*, *looks good*
Public Terms|Referencing public terms, concepts from public language, media, the language of authority, institutions, and responsibility|*discussion*, *amendment*, *corporation*, *authority*, *settlement*
Reasoning|Language that has a reasoning focus, supporting inferences about cause, consequence, generalization, concession, and linear inference either from premise to conclusion or conclusion to premise|*because*, *therefore*, *analysis*, *even if*, *as a result*, *indicating that*
Responsibility|Referencing the language of responsibility|*supposed to*, *requirements*, *obligations*
Strategic|This dimension is active when the text structures strategies activism, advantage-seeking, game-playing cognition, plans, and goal-seeking.|*plan*, *trying to*, *strategy*, *decision*, *coordinate*, *look at the*
Syntactic Complexity|The features in this category are often what are called “function words,” like determiners and prepositions.|*the*, *to*, *for*, *in*, *a lot of*
Uncertainty|References uncertainty, when confidence levels are unknown|*kind of*, *I have no idea*, *for some reason*
Updates|References updates that anticipate someone searching for information and receiving it|*already*, *a new*, *now that*, *here are some*
### BibTeX entry and citation info
```
@incollection{ishizaki2012computer,
title = {Computer-aided rhetorical analysis},
author = {Ishizaki, Suguru and Kaufer, David},
booktitle= {Applied natural language processing: Identification, investigation and resolution},
pages = {276--296},
year = {2012},
publisher= {IGI Global},
url = {https://www.igi-global.com/chapter/content/61054}
}
```
```
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
DeepPavlov/rubert-base-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers",
"has_space"
] | feature-extraction | {
"architectures": [
"BertModel"
],
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}
} | 46,991 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobertpt-all-finetuned-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. -->
# biobertpt-all-finetuned-ner
This model is a fine-tuned version of [pucpr/biobertpt-all](https://huggingface.co/pucpr/biobertpt-all) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3721
- Precision: 0.0179
- Recall: 0.0149
- F1: 0.0163
- Accuracy: 0.6790
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 1 | 2.7864 | 0.0091 | 0.0448 | 0.0152 | 0.3339 |
| No log | 2.0 | 2 | 2.5096 | 0.0097 | 0.0149 | 0.0118 | 0.6292 |
| No log | 3.0 | 3 | 2.3721 | 0.0179 | 0.0149 | 0.0163 | 0.6790 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] | text-classification | {
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"XLMRobertaForSequenceClassification"
],
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}
} | 227 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4779
- Wer: 0.3453
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4307 | 4.0 | 500 | 1.4129 | 0.9980 |
| 0.626 | 8.0 | 1000 | 0.4605 | 0.4499 |
| 0.2199 | 12.0 | 1500 | 0.4457 | 0.3898 |
| 0.1303 | 16.0 | 2000 | 0.4418 | 0.3771 |
| 0.0851 | 20.0 | 2500 | 0.4647 | 0.3548 |
| 0.0604 | 24.0 | 3000 | 0.4603 | 0.3499 |
| 0.0461 | 28.0 | 3500 | 0.4779 | 0.3453 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
DeltaHub/adapter_t5-3b_cola | [
"pytorch",
"transformers"
] | null | {
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} | 3 | null | # Work In Progress
# How to use?
This model can only generate regular text.
# Training details
We continued the pre-training of [gpt2](https://huggingface.co/gpt2).
Dataset:[Natural_Questions_HTML_reduced_all](https://huggingface.co/datasets/SaulLu/Natural_Questions_HTML_reduced_all)
100% of the examples were just plain text.
Training example:
```
start up firms to succeed.[4] Firms like power companies, cable television companies and wireless communication companies with large start up costs fall within this category. A company wishing to enter such industries must have the financial ability to spend millions of dollars before starting operations and generating any revenue.[5] Similarly established firms also have a competitive advantage over new firms. An established firm threatened by a new competitor can lower prices to drive out the competition. Microsoft is a firm that has substantial pricing or market power due to technological superiority in its design and production processes.[4] Finally government created barriers to entry can be a source of market power. A prime example are patents granted to pharmaceutical companies. These patents give the drug companies a virtual monopoly in the protected product for the term of the patent.
Measurement[edit]
Concentration ratios are the most common measures of market power.[6] The four-firm concentration ratio measures the percentage of total industry output attributable to the top four companies. For monopolies the four firm ratio is 100 per cent while the ratio is zero for perfect competition.[7] The four firm concentration domestic (U.S) ratios for cigarettes is 93%; for automobiles, 84% and for beer, 85%.[8]
Another measure of concentration is the Herfindahl-Hirschman Index (HHI) which is calculated by "summing the squares of the percentage market shares of all participants in the market".[8] The HHI index for perfect competition is zero; for monopoly, 10,000.
U.S. courts almost never consider a firm to possess market power if it has a market share of less than 50 percent.[9]
Elasticity of demand[edit]
Market power is the ability to raise price above marginal cost (MC) and earn a positive profit.[10] The degree to which a firm can raise price (P) above marginal cost depends on the shape of the demand curve at the profit maximizing output.[10] That is, elasticity is the critical factor in determining market power. The relationship between market power and the price elasticity of demand (PED) can be summarized by the equation:
P M C = P E D 1 + P E D. {\displaystyle {\frac {P}{MC}}={\frac {PED}{1+PED}}.}
Note that PED will be negative, so the ratio is always greater than one. The higher the P/MC ratio, the more market power the firm possesses. As PED increases in magnitude, the P/MC ratio approaches one, and market power approaches zero.[11] The equation is derived from the monopolist pricing rule:
P − M C P = − 1 P E D. {\displaystyle {\frac {P-MC}{P}}=-{\frac {1}{PED}}.}
Nobel Memorial Prize[edit]
Jean Tirole was awarded the 2014 Nobel Memorial Prize in Economic Sciences for his analysis of market power and economic regulation.
See also[edit]
Bargaining power
Imperfect competition
Market concentration
Natural monopoly
Predatory pricing
Price discrimination
Dominance (economics)
References[edit]
Jump up ^ Vatiero Massimiliano (2010). "The Ordoliberal notion of market power: an institutionalist reassessment". European Competition Journal. 6 (3): 689–707. doi:10.5235/ecj.v6n3.689.
Jump up ^ Vatiero M. (2009), "An Institutionalist Explanation of Market Dominances". World Competition. Law and Economics Review, 32(2):221–226.
Jump up ^ If the power company raised rates the customer either pays the increase or does without power.
^ Jump up to: a b c d e Krugman & Wells, Microeconomics 2d ed. (Worth 2009)
Jump up ^ Often such natural monopolies will also have the benefit of government granted monopolies.
Jump up ^ Samuelson & Nordhaus, Microeconomics, 17th ed. (McGraw-Hill 2001) at 183–184.
Jump up ^ Samuelson & Nordhaus, Microeconomics, 17th ed. (McGraw-Hill 2001) at 183.
^ Jump up to: a b Samuelson & Nordhaus, Microeconomics, 17th ed. (McGraw-Hill 2001) at 184.
Jump up ^ J. Gregory Sidak & Hal J. Singer, Überregulation Without Economics: The World Trade Organization’s Decision in the U.S.-Mexico Arbitration on Telecommunications Services, General Agreement on Trade in Services, GATS, 57 FED. COMM. L.J. 1, 34 (2004), http://www.repository.law.indiana.edu/cgi/viewcontent.cgi?article=1388&context=fclj.
^ Jump up to: a b
```
|
DemangeJeremy/4-sentiments-with-flaubert | [
"pytorch",
"flaubert",
"text-classification",
"fr",
"transformers",
"sentiments",
"french",
"flaubert-large"
] | text-classification | {
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"FlaubertForSequenceClassification"
],
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}
} | 226 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- bshlgrs/autonlp-data-classification
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9522090
## Validation Metrics
- Loss: 0.3541755676269531
- Accuracy: 0.8759671179883946
- Macro F1: 0.5330133182738012
- Micro F1: 0.8759671179883946
- Weighted F1: 0.8482773065757196
- Macro Precision: 0.537738108882869
- Micro Precision: 0.8759671179883946
- Weighted Precision: 0.8241048710814852
- Macro Recall: 0.5316621214820499
- Micro Recall: 0.8759671179883946
- Weighted Recall: 0.8759671179883946
## 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/bshlgrs/autonlp-classification-9522090
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("bshlgrs/autonlp-classification-9522090", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("bshlgrs/autonlp-classification-9522090", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
DheerajPranav/Dialo-GPT-Rick-bot | [] | null | {
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} | 0 | 2021-09-20T08:38:53Z | ---
language: "en"
tags:
- bert
- medical
- clinical
- assertion
- negation
- text-classification
widget:
- text: "Patient denies [entity] SOB [entity]."
---
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
](https://aclanthology.org/2021.nlpmc-1.5/). The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.
The model is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) by Alsentzer et al. and fine-tuned on assertion data from the [2010 i2b2 challenge](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/).
#### How to use the model
You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")
```
The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT(0)`, `ABSENT(1)` or `POSSIBLE(2)`. The entity in question is identified with the special token `[entity]` surrounding it.
Example input and inference:
```
input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
classification = classifier(input)
# [{'label': 'ABSENT', 'score': 0.9842607378959656}]
```
### Cite
When working with the model, please cite our paper as follows:
```bibtex
@inproceedings{van-aken-2021-assertion,
title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?",
author = "van Aken, Betty and
Trajanovska, Ivana and
Siu, Amy and
Mayrdorfer, Manuel and
Budde, Klemens and
Loeser, Alexander",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlpmc-1.5",
doi = "10.18653/v1/2021.nlpmc-1.5"
}
``` |
Dibyaranjan/nl_image_search | [] | null | {
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} | 0 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- librispeech
license: cc-by-4.0
---
## Example ESPnet2 ASR model
### `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3966501
This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### 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 {Enrique Yalta Soplin} 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}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
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 Enrique Yalta Soplin 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}
}
```
|
Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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},
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}
} | 5 | null | # GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from [Urban Dictionary Words And Definitions on Kaggle](https://www.kaggle.com/therohk/urban-dictionary-words-dataset). Data was additionally filtered, normalized, and spell-checked.
## Bias
This model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions. |
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 29 | null | ---
language: "id"
license: "mit"
datasets:
- Indonesian Wikipedia
widget:
- text: "Pulau Dewata sering dikunjungi"
---
# Indonesian GPT2 small model
## Model description
It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness,
we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='cahya/gpt2-small-indonesian-522M')
>>> set_seed(42)
>>> generator("Kerajaan Majapahit adalah", max_length=30, num_return_sequences=5, num_beams=10)
[{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini berdiri pada abad ke-14'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-16. Kerajaan ini berdiri pada abad ke-14'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini berdiri pada abad ke-15'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-16. Kerajaan ini berdiri pada abad ke-15'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini merupakan kelanjutan dari Kerajaan Majapahit yang'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
model_name='cahya/gpt2-small-indonesian-522M'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2Model.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
model_name='cahya/gpt2-small-indonesian-522M'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = TFGPT2Model.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and
a vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens.
|
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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},
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"prefix": null
}
}
} | 38,156 | 2022-02-02T15:26:05Z | ---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2893
- Wer: 0.2713
## 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: 128
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8647 | 14.28 | 200 | 0.2758 | 0.2568 |
| 1.3376 | 28.56 | 400 | 0.2754 | 0.2722 |
| 1.1975 | 42.84 | 600 | 0.2929 | 0.2901 |
| 1.1024 | 57.14 | 800 | 0.2904 | 0.2928 |
| 1.0257 | 71.42 | 1000 | 0.2915 | 0.2823 |
| 0.9628 | 85.7 | 1200 | 0.2936 | 0.2749 |
| 0.9109 | 99.98 | 1400 | 0.2893 | 0.2713 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
} | 4,785,283 | 2022-02-04T14:21:16Z | ---
language:
- tr
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [./checkpoint-1000](https://huggingface.co/./checkpoint-1000) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3282
- Wer: 0.2836
## 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: 96
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.0671 | 2.04 | 200 | 0.3079 | 0.2752 |
| 0.6433 | 4.08 | 400 | 0.2728 | 0.2848 |
| 0.5687 | 6.12 | 600 | 0.2882 | 0.3036 |
| 0.5355 | 8.16 | 800 | 0.2778 | 0.2920 |
| 0.5116 | 10.2 | 1000 | 0.2906 | 0.3014 |
| 0.5313 | 9.16 | 1200 | 0.2984 | 0.3273 |
| 0.4996 | 10.69 | 1400 | 0.3170 | 0.3344 |
| 0.4845 | 12.21 | 1600 | 0.3202 | 0.3634 |
| 0.5092 | 13.74 | 1800 | 0.3167 | 0.3373 |
| 0.4777 | 15.27 | 2000 | 0.3292 | 0.3386 |
| 0.4651 | 16.79 | 2200 | 0.3070 | 0.3427 |
| 0.461 | 18.32 | 2400 | 0.3149 | 0.3561 |
| 0.4481 | 19.85 | 2600 | 0.3292 | 0.3441 |
| 0.4479 | 21.37 | 2800 | 0.3142 | 0.3209 |
| 0.4305 | 22.9 | 3000 | 0.3525 | 0.3547 |
| 0.4254 | 24.43 | 3200 | 0.3414 | 0.3400 |
| 0.4066 | 25.95 | 3400 | 0.3118 | 0.3207 |
| 0.4043 | 27.48 | 3600 | 0.3418 | 0.3483 |
| 0.3985 | 29.01 | 3800 | 0.3254 | 0.3166 |
| 0.3982 | 30.53 | 4000 | 0.3306 | 0.3453 |
| 0.3929 | 32.06 | 4200 | 0.3262 | 0.3229 |
| 0.378 | 33.59 | 4400 | 0.3546 | 0.3336 |
| 0.4062 | 35.11 | 4600 | 0.3174 | 0.3457 |
| 0.3648 | 36.64 | 4800 | 0.3377 | 0.3357 |
| 0.3609 | 38.17 | 5000 | 0.3346 | 0.3520 |
| 0.3483 | 39.69 | 5200 | 0.3350 | 0.3526 |
| 0.3548 | 41.22 | 5400 | 0.3330 | 0.3406 |
| 0.3446 | 42.75 | 5600 | 0.3398 | 0.3372 |
| 0.3346 | 44.27 | 5800 | 0.3449 | 0.3288 |
| 0.3309 | 45.8 | 6000 | 0.3320 | 0.3144 |
| 0.326 | 47.33 | 6200 | 0.3400 | 0.3279 |
| 0.3189 | 48.85 | 6400 | 0.3400 | 0.3150 |
| 0.3165 | 50.38 | 6600 | 0.3359 | 0.2995 |
| 0.3132 | 51.91 | 6800 | 0.3343 | 0.3096 |
| 0.3092 | 53.44 | 7000 | 0.3224 | 0.3029 |
| 0.2995 | 54.96 | 7200 | 0.3205 | 0.2985 |
| 0.304 | 56.49 | 7400 | 0.3523 | 0.3034 |
| 0.2952 | 58.02 | 7600 | 0.3289 | 0.2934 |
| 0.2875 | 59.54 | 7800 | 0.3350 | 0.3008 |
| 0.2868 | 61.07 | 8000 | 0.3537 | 0.3227 |
| 0.2875 | 62.6 | 8200 | 0.3389 | 0.2970 |
| 0.2778 | 64.12 | 8400 | 0.3370 | 0.2960 |
| 0.2706 | 65.65 | 8600 | 0.3250 | 0.2802 |
| 0.2669 | 67.18 | 8800 | 0.3351 | 0.2903 |
| 0.2615 | 68.7 | 9000 | 0.3382 | 0.2989 |
| 0.2563 | 70.23 | 9200 | 0.3312 | 0.2975 |
| 0.2546 | 71.76 | 9400 | 0.3212 | 0.3003 |
| 0.2482 | 73.28 | 9600 | 0.3337 | 0.3091 |
| 0.2504 | 74.81 | 9800 | 0.3308 | 0.3110 |
| 0.2456 | 76.34 | 10000 | 0.3157 | 0.3118 |
| 0.2363 | 77.86 | 10200 | 0.3251 | 0.3144 |
| 0.2319 | 79.39 | 10400 | 0.3253 | 0.3038 |
| 0.2266 | 80.92 | 10600 | 0.3374 | 0.3038 |
| 0.2279 | 82.44 | 10800 | 0.3268 | 0.2964 |
| 0.2231 | 83.97 | 11000 | 0.3278 | 0.2950 |
| 0.2185 | 85.5 | 11200 | 0.3462 | 0.2981 |
| 0.2245 | 87.02 | 11400 | 0.3311 | 0.2895 |
| 0.223 | 88.55 | 11600 | 0.3325 | 0.2877 |
| 0.2121 | 90.08 | 11800 | 0.3337 | 0.2828 |
| 0.2126 | 91.6 | 12000 | 0.3325 | 0.2808 |
| 0.2027 | 93.13 | 12200 | 0.3277 | 0.2820 |
| 0.2058 | 94.66 | 12400 | 0.3308 | 0.2827 |
| 0.1991 | 96.18 | 12600 | 0.3279 | 0.2820 |
| 0.1991 | 97.71 | 12800 | 0.3300 | 0.2822 |
| 0.1986 | 99.24 | 13000 | 0.3285 | 0.2835 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 687 | 2022-01-28T08:43:52Z | ---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: Wav2Vec2 Base Turkish by Cahya
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6.1
type: mozilla-foundation/common_voice_7_0
args: tr
metrics:
- name: Test WER
type: wer
value: 9.437
- name: Test CER
type: cer
value: 3.325
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: tr
metrics:
- name: Test WER
type: wer
value: 8.147
- name: Test CER
type: cer
value: 2.802
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: tr
metrics:
- name: Test WER
type: wer
value: 28.011
- name: Test CER
type: cer
value: 10.66
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: tr
metrics:
- name: Test WER
type: wer
value: 33.62
---
#
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
| | Dataset | WER | CER |
|---|-------------------------------|---------|----------|
| 1 | Common Voice 6.1 | 9.437 | 3.325 |
| 2 | Common Voice 7.0 | 8.147 | 2.802 |
| 3 | Common Voice 8.0 | 8.335 | 2.336 |
| 4 | Speech Recognition Community | 28.011 | 10.66 |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 7.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) 'train', 'validation' and 'other' split were used for training.
- [Media Speech](https://www.openslr.org/108/)
- [Magic Hub](https://magichub.com/)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-06
- train_batch_size: 6
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1224 | 3.45 | 500 | 0.1641 | 0.1396 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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} | 341 | 2021-04-04T16:35:23Z | ---
language: eu
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Basque by Cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice eu
type: common_voice
args: eu
metrics:
- name: Test WER
type: wer
value: 12.44
---
# Wav2Vec2-Large-XLSR-Basque
This is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Basque Common Voice dataset](https://huggingface.co/datasets/common_voice).
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", "eu", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Basque 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", "eu", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
model.to("cuda")
chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\\…\’\ː\'\‹\›\`\´\®\—\→]'
# 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()
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(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"])))
```
**Test Result**: 12.44 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"AlbertForMaskedLM"
],
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}
} | 7,091 | 2021-04-19T13:30:28Z | ---
language: id
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Indonesian with Artificial Voice by Cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice id
type: common_voice
args: id
metrics:
- name: Test WER
type: wer
value: 51.69
---
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
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", "id", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian 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", "id", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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)
# 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"])))
```
**Test Result**: 51.69 %
## Training
The Artificial Common Voice `train`, `validation`, and ... datasets were used for training.
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
(will be available soon)
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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}
}
} | 42,640 | null | ---
language: id
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Indonesian Mix by Cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice id
type: common_voice
args: id
metrics:
- name: Test WER
type: wer
value: 19.36
---
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice) and synthetic voices
generated using [Artificial Common Voicer](https://github.com/cahya-wirawan/artificial-commonvoice), which
again based on Google Text To Speech.
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", "id", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian 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", "id", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
# 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()
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(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"])))
```
**Test Result**: 19.36 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 11,644 | 2021-03-20T06:15:02Z | ---
language: id
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Indonesian by cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice id
type: common_voice
args: id
metrics:
- name: Test WER
type: wer
value: 25.86
---
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
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", "id", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian 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", "id", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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)
# 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"])))
```
**Test Result**: 25.86 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
(will be available soon)
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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} | 8,621,271 | 2021-03-27T12:25:36Z | ---
language: jv
datasets:
- openslr
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Javanese by cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR High quality TTS data for Javanese
type: OpenSLR
args: jv
metrics:
- name: Test WER
type: wer
value: 17.61
---
# Wav2Vec2-Large-XLSR-Javanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [OpenSLR High quality TTS data for Javanese](https://openslr.org/41/).
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, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets.utils.download_manager import DownloadManager
from pathlib import Path
import pandas as pd
def load_dataset_javanese():
urls = [
"https://www.openslr.org/resources/41/jv_id_female.zip",
"https://www.openslr.org/resources/41/jv_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"jv_id_female/wavs",
Path(download_dirs[1])/"jv_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"jv_id_female/line_index.tsv",
Path(download_dirs[1])/"jv_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t', names=["path", "client_id", "sentence"]))
dfs[1] = dfs[1].drop(["client_id"], axis=1)
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_javanese()
test_dataset = dataset['test']
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows or using this
[notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb)
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from datasets.utils.download_manager import DownloadManager
from pathlib import Path
import pandas as pd
def load_dataset_javanese():
urls = [
"https://www.openslr.org/resources/41/jv_id_female.zip",
"https://www.openslr.org/resources/41/jv_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"jv_id_female/wavs",
Path(download_dirs[1])/"jv_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"jv_id_female/line_index.tsv",
Path(download_dirs[1])/"jv_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t', names=["path", "client_id", "sentence"]))
dfs[1] = dfs[1].drop(["client_id"], axis=1)
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_javanese()
test_dataset = dataset['test']
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”_\�]'
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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)
# 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"])))
```
**Test Result**: 17.61 %
## Training
[OpenSLR High quality TTS data for Javanese](https://openslr.org/41/) was used for training.
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb)
and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb)
|
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 3,377,486 | 2021-03-27T12:25:49Z | ---
language: su
datasets:
- openslr
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Sundanese by cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR High quality TTS data for Sundanese
type: OpenSLR
args: su
metrics:
- name: Test WER
type: wer
value: 6.19
---
# Wav2Vec2-Large-XLSR-Sundanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/).
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, load_metric, Dataset
from datasets.utils.download_manager import DownloadManager
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from pathlib import Path
import pandas as pd
def load_dataset_sundanese():
urls = [
"https://www.openslr.org/resources/44/su_id_female.zip",
"https://www.openslr.org/resources/44/su_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"su_id_female/wavs",
Path(download_dirs[1])/"su_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"su_id_female/line_index.tsv",
Path(download_dirs[1])/"su_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_sundanese()
test_dataset = dataset['test']
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio 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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows or using the [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb).
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets.utils.download_manager import DownloadManager
import re
from pathlib import Path
import pandas as pd
def load_dataset_sundanese():
urls = [
"https://www.openslr.org/resources/44/su_id_female.zip",
"https://www.openslr.org/resources/44/su_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"su_id_female/wavs",
Path(download_dirs[1])/"su_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"su_id_female/line_index.tsv",
Path(download_dirs[1])/"su_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_sundanese()
test_dataset = dataset['test']
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”_\�]'
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):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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)
# Preprocessing the datasets.
# We need to read the audio 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"])))
```
**Test Result**: 6.19 %
## Training
[OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/) was used for training.
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)
and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)
|
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 175,983 | 2021-04-22T15:24:32Z | ---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Turkish by Cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice tr
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
value: 14.61
---
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned
[cahya/wav2vec2-large-xlsr-turkish-artificial](https://huggingface.co/cahya/wav2vec2-large-xlsr-turkish-artificial)
model on [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice).
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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish 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", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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()
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(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"])))
```
**Test Result**: 14.61 %
## Training
The Common Voice `train`, `validation`, other and invalidated
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 1,814 | 2021-04-22T05:12:54Z | ---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Turkish with Artificial Voices by Cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice tr
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
value: 66.98
---
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish 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", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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()
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(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"])))
```
**Test Result**: 66.98 %
## Training
The Artificial Common Voice `train`, `validation` is used to fine tune the model
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
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} | 68,305 | 2021-04-18T17:34:05Z | ---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Turkish by Cahya
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice tr
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
value: 21.13
---
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Turkish Common Voice dataset](https://huggingface.co/datasets/common_voice).
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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish 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", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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()
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(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"])))
```
**Test Result**: 21.13 %
## Training
The Common Voice `train`, `validation`, other and invalidated
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
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"mk",
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"min",
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"oc",
"fa",
"pms",
"pl",
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"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
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} | 4,749,504 | 2022-02-07T08:47:31Z | ---
language: lg
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- common_voice
- hf-asr-leaderboard
- lg
- robust-speech-event
- speech
license: apache-2.0
model-index:
- name: Wav2Vec2 Luganda by Indonesian-NLP
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice lg
type: common_voice
args: lg
metrics:
- name: Test WER
type: wer
value: 9.332
- name: Test CER
type: cer
value: 1.987
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: lg
metrics:
- name: Test WER
type: wer
value: 13.844
- name: Test CER
type: cer
value: 2.68
---
# Automatic Speech Recognition for Luganda
This is the model built for the
[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0.
We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model.
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", "lg", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
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):
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian 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", "lg", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model.to("cuda")
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"]
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
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)
# Preprocessing the datasets.
# We need to read the audio 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"])))
```
WER without KenLM: 15.38 %
WER With KenLM:
**Test Result**: 7.53 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr)
|
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
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} | 480,510 | 2022-01-28T07:34:48Z | ---
language:
- ab
tags:
- ab
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 135.4675
- 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.10.3
|
ctrl | [
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null | {
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} | 17,007 | 2021-11-18T23:19:46Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-md
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-finetuned-md
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: 0.3329
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2415 | 1.0 | 1044 | 0.2084 |
| 0.1244 | 2.0 | 2088 | 0.2903 |
| 0.0427 | 3.0 | 3132 | 0.3329 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.10.3
|
Ab0/keras-dummy-sequential-demo | [
"keras"
] | null | {
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} | 0 | 2022-01-20T11:35:08Z | ---
language: en
tags:
- timelms
- twitter
license: mit
datasets:
- twitter-api
---
# Twitter September 2020 (RoBERTa-base, 103M)
This is a RoBERTa-base model trained on 102.86M tweets until the end of September 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-sep2020"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.55215 not
2) 0.16466 getting
3) 0.08991 fully
4) 0.05542 being
5) 0.01733 still
------------------------------
I keep forgetting to bring a <mask>.
1) 0.18145 mask
2) 0.04476 book
3) 0.03751 knife
4) 0.03713 laptop
5) 0.02873 bag
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.53243 the
2) 0.24435 The
3) 0.04717 End
4) 0.02421 this
5) 0.00958 Championship
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-sep2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99045 The movie was great
2) 0.96650 Just finished reading 'Embeddings in NLP'
3) 0.95947 I just ordered fried chicken 🐣
4) 0.95707 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-sep2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
``` |
AbhijeetA/PIE | [] | null | {
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} | 0 | 2021-04-16T23:30:00Z | ---
language: multilingual
widget:
- text: "🤗"
- text: "T'estimo! ❤️"
- text: "I love you!"
- text: "I hate you 🤮"
- text: "Mahal kita!"
- text: "사랑해!"
- text: "난 너가 싫어"
- text: "😍😍😍"
---
# twitter-XLM-roBERTa-base for Sentiment Analysis
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).
- Paper: [XLM-T: A Multilingual Language Model Toolkit for Twitter](https://arxiv.org/abs/2104.12250).
- Git Repo: [XLM-T official repository](https://github.com/cardiffnlp/xlm-t).
This model has been integrated into the [TweetNLP library](https://github.com/cardiffnlp/tweetnlp).
## Example Pipeline
```python
from transformers import pipeline
model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("T'estimo!")
```
```
[{'label': 'Positive', 'score': 0.6600581407546997}]
```
## Full classification example
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
MODEL = f"cardiffnlp/twitter-xlm-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Good night 😊"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
# Print labels and scores
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = config.id2label[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) Positive 0.7673
2) Neutral 0.2015
3) Negative 0.0313
```
|
AdapterHub/bert-base-uncased-pf-mnli | [
"bert",
"en",
"dataset:multi_nli",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/multinli"
] | text-classification | {
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} | 7 | null | This model is converted from the original BPR [repo](https://github.com/studio-ousia/bpr) and fitted into Pyserini:
> Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. arXiv:2106.00882. |
AdapterHub/roberta-base-pf-comqa | [
"roberta",
"en",
"dataset:com_qa",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] | question-answering | {
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} | 0 | null | An NER model to detect company and person names from news articles. |
AdapterHub/roberta-base-pf-hellaswag | [
"roberta",
"en",
"dataset:hellaswag",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/hellaswag"
] | null | {
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}
} | 0 | null | ---
language: en
tags:
- long context
---
# LSG model
**Transformers >= 4.23.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https://github.com/ccdv-ai/convert_checkpoint_to_lsg).
* [Usage](#usage)
* [Parameters](#parameters)
* [Sparse selection type](#sparse-selection-type)
* [Tasks](#tasks)
* [Training global tokens](#training-global-tokens)
This model can handle long sequences but faster and more efficiently than Longformer or BigBird (from Transformers) and relies on Local + Sparse + Global attention (LSG).
The model requires sequences whose length is a multiple of the block size. The model is "adaptive" and automatically pads the sequences if needed (adaptive=True in config). It is however recommended, thanks to the tokenizer, to truncate the inputs (truncation=True) and optionally to pad with a multiple of the block size (pad_to_multiple_of=...). \
The model is trained starting from a RoBERTa-base checkpoint on 16Gb of data (Wikipedia, Bookcorpus etc...) using the same number of parameters/layers and the same tokenizer.
Support encoder-decoder and causal masking but I didnt test it extensively.\
Implemented in PyTorch.
## Usage
The model relies on a custom modeling file, you need to add trust_remote_code=True to use it.
```python:
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("ccdv/lsg-base-4096", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-base-4096")
```
## Parameters
You can change various parameters like :
* the number of global tokens (num_global_tokens=1)
* local block size (block_size=128)
* sparse block size (sparse_block_size=128)
* sparsity factor (sparsity_factor=2)
* mask_first_token (mask first token since it is redundant with the first global token)
* see config.json file
Default parameters work well in practice. If you are short on memory, reduce block sizes, increase sparsity factor and remove dropout in the attention score matrix.
```python:
from transformers import AutoModel
model = AutoModel.from_pretrained("ccdv/lsg-base-4096",
trust_remote_code=True,
num_global_tokens=16,
block_size=64,
sparse_block_size=64,
attention_probs_dropout_prob=0.0
sparsity_factor=4,
sparsity_type="none",
mask_first_token=True
)
```
## Sparse selection type
There are 5 different sparse selection patterns. The best type is task dependent. \
Note that for sequences with length < 2*block_size, the type has no effect.
* sparsity_type="norm", select highest norm tokens
* Works best for a small sparsity_factor (2 to 4)
* Additional parameters:
* None
* sparsity_type="pooling", use average pooling to merge tokens
* Works best for a small sparsity_factor (2 to 4)
* Additional parameters:
* None
* sparsity_type="lsh", use the LSH algorithm to cluster similar tokens
* Works best for a large sparsity_factor (4+)
* LSH relies on random projections, thus inference may differ slightly with different seeds
* Additional parameters:
* lsg_num_pre_rounds=1, pre merge tokens n times before computing centroids
* sparsity_type="stride", use a striding mecanism per head
* Each head will use different tokens strided by sparsify_factor
* Not recommended if sparsify_factor > num_heads
* sparsity_type="block_stride", use a striding mecanism per head
* Each head will use block of tokens strided by sparsify_factor
* Not recommended if sparsify_factor > num_heads
## Tasks
Fill mask example:
```python:
from transformers import FillMaskPipeline, AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("ccdv/lsg-base-4096", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-base-4096")
SENTENCES = ["Paris is the <mask> of France.", "The goal of life is <mask>."]
pipeline = FillMaskPipeline(model, tokenizer)
output = pipeline(SENTENCES, top_k=1)
output = [o[0]["sequence"] for o in output]
> ['Paris is the capital of France.', 'The goal of life is happiness.']
```
Classification example:
```python:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ccdv/lsg-base-4096",
trust_remote_code=True,
pool_with_global=True, # pool with a global token instead of first token
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-base-4096")
SENTENCE = "This is a test for sequence classification. " * 300
token_ids = tokenizer(
SENTENCE,
return_tensors="pt",
#pad_to_multiple_of=... # Optional
truncation=True
)
output = model(**token_ids)
> SequenceClassifierOutput(loss=None, logits=tensor([[-0.3051, -0.1762]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
```
## Training global tokens
To train global tokens and the classification head only:
```python:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ccdv/lsg-base-4096",
trust_remote_code=True,
pool_with_global=True, # pool with a global token instead of first token
num_global_tokens=16
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-base-4096")
for name, param in model.named_parameters():
if "global_embeddings" not in name:
param.requires_grad = False
else:
param.required_grad = True
```
|
AdapterHub/roberta-base-pf-hotpotqa | [
"roberta",
"en",
"dataset:hotpot_qa",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] | question-answering | {
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} | 35 | null | ---
tags:
- summarization
- pegasus
- long context
language:
- en
pipeline_tag: fill-mask
---
# LSG model
**Transformers >= 4.23.1**\
**This model relies on a custom modeling file, you need to add trust_remote_code=True**\
**See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https://github.com/ccdv-ai/convert_checkpoint_to_lsg).
* [Usage](#usage)
* [Parameters](#parameters)
* [Sparse selection type](#sparse-selection-type)
* [Tasks](#tasks)
This model is adapted from [Pegasus-large](https://huggingface.co/google/pegasus-large) for encoder-decoder tasks without additional pretraining. It uses the same number of parameters/layers and the same tokenizer.
This model can handle long sequences but faster and more efficiently than Longformer (LED) or BigBird (Pegasus) from the hub and relies on Local + Sparse + Global attention (LSG).
The model requires sequences whose length is a multiple of the block size. The model is "adaptive" and automatically pads the sequences if needed (adaptive=True in config). It is however recommended, thanks to the tokenizer, to truncate the inputs (truncation=True) and optionally to pad with a multiple of the block size (pad_to_multiple_of=...). \
Implemented in PyTorch.
## Usage
The model relies on a custom modeling file, you need to add trust_remote_code=True to use it.
```python:
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("ccdv/lsg-pegasus-large-4096", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-pegasus-large-4096")
```
## Parameters
You can change various parameters like :
* the number of global tokens (num_global_tokens=1)
* local block size (block_size=128)
* sparse block size (sparse_block_size=128)
* sparsity factor (sparsity_factor=2)
* see config.json file
Default parameters work well in practice. If you are short on memory, reduce block sizes, increase sparsity factor and remove dropout in the attention score matrix.
```python:
from transformers import AutoModel
model = AutoModel.from_pretrained("ccdv/lsg-pegasus-large-4096",
trust_remote_code=True,
num_global_tokens=16,
block_size=64,
sparse_block_size=64,
attention_probs_dropout_prob=0.0
sparsity_factor=4,
sparsity_type="none",
mask_first_token=True
)
```
## Sparse selection type
There are 5 different sparse selection patterns. The best type is task dependent. \
Note that for sequences with length < 2*block_size, the type has no effect.
* sparsity_type="norm", select highest norm tokens
* Works best for a small sparsity_factor (2 to 4)
* Additional parameters:
* None
* sparsity_type="pooling", use average pooling to merge tokens
* Works best for a small sparsity_factor (2 to 4)
* Additional parameters:
* None
* sparsity_type="lsh", use the LSH algorithm to cluster similar tokens
* Works best for a large sparsity_factor (4+)
* LSH relies on random projections, thus inference may differ slightly with different seeds
* Additional parameters:
* lsg_num_pre_rounds=1, pre merge tokens n times before computing centroids
* sparsity_type="stride", use a striding mecanism per head
* Each head will use different tokens strided by sparsify_factor
* Not recommended if sparsify_factor > num_heads
* sparsity_type="block_stride", use a striding mecanism per head
* Each head will use block of tokens strided by sparsify_factor
* Not recommended if sparsify_factor > num_heads
## Tasks
Seq2Seq example for summarization:
```python:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-pegasus-large-4096",
trust_remote_code=True,
pass_global_tokens_to_decoder=True, # Pass encoder global tokens to decoder
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-pegasus-large-4096")
SENTENCE = "This is a test sequence to test the model. " * 300
token_ids = tokenizer(
SENTENCE,
return_tensors="pt",
#pad_to_multiple_of=... # Optional
truncation=True
)
output = model(**token_ids)
```
Classification example:
```python:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ccdv/lsg-pegasus-large-4096",
trust_remote_code=True,
pass_global_tokens_to_decoder=True, # Pass encoder global tokens to decoder
)
tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-pegasus-large-4096")
SENTENCE = "This is a test sequence to test the model. " * 300
token_ids = tokenizer(
SENTENCE,
return_tensors="pt",
padding="max_length", # Optional but recommended
truncation=True # Optional but recommended
)
output = model(**token_ids)
> SequenceClassifierOutput(loss=None, logits=tensor([[-0.3051, -0.1762]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
```
**Pegasus**
```
@misc{zhang2019pegasus,
title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},
author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},
year={2019},
eprint={1912.08777},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
AdapterHub/roberta-base-pf-imdb | [
"roberta",
"en",
"dataset:imdb",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/imdb"
] | text-classification | {
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}
} | 0 | 2021-05-25T21:35:58Z | ---
language: ca
datasets:
- common_voice
- parlament_parla
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- speech-to-text
license: apache-2.0
model-index:
- name: Catalan VoxPopuli Wav2Vec2 Large
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
datasets:
- name: Common Voice ca
type: common_voice
args: ca
- name: ParlamentParla
url: https://www.openslr.org/59/
metrics:
- name: Test WER
type: wer
value: 5.98
- name: Google Crowsourced Corpus WER
type: wer
value: 12.14
- name: Audiobook “La llegenda de Sant Jordi” WER
type: wer
value: 12.02
---
# Wav2Vec2-Large-100k-VoxPopuli-Català
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL:**
https://huggingface.co/softcatala/wav2vec2-large-100k-voxpopuli-catala
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets.
**Attention:** The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoice and ParlamentParla dataset and can be found [here](https://github.com/ccoreilly/wav2vec2-catala). Evaluating on the CV test dataset will produce a biased WER as 1144 audio files of that dataset were used in training/evaluation of this model.
WER was calculated using this [test.csv](https://github.com/ccoreilly/wav2vec2-catala/blob/master/test-filtered.csv) which was not seen by the model during training/evaluation.
You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala)
When using this model, make sure that your speech input is sampled at 16kHz.
## Results
Word error rate was evaluated on the following datasets unseen by the model:
| Dataset | WER |
| ------- | --- |
| [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test-filtered.csv)) | 5.98% |
| [Google Crowsourced Corpus](https://www.openslr.org/69/) | 12.14% |
| Audiobook “La llegenda de Sant Jordi” | 12.02% |
## 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", "ca", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio 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])
``` |
AdapterHub/roberta-base-pf-mit_movie_trivia | [
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:ner/mit_movie_trivia"
] | token-classification | {
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"model_type": "roberta",
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} | 0 | 2021-03-27T22:36:00Z | ---
language: ca
datasets:
- common_voice
- parlament_parla
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Catalan XLSR Wav2Vec2 Large
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
datasets:
- name: Common Voice ca
type: common_voice
args: ca
- name: ParlamentParla
url: https://www.openslr.org/59/
metrics:
- name: Test WER
type: wer
value: 6.92
- name: Google Crowsourced Corpus WER
type: wer
value: 12.99
- name: Audiobook “La llegenda de Sant Jordi” WER
type: wer
value: 13.23
---
# Wav2Vec2-Large-XLSR-Català
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets.
**Attention:** The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoice and ParlamentParla dataset and can be found [here](https://github.com/ccoreilly/wav2vec2-catala). Evaluating on the CV test dataset will produce a biased WER as 1144 audio files of that dataset were used in training/evaluation of this model.
WER was calculated using this [test.csv](https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv) which was not seen by the model during training/evaluation.
You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala)
When using this model, make sure that your speech input is sampled at 16kHz.
## Results
Word error rate was evaluated on the following datasets unseen by the model:
| Dataset | WER |
| ------- | --- |
| [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv)) | 6.92% |
| [Google Crowsourced Corpus](https://www.openslr.org/69/) | 12.99% |
| Audiobook “La llegenda de Sant Jordi” | 13.23% |
## 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", "ca", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala")
model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-xlsr-catala")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio 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])
``` |
AdapterHub/roberta-base-pf-mnli | [
"roberta",
"en",
"dataset:multi_nli",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/multinli"
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} | 5 | null | ---
tags:
- Text Generation
---
# GIMPLEARN knows modeltest2
# To generate conversation use input such as Human: What should I do?\nAI: |
AdapterHub/roberta-base-pf-record | [
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:rc/record"
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} | 0 | null | ---
language:
- zh
tags:
- bert
- pytorch
- environment
- multi-class
- classification
---
中文环境文本分类模型，1.6M的数据集，在env-bert-chinese上进行fine-tuning。
分为环境影响评价与控制、碳排放控制、水污染控制、大气污染控制、土壤污染控制、环境生态、固体废物、环境毒理与健康、环境微生物、环境政策与经济10类。
项目正在进行中，后续会陆续更新相关内容。
清华大学环境学院课题组
有相关需求、建议，联系[email protected] |
AdapterHub/roberta-base-pf-rte | [
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"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/rte"
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} | 6 | 2022-01-10T07:20:45Z | ---
language: zh
widget:
- text: "美国退出《巴黎协定》"
- text: "污水处理厂中的功耗需要减少"
tags:
- pretrain
- pytorch
- environment
- classification
- topic classification
---
话题分类模型，使用某乎"环境"话题下所有子话题，过滤后得69类。
top1 acc 60.7,
top3 acc 81.6，
可以用于中文环境文本挖掘的预处理步骤。
标签：
"生态环境","水污染", "野生动物保护", "太阳能", "环保经济", "污水处理", "绿色建筑", "水处理", "噪音污染", "温室效应", "净水设备",
"净水器", "自来水", "生活", "环境评估", "空气污染", "环境评价", "工业污染", "雾霾", "植树", "环保行业", "水处理工程", "沙漠治理",
"巴黎协定", "核能", "噪音", "环评工程师", "二氧化碳", "低碳", "自然环境", "沙尘暴", "环境工程", "秸秆焚烧", "PM 2.5", "太空垃圾",
"穹顶之下（纪录片）", "垃圾", "环境科学", "净水", "污水排放", "室内空气污染", "环境污染", "全球变暖", "邻居噪音", "土壤污染", "生物多样性",
"碳交易", "污染治理", "雾霾治理", "碳金融", "建筑节能", "风能及风力发电", "温室气体", "环境保护", "碳排放", "垃圾处理器", "气候变化", "化学污染",
"地球一小时", "环保组织", "物种多样性", "节能减排", "核污染", "环保督查", "垃圾处理", "垃圾分类", "重金属污染", "环境伦理学", "垃圾焚烧" |
AdapterHub/roberta-base-pf-snli | [
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"en",
"dataset:snli",
"arxiv:2104.08247",
"adapter-transformers",
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} | 2 | null | tags:
- array
- of
- tags
license: "any valid license identifier" |
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} | 0 | null | ---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Turkish by Ceyda Cinarel
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice tr
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
value: 27.59
---
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice)
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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish")
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])
```
## Evaluation
The model can be evaluated as follows on the Turkish 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", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\]\[\’»«]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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)
# 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"])))
```
**Test Result**: 27.59 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://github.com/cceyda/wav2vec2) |
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} | 0 | null | ---
tags:
- conversational
---
# Rick DialoGPT model |
AetherIT/DialoGPT-small-Hal | [
"conversational"
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} | 0 | null | ---
tags:
- conversational
---
# DialoGPT Medium JAB
|
AethiQs-Max/AethiQs_GemBERT_bertje_50k | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 11 | null | ---
tags:
- conversational
---
# DialoGPT Small JAB |
AethiQs-Max/cross_encoder | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-finetuned-kaggglenews-baseline-final
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. -->
# bart-base-finetuned-kaggglenews-baseline-final
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6942
- Rouge1: 28.581
- Rouge2: 16.3417
- Rougel: 24.1277
- Rougelsum: 25.9797
- Gen Len: 20.0
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 495 | 1.7514 | 27.911 | 15.7038 | 23.6466 | 25.2111 | 20.0 |
| 2.0585 | 2.0 | 990 | 1.6655 | 28.7581 | 16.4875 | 24.2669 | 26.1676 | 20.0 |
| 1.4173 | 3.0 | 1485 | 1.6942 | 28.581 | 16.3417 | 24.1277 | 25.9797 | 20.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AidenGO/KDXF_Bert4MaskedLM | [
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"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base-finetuned-kaggglenews-fact-corrector-II
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. -->
# bart-base-finetuned-kaggglenews-fact-corrector-II
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 305 | 1.5749 | 27.9313 | 15.1004 | 23.3282 | 25.2336 | 20.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-finetuned-kaggglenews
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. -->
# bart-base-finetuned-kaggglenews
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6240
- Rouge1: 28.3618
- Rouge2: 15.9828
- Rougel: 24.078
- Rougelsum: 25.565
- Gen Len: 20.0
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:|
| 1.9433 | 1.0 | 989 | 1.6240 | 28.3618 | 15.9828 | 24.078 | 25.565 | 20.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ba",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"has_space"
] | automatic-speech-recognition | {
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} | 64 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-finetuned-kagglenews-entityfiltering
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. -->
# bart-base-finetuned-kagglenews-entityfiltering
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5703
- Rouge1: 28.2719
- Rouge2: 15.6883
- Rougel: 24.0674
- Rougelsum: 25.616
- Gen Len: 20.0
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9187 | 1.0 | 863 | 1.5703 | 28.2719 | 15.6883 | 24.0674 | 25.616 | 20.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.14.0
- Tokenizers 0.10.3
|
Akira-Yana/distilbert-base-uncased-finetuned-cola | [] | null | {
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} | 0 | null | ---
tags:
- conversational
---
#Chizuru Ichinose~ DialoGPT Model |
AlchemistDude/DialoGPT-medium-Gon | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-commentaries_hdwriter
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. -->
# bart-large-commentaries_hdwriter
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1619
- Rouge1: 26.1101
- Rouge2: 9.928
- Rougel: 22.9007
- Rougelsum: 23.117
- Gen Len: 15.9536
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6237 | 1.0 | 5072 | 2.5309 | 26.4063 | 9.1795 | 22.6699 | 22.9125 | 17.3103 |
| 1.8808 | 2.0 | 10144 | 2.5049 | 25.3706 | 8.7568 | 21.8594 | 22.1233 | 15.8579 |
| 1.3084 | 3.0 | 15216 | 2.6680 | 26.6284 | 9.9914 | 23.1477 | 23.3625 | 16.8832 |
| 0.9247 | 4.0 | 20288 | 2.8923 | 26.3827 | 9.8217 | 22.9524 | 23.1651 | 15.4529 |
| 0.692 | 5.0 | 25360 | 3.1619 | 26.1101 | 9.928 | 22.9007 | 23.117 | 15.9536 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ale/Alen | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-sgnews
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. -->
# distilgpt2-sgnews
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1516
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3558 | 1.0 | 23769 | 3.2316 |
| 3.2558 | 2.0 | 47538 | 3.1683 |
| 3.2321 | 3.0 | 71307 | 3.1516 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
AlekseyKorshuk/horror-scripts | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 19 | null | ---
tags:
- generated_from_trainer
model-index:
- name: finetune-paraphrase-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune-paraphrase-model
This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) 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: 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: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.1 | 200 | 3.0116 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
AlgoveraAI/dcgan | [
"pytorch",
"transformers"
] | null | {
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} | 12 | null | ---
language: th
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning
license: apache-2.0
---
# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)
|
AliReza/distilbert-emotion | [] | null | {
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} | 0 | null | Test English-Dhivehi/Dhivehi-English NMT
Would need a lot more data to get accurate translations. |
Aliraza47/BERT | [] | null | {
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} | 0 | 2021-03-26T09:33:33Z | ---
language: fon
datasets:
- fon_dataset
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: Fon XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: fon
type: fon_dataset
args: fon
metrics:
- name: Test WER
type: wer
value: 14.97
---
# Wav2Vec2-Large-XLSR-53-Fon
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data).
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 json
import random
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
#Load test_dataset from saved files in folder
from datasets import load_dataset, load_metric
#for test
for root, dirs, files in os.walk(test/):
test_dataset= load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
#Remove unnecessary chars
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
def remove_special_characters(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
test_dataset = test_dataset.map(remove_special_characters)
processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
#No need for resampling because audio dataset already at 16kHz
#resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"]=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():
tlogits = 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])
```
## Evaluation
The model can be evaluated as follows on our unique Fon test data.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
for root, dirs, files in os.walk(test/):
test_dataset = load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]'
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
test_dataset = test_dataset.map(remove_special_characters)
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon")
model.to("cuda")
# 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"] = speech_array[0].numpy()
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["sentence"]
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
#Evaluation on test dataset
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"])))
```
**Test Result**: 14.97 %
## Training
The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples).
The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing)
# Collaborators on this project
- Chris C. Emezue ([Twitter](https://twitter.com/ChrisEmezue))|([email protected])
- Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))|([Twitter](https://twitter.com/bonadossou))|([email protected])
## This is a joint project continuing our research on [OkwuGbé: End-to-End Speech Recognition for Fon and Igbo](https://arxiv.org/abs/2103.07762) |
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} | 0 | 2021-10-17T20:31:21Z | ---
language: african-languages
tags:
- african-languages
- machine-translation
- text
license: apache-2.0
model-index:
- name: Masakhane Benchmark Models
results:
- task:
name: Machine Translation
type: machine-translation
dataset:
name: masakhane benchmarks
args: african-languages
---
# Interacting with the Masakhane Benchmark Models
I created this demo for very easy interaction with the [benchmark models on Masakhane](https://github.com/masakhane-io/masakhane-mt/tree/master/benchmarks) which were trained with [JoeyNMT](https://github.com/chrisemezue/joeynmt)(my forked version).
To access the space click [here](https://huggingface.co/spaces/chrisjay/masakhane-benchmarks).
To include your language, all you need to do is:
1. Create a folder in the format *src-tgt/main* for your language pair, if it does not exist.
2. Inside the *main* folder put the following files:
1. model checkpoint. Rename it to `best.ckpt`.
2. `config.yaml` file. This is the JoeyNMT config file which loads the model an pre-processing parameters.
3. `src_vocab.txt` file.
4. `trg_vocab.txt` file.
The space currently supports these languages:
| source language | target language |
|:---------------:|:---------------:|
| English | Swahili |
| English | Afrikaans |
| English | Arabic |
| English | Urhobo |
| English | Ẹ̀dó |
| Efik | English |
| English | Hausa |
| English | Igbo |
| English | Fon |
| English | Twi |
| English | Dendi |
| English | Ẹ̀sán |
| English | Isoko |
| English | Kamba |
| English | Luo |
| English | Southern Ndebele |
| English | Tshivenda |
| Shona | English |
| Swahili | English |
| Yoruba | English |
TO DO:
1. Include more languages from the benchmark. |
Alireza1044/albert-base-v2-mrpc | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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"AlbertForSequenceClassification"
],
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}
}
} | 204 | null | ---
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_stylecheck
results: []
---
Check style on English text (currently passive text).
| Feature | Description |
| --- | --- |
| **Name** | `en_stylecheck` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` |
| **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (115 labels for 5 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`senter`** | `I`, `S` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
| **`entity_ruler`** | `PASSIVE` |
</details> |
Alireza1044/albert-base-v2-qqp | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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"AlbertForSequenceClassification"
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}
}
} | 37 | null | ---
language: "en"
tags:
- gpt2
- arxiv
- transformers
datasets:
- https://github.com/staeiou/arxiv_archive/tree/v1.0.1
---
# ArXiv AI GPT-2
## Model description
This GPT-2 (774M) model is capable of generating abstracts given paper titles. It was trained using all research paper titles and abstracts under artificial intelligence (AI), machine learning (LG), computation and language (CL), and computer vision and pattern recognition (CV) on arXiv.
## Intended uses & limitations
#### How to use
To generate paper abstracts, use the provided `generate.py` [here](https://gist.github.com/chrisliu298/ccb8144888eace069da64ad3e6472d64). This is very similar to the HuggingFace's `run_generation.py` [here](https://github.com/huggingface/transformers/tree/master/examples/text-generation). You can simply replace the text with with your own model path (line 89) and change the input string to your paper title (line 127). If you want to use your own script, make sure to prepend `<|startoftext|> ` at the front and append ` <|sep|>` at the end of the paper title.
## Training data
I selected a subset of the [arXiv Archive](https://github.com/staeiou/arxiv_archive) dataset (Geiger, 2019) as the training and evaluation data to fine-tune GPT-2. The original arXiv Archive dataset contains a full archive of metadata about papers on arxiv.org, from the start of the site in 1993 to the end of 2019. Our subset includes all the paper titles (query) and abstracts (context) under the Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Computation and Language (cs.CL), and Computer Vision and Pattern Recognition (cs.CV) categories. I provide the information of the sub-dataset and the distribution of the training and evaluation dataset as follows.
| Splits | Count | Percentage (%) | BPE Token Count |
| :--------: | :--------: | :------------: | :-------------: |
| Train | 90,000 | 90.11 | 20,834,012 |
| Validation | 4,940 | 4.95 | 1,195,056 |
| Test | 4,940 | 4.95 | 1,218,754 |
| **Total** | **99,880** | **100** | **23,247,822** |
The original dataset is in the format of a tab-separated value, so we wrote a simple preprocessing script to convert it into a text file format, which is the input file type (a document) of the GPT-2 model. An example of a paper’s title and its abstract is shown below.
```text
<|startoftext|> Some paper title <|sep|> Some paper abstract <|endoftext|>
```
Because there are a lot of cross-domain papers in the dataset, I deduplicate the dataset using the arXiv ID, which is unique for every paper. I sort the paper by submission date, by doing so, one can examine GPT-2’s ability to use learned terminologies when it is prompted with paper titles from the “future.”
## Training procedure
I used block size = 512, batch size = 1, gradidnet accumulation = 1, learning rate = 1e-5, epochs = 5, and everything else follows the default model configuration.
## Eval results
The resulting GPT-2 large model's perplexity score on the test set is **14.9413**.
## Reference
```bibtex
@dataset{r_stuart_geiger_2019_2533436,
author= {R. Stuart Geiger},
title={{ArXiV Archive: A tidy and complete archive of metadata for papers on arxiv.org, 1993-2019}},
month=jan,
year= 2019,
publisher={Zenodo},
version= {v1.0.1},
doi={10.5281/zenodo.2533436},
url={https://doi.org/10.5281/zenodo.2533436}
}
```
|
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}
} | 0 | null | ---
language:
- en
tags:
- pytorch
- coai
pipeline_tag: conversational
---
[blenderbot-400M-distill](https://huggingface.co/facebook/blenderbot-400M-distill) fine-tuned on the [ESConv dataset](https://github.com/thu-coai/Emotional-Support-Conversation). Usage example:
```python
import torch
from transformers import AutoTokenizer
from transformers.models.blenderbot import BlenderbotTokenizer, BlenderbotForConditionalGeneration
def _norm(x):
return ' '.join(x.strip().split())
tokenizer = BlenderbotTokenizer.from_pretrained('thu-coai/blenderbot-400M-esconv')
model = BlenderbotForConditionalGeneration.from_pretrained('thu-coai/blenderbot-400M-esconv')
model.eval()
utterances = [
"I am having a lot of anxiety about quitting my current job. It is too stressful but pays well",
"What makes your job stressful for you?",
"I have to deal with many people in hard financial situations and it is upsetting",
"Do you help your clients to make it to a better financial situation?",
"I do, but often they are not going to get back to what they want. Many people are going to lose their home when safeguards are lifted",
]
input_sequence = ' '.join([' ' + e for e in utterances]) + tokenizer.eos_token # add space prefix and separate utterances with two spaces
input_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(input_sequence))[-128:]
input_ids = torch.LongTensor([input_ids])
model_output = model.generate(input_ids, num_beams=1, do_sample=True, top_p=0.9, num_return_sequences=5, return_dict=False)
generation = tokenizer.batch_decode(model_output, skip_special_tokens=True)
generation = [_norm(e) for e in generation]
print(generation)
utterances.append(generation[0]) # for future loop
```
Please kindly cite the [original paper](https://arxiv.org/abs/2106.01144) if you use this model:
```bib
@inproceedings{liu-etal-2021-towards,
title={Towards Emotional Support Dialog Systems},
author={Liu, Siyang and
Zheng, Chujie and
Demasi, Orianna and
Sabour, Sahand and
Li, Yu and
Yu, Zhou and
Jiang, Yong and
Huang, Minlie},
booktitle={Proceedings of the 59th annual meeting of the Association for Computational Linguistics},
year={2021}
}
```
|
Amirosein/distilbert_v1 | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"DistilBertForMaskedLM"
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}
} | 6 | null | ---
language:
- ru
- en
---
## EnDR-BERT
EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \
link: https://yadi.sk/d/-PTn0xhk1PqvgQ
## Citing & Authors
If you find this repository helpful, feel free to cite our publication:
[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020.
preprint: https://arxiv.org/abs/2004.03659
```
@article{10.1093/bioinformatics/btaa675,
author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa675},
url = {https://doi.org/10.1093/bioinformatics/btaa675},
note = {btaa675},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
}
```
[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.
[link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)
```
@article{tutubalina2017using,
title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
journal={Russian Chemical Bulletin},
volume={66},
number={11},
pages={2180--2189},
year={2017},
publisher={Springer}
}
```
|
Amirosein/roberta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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}
} | 6 | null | ---
language:
- ru
- en
---
## EnRuDR-BERT
EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \
link: https://yadi.sk/d/-PTn0xhk1PqvgQ
## Citing & Authors
If you find this repository helpful, feel free to cite our publication:
[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020.
preprint: https://arxiv.org/abs/2004.03659
```
@article{10.1093/bioinformatics/btaa675,
author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa675},
url = {https://doi.org/10.1093/bioinformatics/btaa675},
note = {btaa675},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
}
```
[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.
[link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)
```
@article{tutubalina2017using,
title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
journal={Russian Chemical Bulletin},
volume={66},
number={11},
pages={2180--2189},
year={2017},
publisher={Springer}
}
```
|
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} | 0 | 2020-07-09T14:44:58Z | ## RuDR-BERT
RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \
link: https://yadi.sk/d/-PTn0xhk1PqvgQ
## Citing & Authors
If you find this repository helpful, feel free to cite our publication:
[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.
preprint: https://arxiv.org/abs/2004.03659
```
@article{10.1093/bioinformatics/btaa675,
author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey},
title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}",
journal = {Bioinformatics},
year = {2020},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa675},
url = {https://doi.org/10.1093/bioinformatics/btaa675},
note = {btaa675},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf},
}
```
[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.
[link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf)
```
@article{tutubalina2017using,
title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects},
author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE},
journal={Russian Chemical Bulletin},
volume={66},
number={11},
pages={2180--2189},
year={2017},
publisher={Springer}
}
``` |
Amitabh/doc-classification | [] | null | {
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}
}
} | 0 | null | ---
language:
- sw
tags:
- generated_from_trainer
datasets:
- tydiqa
model-index:
- name: afriberta_base-finetuned-tydiqa
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. -->
# afriberta_base-finetuned-tydiqa
This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the tydiqa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3728
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 192 | 2.1359 |
| No log | 2.0 | 384 | 2.3409 |
| 0.8353 | 3.0 | 576 | 2.3728 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Anamika/autonlp-Feedback1-479512837 | [
"pytorch",
"xlm-roberta",
"text-classification",
"unk",
"dataset:Anamika/autonlp-data-Feedback1",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 34 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nlu_sherlock_model
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. -->
# nlu_sherlock_model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -947, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Anamika/autonlp-fa-473312409 | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:Anamika/autonlp-data-fa",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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},
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 35 | 2022-02-20T09:01:48Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nlu_sherlock_model_20220220
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. -->
# nlu_sherlock_model_20220220
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -955, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Anders/itu-ams-summa | [] | null | {
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"model_type": null,
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},
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},
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},
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}
}
} | 0 | 2020-12-14T07:29:42Z | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ner')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
Andi/bert-tt-ner-1 | [] | null | {
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},
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"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
} | 0 | null | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-pos')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
Andranik/TestPytorchClassification | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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},
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},
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}
}
} | 36 | 2020-12-14T07:29:31Z | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ws')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
AndreLiu1225/t5-news | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 18 | null | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ws')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
Andres2015/HiggingFaceTest | [] | null | {
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},
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"max_length": null
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"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 0 | null | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
AndrewMcDowell/wav2vec2-xls-r-300m-arabic | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
} | 4 | null | ---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- gpt2
- zh
license: gpl-3.0
---
# CKIP GPT2 Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/gpt2-base-chinese')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
Andrija/M-bert-NER | [
"pytorch",
"bert",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"BertForTokenClassification"
],
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}
} | 8 | null | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東北大学で[MASK]の研究をしています。
---
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by character-level tokenization.
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0).
## Model architecture
The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
## Training Data
The models are trained on the Japanese version of Wikipedia.
The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.
The generated corpus files are 4.0GB in total, containing approximately 30M sentences.
We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences.
## Tokenization
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.
The vocabulary size is 6144.
We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization.
## Training
The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/).
The training took about 5 days to finish.
## Licenses
The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
## Acknowledgments
This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
|
Anirbanbhk/Hate-speech-Pretrained-movies | [
"tf",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 20 | 2021-05-21T11:21:07Z | ---
language:
- hr
- bs
- sr
- cnr
- hbs
tags:
- masked-lm
widget:
- text: "Zovem se Marko i radim u [MASK]."
license: apache-2.0
---
# BERTić* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian
* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).
This is the smaller generator of the main [discriminator model](https://huggingface.co/classla/bcms-bertic), useful if you want to continue pre-training the discriminator model.
If you use the model, please cite the following paper:
```
@inproceedings{ljubesic-lauc-2021-bertic,
title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian",
author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5",
pages = "37--42",
}
```
|
Ankitha/DialoGPT-small-harrypottery | [] | null | {
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}
} | 0 | null | ---
language: "en"
license: "cc-by-sa-4.0"
tags:
- text-classification
- hate-speech
widget:
- text: "Gay is okay."
---
# roberta-base-frenk-hate
Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).
## Fine-tuning hyperparameters
Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:
```python
model_args = {
"num_train_epochs": 6,
"learning_rate": 3e-6,
"train_batch_size": 69}
```
## Performance
The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
| model | average accuracy | average macro F1|
|---|---|---|
|roberta-base-frenk-hate|0.7915|0.7785|
|xlm-roberta-large |0.7904|0.77876|
|xlm-roberta-base |0.7577|0.7402|
|fasttext|0.725 |0.707 |
From recorded accuracies and macro F1 scores p-values were also calculated:
Comparison with `xlm-roberta-base`:
| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U-test|0.00108|0.00108|
|Student t-test | 1.35e-08 | 1.05e-07|
Comparison with `xlm-roberta-large` yielded inconclusive results. `roberta-base` has average accuracy 0.7915, while `xlm-roberta-large` has average accuracy of 0.7904. If macro F1 scores were to be compared, `roberta-base` actually has lower average than `xlm-roberta-large`: 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that `roberta-base` has greater metrics, and the results are given below.
| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.188|0.406|
|Mann Whithey|0.375|0.649|
|Student t-test | 0.681| 0.934|
With reversed premise (i.e., that `xlm-roberta-large` has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another.
## Use examples
```python
from simpletransformers.classification import ClassificationModel
model_args = {
"num_train_epochs": 6,
"learning_rate": 3e-6,
"train_batch_size": 69}
model = ClassificationModel(
"roberta", "5roop/roberta-base-frenk-hate", use_cuda=True,
args=model_args
)
predictions, logit_output = model.predict(["Build the wall",
"Build the wall of trust"]
)
predictions
### Output:
### array([1, 0])
```
## Citation
If you use the model, please cite the following paper on which the original model is based:
```
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
and the dataset used for fine-tuning:
```
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
```
|
Ann2020/distilbert-base-uncased-finetuned-ner | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
}
} | 4 | null | ---
language: "sl"
license: "cc-by-sa-4.0"
tags:
- text-classification
- hate-speech
widget:
- text: "Silva, ti si grda in neprijazna"
---
Text classification model based on `EMBEDDIA/sloberta` and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the slovenian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).
## Fine-tuning hyperparameters
Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:
```python
model_args = {
"num_train_epochs": 14,
"learning_rate": 1e-5,
"train_batch_size": 21,
}
```
## Performance
The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
| model | average accuracy | average macro F1|
|---|---|---|
|sloberta-frenk-hate|0.7785|0.7764|
|EMBEDDIA/crosloengual-bert |0.7616|0.7585|
|xlm-roberta-base |0.686|0.6827|
|fasttext|0.709 |0.701 |
From recorded accuracies and macro F1 scores p-values were also calculated:
Comparison with `crosloengual-bert`:
| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U test|0.00163|0.00108|
|Student t-test |0.000101|3.95e-05|
Comparison with `xlm-roberta-base`:
| test | accuracy p-value | macro F1 p-value|
| --- | --- | --- |
|Wilcoxon|0.00781|0.00781|
|Mann Whithney U test|0.00108|0.00108|
|Student t-test |9.46e-11|6.94e-11|
## Use examples
```python
from simpletransformers.classification import ClassificationModel
model_args = {
"num_train_epochs": 6,
"learning_rate": 3e-6,
"train_batch_size": 69}
model = ClassificationModel(
"camembert", "5roop/sloberta-frenk-hate", use_cuda=True,
args=model_args
)
predictions, logit_output = model.predict(["Silva, ti si grda in neprijazna", "Naša hiša ima dimnik"])
predictions
### Output:
### array([1, 0])
```
## Citation
If you use the model, please cite the following paper on which the original model is based:
```
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
and the dataset used for fine-tuning:
```
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
``` |
Ann2020/rubert-base-cased-finetuned-ner | [] | null | {
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} | 0 | null | ---
language: hr
datasets:
- parlaspeech-hr
tags:
- audio
- automatic-speech-recognition
- parlaspeech
widget:
- example_title: example 1
src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a
- example_title: example 2
src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav
---
# wav2vec2-xls-r-parlaspeech-hr
This model for Croatian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494).
If you use this model, please cite the following paper:
Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/pdf/2022.parlaclariniii-1.16.pdf
## Metrics
Evaluation is performed on the dev and test portions of the [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494) dataset.
|split|CER|WER|
|---|---|---|
|dev|0.0335|0.1046|
|test|0.0234|0.0761|
There are multiple models available, and in terms of CER and WER, the best-performing model is [wav2vec2-large-slavic-parlaspeech-hr-lm](https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr-lm).
## Usage in `transformers`
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import soundfile as sf
import torch
import os
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained(
"classla/wav2vec2-xls-r-parlaspeech-hr")
model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-xls-r-parlaspeech-hr")
# download the example wav files:
os.system("wget https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav")
# read the wav file
speech, sample_rate = sf.read("00020570a.flac.wav")
input_values = processor(speech, sampling_rate=sample_rate, return_tensors="pt").input_values.to(device)
# remove the raw wav file
os.system("rm 00020570a.flac.wav")
# retrieve logits
logits = model.to(device)(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0]).lower()
# transcription: 'veliki broj poslovnih subjekata posluje sa minusom velik dio'
```
## Training hyperparameters
In fine-tuning, the following arguments were used:
| arg | value |
|-------------------------------|-------|
| `per_device_train_batch_size` | 16 |
| `gradient_accumulation_steps` | 4 |
| `num_train_epochs` | 8 |
| `learning_rate` | 3e-4 |
| `warmup_steps` | 500 | |
AnonymousSub/EManuals_RoBERTa_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"RobertaForQuestionAnswering"
],
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} | 4 | 2022-03-01T12:18:32Z | ---
language:
- de
- fr
- it
pipeline_tag: fill-mask
license: cc-by-nc-sa-4.0
tags:
- legal
- fairlex
widget:
- text: "Aus seinem damaligen strafbaren Verhalten resultierte eine Forderung der Nachlassverwaltung eines <mask>, worüber eine aussergerichtliche Vereinbarung über Fr. 500'000."
- text: " Elle avait pour but social les <mask> dans le domaine des changes, en particulier l'exploitation d'une plateforme internet."
- text: "Il Pretore ha accolto la petizione con sentenza 16 luglio 2015, accordando all'attore l'importo <mask>, con interessi di mora a partire dalla notifica del precetto esecutivo, e ha rigettato in tale misura l'opposizione interposta a quest'ultimo."
---
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
## Pre-training details
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
## Models list
| Model name | Training corpora | Language |
|-----------------------------------|------------------|--------------------|
| `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` |
| `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` |
| `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] |
| `coastalcph/fairlex-cail-minlm` | CAIL | `zh` |
## Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-fscs-minlm")
model = AutoModel.from_pretrained("coastalcph/fairlex-fscs-minlm")
```
## Evaluation on downstream tasks
Consider the experiments in the article:
_Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._
## Author - Publication
```
@inproceedings{chalkidis-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io)
| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) | |
AnonymousSub/EManuals_RoBERTa_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
} | 29 | null | ---
language: en
pipeline_tag: fill-mask
license: cc-by-nc-sa-4.0
tags:
- legal
- fairlex
widget:
- text: "Because the Court granted <mask> before judgment, the Court effectively stands in the shoes of the Court of Appeals and reviews the defendants’ appeals."
---
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
## Pre-training details
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
## Models list
| Model name | Training corpora | Language |
|-----------------------------------|------------------|--------------------|
| `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` |
| `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` |
| `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] |
| `coastalcph/fairlex-cail-minlm` | CAIL | `zh` |
## Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-scotus-minlm")
model = AutoModel.from_pretrained("coastalcph/fairlex-scotus-minlm")
```
## Evaluation on downstream tasks
Consider the experiments in the article:
_Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._
## Author - Publication
```
@inproceedings{chalkidis-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io)
| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) | |
AnonymousSub/SR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
} | 1 | null | HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was proposed which introduced a new layer to the Novel Facebook Wav2vec to capture the disparity between the baseline model and Nigerian English Speeches. A CTC loss was also inserted on top of the model which adds flexibility to the speech-text alignment. This resulted in over 20% improvement in the performance for NAE.T
Fine-tuned facebook/wav2vec2-large on English using the UISpeech Corpus. When using this model, make sure that your speech input is sampled at 16kHz.
The script used for training can be found here: https://github.com/amceejay/HIYACCENT-NE-Speech-Recognition-System
##Usage: The model can be used directly (without a language model) as follows...
#Using the ASRecognition library:
from asrecognition import ASREngine
asr = ASREngine("fr", model_path="codeceejay/HIYACCENT_Wav2Vec2")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)
##Writing your own inference speech:
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "codeceejay/HIYACCENT_Wav2Vec2"
SAMPLES = 10
#You can use common_voice/timit or Nigerian Accented Speeches can also be found here: https://openslr.org/70/
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], 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)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
|
AnonymousSub/SR_SDR_HF_model_base | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
}
} | 1 | null | ---
language: "ca"
tags:
- masked-lm
- catalan
- exbert
license: mit
---
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version and `base-uncased` (the one you're looking at) as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/).
For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert)
## Pre-trained models
| Model | Arch. | Training data |
| ----------------------------------- | -------------- | ---------------------- |
| `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) |
| `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) |
## How to use Calbert with HuggingFace
#### Load Calbert and its tokenizer:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-base-uncased")
model = AutoModel.from_pretrained("codegram/calbert-base-uncased")
model.eval() # disable dropout (or leave in train mode to finetune
```
#### Filling masks using pipeline
```python
from transformers import pipeline
calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-base-uncased", tokenizer="codegram/calbert-base-uncased")
results = calbert_fill_mask("M'agrada [MASK] això")
# results
# [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.614592969417572, 'token': 61},
# {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.06058056280016899, 'token': 4867},
# {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.017195818945765495, 'token': 43},
# {'sequence': "[CLS] m'agrada llegir aixo[SEP]", 'score': 0.016321714967489243, 'token': 684},
# {'sequence': "[CLS] m'agrada escriure aixo[SEP]", 'score': 0.012185849249362946, 'token': 1306}]
```
#### Extract contextual embedding features from Calbert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("M'és una mica igual")
# ['▁m', "'", 'es', '▁una', '▁mica', '▁igual']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [2, 109, 7, 71, 36, 371, 1103, 3]
# NB: Can be done in one step : tokenize.encode("M'és una mica igual")
# Feed tokens to Calbert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = model(encoded_sentence)
embeddings.size()
# torch.Size([1, 8, 768])
embeddings.detach()
# tensor([[[-0.0261, 0.1166, -0.1075, ..., -0.0368, 0.0193, 0.0017],
# [ 0.1289, -0.2252, 0.9881, ..., -0.1353, 0.3534, 0.0734],
# [-0.0328, -1.2364, 0.9466, ..., 0.3455, 0.7010, -0.2085],
# ...,
# [ 0.0397, -1.0228, -0.2239, ..., 0.2932, 0.1248, 0.0813],
# [-0.0261, 0.1165, -0.1074, ..., -0.0368, 0.0193, 0.0017],
# [-0.1934, -0.2357, -0.2554, ..., 0.1831, 0.6085, 0.1421]]])
```
## Authors
CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research.
<a href="https://huggingface.co/exbert/?model=codegram/calbert-base-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
AnonymousSub/SR_bert-base-uncased | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 3 | null | ---
language: "ca"
tags:
- masked-lm
- catalan
- exbert
license: mit
---
# Calbert: a Catalan Language Model
## Introduction
CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture.
It is now available on Hugging Face in its `tiny-uncased` version (the one you're looking at) and `base-uncased` as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/).
For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert)
## Pre-trained models
| Model | Arch. | Training data |
| ----------------------------------- | -------------- | ---------------------- |
| `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) |
| `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) |
## How to use Calbert with HuggingFace
#### Load Calbert and its tokenizer:
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-tiny-uncased")
model = AutoModel.from_pretrained("codegram/calbert-tiny-uncased")
model.eval() # disable dropout (or leave in train mode to finetune
```
#### Filling masks using pipeline
```python
from transformers import pipeline
calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-tiny-uncased", tokenizer="codegram/calbert-tiny-uncased")
results = calbert_fill_mask("M'agrada [MASK] això")
# results
# [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.4403671622276306, 'token': 61},
# {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.050061386078596115, 'token': 43},
# {'sequence': "[CLS] m'agrada veure aixo[SEP]", 'score': 0.026286985725164413, 'token': 157},
# {'sequence': "[CLS] m'agrada bastant aixo[SEP]", 'score': 0.022483550012111664, 'token': 2143},
# {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.014491282403469086, 'token': 4867}]
```
#### Extract contextual embedding features from Calbert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("M'és una mica igual")
# ['▁m', "'", 'es', '▁una', '▁mica', '▁igual']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [2, 109, 7, 71, 36, 371, 1103, 3]
# NB: Can be done in one step : tokenize.encode("M'és una mica igual")
# Feed tokens to Calbert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = model(encoded_sentence)
embeddings.size()
# torch.Size([1, 8, 312])
embeddings.detach()
# tensor([[[-0.2726, -0.9855, 0.9643, ..., 0.3511, 0.3499, -0.1984],
# [-0.2824, -1.1693, -0.2365, ..., -3.1866, -0.9386, -1.3718],
# [-2.3645, -2.2477, -1.6985, ..., -1.4606, -2.7294, 0.2495],
# ...,
# [ 0.8800, -0.0244, -3.0446, ..., 0.5148, -3.0903, 1.1879],
# [ 1.1300, 0.2425, 0.2162, ..., -0.5722, -2.2004, 0.4045],
# [ 0.4549, -0.2378, -0.2290, ..., -2.1247, -2.2769, -0.0820]]])
```
## Authors
CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research.
<a href="https://huggingface.co/exbert/?model=codegram/calbert-tiny-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
AnonymousSub/SR_consert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"BertModel"
],
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}
} | 2 | null | This model is a paraphraser designed for the Adversarial Paraphrasing Task described and used in this paper: https://aclanthology.org/2021.acl-long.552/.
Please refer to `nap_generation.py` on the github repository for ways to better utilize this model using concepts of top-k sampling and top-p sampling. The demo on huggingface will output only one sentence which will most likely be the same as the input sentence since the model is supposed to output using beam search and sampling.
Github repository: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git
Please cite the following if you use this model:
```bib
@inproceedings{nighojkar-licato-2021-improving,
title = "Improving Paraphrase Detection with the Adversarial Paraphrasing Task",
author = "Nighojkar, Animesh and
Licato, John",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.552",
pages = "7106--7116",
abstract = "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.",
}
``` |
AnonymousSub/SR_declutr | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
} | 6 | null | This model is a paraphrase detector trained on the Adversarial Paraphrasing datasets described and used in this paper: https://aclanthology.org/2021.acl-long.552/.
Github repository: https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt.git
Please cite the following if you use this model:
```bib
@inproceedings{nighojkar-licato-2021-improving,
title = "Improving Paraphrase Detection with the Adversarial Paraphrasing Task",
author = "Nighojkar, Animesh and
Licato, John",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.552",
pages = "7106--7116",
abstract = "If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.",
}
``` |
AnonymousSub/SR_rule_based_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9843042559613643
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
- Precision: 0.9272
- Recall: 0.9382
- F1: 0.9327
- Accuracy: 0.9843
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2432 | 1.0 | 878 | 0.0689 | 0.9132 | 0.9203 | 0.9168 | 0.9813 |
| 0.0507 | 2.0 | 1756 | 0.0608 | 0.9208 | 0.9346 | 0.9276 | 0.9835 |
| 0.03 | 3.0 | 2634 | 0.0611 | 0.9272 | 0.9382 | 0.9327 | 0.9843 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | This is a RoBERTa-large classifier trained on the CoLA corpus [Warstadt et al., 2019](https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00290),
which contains sentences paired with grammatical acceptability judgments. The model can be used to evaluate fluency of machine-generated English sentences, e.g. for evaluation of text style transfer.
The model was trained in the paper [Krishna et al, 2020. Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700), and its original version is available at [their project page](http://style.cs.umass.edu). We converted this model from Fairseq to Transformers format. All credit goes to the authors of the original paper.
## Citation
If you found this model useful and refer to it, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
``` |
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
language: ["ru"]
tags:
- sentence-similarity
- text-classification
datasets:
- merionum/ru_paraphraser
---
This is a version of paraphrase detector by DeepPavlov ([details in the documentation](http://docs.deeppavlov.ai/en/master/features/overview.html#ranking-model-docs)) ported to the `Transformers` format.
All credit goes to the authors of DeepPavlov.
The model has been trained on the dataset from http://paraphraser.ru/.
It classifies texts as paraphrases (class 1) or non-paraphrases (class 0).
```python
import torch
from transformers import AutoModelForSequenceClassification, BertTokenizer
model_name = 'cointegrated/rubert-base-cased-dp-paraphrase-detection'
model = AutoModelForSequenceClassification.from_pretrained(model_name).cuda()
tokenizer = BertTokenizer.from_pretrained(model_name)
def compare_texts(text1, text2):
batch = tokenizer(text1, text2, return_tensors='pt').to(model.device)
with torch.inference_mode():
proba = torch.softmax(model(**batch).logits, -1).cpu().numpy()
return proba[0] # p(non-paraphrase), p(paraphrase)
print(compare_texts('Сегодня на улице хорошая погода', 'Сегодня на улице отвратительная погода'))
# [0.7056226 0.2943774]
print(compare_texts('Сегодня на улице хорошая погода', 'Отличная погодка сегодня выдалась'))
# [0.16524374 0.8347562 ]
```
P.S. In the DeepPavlov repository, the tokenizer uses `max_seq_length=64`.
This model, however, uses `model_max_length=512`.
Therefore, the results on long texts may be inadequate. |
AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
language: ru
pipeline_tag: zero-shot-classification
tags:
- rubert
- russian
- nli
- rte
- zero-shot-classification
widget:
- text: "Я хочу поехать в Австралию"
candidate_labels: "спорт,путешествия,музыка,кино,книги,наука,политика"
hypothesis_template: "Тема текста - {}."
---
# RuBERT for NLI (natural language inference)
This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.
## Usage
How to run the model for NLI:
```python
# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_checkpoint = 'cointegrated/rubert-base-cased-nli-threeway'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
model.cuda()
text1 = 'Сократ - человек, а все люди смертны.'
text2 = 'Сократ никогда не умрёт.'
with torch.inference_mode():
out = model(**tokenizer(text1, text2, return_tensors='pt').to(model.device))
proba = torch.softmax(out.logits, -1).cpu().numpy()[0]
print({v: proba[k] for k, v in model.config.id2label.items()})
# {'entailment': 0.009525929, 'contradiction': 0.9332064, 'neutral': 0.05726764}
```
You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis:
```python
def predict_zero_shot(text, label_texts, model, tokenizer, label='entailment', normalize=True):
label_texts
tokens = tokenizer([text] * len(label_texts), label_texts, truncation=True, return_tensors='pt', padding=True)
with torch.inference_mode():
result = torch.softmax(model(**tokens.to(model.device)).logits, -1)
proba = result[:, model.config.label2id[label]].cpu().numpy()
if normalize:
proba /= sum(proba)
return proba
classes = ['Я доволен', 'Я недоволен']
predict_zero_shot('Какая гадость эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.05609814, 0.9439019 ], dtype=float32)
predict_zero_shot('Какая вкусная эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.9059292 , 0.09407079], dtype=float32)
```
Alternatively, you can use [Huggingface pipelines](https://huggingface.co/transformers/main_classes/pipelines.html) for inference.
## Sources
The model has been trained on a series of NLI datasets automatically translated to Russian from English.
Most datasets were taken [from the repo of Felipe Salvatore](https://github.com/felipessalvatore/NLI_datasets):
[JOCI](https://github.com/sheng-z/JOCI),
[MNLI](https://cims.nyu.edu/~sbowman/multinli/),
[MPE](https://aclanthology.org/I17-1011/),
[SICK](http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf),
[SNLI](https://nlp.stanford.edu/projects/snli/).
Some datasets obtained from the original sources:
[ANLI](https://github.com/facebookresearch/anli),
[NLI-style FEVER](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md),
[IMPPRES](https://github.com/facebookresearch/Imppres).
## Performance
The table below shows ROC AUC (one class vs rest) for five models on the corresponding *dev* sets:
- [tiny](https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli): a small BERT predicting entailment vs not_entailment
- [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway): a base-sized BERT predicting entailment vs not_entailment
- [threeway](https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway) (**this model**): a base-sized BERT predicting entailment vs contradiction vs neutral
- [vicgalle-xlm](https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli): a large multilingual NLI model
- [facebook-bart](https://huggingface.co/facebook/bart-large-mnli): a large multilingual NLI model
|model |add_one_rte|anli_r1|anli_r2|anli_r3|copa|fever|help|iie |imppres|joci|mnli |monli|mpe |scitail|sick|snli|terra|total |
|------------------------|-----------|-------|-------|-------|----|-----|----|-----|-------|----|-----|-----|----|-------|----|----|-----|------|
|n_observations |387 |1000 |1000 |1200 |200 |20474|3355|31232|7661 |939 |19647|269 |1000|2126 |500 |9831|307 |101128|
|tiny/entailment |0.77 |0.59 |0.52 |0.53 |0.53|0.90 |0.81|0.78 |0.93 |0.81|0.82 |0.91 |0.81|0.78 |0.93|0.95|0.67 |0.77 |
|twoway/entailment |0.89 |0.73 |0.61 |0.62 |0.58|0.96 |0.92|0.87 |0.99 |0.90|0.90 |0.99 |0.91|0.96 |0.97|0.97|0.87 |0.86 |
|threeway/entailment |0.91 |0.75 |0.61 |0.61 |0.57|0.96 |0.56|0.61 |0.99 |0.90|0.91 |0.67 |0.92|0.84 |0.98|0.98|0.90 |0.80 |
|vicgalle-xlm/entailment |0.88 |0.79 |0.63 |0.66 |0.57|0.93 |0.56|0.62 |0.77 |0.80|0.90 |0.70 |0.83|0.84 |0.91|0.93|0.93 |0.78 |
|facebook-bart/entailment|0.51 |0.41 |0.43 |0.47 |0.50|0.74 |0.55|0.57 |0.60 |0.63|0.70 |0.52 |0.56|0.68 |0.67|0.72|0.64 |0.58 |
|threeway/contradiction | |0.71 |0.64 |0.61 | |0.97 | | |1.00 |0.77|0.92 | |0.89| |0.99|0.98| |0.85 |
|threeway/neutral | |0.79 |0.70 |0.62 | |0.91 | | |0.99 |0.68|0.86 | |0.79| |0.96|0.96| |0.83 |
For evaluation (and for training of the [tiny](https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli) and [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway) models), some extra datasets were used:
[Add-one RTE](https://cs.brown.edu/people/epavlick/papers/ans.pdf),
[CoPA](https://people.ict.usc.edu/~gordon/copa.html),
[IIE](https://aclanthology.org/I17-1100), and
[SCITAIL](https://allenai.org/data/scitail) taken from [the repo of Felipe Salvatore](https://github.com/felipessalvatore/NLI_datasets) and translatted,
[HELP](https://github.com/verypluming/HELP) and [MoNLI](https://github.com/atticusg/MoNLI) taken from the original sources and translated,
and Russian [TERRa](https://russiansuperglue.com/ru/tasks/task_info/TERRa).
|
AnonymousSub/SR_rule_based_roberta_only_classfn_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
language: ["ru"]
tags:
- russian
- classification
- toxicity
- multilabel
widget:
- text: "Иди ты нафиг!"
---
This is the [cointegrated/rubert-tiny](https://huggingface.co/cointegrated/rubert-tiny) model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks.
The problem is formulated as multilabel classification with the following classes:
- `non-toxic`: the text does NOT contain insults, obscenities, and threats, in the sense of the [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) competition.
- `insult`
- `obscenity`
- `threat`
- `dangerous`: the text is inappropriate, in the sense of [Babakov et.al.](https://arxiv.org/abs/2103.05345), i.e. it can harm the reputation of the speaker.
A text can be considered safe if it is BOTH `non-toxic` and NOT `dangerous`.
## Usage
The function below estimates the probability that the text is either toxic OR dangerous:
```python
# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_checkpoint = 'cointegrated/rubert-tiny-toxicity'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
model.cuda()
def text2toxicity(text, aggregate=True):
""" Calculate toxicity of a text (if aggregate=True) or a vector of toxicity aspects (if aggregate=False)"""
with torch.no_grad():
inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()
if isinstance(text, str):
proba = proba[0]
if aggregate:
return 1 - proba.T[0] * (1 - proba.T[-1])
return proba
print(text2toxicity('я люблю нигеров', True))
# 0.9350118728093193
print(text2toxicity('я люблю нигеров', False))
# [0.9715758 0.0180863 0.0045551 0.00189755 0.9331106 ]
print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], True))
# [0.93501186 0.04156357]
print(text2toxicity(['я люблю нигеров', 'я люблю африканцев'], False))
# [[9.7157580e-01 1.8086294e-02 4.5550885e-03 1.8975559e-03 9.3311059e-01]
# [9.9979788e-01 1.9048342e-04 1.5297388e-04 1.7452303e-04 4.1369814e-02]]
```
## Training
The model has been trained on the joint dataset of [OK ML Cup](https://cups.mail.ru/ru/tasks/1048) and [Babakov et.al.](https://arxiv.org/abs/2103.05345) with `Adam` optimizer, the learning rate of `1e-5`, and batch size of `64` for `15` epochs. A text was considered inappropriate if its inappropriateness score was higher than 0.8, and appropriate - if it was lower than 0.2. The per-label ROC AUC on the dev set is:
```
non-toxic : 0.9937
insult : 0.9912
obscenity : 0.9881
threat : 0.9910
dangerous : 0.8295
``` |
AnonymousSub/SR_rule_based_roberta_only_classfn_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 3 | null | ---
language:
- ru
- en
tags:
- russian
- fill-mask
- pretraining
- embeddings
- masked-lm
- tiny
- feature-extraction
- sentence-similarity
license: mit
widget:
- text: Миниатюрная модель для [MASK] разных задач.
pipeline_tag: fill-mask
---
This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English (45 MB, 12M parameters). There is also an **updated version of this model**, [rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2), with a larger vocabulary and better quality on practically all Russian NLU tasks.
This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than a base-sized BERT. Its `[CLS]` embeddings can be used as a sentence representation aligned between Russian and English.
It was trained on the [Yandex Translate corpus](https://translate.yandex.ru/corpus), [OPUS-100](https://huggingface.co/datasets/opus100) and [Tatoeba](https://huggingface.co/datasets/tatoeba), using MLM loss (distilled from [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)), translation ranking loss, and `[CLS]` embeddings distilled from [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), [rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence), Laser and USE.
There is a more detailed [description in Russian](https://habr.com/ru/post/562064/).
Sentence embeddings can be produced as follows:
```python
# pip install transformers sentencepiece
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny")
model = AutoModel.from_pretrained("cointegrated/rubert-tiny")
# model.cuda() # uncomment it if you have a GPU
def embed_bert_cls(text, model, tokenizer):
t = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**{k: v.to(model.device) for k, v in t.items()})
embeddings = model_output.last_hidden_state[:, 0, :]
embeddings = torch.nn.functional.normalize(embeddings)
return embeddings[0].cpu().numpy()
print(embed_bert_cls('привет мир', model, tokenizer).shape)
# (312,)
``` |
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | ---
language: ["ru"]
tags:
- russian
- summarization
datasets:
- IlyaGusev/gazeta
- csebuetnlp/xlsum
- mlsum
- wiki_lingua
license: mit
widget:
- text: "Высота башни составляет 324 метра (1063 фута), примерно такая же высота, как у 81-этажного здания, и самое высокое сооружение в Париже. Его основание квадратно, размером 125 метров (410 футов) с любой стороны. Во время строительства Эйфелева башня превзошла монумент Вашингтона, став самым высоким искусственным сооружением в мире, и этот титул она удерживала в течение 41 года до завершения строительство здания Крайслер в Нью-Йорке в 1930 году. Это первое сооружение которое достигло высоты 300 метров. Из-за добавления вещательной антенны на вершине башни в 1957 году она сейчас выше здания Крайслер на 5,2 метра (17 футов). За исключением передатчиков, Эйфелева башня является второй самой высокой отдельно стоящей структурой во Франции после виадука Мийо."
---
This is a model for abstractive Russian summarization, based on [cointegrated/rut5-base-multitask](https://huggingface.co/cointegrated/rut5-base-multitask) and fine-tuned on 4 datasets.
It can be used as follows:
```python
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'cointegrated/rut5-base-absum'
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
model.cuda();
model.eval();
def summarize(
text, n_words=None, compression=None,
max_length=1000, num_beams=3, do_sample=False, repetition_penalty=10.0,
**kwargs
):
"""
Summarize the text
The following parameters are mutually exclusive:
- n_words (int) is an approximate number of words to generate.
- compression (float) is an approximate length ratio of summary and original text.
"""
if n_words:
text = '[{}] '.format(n_words) + text
elif compression:
text = '[{0:.1g}] '.format(compression) + text
x = tokenizer(text, return_tensors='pt', padding=True).to(model.device)
with torch.inference_mode():
out = model.generate(
**x,
max_length=max_length, num_beams=num_beams,
do_sample=do_sample, repetition_penalty=repetition_penalty,
**kwargs
)
return tokenizer.decode(out[0], skip_special_tokens=True)
text = """Высота башни составляет 324 метра (1063 фута), примерно такая же высота, как у 81-этажного здания, и самое высокое сооружение в Париже. Его основание квадратно, размером 125 метров (410 футов) с любой стороны. Во время строительства Эйфелева башня превзошла монумент Вашингтона, став самым высоким искусственным сооружением в мире, и этот титул она удерживала в течение 41 года до завершения строительство здания Крайслер в Нью-Йорке в 1930 году. Это первое сооружение которое достигло высоты 300 метров. Из-за добавления вещательной антенны на вершине башни в 1957 году она сейчас выше здания Крайслер на 5,2 метра (17 футов). За исключением передатчиков, Эйфелева башня является второй самой высокой отдельно стоящей структурой во Франции после виадука Мийо."""
print(summarize(text))
# Эйфелева башня достигла высоты 300 метров.
print(summarize(text, n_words=10))
# Французская Эйфелева башня достигла высоты 300 метров.
``` |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | ---
language:
- ru
- en
- multilingual
license: mit
tags:
- russian
---
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Russian and some English embeddings left.
* The original model has 582M parameters, with 384M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 30K (top 10K English and top 20K Russian tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
The creation of this model is described in the post [How to adapt a multilingual T5 model for a single language](https://cointegrated.medium.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) along with the source code.
|
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