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flax-community/roberta-hindi | flax-community | 2021-07-20T12:50:29Z | 105 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
widget:
- text: "मुझे उनसे बात करना <mask> अच्छा लगा"
- text: "हम आपके सुखद <mask> की कामना करते हैं"
- text: "सभी अच्छी चीजों का एक <mask> होता है"
---
# RoBERTa base model for Hindi language
Pretrained model on Hindi language using a masked language modeling (MLM) objective. [A more interactive & comparison demo is available here](https://huggingface.co/spaces/flax-community/roberta-hindi).
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-roberta-from-scratch-in-hindi/7091), organized by [Hugging Face](https://huggingface.co/) and TPU usage sponsored by Google.
## Model description
RoBERTa Hindi is a transformers model pretrained on a large corpus of Hindi data(a combination of **mc4, oscar and indic-nlp** datasets)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-hindi')
>>> unmasker("हम आपके सुखद <mask> की कामना करते हैं")
[{'score': 0.3310680091381073,
'sequence': 'हम आपके सुखद सफर की कामना करते हैं',
'token': 1349,
'token_str': ' सफर'},
{'score': 0.15317578613758087,
'sequence': 'हम आपके सुखद पल की कामना करते हैं',
'token': 848,
'token_str': ' पल'},
{'score': 0.07826550304889679,
'sequence': 'हम आपके सुखद समय की कामना करते हैं',
'token': 453,
'token_str': ' समय'},
{'score': 0.06304813921451569,
'sequence': 'हम आपके सुखद पहल की कामना करते हैं',
'token': 404,
'token_str': ' पहल'},
{'score': 0.058322224766016006,
'sequence': 'हम आपके सुखद अवसर की कामना करते हैं',
'token': 857,
'token_str': ' अवसर'}]
```
## Training data
The RoBERTa Hindi model was pretrained on the reunion of the following datasets:
- [OSCAR](https://huggingface.co/datasets/oscar) is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
- [mC4](https://huggingface.co/datasets/mc4) is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus.
- [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) is a natural language understanding benchmark.
- [Samanantar](https://indicnlp.ai4bharat.org/samanantar/) is a parallel corpora collection for Indic language.
- [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-and-large-summarization-corpus) is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites.
- [Hindi Text Short Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-summarization-corpus) is a collection of ~330k articles with their headlines collected from Hindi News Websites.
- [Old Newspapers Hindi](https://www.kaggle.com/crazydiv/oldnewspapershindi) is a cleaned subset of HC Corpora newspapers.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`.
- We had to perform cleanup of **mC4** and **oscar** datasets by removing all non hindi (non Devanagari) characters from the datasets.
- We tried to filter out evaluation set of WikiNER of [IndicGlue](https://indicnlp.ai4bharat.org/indic-glue/) benchmark by [manual labelling](https://github.com/amankhandelia/roberta_hindi/blob/master/wikiner_incorrect_eval_set.csv) where the actual labels were not correct and modifying the [downstream evaluation dataset](https://github.com/amankhandelia/roberta_hindi/blob/master/utils.py).
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores).A randomized shuffle of combined dataset of **mC4, oscar** and other datasets listed above was used to train the model. Training logs are present in [wandb](https://wandb.ai/wandb/hf-flax-roberta-hindi).
## Evaluation Results
RoBERTa Hindi is evaluated on various downstream tasks. The results are summarized below.
| Task | Task Type | IndicBERT | HindiBERTa | Indic Transformers Hindi BERT | RoBERTa Hindi Guj San | RoBERTa Hindi |
|-------------------------|----------------------|-----------|------------|-------------------------------|-----------------------|---------------|
| BBC News Classification | Genre Classification | **76.44** | 66.86 | **77.6** | 64.9 | 73.67 |
| WikiNER | Token Classification | - | 90.68 | **95.09** | 89.61 | **92.76** |
| IITP Product Reviews | Sentiment Analysis | **78.01** | 73.23 | **78.39** | 66.16 | 75.53 |
| IITP Movie Reviews | Sentiment Analysis | 60.97 | 52.26 | **70.65** | 49.35 | **61.29** |
## Team Members
- Aman K ([amankhandelia](https://huggingface.co/amankhandelia))
- Haswanth Aekula ([hassiahk](https://huggingface.co/hassiahk))
- Kartik Godawat ([dk-crazydiv](https://huggingface.co/dk-crazydiv))
- Prateek Agrawal ([prateekagrawal](https://huggingface.co/prateekagrawal))
- Rahul Dev ([mlkorra](https://huggingface.co/mlkorra))
## Credits
Huge thanks to Hugging Face 🤗 & Google Jax/Flax team for such a wonderful community week, especially for providing such massive computing resources. Big thanks to [Suraj Patil](https://huggingface.co/valhalla) & [Patrick von Platen](https://huggingface.co/patrickvonplaten) for mentoring during the whole week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:medium> |
huggingtweets/yigitckahyaoglu | huggingtweets | 2021-07-20T03:00:16Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/yigitckahyaoglu/1626750011426/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1407084026507182089/ywRe7M0Z_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">yiğit</div>
<div style="text-align: center; font-size: 14px;">@yigitckahyaoglu</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from yiğit.
| Data | yiğit |
| --- | --- |
| Tweets downloaded | 1671 |
| Retweets | 165 |
| Short tweets | 64 |
| Tweets kept | 1442 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2cqhj21l/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @yigitckahyaoglu's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2k3eal89/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/yigitckahyaoglu')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
mnaylor/base-bert-finetuned-mtsamples | mnaylor | 2021-07-19T15:53:35Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # BERT Base Fine-tuned on MTSamples
This model is [BERT-base](https://huggingface.co/bert-base-uncased) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
|
mnaylor/bioclinical-bert-finetuned-mtsamples | mnaylor | 2021-07-19T15:52:36Z | 12 | 3 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # BioClinical BERT Fine-tuned on MTSamples
This model is simply [Alsentzer's Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp). |
BumBelDumBel/ZORK_AI_SCIFI | BumBelDumBel | 2021-07-19T14:51:33Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
model_index:
- name: ZORK_AI_SCIFI
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# ZORK_AI_SCIFI
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
flax-community/wav2vec2-dhivehi | flax-community | 2021-07-19T09:40:30Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"wav2vec2",
"pretraining",
"automatic-speech-recognition",
"dv",
"dataset:common_voice",
"arxiv:2006.11477",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: dv
tags:
- automatic-speech-recognition
datasets:
- common_voice
---
# Wav2Vec2 Dhivehi
Wav2vec2 pre-pretrained from scratch using common voice dhivehi dataset. The model was trained with Flax during the [Flax/Jax Community Week](https://discss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organised by HuggingFace.
## Model description
The model used for training is [Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) by FacebookAI. It was introduced in the paper
"wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations" by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli (https://arxiv.org/abs/2006.11477).
This model is available in the 🤗 [Model Hub](https://huggingface.co/facebook/wav2vec2-base-960h).
## Training data
Dhivehi data from [Common Voice](https://commonvoice.mozilla.org/en/datasets).
The dataset is also available in the 🤗 [Datasets](https://huggingface.co/datasets/common_voice) library.
## Team members
- Shahu Kareem ([@shahukareem](https://huggingface.co/shahukareem))
- Eyna ([@eyna](https://huggingface.co/eyna))
|
huggingtweets/deathbattlebot | huggingtweets | 2021-07-19T06:42:58Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/deathbattlebot/1626676974616/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1295317265257238529/8q3IptgS_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Death Battle Bot</div>
<div style="text-align: center; font-size: 14px;">@deathbattlebot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Death Battle Bot.
| Data | Death Battle Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1 |
| Short tweets | 20 |
| Tweets kept | 3229 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hcf8oqg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deathbattlebot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2d0vuhj5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2d0vuhj5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/deathbattlebot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/Image-captioning-Indonesia | flax-community | 2021-07-19T05:59:03Z | 7 | 0 | transformers | [
"transformers",
"jax",
"clip-vision-marian",
"text2text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: id
---
# Image-captioning-Indonesia
This is an encoder-decoder image captioning model using [CLIP](https://huggingface.co/transformers/model_doc/clip.html) as the visual encoder and [Marian](https://huggingface.co/transformers/model_doc/marian.html) as the textual decoder on datasets with Indonesian captions.
This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
## How to use
At time of writing, you will need to install [HuggingFace](https://github.com/huggingface/) from its latest master branch in order to load `FlaxMarian`.
You will also need to have the [`flax_clip_vision_marian` folder](https://github.com/indonesian-nlp/Indonesia-Image-Captioning/tree/main/flax_clip_vision_marian) in your project directory to load the model using the `FlaxCLIPVisionMarianForConditionalGeneration` class.
```python
from torchvision.io import ImageReadMode, read_image
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torchvision.transforms.functional import InterpolationMode
import torch
import numpy as np
from transformers import MarianTokenizer
from flax_clip_vision_marian.modeling_clip_vision_marian import FlaxCLIPVisionMarianForConditionalGeneration
clip_marian_model_name = 'flax-community/Image-captioning-Indonesia'
model = FlaxCLIPVisionMarianForConditionalGeneration.from_pretrained(clip_marian_model_name)
marian_model_name = 'Helsinki-NLP/opus-mt-en-id'
tokenizer = MarianTokenizer.from_pretrained(marian_model_name)
config = model.config
image_size = config.clip_vision_config.image_size
# Image transformation
transforms = torch.nn.Sequential(
Resize([image_size], interpolation=InterpolationMode.BICUBIC),
CenterCrop(image_size),
ConvertImageDtype(torch.float),
Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
)
# Hyperparameters
max_length = 8
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def generate_step(batch):
output_ids = model.generate(pixel_values, **gen_kwargs)
token_ids = np.array(output_ids.sequences)[0]
caption = tokenizer.decode(token_ids)
return caption
image_file_path = image_file_path
image = read_image(image_file_path, mode=ImageReadMode.RGB)
image = transforms(image)
pixel_values = torch.stack([image]).permute(0, 2, 3, 1).numpy()
generated_ids = generate_step(pixel_values)
print(generated_ids)
```
## Training data
The Model was trained on translated Coco,Flickr and ViZWiz, each of them were translated using google translate and marian mt. we took only random 2 captions per image for each datasets
## Training procedure
The model was trained on a TPUv3-8 VM provided by the Google Cloud team.
## Team members
- Cahya Wirawan ([@cahya](https://huggingface.co/cahya))
- Galuh Sahid ([@Galuh](https://huggingface.co/Galuh))
- Muhammad Agung Hambali ([@AyameRushia](https://huggingface.co/AyameRushia))
- Samsul Rahmadani ([@munggok](https://huggingface.co/munggok)) |
huggingtweets/heyarav | huggingtweets | 2021-07-19T01:28:18Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1416877970132672512/942NnDJA_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Arav</div>
<div style="text-align: center; font-size: 14px;">@heyarav</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Arav.
| Data | Arav |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 411 |
| Short tweets | 786 |
| Tweets kept | 2049 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n441q7z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @heyarav's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s8u4vm6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s8u4vm6/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/heyarav')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/ellis_hughes | huggingtweets | 2021-07-18T18:42:16Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/ellis_hughes/1626633732954/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1004536007012651008/ZWJUeJ2W_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ellis Hughes</div>
<div style="text-align: center; font-size: 14px;">@ellis_hughes</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ellis Hughes.
| Data | Ellis Hughes |
| --- | --- |
| Tweets downloaded | 2170 |
| Retweets | 396 |
| Short tweets | 91 |
| Tweets kept | 1683 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rqrdlum/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ellis_hughes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3n17xu9k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3n17xu9k/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ellis_hughes')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
imvladikon/bert-base-uncased-jigsaw | imvladikon | 2021-07-18T15:46:05Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"generated_from_trainer",
"en",
"dataset:jigsaw",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language:
- en
license:
tags:
- generated_from_trainer
datasets:
- jigsaw
model_index:
- name: bert-base-uncased
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
This model is a fine-tuned version of [](https://huggingface.co/) on the jigsaw dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0393
- Precision Micro: 0.7758
- Recall Micro: 0.7858
- F1 Micro: 0.7808
- F2 Micro: 0.7838
- Precision Macro: 0.6349
- Recall Macro: 0.5972
- F1 Macro: 0.6105
- F2 Macro: 0.6015
- Overall Precision: 0.9841
- Overall Recall: 0.9841
- Overall F1: 0.9841
- Overall F2: 0.9841
- Overall Accuracy: 0.9841
- Matthews Corrcoef: 0.7725
- Aucroc Macro: 0.9897
- Aucroc Micro: 0.9920
- Accuracy Toxic: 0.9678
- F1 Toxic: 0.8295
- Accuracy Severe Toxic: 0.9899
- F1 Severe Toxic: 0.3313
- Accuracy Obscene: 0.9816
- F1 Obscene: 0.8338
- Accuracy Threat: 0.9974
- F1 Threat: 0.4545
- Accuracy Insult: 0.9763
- F1 Insult: 0.7662
- Accuracy Identity Hate: 0.9914
- F1 Identity Hate: 0.4480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Recall Micro | F1 Micro | F2 Micro | Precision Macro | Recall Macro | F1 Macro | F2 Macro | Overall Precision | Overall Recall | Overall F1 | Overall F2 | Overall Accuracy | Matthews Corrcoef | Aucroc Macro | Aucroc Micro | Accuracy Toxic | F1 Toxic | Accuracy Severe Toxic | F1 Severe Toxic | Accuracy Obscene | F1 Obscene | Accuracy Threat | F1 Threat | Accuracy Insult | F1 Insult | Accuracy Identity Hate | F1 Identity Hate |
|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:--------:|:---------------:|:------------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:----------:|:----------------:|:-----------------:|:------------:|:------------:|:--------------:|:--------:|:---------------------:|:---------------:|:----------------:|:----------:|:---------------:|:---------:|:---------------:|:---------:|:----------------------:|:----------------:|
| 0.0433 | 1.0 | 2659 | 0.0423 | 0.7607 | 0.7798 | 0.7702 | 0.7759 | 0.6398 | 0.5561 | 0.5585 | 0.5535 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | 0.7615 | 0.9887 | 0.9908 | 0.9671 | 0.8211 | 0.9878 | 0.4354 | 0.9805 | 0.8265 | 0.9974 | 0.2243 | 0.9746 | 0.7602 | 0.9918 | 0.2834 |
| 0.0366 | 2.0 | 5318 | 0.0393 | 0.7758 | 0.7858 | 0.7808 | 0.7838 | 0.6349 | 0.5972 | 0.6105 | 0.6015 | 0.9841 | 0.9841 | 0.9841 | 0.9841 | 0.9841 | 0.7725 | 0.9897 | 0.9920 | 0.9678 | 0.8295 | 0.9899 | 0.3313 | 0.9816 | 0.8338 | 0.9974 | 0.4545 | 0.9763 | 0.7662 | 0.9914 | 0.4480 |
| 0.0305 | 3.0 | 7977 | 0.0399 | 0.7608 | 0.8186 | 0.7887 | 0.8064 | 0.6621 | 0.6856 | 0.6715 | 0.6794 | 0.9842 | 0.9842 | 0.9842 | 0.9842 | 0.9842 | 0.7810 | 0.9897 | 0.9919 | 0.9662 | 0.8272 | 0.9892 | 0.4772 | 0.9815 | 0.8347 | 0.9977 | 0.5629 | 0.9772 | 0.7740 | 0.9931 | 0.5528 |
| 0.0263 | 4.0 | 10636 | 0.0435 | 0.7333 | 0.8336 | 0.7803 | 0.8114 | 0.6395 | 0.7039 | 0.6687 | 0.6890 | 0.9830 | 0.9830 | 0.9830 | 0.9830 | 0.9830 | 0.7732 | 0.9897 | 0.9912 | 0.9608 | 0.8083 | 0.9898 | 0.4791 | 0.9812 | 0.8319 | 0.9972 | 0.5368 | 0.9756 | 0.7700 | 0.9935 | 0.5861 |
| 0.0218 | 5.0 | 13295 | 0.0456 | 0.7480 | 0.8108 | 0.7781 | 0.7974 | 0.6661 | 0.6720 | 0.6662 | 0.6691 | 0.9833 | 0.9833 | 0.9833 | 0.9833 | 0.9833 | 0.7701 | 0.9890 | 0.9907 | 0.9612 | 0.8071 | 0.9894 | 0.4642 | 0.9823 | 0.8354 | 0.9977 | 0.5325 | 0.9754 | 0.7613 | 0.9936 | 0.5968 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3
|
sehandev/koelectra-qa | sehandev | 2021-07-18T14:21:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: koelectra-qa
results:
- task:
name: Question Answering
type: question-answering
---
<!-- 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. -->
# koelectra-qa
This model was trained from scratch on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1
- Datasets 1.9.0
- Tokenizers 0.10.3
|
jacobshein/danish-bert-botxo-qa-squad | jacobshein | 2021-07-18T11:19:49Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"danish",
"question answering",
"squad",
"machine translation",
"botxo",
"da",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language: da
tags:
- danish
- bert
- question answering
- squad
- machine translation
- botxo
license: cc-by-4.0
datasets:
- common_crawl
- wikipedia
- dindebat.dk
- hestenettet.dk
- danish OpenSubtitles
widget:
- context: Stine sagde hej, men Jacob sagde halløj.
---
# Danish BERT (version 2, uncased) by [BotXO](https://github.com/botxo/nordic_bert) fine-tuned for Question Answering (QA) on the [machine-translated SQuAD-da dataset](https://github.com/ccasimiro88/TranslateAlignRetrieve/tree/multilingual/squads-tar/da)
```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("jacobshein/danish-bert-botxo-qa-squad")
model = AutoModelForQuestionAnswering.from_pretrained("jacobshein/danish-bert-botxo-qa-squad")
```
#### Contact
For further information on usage or fine-tuning procedure, please reach out by email through [jacobhein.com](https://jacobhein.com/#contact).
|
huggingtweets/trevorthalacker | huggingtweets | 2021-07-18T07:39:42Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/trevorthalacker/1626593979307/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1414806244536242178/hxQldoS0_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Trevor</div>
<div style="text-align: center; font-size: 14px;">@trevorthalacker</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Trevor.
| Data | Trevor |
| --- | --- |
| Tweets downloaded | 1792 |
| Retweets | 123 |
| Short tweets | 227 |
| Tweets kept | 1442 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kmb92alm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @trevorthalacker's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37c7xs83) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37c7xs83/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/trevorthalacker')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sehandev/koelectra-long-qa | sehandev | 2021-07-18T06:01:25Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: koelectra-long-qa
results:
- task:
name: Question Answering
type: question-answering
---
<!-- 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. -->
# koelectra-long-qa
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1
- Datasets 1.9.0
- Tokenizers 0.10.3
|
johnpaulbin/gpt2-skript-80-v3 | johnpaulbin | 2021-07-18T04:53:22Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | GPT-2 Skript 80k lines. v3
Training loss: `0.594200`
1.5 GB
Inferencing colab: https://colab.research.google.com/drive/1uTAPLa1tuNXFpG0qVLSseMro6iU9-xNc |
flax-community/roberta-base-mr | flax-community | 2021-07-17T15:30:40Z | 28 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
widget:
- text: "अध्यक्ष <mask> पवार आणि उपमुख्यमंत्री अजित पवार यांची भेट घेतली."
- text: "मोठी बातमी! उद्या दुपारी <mask> वाजता जाहीर होणार दहावीचा निकाल"
---
# RoBERTa base model for Marathi language (मराठी भाषा)
Pretrained model on Marathi language using a masked language modeling (MLM) objective. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). We trained RoBERTa model for Marathi Language during community week hosted by Huggingface 🤗 using JAX/Flax for NLP & CV jax.
<img src="https://user-images.githubusercontent.com/15062408/126040902-ea8808db-ec30-4a3f-bf95-5d3b10d674e9.png" alt="huggingface-marathi-roberta" width="350" height="350" style="text-align: center">
## Model description
Marathi RoBERTa is a transformers model pretrained on a large corpus of Marathi data in a self-supervised fashion.
## Intended uses & limitations❗️
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. We used this model to fine tune on text classification task for iNLTK and indicNLP news text classification problem statement. Since marathi mc4 dataset is made by scraping marathi newspapers text, it will involve some biases which will also affect all fine-tuned versions of this model.
### How to use❓
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-base-mr')
>>> unmasker("मोठी बातमी! उद्या दुपारी <mask> वाजता जाहीर होणार दहावीचा निकाल")
[{'score': 0.057209037244319916,'sequence': 'मोठी बातमी! उद्या दुपारी आठ वाजता जाहीर होणार दहावीचा निकाल',
'token': 2226,
'token_str': 'आठ'},
{'score': 0.02796074189245701,
'sequence': 'मोठी बातमी! उद्या दुपारी २० वाजता जाहीर होणार दहावीचा निकाल',
'token': 987,
'token_str': '२०'},
{'score': 0.017235398292541504,
'sequence': 'मोठी बातमी! उद्या दुपारी नऊ वाजता जाहीर होणार दहावीचा निकाल',
'token': 4080,
'token_str': 'नऊ'},
{'score': 0.01691395975649357,
'sequence': 'मोठी बातमी! उद्या दुपारी २१ वाजता जाहीर होणार दहावीचा निकाल',
'token': 1944,
'token_str': '२१'},
{'score': 0.016252165660262108,
'sequence': 'मोठी बातमी! उद्या दुपारी ३ वाजता जाहीर होणार दहावीचा निकाल',
'token': 549,
'token_str': ' ३'}]
```
## Training data 🏋🏻♂️
The RoBERTa Marathi model was pretrained on `mr` dataset of C4 multilingual dataset:
<br>
<br>
[C4 (Colossal Clean Crawled Corpus)](https://yknzhu.wixsite.com/mbweb), Introduced by Raffel et al. in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://paperswithcode.com/paper/exploring-the-limits-of-transfer-learning).
The dataset can be downloaded in a pre-processed form from [allennlp](https://github.com/allenai/allennlp/discussions/5056) or huggingface's datsets - [mc4 dataset](https://huggingface.co/datasets/mc4).
Marathi (`mr`) dataset consists of 14 billion tokens, 7.8 million docs and with weight ~70 GB of text.
## Data Cleaning 🧹
Though initial `mc4` marathi corpus size ~70 GB, Through data exploration, it was observed it contains docs from different languages especially thai, chinese etc. So we had to clean the dataset before traning tokenizer and model. Surprisingly, results after cleaning Marathi mc4 corpus data:
#### **Train set:**
Clean docs count 1581396 out of 7774331. <br>
**~20.34%** of whole marathi train split is actually Marathi.
#### **Validation set**
Clean docs count 1700 out of 7928. <br>
**~19.90%** of whole marathi validation split is actually Marathi.
## Training procedure 👨🏻💻
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 42K steps with a batch size of 128 and a sequence length of 128. The
optimizer used is Adam with a learning rate of 3e-4, β1 = 0.9, β2 = 0.98 and
ε = 1e-8, a weight decay of 0.01, learning rate warmup for 1,000 steps and linear decay of the learning
rate after.
We tracked experiments and hyperparameter tunning on weights and biases platform. Here is link to main dashboard: <br>
[Link to Weights and Biases Dashboard for Marathi RoBERTa model](https://wandb.ai/nipunsadvilkar/roberta-base-mr/runs/19qtskbg?workspace=user-nipunsadvilkar)
#### **Pretraining Results 📊**
RoBERTa Model reached **eval accuracy of 85.28%** around ~35K step **with train loss at 0.6507 and eval loss at 0.6219**.
## Fine Tuning on downstream tasks
We performed fine-tuning on downstream tasks. We used following datasets for classification:
1. [IndicNLP Marathi news classification](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets)
2. [iNLTK Marathi news headline classification](https://www.kaggle.com/disisbig/marathi-news-dataset)
### **Fine tuning on downstream task results (Segregated)**
#### 1. [IndicNLP Marathi news classification](https://github.com/ai4bharat-indicnlp/indicnlp_corpus#publicly-available-classification-datasets)
IndicNLP Marathi news dataset consists 3 classes - `['lifestyle', 'entertainment', 'sports']` - with following docs distribution as per classes:
| train | eval | test
| -- | -- | --
| 9672 | 477 | 478
💯 Our Marathi RoBERTa **`roberta-base-mr` model outperformed both classifier ** mentioned in [Arora, G. (2020). iNLTK](https://www.semanticscholar.org/paper/iNLTK%3A-Natural-Language-Toolkit-for-Indic-Languages-Arora/5039ed9e100d3a1cbbc25a02c82f6ee181609e83/figure/3) and [Kunchukuttan, Anoop et al. AI4Bharat-IndicNLP.](https://www.semanticscholar.org/paper/AI4Bharat-IndicNLP-Corpus%3A-Monolingual-Corpora-and-Kunchukuttan-Kakwani/7997d432925aff0ba05497d2893c09918298ca55/figure/4)
Dataset | FT-W | FT-WC | INLP | iNLTK | **roberta-base-mr 🏆**
-- | -- | -- | -- | -- | --
iNLTK Headlines | 83.06 | 81.65 | 89.92 | 92.4 | **97.48**
**🤗 Huggingface Model hub repo:**<br>
`roberta-base-mr` fine tuned on iNLTK Headlines classification dataset model:
[**`flax-community/mr-indicnlp-classifier`**](https://huggingface.co/flax-community/mr-indicnlp-classifier)
🧪 Fine tuning experiment's weight and biases dashboard [link](https://wandb.ai/nipunsadvilkar/huggingface/runs/1242bike?workspace=user-nipunsadvilkar
)
#### 2. [iNLTK Marathi news headline classification](https://www.kaggle.com/disisbig/marathi-news-dataset)
This dataset consists 3 classes - `['state', 'entertainment', 'sports']` - with following docs distribution as per classes:
| train | eval | test
| -- | -- | --
| 9658 | 1210 | 1210
💯 Here as well **`roberta-base-mr` outperformed `iNLTK` marathi news text classifier**.
Dataset | iNLTK ULMFiT | **roberta-base-mr 🏆**
-- | -- | --
iNLTK news dataset (kaggle) | 92.4 | **94.21**
**🤗 Huggingface Model hub repo:**<br>
`roberta-base-mr` fine tuned on iNLTK news classification dataset model:
[**`flax-community/mr-inltk-classifier`**](https://huggingface.co/flax-community/mr-inltk-classifier)
Fine tuning experiment's weight and biases dashboard [link](https://wandb.ai/nipunsadvilkar/huggingface/runs/2u5l9hon?workspace=user-nipunsadvilkar
)
## **Want to check how above models generalise on real world Marathi data?**
Head to 🤗 Huggingface's spaces 🪐 to play with all three models:
1. Mask Language Modelling with Pretrained Marathi RoBERTa model: <br>
[**`flax-community/roberta-base-mr`**](https://huggingface.co/flax-community/roberta-base-mr)
2. Marathi Headline classifier: <br>
[**`flax-community/mr-indicnlp-classifier`**](https://huggingface.co/flax-community/mr-indicnlp-classifier)
3. Marathi news classifier: <br>
[**`flax-community/mr-inltk-classifier`**](https://huggingface.co/flax-community/mr-inltk-classifier)
[Streamlit app of Pretrained Roberta Marathi model on Huggingface Spaces](https://huggingface.co/spaces/flax-community/roberta-base-mr)
## Team Members
- Nipun Sadvilkar [@nipunsadvilkar](https://github.com/nipunsadvilkar)
- Haswanth Aekula [@hassiahk](https://github.com/hassiahk)
## Credits
Huge thanks to Huggingface 🤗 & Google Jax/Flax team for such a wonderful community week. Especially for providing such massive computing resource. Big thanks to [@patil-suraj](https://github.com/patil-suraj) & [@patrickvonplaten](https://github.com/patrickvonplaten) for mentoring during whole week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
birgermoell/t5-base-swedish | birgermoell | 2021-07-17T07:52:39Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"feature-extraction",
"summarization",
"translation",
"sv",
"dataset:oscar",
"arxiv:1910.10683",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | translation | 2022-03-02T23:29:05Z | ---
language:
- sv
datasets:
- oscar
tags:
- summarization
- translation
license: apache-2.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
Pretraining Dataset: [C4](https://huggingface.co/datasets/oscar)
Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu*
## Abstract
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/flax-community/nordic-gpt-wiki
## Dansk gpt wiki
https://huggingface.co/flax-community/dansk-gpt-wiki
## Norsk gpt wiki
https://huggingface.co/flax-community/norsk-gpt-wiki
## Roberta models
## Nordic Roberta Wiki
https://huggingface.co/flax-community/nordic-roberta-wiki
## Swe Roberta Wiki Oscar
https://huggingface.co/flax-community/swe-roberta-wiki-oscar
## Roberta Swedish Scandi
https://huggingface.co/birgermoell/roberta-swedish-scandi
## Roberta Swedish
https://huggingface.co/birgermoell/roberta-swedish
## Swedish T5 model
https://huggingface.co/birgermoell/t5-base-swedish
|
birgermoell/swedish-gpt | birgermoell | 2021-07-17T07:45:52Z | 30 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"sv",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: sv
widget:
- text: "Jag är en svensk språkmodell."
---
## Model series
This model is part of a series of models training on TPU with Flax Jax during Huggingface Flax/Jax challenge.
## Gpt models
## Swedish Gpt
https://huggingface.co/birgermoell/swedish-gpt/
## Swedish gpt wiki
https://huggingface.co/flax-community/swe-gpt-wiki
# Nordic gpt wiki
https://huggingface.co/flax-community/nordic-gpt-wiki
## Dansk gpt wiki
https://huggingface.co/flax-community/dansk-gpt-wiki
## Norsk gpt wiki
https://huggingface.co/flax-community/norsk-gpt-wiki
## Roberta models
## Nordic Roberta Wiki
https://huggingface.co/flax-community/nordic-roberta-wiki
## Swe Roberta Wiki Oscar
https://huggingface.co/flax-community/swe-roberta-wiki-oscar
## Roberta Swedish Scandi
https://huggingface.co/birgermoell/roberta-swedish-scandi
## Roberta Swedish
https://huggingface.co/birgermoell/roberta-swedish
## Swedish T5 model
https://huggingface.co/birgermoell/t5-base-swedish
# GPT-svenska-wikipedia
A swedish GPT2 style model trained using Flax CLM pipeline on the Swedish
part of the wiki40b dataset and the Oscar dataset.
https://huggingface.co/datasets/wiki40b
The model was trained for around 22600 steps (42 hours) as part of the Huggingface Jax/Flax challenge with the following loss and learning rate
Loss: 3.1715331077575684, Learning Rate: 0.0024816440418362617)
The model could likely be trained for longer.
## Data cleaning and preprocessing
The data was cleaned and preprocessed using the following script. Make sure to install depencies for beam_runner to make the dataset work.
```python
from datasets import load_dataset
def load_and_clean_wiki():
dataset = load_dataset('wiki40b', 'sv', beam_runner='DirectRunner', split="train")
#dataset = load_dataset('wiki40b', 'sv', beam_runner='DirectRunner')
dataset = dataset.remove_columns(['wikidata_id', 'version_id'])
filtered_dataset = dataset.map(filter_wikipedia)
# filtered_dataset[:3]
# print(filtered_dataset[:3])
return filtered_dataset
def filter_wikipedia(batch):
batch["text"] = " ".join(batch["text"].split("\
_START_SECTION_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_ARTICLE_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_ARTICLE_\
"))
batch["text"] = " ".join(batch["text"].split("\
_START_PARAGRAPH_\
"))
batch["text"] = " ".join(batch["text"].split("_NEWLINE_"))
batch["text"] = " ".join(batch["text"].split("\xa0"))
return batch
```
## Training script
The following training script was used to train the model.
```bash
./run_clm_flax.py --output_dir="${MODEL_DIR}" --model_type="gpt2" --config_name="${MODEL_DIR}" --tokenizer_name="${MODEL_DIR}" --dataset_name="wiki40b" --dataset_config_name="sv" --do_train --do_eval --block_size="512" --per_device_train_batch_size="64" --per_device_eval_batch_size="64" --learning_rate="5e-3" --warmup_steps="1000" --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" --overwrite_output_dir --num_train_epochs="20" --logging_steps="500" --save_steps="1000" --eval_steps="2500" --push_to_hub
```
|
flax-community/koclip | flax-community | 2021-07-17T05:08:38Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | # KoCLIP
This repository includes
## Installation
Create a virtual env and install `requirements.txt`.
```
pip install -r requirements.txt
```
For Google Cloud TPU VM please follow necessary installation steps here:
[Pytorch on TPU VM](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm)
[JAX/Flax on TPU VM](https://cloud.google.com/tpu/docs/jax-quickstart-tpu-vm)
|
BumBelDumBel/TRUMP | BumBelDumBel | 2021-07-16T19:14:17Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
license: mit
tags:
- generated_from_trainer
model_index:
- name: TRUMP
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# TRUMP
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
huggingtweets/vishigondi | huggingtweets | 2021-07-16T19:01:40Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/vishigondi/1626462038417/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1019963234793684992/LVdF4ah2_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Vishi Gondi</div>
<div style="text-align: center; font-size: 14px;">@vishigondi</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Vishi Gondi.
| Data | Vishi Gondi |
| --- | --- |
| Tweets downloaded | 1154 |
| Retweets | 166 |
| Short tweets | 21 |
| Tweets kept | 967 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2txfnkwt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vishigondi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/eshvn0h5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/eshvn0h5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/vishigondi')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BumBelDumBel/ZORK-AI-TEST | BumBelDumBel | 2021-07-16T17:12:42Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
license: mit
tags:
- generated_from_trainer
model_index:
- name: ZORK-AI-TEST
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# ZORK-AI-TEST
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
keshan/sinhala-gpt2-newswire | keshan | 2021-07-16T15:46:36Z | 5 | 2 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"sinhala",
"si",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: si
tags:
- sinhala
- gpt2
pipeline_tag: text-generation
widget:
- text: "මම"
---
This is a finetunes version of keshan/sinhala-gpt2 with newswire articles. This was finetuned on ~12MB of data
- Num examples=8395
- Batch size =8
It got a Perplexity of 3.15 |
flax-community/gpt2-medium-persian | flax-community | 2021-07-16T13:01:08Z | 364 | 9 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"fa",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: fa
tags:
- text-generation
widget:
- text: "در یک اتفاق شگفت انگیز، پژوهشگران"
- text: "گرفتگی بینی در کودکان و بهخصوص نوزادان باعث میشود"
- text: "امیدواریم نوروز امسال سالی"
---
# GPT2 Medium 4 Persian
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-gpt2-from-scratch-in-persian/7560), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Team Members
- [Mehrdad Farahani](huggingface.co/m3hrdadfi)
- [Saied Alimoradi](https://discuss.huggingface.co/u/saied)
- [M. Reza Zerehpoosh](huggingface.co/ironcladgeek)
- [Hooman Sedghamiz](https://discuss.huggingface.co/u/hooman650)
- [Mazeyar Moeini Feizabadi](https://discuss.huggingface.co/u/mazy1998)
## Dataset
We used [Oscar](https://huggingface.co/datasets/oscar) dataset, which is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus.
## 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('flax-community/gpt2-medium-persian')
model = GPT2LMHeadModel.from_pretrained('flax-community/gpt2-medium-persian')
generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':100})
generated_text = generator('در یک اتفاق شگفت انگیز، پژوهشگران')
```
For using Tensorflow import TFGPT2LMHeadModel instead of GPT2LMHeadModel.
## Demo
... SOON
## Evaluation
... SOON |
liam168/trans-opus-mt-en-zh | liam168 | 2021-07-16T04:17:11Z | 446 | 29 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2022-03-02T23:29:05Z | ---
language:
- en
- zh
tags:
- translation
widget:
- text: "I like to study Data Science and Machine Learning."
---
# liam168/trans-opus-mt-en-zh
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
* target language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
## How to use
```python
>>> from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
>>> mode_name = 'liam168/trans-opus-mt-en-zh'
>>> model = AutoModelWithLMHead.from_pretrained(mode_name)
>>> tokenizer = AutoTokenizer.from_pretrained(mode_name)
>>> translation = pipeline("translation_en_to_zh", model=model, tokenizer=tokenizer)
>>> translation('I like to study Data Science and Machine Learning.', max_length=400)
[{'translation_text': '我喜欢学习数据科学和机器学习'}]
```
## Contact
[email protected]
|
liam168/trans-opus-mt-zh-en | liam168 | 2021-07-16T03:34:38Z | 251 | 21 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2022-03-02T23:29:05Z | ---
language:
- en
- zh
tags:
- translation
widget:
- text: "我喜欢学习数据科学和机器学习。"
---
# liam168/trans-opus-mt-zh-en
## Model description
* source group: English
* target group: Chinese
* model: transformer
* source language(s): eng
## How to use
```python
>>> from transformers import AutoModelWithLMHead,AutoTokenizer,pipeline
>>> mode_name = 'liam168/trans-opus-mt-zh-en'
>>> model = AutoModelWithLMHead.from_pretrained(mode_name)
>>> tokenizer = AutoTokenizer.from_pretrained(mode_name)
>>> translation = pipeline("translation_zh_to_en", model=model, tokenizer=tokenizer)
>>> translation('我喜欢学习数据科学和机器学习。', max_length=400)
[{'translation_text': 'I like to study data science and machine learning.'}]
```
## Contact
[email protected]
|
huggingtweets/gambsvns | huggingtweets | 2021-07-15T21:50:46Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/gambsvns/1626385842515/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1415310065960198148/w9Yr9mLK_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">gãmbs</div>
<div style="text-align: center; font-size: 14px;">@gambsvns</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from gãmbs.
| Data | gãmbs |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 86 |
| Short tweets | 308 |
| Tweets kept | 2852 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wahjzcj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gambsvns's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1td3tcaf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1td3tcaf/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/gambsvns')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/bladeecity-robber0540 | huggingtweets | 2021-07-15T06:48:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/bladeecity-robber0540/1626331680252/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1406669132527976453/Sv0lEtmk_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/822229503212666880/L4UutyTM_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim & Combat Ballerina</div>
<div style="text-align: center; font-size: 14px;">@bladeecity-robber0540</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Aim & Combat Ballerina.
| Data | Aim | Combat Ballerina |
| --- | --- | --- |
| Tweets downloaded | 1604 | 671 |
| Retweets | 314 | 66 |
| Short tweets | 487 | 303 |
| Tweets kept | 803 | 302 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uvtcfjv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bladeecity-robber0540's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/36qst0l8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/36qst0l8/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/bladeecity-robber0540')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/theisaiahw | huggingtweets | 2021-07-14T21:05:53Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/theisaiahw/1626296749614/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1388820869762322434/v3h5S7mu_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Isaiah Williams</div>
<div style="text-align: center; font-size: 14px;">@theisaiahw</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Isaiah Williams.
| Data | Isaiah Williams |
| --- | --- |
| Tweets downloaded | 620 |
| Retweets | 65 |
| Short tweets | 72 |
| Tweets kept | 483 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/336gn9be/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @theisaiahw's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ohqpafvm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ohqpafvm/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/theisaiahw')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
YusufSahin99/IFIS_ZORK_AI_FANTASY | YusufSahin99 | 2021-07-14T13:18:10Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model_index:
- name: IFIS_ZORK_AI_FANTASY
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# IFIS_ZORK_AI_FANTASY
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
ehdwns1516/klue-roberta-base-kornli | ehdwns1516 | 2021-07-14T08:11:08Z | 19 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # klue-roberta-base-kornli
* This model trained with Korean dataset.
* Input premise sentence and hypothesis sentence.
* You can use English, but don't expect accuracy.
* If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
klue-roberta-base-kornli DEMO: [Ainize DEMO](https://main-klue-roberta-base-kornli-ehdwns1516.endpoint.ainize.ai/)
klue-roberta-base-kornli API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/klue-roberta-base_kornli)
## Overview
Language model: [klue/roberta-base](https://huggingface.co/klue/roberta-base)
Language: Korean
Training data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)
Eval data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/klue-roberta-base_finetunning_ex)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/klue-roberta-base-kornli")
classifier = pipeline(
"text-classification",
model="ehdwns1516/klue-roberta-base-kornli",
return_all_scores=True,
)
premise = "your premise"
hypothesis = "your hypothesis"
result = dict()
result[0] = classifier(premise + tokenizer.sep_token + hypothesis)[0]
```
|
cstorm125/wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa | cstorm125 | 2021-07-14T07:45:06Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"deberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=wangchan-deberta_v1-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--revision mlm@ckp-41100 \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--model_max_length 400 \
--pad_on_right \
--fp16 \
--use_auth_token
``` |
airesearch/xlm-roberta-base-finetune-qa | airesearch | 2021-07-14T07:13:00Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# xlm-roberta-base-finetune-qa
Finetuning `xlm-roberta-base` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Train with:
```
export WANDB_PROJECT=wangchanberta-qa
export MODEL_NAME=xlm-roberta-base
python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--pad_on_right \
--fp16
```
|
airesearch/bert-base-multilingual-cased-finetune-qa | airesearch | 2021-07-14T05:50:52Z | 41 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
widget:
- text: "สวนกุหลาบเป็นโรงเรียนอะไร"
context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย"
---
# bert-base-multilingual-cased
Finetuning `bert-base-multilingual-cased` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=bert-base-multilingual-cased
python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--pad_on_right \
--fp16
``` |
andi611/roberta-base-ner-conll2003 | andi611 | 2021-07-14T00:25:37Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
model_index:
- name: roberta-base-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0814
- eval_precision: 0.9101
- eval_recall: 0.9336
- eval_f1: 0.9217
- eval_accuracy: 0.9799
- eval_runtime: 10.2964
- eval_samples_per_second: 315.646
- eval_steps_per_second: 39.529
- epoch: 1.14
- step: 500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
huggingtweets/jplatzhalter | huggingtweets | 2021-07-13T22:13:16Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/jplatzhalter/1626214256716/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1204103314733821954/O_QCiMdI_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Julia Platz-Halter</div>
<div style="text-align: center; font-size: 14px;">@jplatzhalter</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Julia Platz-Halter.
| Data | Julia Platz-Halter |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 270 |
| Short tweets | 373 |
| Tweets kept | 2592 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2z39jb5g/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jplatzhalter's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1deih6g9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1deih6g9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jplatzhalter')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
YusufSahin99/Zork_AI_SciFi | YusufSahin99 | 2021-07-13T14:58:01Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model_index:
- name: Zork_AI_SciFi
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- 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. -->
# Zork_AI_SciFi
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
|
AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2 | AIDA-UPM | 2021-07-13T14:12:45Z | 292 | 12 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"multilingual",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:04Z | ---
pipeline_tag: sentence-similarity
language: "multilingual"
tags:
- feature-extraction
- sentence-similarity
- transformers
- multilingual
---
# mstsb-paraphrase-multilingual-mpnet-base-v2
This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences.
<!--- Describe your model here -->
This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at [GitHub](https://github.com/Huertas97/Multilingual-STSB). The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below).
<!-- ## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
# It support several languages
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]
# The pooling technique is automatically detected (mean pooling)
model = SentenceTransformer('mstsb-paraphrase-multilingual-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
``` -->
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# We should define the proper pooling function: Mean pooling
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["This is an example sentence", "Esta es otra frase de ejemplo", "最後の例文"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
model = AutoModel.from_pretrained('AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
Check the test results in the Semantic Textual Similarity Tasks. The 15 languages available at the [Multilingual STSB](https://github.com/Huertas97/Multilingual-STSB) have been combined into monolingual and cross-lingual tasks, giving a total of 31 tasks. Monolingual tasks have both sentences from the same language source (e.g., Ar-Ar, Es-Es), while cross-lingual tasks have two sentences, each in a different language being one of them English (e.g., en-ar, en-es).
Here we compare the average multilingual semantic textual similairty capabilities between the `paraphrase-multilingual-mpnet-base-v2` based model and the `mstsb-paraphrase-multilingual-mpnet-base-v2` fine-tuned model across the 31 tasks. It is worth noting that both models are multilingual, but the second model is adjusted with multilingual data for semantic similarity. The average of correlation coefficients is computed by transforming each correlation coefficient to a Fisher's z value, averaging them, and then back-transforming to a correlation coefficient.
| Model | Average Spearman Cosine Test |
|:---------------------------------------------:|:------------------------------:|
| mstsb-paraphrase-multilingual-mpnet-base-v2 | 0.835890 |
| paraphrase-multilingual-mpnet-base-v2 | 0.818896 |
<br>
The following tables breakdown the performance of `mstsb-paraphrase-multilingual-mpnet-base-v2` according to the different tasks. For the sake of readability tasks have been splitted into monolingual and cross-lingual tasks.
| Monolingual Task | Pearson Cosine test | Spearman Cosine test |
|:------------------:|:---------------------:|:-----------------------:|
| en;en | 0.868048310692506 | 0.8740170943535747 |
| ar;ar | 0.8267139454193487 | 0.8284459741532022 |
| cs;cs | 0.8466821720942157 | 0.8485417688803879 |
| de;de | 0.8517285961812183 | 0.8557680051557893 |
| es;es | 0.8519185309064691 | 0.8552243211580456 |
| fr;fr | 0.8430951067985064 | 0.8466614534379704 |
| hi;hi | 0.8178258630578092 | 0.8176462079184331 |
| it;it | 0.8475909574305637 | 0.8494216064459076 |
| ja;ja | 0.8435588859386477 | 0.8456031494178619 |
| nl;nl | 0.8486765104527032 | 0.8520856765262531 |
| pl;pl | 0.8407840177883407 | 0.8443070467300299 |
| pt;pt | 0.8534880178249296 | 0.8578544068829622 |
| ru;ru | 0.8390897585455678 | 0.8423041443534423 |
| tr;tr | 0.8382125451820572 | 0.8421587450058385 |
| zh-CN;zh-CN | 0.826233678946644 | 0.8248515460782744 |
| zh-TW;zh-TW | 0.8242683809675422 | 0.8235506799952028 |
<br>
| Cross-lingual Task | Pearson Cosine test | Spearman Cosine test |
|:--------------------:|:---------------------:|:-----------------------:|
| en;ar | 0.7990830340462535 | 0.7956792016468148 |
| en;cs | 0.8381274879061265 | 0.8388713450024455 |
| en;de | 0.8414439600928739 | 0.8441971698649943 |
| en;es | 0.8442337511356952 | 0.8445035292903559 |
| en;fr | 0.8378437644605063 | 0.8387903367907733 |
| en;hi | 0.7951955086055527 | 0.7905052217683244 |
| en;it | 0.8415686372978766 | 0.8419480899107785 |
| en;ja | 0.8094306665283388 | 0.8032512280936449 |
| en;nl | 0.8389526140129767 | 0.8409310421803277 |
| en;pl | 0.8261309163979578 | 0.825976253023656 |
| en;pt | 0.8475546209070765 | 0.8506606391790897 |
| en;ru | 0.8248514914263723 | 0.8224871183202255 |
| en;tr | 0.8191803661207868 | 0.8194200775744044 |
| en;zh-CN | 0.8147678083378249 | 0.8102089470690433 |
| en;zh-TW | 0.8107272160374955 | 0.8056129680510944 |
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 687 with parameters:
```
{'batch_size': 132, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 2,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 140,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
lewtun/dummy-translation | lewtun | 2021-07-13T12:43:13Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model_index:
- name: dummy-translation
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
---
<!-- 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. -->
# dummy-translation
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3
|
nateraw/test_model_a | nateraw | 2021-07-13T04:52:00Z | 128 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- image_folder
model_index:
- name: test_model_a
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
---
<!-- 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. -->
# test_model_a
This model is a fine-tuned version of [lysandre/tiny-vit-random](https://huggingface.co/lysandre/tiny-vit-random) on the image_folder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 40
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.1.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/gpt2-als-demo | patrickvonplaten | 2021-07-12T14:20:19Z | 8 | 0 | transformers | [
"transformers",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | # roberta-base-als-demo
**roberta-base-als-demo** is a model trained by Patrick von Platen to demonstrate how to train a roberta-base model from scratch on the Alemannic language.
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Useful links
- [Community Week timeline](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104#summary-timeline-calendar-6)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Masked Language Modelling example scripts](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
- [Model Repository](https://huggingface.co/flax-community/roberta-base-als-demo)
|
jaimin/Gujarati-Model | jaimin | 2021-07-12T13:23:21Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | tokenizer = AutoTokenizer.from_pretrained("jaimin/Gujarati-Model")
model = AutoModel.from_pretrained("jaimin/Gujarati-Model") |
Andrija/RobertaFastBPE | Andrija | 2021-07-12T11:11:21Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:04Z | from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('Andrija/RobertaFastBPE', bos_token="<s>", eos_token="</s>")
encoded = tokenizer('Stručnjaci te bolnice, predvođeni dr Alisom Lim')
# {'input_ids': [0, 47541, 34632, 603, 24817, 16, 27540, 6768, 2350, 2803, 3991, 2733, 81, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
tokenizer.decode(encoded['input_ids'])
# <s>Stručnjaci te bolnice, predvođeni dr Alisom Lim</s> |
huggingtweets/pontifex_es | huggingtweets | 2021-07-12T08:26:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/pontifex_es/1626078305422/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/507819548834148352/jyx1JOS-_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Papa Francisco</div>
<div style="text-align: center; font-size: 14px;">@pontifex_es</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Papa Francisco.
| Data | Papa Francisco |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 45 |
| Tweets kept | 3205 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2a8o5bwd/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pontifex_es's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3183nmsb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3183nmsb/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/pontifex_es')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Vivek/flax-gpt2-common-sense-reasoning | Vivek | 2021-07-12T04:19:26Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also. |
Littlejohn/analisis_sentimientos | Littlejohn | 2021-07-12T00:22:27Z | 11 | 0 | transformers | [
"transformers",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
language:
- en
pipeline_tag: text-classification
---
# bert-base-cased-sentiment
Es un modelo de BERT (bert-base-cased) afinado para el analisis de sentimientos para dos clases.
El sentimiento solo se define como positivo negativo según sea el caso de la oración suministrada.
## Training data
El set de datos utilizado para el entrenamiento del modelo fue a traves de una recopilación de reseñas de amazón, el cual se puede descargar desde el autor original en Kaggle [Adam Bittlingmayer](https://www.kaggle.com/bittlingmayer/amazonreviews) Amazon Reviews for Sentiment Analysis.
El numero de datos fue solo de 40 000 oraciones de las cuales solo se tomaron las primeras 100 palabras para conformar cada una de las oraciones.
## Accuaracy
El modelo afinado fue sometido a 3 pruebas para conocer su precisión.
- La primera prueba fue en un set de datos de Reseñas de hoteles
| Accuracy (Precisión) |
| -------- |
| 95% |
- La segunda prueba fue en un set de datos de Reseñas de comida
| Accuracy (Precisión) |
| -------- |
| 88% |
- La tercera prueba fue en un set de datos de Sentimientos generales
| Accuracy (Precisión) |
| -------- |
| 65% |
## Contact
Contacto a traves de github [Murdoocc7](https://github.com/murdoocc) |
CarlosPR/mt5-spanish-memmories-analysis | CarlosPR | 2021-07-11T15:11:55Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | **mt5-spanish-memmories-analysis**
**// ES**
Este es un trabajo en proceso.
Este modelo aún es solo un punto de control inicial que mejoraré en los próximos meses.
El objetivo es proporcionar un modelo capaz de, utilizando una combinación de tareas del modelo mT5, comprender los recuerdos y proporcionar una interacción útil para las personas con alzeimer o personas como mi propio abuelo que escribió sus recuerdos, pero ahora es solo un libro en la estantería. por lo que este modelo puede hacer que esos recuerdos parezcan "vivos".
Pronto (si aún no está cargado) cargaré un cuaderno de **Google Colaboratory con una aplicación visual** que al usar este modelo proporcionará toda la interacción necesaria y deseada con una interfaz fácil de usar.
**LINK APLICACIÓN (sobre él se actualizará la versión):** https://drive.google.com/drive/folders/1ewGcxxCYHHwhHhWtGlLiryZfV8wEAaBa?usp=sharing
-> Debe descargarse la carpeta "memorium" del enlace y subirse a Google Drive sin incluir en ninguna otra carpeta (directamente en "Mi unidad").
-> A continuación se podrá abrir la app, encontrada dentro de dicha carpeta "memorium" con nombre "APP-Memorium" (el nombre puede incluir además un indicador de versión).
-> Si haciendo doble click en el archivo de la app no permite abrirla, debe hacerse pulsando el botón derecho sobre el archivo y seleccionar "Abrir con", "Conectar más aplicaciones", y a continuación escoger Colaboratory (se pedirá instalar). Completada la instalación (tiempo aproximado: 2 minutos) se podrá cerrar la ventana de instalación para volver a visualizar la carpeta donde se encuentra el fichero de la app, que de ahora en adelante se podrá abrir haciendo doble click.
-> Se podrán añadir memorias en la carpeta "perfiles" como se indica en la aplicación en el apartado "crear perfil".
**// EN**
This is a work in process.
This model is just an initial checkpoint yet that I will be improving the following months.
**APP LINK (it will contain the latest version):** https://drive.google.com/drive/folders/1ewGcxxCYHHwhHhWtGlLiryZfV8wEAaBa?usp=sharing
-> The folder "memorium" must be downloaded and then uploaded to Google Drive at "My Drive", NOT inside any other folder.
The aim is to provide a model able to, using a mixture of mT5 model's tasks, understand memories and provide an interaction useful for people with alzeimer or people like my own grandfather who wrote his memories but it is now just a book in the shelf, so this model can make those memories seem 'alive'.
I will soon (if it is´t uploaded by now) upload a **Google Colaboratory notebook with a visual App** that using this model will provide all the needed and wanted interaction with an easy-to-use Interface.
|
nateraw/donut-or-bagel | nateraw | 2021-07-10T19:54:49Z | 71 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: donut-or-bagel
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
---
# donut-or-bagel
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### bagel
#### donut
 |
huggingtweets/averagesmasher | huggingtweets | 2021-07-10T13:47:30Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/averagesmasher/1625924846625/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1368753714568327168/oh6BSjqX_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">AverageVermontSmasher</div>
<div style="text-align: center; font-size: 14px;">@averagesmasher</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from AverageVermontSmasher.
| Data | AverageVermontSmasher |
| --- | --- |
| Tweets downloaded | 41 |
| Retweets | 0 |
| Short tweets | 2 |
| Tweets kept | 39 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/auyr340s/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @averagesmasher's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qnfjchi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qnfjchi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/averagesmasher')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sourabharsh/wav2vec2_10july | sourabharsh | 2021-07-10T08:25:13Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"de",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: de
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 German by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice de
type: common_voice
args: de
metrics:
- name: Test WER
type: wer
value: 10.55
- name: Test CER
type: cer
value: 2.81
--- |
huggingtweets/the_robisho | huggingtweets | 2021-07-10T03:40:16Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/the_robisho/1625888412499/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1311895700582666243/6N1xGNV9_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Nick Landback</div>
<div style="text-align: center; font-size: 14px;">@the_robisho</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Nick Landback.
| Data | Nick Landback |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 1018 |
| Short tweets | 21 |
| Tweets kept | 2166 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/bbz9lc2n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @the_robisho's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/gh2qryi3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gh2qryi3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/the_robisho')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/i_apx_86 | huggingtweets | 2021-07-10T03:25:37Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/i_apx_86/1625887532973/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1404915017703575558/05H2noyT_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🍮CC🍮</div>
<div style="text-align: center; font-size: 14px;">@i_apx_86</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 🍮CC🍮.
| Data | 🍮CC🍮 |
| --- | --- |
| Tweets downloaded | 701 |
| Retweets | 391 |
| Short tweets | 22 |
| Tweets kept | 288 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xwg9l0v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @i_apx_86's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/11srzptq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/11srzptq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/i_apx_86')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
shahukareem/dhivehi-roberta-base | shahukareem | 2021-07-10T00:19:12Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"dv",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: dv
tags:
- dv
- roberta
widget:
- text: "<mask> މާލެ އަކީ ދިވެހިރާއްޖޭގެ"
---
# Dhivehi Roberta Base - Oscar
## Description
RoBERTA pretrained from scratch using Jax/Flax backend and with the Dhivehi Oscar Corpus only.
|
huggingtweets/marxhaunting | huggingtweets | 2021-07-09T22:04:38Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/marxhaunting/1625868274804/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1323823559182045184/Vqrrga8t_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Karl Marx</div>
<div style="text-align: center; font-size: 14px;">@marxhaunting</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Karl Marx.
| Data | Karl Marx |
| --- | --- |
| Tweets downloaded | 1287 |
| Retweets | 16 |
| Short tweets | 25 |
| Tweets kept | 1246 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zcjng5j/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @marxhaunting's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nimlh0s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nimlh0s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/marxhaunting')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/freudotheism | huggingtweets | 2021-07-09T21:54:33Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/freudotheism/1625867628365/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1412918415703019521/J2TQHTDo_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Evelyn🪶🇰🇵</div>
<div style="text-align: center; font-size: 14px;">@freudotheism</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Evelyn🪶🇰🇵.
| Data | Evelyn🪶🇰🇵 |
| --- | --- |
| Tweets downloaded | 3231 |
| Retweets | 333 |
| Short tweets | 968 |
| Tweets kept | 1930 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rbzyyts/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @freudotheism's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/elt06ed5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/elt06ed5/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/freudotheism')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
gagan3012/k2t-test3 | gagan3012 | 2021-07-09T19:57:47Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"keytotext",
"k2t",
"Keywords to Sentences",
"en",
"dataset:WebNLG",
"dataset:Dart",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: "en"
thumbnail: "Keywords to Sentences"
tags:
- keytotext
- k2t
- Keywords to Sentences
license: "MIT"
datasets:
- WebNLG
- Dart
metrics:
- NLG
model-index:
- name: k2t-test3
---
#keytotext
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/project/keytotext)
[](https://colab.research.google.com/github/gagan3012/keytotext/blob/master/notebooks/K2T.ipynb)
[](https://share.streamlit.io/gagan3012/keytotext/UI/app.py)
[](https://github.com/gagan3012/keytotext#api)
[](https://hub.docker.com/r/gagan30/keytotext)
[](https://huggingface.co/models?filter=keytotext)
[](https://keytotext.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/psf/black)
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
Potential use case can include:
- Marketing
- Search Engine Optimization
- Topic generation etc.
- Fine tuning of topic modeling models |
flax-community/t5-covid-qa | flax-community | 2021-07-09T19:03:44Z | 1 | 0 | null | [
"arxiv:2002.08910",
"region:us"
] | null | 2022-03-02T23:29:05Z | # Covid19 Related Question Answering (Closed book question answering)
In 2020, COVID-19 which is caused by a coronavirus called SARS-CoV-2 took over the world. It touched the lives of many people and caused a lot of hardship for humanity. There are still many questions in regards to COVID-19 and it is often difficult to get the right answers. The aim of this project is to finetune models for closed book question answering. In closed-book QA, we feed the model a question *without any context or access to external knowledge* and train it to predict the answer. Since the model doesn't receive any context, the primary way it can learn to answer these questions is based on the "knowledge" it obtained during pre-training [[1]](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb#scrollTo=zSeyoqE7WMwu) [[2]](https://arxiv.org/abs/2002.08910).
The main goals of this project are:
1. Train a model for question answering in regards to COVID-19
2. Release the top performing models for further research and enhancement
3. Release all of the preprocessing and postprocessing scripts and findings for future research.
## TO DO LIST:
- [x] Team members met and the following was discussed:
- Data preparation script is prepared that mixes CORD-19 and Pubmed.
- Agreed to finalize the training scripts by 9pm PDT 7/9/2021.
- Tokenizer is now trained.
- [ ] Setup the pretraining script
- [ ] Prepare the finetuning tasks inspired from [T5 Trivia Colab](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb)
- What datasets we want to go with?
- [Covid-QA](https://huggingface.co/datasets/covid_qa_deepset) (Maybe as test set?)
- [Trivia](https://huggingface.co/datasets/covid_qa_deepset)
- [CDC-QA](https://www.cdc.gov/coronavirus/2019-ncov/faq.html) (We can scrape quickly using beautiful soup or something)
- [More Medical Datasets](https://aclanthology.org/2020.findings-emnlp.289.pdf) (See the dataset section for inspiratio)
## 1. Model
We will be using T5 model.
## 2. Datasets
The following datasets would be used for finetuning the model. Note that the last dataset is optional and the model is evaluated only using Covid-QA.
For **Intermediate Pre-Training**:
1. [CORD-19](https://allenai.org/data/cord-19)
For **Fine-Tuning** :
1. [Covid-QA](https://huggingface.co/datasets/covid_qa_deepset)
2. [CDC-QA](https://www.cdc.gov/coronavirus/2019-ncov/faq.html)
4. Optional - [Trivia-QA](https://nlp.cs.washington.edu/triviaqa/)
## 3. Training Scripts
We can make use of :
1. [For preprocessing and mixing datasets](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb#:~:text=In%20this%20notebook%2C%20we'll,it%20to%20predict%20the%20answer.)
2. [For T5 training](https://github.com/huggingface/transformers/blob/master/src/transformers/models/t5/modeling_flax_t5.py)
## 4. Additional Reading
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://arxiv.org/pdf/2002.08910.pdf)
|
huggingtweets/leduans1 | huggingtweets | 2021-07-09T18:01:11Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/leduans1/1625853639603/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1400417811407814659/XYjQArW4_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Comrade Based</div>
<div style="text-align: center; font-size: 14px;">@leduans1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Comrade Based.
| Data | Comrade Based |
| --- | --- |
| Tweets downloaded | 536 |
| Retweets | 9 |
| Short tweets | 176 |
| Tweets kept | 351 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2xffg6kj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @leduans1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2g7o54j4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2g7o54j4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/leduans1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/robit-roberta-base-it | flax-community | 2021-07-09T16:47:58Z | 8 | 1 | transformers | [
"transformers",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # RobIt
**RobIt** is a RoBERTa-base model for Italian. It has been trained from scratch on the Italian portion of the OSCAR dataset using [Flax](https://github.com/google/flax), including training scripts.
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Team members
- Prateek Agrawal (prateekagrawal)
- Tanay Mehta (yotanay)
- Shreya Gupta (Sheyz-max)
- Ruchi Bhatia (ruchi798)
## Dataset :
[OSCAR](https://huggingface.co/datasets/oscar)
- config : **unshuffled_deduplicated_it**
- Size of downloaded dataset files: **26637.62 MB**
- Size of the generated dataset: **70661.48 MB**
- Total amount of disk used: **97299.10 MB**
## Useful links
- [Community Week timeline](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104#summary-timeline-calendar-6)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Community Week thread](https://discuss.huggingface.co/t/robit-pretrain-roberta-base-from-scratch-in-italian/7564)
- [Community Week channel](https://discord.gg/NTyQNUNs)
- [Masked Language Modelling example scripts](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
- [Model Repository](https://huggingface.co/flax-community/robit-roberta-base-it/)
|
jephthah/dfjgidfhj | jephthah | 2021-07-09T12:39:42Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | https://natureecoevocommunity.nature.com/users/123movies-hd-watch-hitman-s-wife-s-bodyguard-2021-full-movie-online
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-a-quiet-place-part-2-2021-full-movie-online-free-1d44a4a0-bbe0-4b52-a56c-c86e7ce72c1c
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-a-quiet-place-part-2-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-the-conjuring-3-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-luca-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-space-jam-2-a-new-legacy-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-cruella-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-the-forever-purge-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-the-boss-baby-2-family-business-2021-full-movie-online-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-fast-and-furious-9-2021-online-full-movie-free-reddit
https://natureecoevocommunity.nature.com/users/123movies-hd-watch-black-widow-2021-online-full-movie-free-reddit |
flax-community/roberta-base-als | flax-community | 2021-07-09T12:18:18Z | 4 | 0 | transformers | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | This project pretrains a [`roberta-base`](https://huggingface.co/roberta-base) on the *Alemannic* (`als`) data subset of the [OSCAR](https://oscar-corpus.com/) corpus in JAX/Flax.
We will be using the masked-language modeling loss for pretraining. |
flax-community/roberta-base-als-demo | flax-community | 2021-07-09T12:18:07Z | 5 | 0 | transformers | [
"transformers",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # roberta-base-als-demo
**roberta-base-als-demo** is a model trained by Patrick von Platen to demonstrate how to train a roberta-base model from scratch on the Alemannic language.
This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.
## Useful links
- [Community Week timeline](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104#summary-timeline-calendar-6)
- [Community Week README](https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md)
- [Masked Language Modelling example scripts](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
- [Model Repository](https://huggingface.co/flax-community/roberta-base-als-demo)
|
Alireza1044/bert_classification_lm | Alireza1044 | 2021-07-09T08:50:58Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | A simple model trained on dialogues of characters in NBC series, `The Office`. The model can do a binary classification between `Michael Scott` and `Dwight Shrute`'s dialogues.
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top}
</style>
<table class="tg">
<thead>
<tr>
<th class="tg-c3ow" colspan="2">Label Definitions</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-c3ow">Label 0</td>
<td class="tg-c3ow">Michael</td>
</tr>
<tr>
<td class="tg-c3ow">Label 1</td>
<td class="tg-c3ow">Dwight</td>
</tr>
</tbody>
</table> |
ThomasNLG/t5-qg_webnlg_synth-en | ThomasNLG | 2021-07-09T07:45:44Z | 259 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"question",
"generation",
"SQuAD",
"data2text",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad_v2",
"arxiv:2104.07555",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
tags:
- qa
- question
- generation
- SQuAD
- data2text
- metric
- nlg
- t5-small
license: mit
datasets:
- squad_v2
model-index:
- name: t5-qg_webnlg_synth-en
results:
- task:
name: Data Question Generation
type: Text To Text Generation
widget:
- text: "The Eagle </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"
---
# t5-qg_webnlg_synth-en
## Model description
This model is a *Data Question Generation* model based on T5-small, that generates questions, given a structured table as input and the conditioned answer.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QG only.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "{ANSWER} </s> {CONTEXT}"`
where `CONTEXT is a structured table that is linearised this way:
`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"`
## Training data
The model was trained on synthetic data as described in [Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation](https://arxiv.org/abs/2104.07555).
### Citation info
```bibtex
@article{rebuffel2021data,
title={Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation},
author={Rebuffel, Cl{\'e}ment and Scialom, Thomas and Soulier, Laure and Piwowarski, Benjamin and Lamprier, Sylvain and Staiano, Jacopo and Scoutheeten, Geoffrey and Gallinari, Patrick},
journal={arXiv preprint arXiv:2104.07555},
year={2021}
}
``` |
ThomasNLG/t5-qa_webnlg_synth-en | ThomasNLG | 2021-07-09T07:45:27Z | 260 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"qa",
"question",
"answering",
"SQuAD",
"data2text",
"metric",
"nlg",
"t5-small",
"en",
"dataset:squad_v2",
"arxiv:2104.07555",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
tags:
- qa
- question
- answering
- SQuAD
- data2text
- metric
- nlg
- t5-small
license: mit
datasets:
- squad_v2
model-index:
- name: t5-qa_webnlg_synth-en
results:
- task:
name: Data Question Answering
type: extractive-qa
widget:
- text: "What is the food type at The Eagle? </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"
---
# t5-qa_webnlg_synth-en
## Model description
This model is a *Data Question Answering* model based on T5-small, that answers questions given a structured table as input.
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only.
## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qa_webnlg_synth-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_webnlg_synth-en")
```
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
`text_input = "{QUESTION} </s> {CONTEXT}"`
where `CONTEXT` is a structured table that is linearised this way:
`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"`
## Training data
The model was trained on synthetic data as described in [Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation](https://arxiv.org/abs/2104.07555).
### Citation info
```bibtex
@article{rebuffel2021data,
title={Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation},
author={Rebuffel, Cl{\'e}ment and Scialom, Thomas and Soulier, Laure and Piwowarski, Benjamin and Lamprier, Sylvain and Staiano, Jacopo and Scoutheeten, Geoffrey and Gallinari, Patrick},
journal={arXiv preprint arXiv:2104.07555},
year={2021}
}
``` |
mboth/distil-eng-quora-sentence | mboth | 2021-07-09T06:00:21Z | 130 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# mboth/distil-eng-quora-sentence
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mboth/distil-eng-quora-sentence')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('mboth/distil-eng-quora-sentence')
model = AutoModel.from_pretrained('mboth/distil-eng-quora-sentence')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=mboth/distil-eng-quora-sentence)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
franklu/pubmed_bert_squadv2 | franklu | 2021-07-09T05:25:26Z | 42 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | **[`microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext`](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_qa.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py)**
Tunning script:
```bash
BASE_MODEL=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
OUTPUT_DIR=~/Documents/projects/tunned_models/ms_pubmed_bert_squadv2/
python run_qa.py \
--model_name_or_path $BASE_MODEL\
--dataset_name squad_v2 \
--do_train \
--do_eval \
--version_2_with_negative \
--per_device_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir $OUTPUT_DIR
``` |
kittinan/exercise-feedback-classification | kittinan | 2021-07-08T17:44:43Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # Reddit exercise feedback classification
Model to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it locally,
### Usage:
```py
from transformers import pipeline
classifier = pipeline("text-classification", "kittinan/exercise-feedback-classification")
classifier("search for alan thrall deadlift video he will explain basic ques")
#[{'label': 'correction', 'score': 0.9998193979263306}]
``` |
liam168/c2-roberta-base-finetuned-dianping-chinese | liam168 | 2021-07-08T01:50:53Z | 174 | 23 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: zh
widget:
- text: "我喜欢下雨。"
- text: "我讨厌他。"
---
# liam168/c2-roberta-base-finetuned-dianping-chinese
## Model description
用中文对话情绪语料训练的模型，2分类：乐观和悲观。
## Overview
- **Language model**: BertForSequenceClassification
- **Model size**: 410M
- **Language**: Chinese
## Example
```python
>>> from transformers import AutoModelForSequenceClassification , AutoTokenizer, pipeline
>>> model_name = "liam168/c2-roberta-base-finetuned-dianping-chinese"
>>> class_num = 2
>>> ts_texts = ["我喜欢下雨。", "我讨厌他."]
>>> model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=class_num)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
>>> classifier(ts_texts[0])
>>> classifier(ts_texts[1])
[{'label': 'positive', 'score': 0.9973447918891907}]
[{'label': 'negative', 'score': 0.9972558617591858}]
```
|
tdopierre/ProtAugment-ParaphraseGenerator | tdopierre | 2021-07-07T14:15:07Z | 4 | 5 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"Paraphase Generation",
"Data Augmentation",
"en",
"dataset:Quora",
"dataset:MSR",
"dataset:Google-PAWS",
"arxiv:2105.12995",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: "en"
tags:
- Paraphase Generation
- Data Augmentation
datasets:
- Quora
- MSR
- Google-PAWS
---
[](https://arxiv.org/abs/2105.12995)
This model is used to generate paraphrases. It has been trained on a mix of 3 different paraphrase detection datasets: MSR, Quora, Google-PAWS.
We use this model in our ACL'21 Paper ["PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning"](https://arxiv.org/abs/2105.12995)
Jointly used with generation constraints, this model allows to generate diverse paraphrases. We use those paraphrases as a data augmentation technique to further boosts a classification model's generalization capability. Feel free to play with the [code](https://github.com/tdopierre/ProtAugment)!
If you use this model, please consider citing our paper.
```
@article{Dopierre2021ProtAugmentUD,
title={ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning},
author={Thomas Dopierre and C. Gravier and Wilfried Logerais},
journal={ArXiv},
year={2021},
volume={abs/2105.12995}
}
```
|
shreeshaaithal/DialoGPT-small-Michael-Scott | shreeshaaithal | 2021-07-07T11:56:25Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on WhatsApp chats
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on WhatsApp chats or you can train this model on [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
feel free to ask me questions on discord server [discord server](https://discord.gg/Gqhje8Z7DX)
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("harrydonni/DialoGPT-small-Michael-Scott")
model = AutoModelWithLMHead.from_pretrained("harrydonni/DialoGPT-small-Michael-Scott")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("Michael: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
this is done by shreesha thank you...... |
ainize/klue-bert-base-re | ainize | 2021-07-07T09:55:52Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # bert-base for KLUE Relation Extraction task.
Fine-tuned klue/bert-base using KLUE RE dataset.
- <a href="https://klue-benchmark.com/">KLUE Benchmark Official Webpage</a>
- <a href="https://github.com/KLUE-benchmark/KLUE">KLUE Official Github</a>
- <a href="https://github.com/ainize-team/klue-re-workspace">KLUE RE Github</a>
- Run KLUE RE on free GPU : <a href="https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ainize-team/klue-re-workspace">Ainize Workspace</a>
<br>
# Usage
<pre><code>
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ainize/klue-bert-base-re")
model = AutoModelForSequenceClassification.from_pretrained("ainize/klue-bert-base-re")
# Add "<subj>", "</subj>" to both ends of the subject object and "<obj>", "</obj>" to both ends of the object object.
sentence = "<subj>손흥민</subj>은 <obj>대한민국</obj>에서 태어났다."
encodings = tokenizer(sentence,
max_length=128,
truncation=True,
padding="max_length",
return_tensors="pt")
outputs = model(**encodings)
logits = outputs['logits']
preds = torch.argmax(logits, dim=1)
</code></pre>
<br>
# About us
- <a href="https://ainize.ai/teachable-nlp">Teachable NLP</a> - Train NLP models with your own text without writing any code
- <a href="https://ainize.ai/">Ainize</a> - Deploy ML project using free gpu |
andi611/bert-base-uncased-ner-conll2003 | andi611 | 2021-07-07T09:31:59Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-base-uncased-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.19881805328292054
---
<!-- 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-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1258
- Precision: 0.0269
- Recall: 0.1379
- F1: 0.0451
- Accuracy: 0.1988
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 4 | 2.1296 | 0.0270 | 0.1389 | 0.0452 | 0.1942 |
| No log | 2.0 | 8 | 2.1258 | 0.0269 | 0.1379 | 0.0451 | 0.1988 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
vionwinnie/t5-reddit | vionwinnie | 2021-07-07T08:15:48Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | This T5 small model finetuned on Reddit data.
It has two subtasks:
1. title generation
2. tag classification
|
shreeshaaithal/whatsapp-medium-bot-2 | shreeshaaithal | 2021-07-07T06:28:15Z | 7 | 2 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on WhatsApp chats
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on WhatsApp chats or you can train this model on [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
feel free to ask me questions on discord server [discord server](https://discord.gg/Gqhje8Z7DX)
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("harrydonni/whatsapp-medium-bot-2")
model = AutoModelWithLMHead.from_pretrained("harrydonni/whatsapp-medium-bot-2")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("Messi: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
this is done by shreesha thank you...... |
huggingtweets/gohere4porn-onlinepete | huggingtweets | 2021-07-07T06:07:15Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/gohere4porn-onlinepete/1625638031693/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/456958582731603969/QZKpv6eI_400x400.jpeg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1324123540556316673/YQjGLFLJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">im pete online & Grateful King</div>
<div style="text-align: center; font-size: 14px;">@gohere4porn-onlinepete</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from im pete online & Grateful King.
| Data | im pete online | Grateful King |
| --- | --- | --- |
| Tweets downloaded | 3190 | 2141 |
| Retweets | 94 | 557 |
| Short tweets | 1003 | 217 |
| Tweets kept | 2093 | 1367 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w0274vc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gohere4porn-onlinepete's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rvkp85n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rvkp85n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/gohere4porn-onlinepete')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
byeongal/bart-base | byeongal | 2021-07-07T05:58:29Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
language: en
tags:
- bart
---
# BART base model for Teachable NLP
- This model forked from [bart-base](https://huggingface.co/facebook/bart-base) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
The Authors’ code can be found here:
https://github.com/pytorch/fairseq/tree/master/examples/bart
|
huggingtweets/alice333ai-alicecweam | huggingtweets | 2021-07-06T22:53:45Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/alice333ai-alicecweam/1625611976936/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410515009252302852/sah1ksNb_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1393311358293356546/tXc-X9fx_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Alice Cream 🐇🍓 Vtuber & 👁️⃤ lison</div>
<div style="text-align: center; font-size: 14px;">@alice333ai-alicecweam</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Alice Cream 🐇🍓 Vtuber & 👁️⃤ lison.
| Data | Alice Cream 🐇🍓 Vtuber | 👁️⃤ lison |
| --- | --- | --- |
| Tweets downloaded | 3244 | 3216 |
| Retweets | 359 | 1062 |
| Short tweets | 463 | 200 |
| Tweets kept | 2422 | 1954 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/4cfpc23c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @alice333ai-alicecweam's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r62epp4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r62epp4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/alice333ai-alicecweam')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/alice333ai-jj_visuals | huggingtweets | 2021-07-06T20:56:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/alice333ai-jj_visuals/1625605011527/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1393311358293356546/tXc-X9fx_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1412466315240030217/yDDNt3-0_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">👁️⃤ lison & JJ (comms closed)</div>
<div style="text-align: center; font-size: 14px;">@alice333ai-jj_visuals</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 👁️⃤ lison & JJ (comms closed).
| Data | 👁️⃤ lison | JJ (comms closed) |
| --- | --- | --- |
| Tweets downloaded | 3216 | 3221 |
| Retweets | 1062 | 781 |
| Short tweets | 200 | 229 |
| Tweets kept | 1954 | 2211 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sqkkxt9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @alice333ai-jj_visuals's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/327x2oet) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/327x2oet/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/alice333ai-jj_visuals')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
flax-community/gpt-2-german | flax-community | 2021-07-06T17:10:39Z | 0 | 0 | null | [
"arxiv:1904.09751",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language:
-
-
thumbnail:
tags:
-
-
-
license:
datasets:
-
-
metrics:
-
-
---
# GPT-2 GERMAN
## Model description
See [Open AI's model card](https://github.com/openai/gpt-2/blob/master/model_card.md) and [Huggingface's model card](https://huggingface.co/gpt2) for the original model.
## Intended uses & limitations
#### How to use
```python
def foo(bar)
bar +=1
return bar
```
#### Limitations and bias
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? ?? https://dl.acm.org/doi/10.1145/3442188.3445922
```
@inproceedings{10.1145/3442188.3445922,
author = {Bender, Emily M. and Gebru, Timnit and McMillan-Major, Angelina and Shmitchell, Shmargaret},
title = {On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? ??},
year = {2021},
isbn = {9781450383097},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3442188.3445922},
doi = {10.1145/3442188.3445922},
abstract = {The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.},
booktitle = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
pages = {610?623},
numpages = {14},
location = {Virtual Event, Canada},
series = {FAccT '21}
}
```
## Training data
https://huggingface.co/datasets/german-nlp-group/german_common_crawl
```json
{'url': 'http://my-shop.ru/shop/books/545473.html',
'date_download': '2016-10-20T19:38:58Z',
'digest': 'sha1:F62EMGYLZDIKF4UL5JZYU47KWGGUBT7T',
'length': 1155,
'nlines': 4,
'source_domain': 'my-shop.ru',
'title': 'Grammatikalische Liebeslieder. Methodische Vorschläge',
'raw_content': 'Grammatikalische Liebeslieder. [....]',
'cc_segment': 'crawl-data/CC-MAIN-2016-44/segments/1476988717783.68/wet/CC-MAIN-20161020183837-00354-ip-10-171-6-4.ec2.internal.warc.wet.gz',
'original_nlines': 99,
'original_length': 2672,
'language': 'de',
'language_score': 1.0,
'perplexity': 283.0,
'bucket': 'head'}"
```
## Training procedure
TODO (See [training](training.md))
## Eval results
TODO: Self-BLEU, Diversity, and other metrics from https://arxiv.org/abs/1904.09751
```
@inproceedings{DBLP:conf/iclr/HoltzmanBDFC20,
author = {Ari Holtzman and
Jan Buys and
Li Du and
Maxwell Forbes and
Yejin Choi},
title = {The Curious Case of Neural Text Degeneration},
booktitle = {8th International Conference on Learning Representations, {ICLR} 2020,
Addis Ababa, Ethiopia, April 26-30, 2020},
publisher = {OpenReview.net},
year = {2020},
url = {https://openreview.net/forum?id=rygGQyrFvH},
timestamp = {Thu, 21 Jan 2021 17:36:46 +0100},
biburl = {https://dblp.org/rec/conf/iclr/HoltzmanBDFC20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### BibTeX entry and citation info
Does the Huggingface hub generate DOIs? Otherwise maybe Kaggle or Zenodo to generate one.
```bibtex
@inproceedings{...,
year={2021}
}
```
|
mrshu/wav2vec2-large-xlsr-slovene | mrshu | 2021-07-06T13:25:51Z | 5 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: sl
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Slovene
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice sl
type: common_voice
args: sl
metrics:
- name: Test WER
type: wer
value: 36.97
---
# Wav2Vec2-Large-XLSR-53-Slovene
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Slovene 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", "sl", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("mrshu/wav2vec2-large-xlsr-slovene")
model = Wav2Vec2ForCTC.from_pretrained("mrshu/wav2vec2-large-xlsr-slovene")
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 Slovene 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", "sl", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("mrshu/wav2vec2-large-xlsr-slovene")
model = Wav2Vec2ForCTC.from_pretrained("mrshu/wav2vec2-large-xlsr-slovene")
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**: 36.97 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://colab.research.google.com/drive/14uahdilysnFsiYniHxY9fyKjFGuYQe7p)
|
mrm8488/wav2vec2-large-xlsr-53-ukrainian | mrm8488 | 2021-07-06T13:20:13Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"uk",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: uk
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Ukrainian Manuel Romero
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice uk
type: common_voice
args: uk
metrics:
- name: Test WER
type: wer
value: 41.82
---
# Wav2Vec2-Large-XLSR-53-ukrainian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Ukrainian 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", "uk", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
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 Ukrainian 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", "uk", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-ukrainian")
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**: 41.82 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found ???
|
mbsouksu/wav2vec2-large-xlsr-turkish-large | mbsouksu | 2021-07-06T12:43:27Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
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 Mehmet Berk Souksu
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: 29.80
---
# 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("mbsouksu/wav2vec2-large-xlsr-turkish-large")
model = Wav2Vec2ForCTC.from_pretrained("mbsouksu/wav2vec2-large-xlsr-turkish-large")
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("mbsouksu/wav2vec2-large-xlsr-turkish-large")
model = Wav2Vec2ForCTC.from_pretrained("mbsouksu/wav2vec2-large-xlsr-turkish-large")
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**: 29.80 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://github.com/mbsouksu/wav2vec2-turkish) |
mbien/fma2vec2popularity | mbien | 2021-07-06T12:36:26Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | # Predicting music popularity using DNNs
This is a model fine-tuned for music popularity classification, created as part of DH-401: Digital Musicology class on EPFL
## Team
* Elisa ([email protected])
* Michał ([email protected])
* Noé ([email protected])
## Milestone 3
Main notebook presenting out results is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3.ipynb)
Notebook describing the details of Wav2Vec2.0 pre-training and fine-tuning for the task is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone3-wav2vec2.ipynb)
## Milestone 2
Exploratory data analysis notebook is available [here](https://nbviewer.jupyter.org/github/Glorf/DH-401/blob/main/milestone2.ipynb)
## Milestone 1
Refined project proposal is available [here](https://github.com/Glorf/DH-401/blob/main/milestone0.md)
## Milestone 0
Original project proposal is available in git history [here](https://github.com/Glorf/DH-401/blob/bb14813ff2bbbd9cdc6b6eecf34c9e3c160598eb/milestone0.md) |
marma/wav2vec2-large-xlsr-swedish | marma | 2021-07-06T12:28:48Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sv",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: sv
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Swedish by Marma
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice sv-SE
type: common_voice
args: sv
metrics:
- name: Test WER
type: wer
value: 23.33
---
# Wav2Vec2-Large-XLSR-53-Swedish
This model has moved [here](https://huggingface.co/KBLab/wav2vec2-large-xlsr-53-swedish) |
marcel/wav2vec2-large-xlsr-german-demo | marcel | 2021-07-06T12:09:00Z | 24 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"de",
"dataset:common_voice",
"dataset:wer",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: de
datasets:
- common_voice
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice de
type: common_voice
args: de
metrics:
- name: Test WER
type: wer
value: 29.35
---
# Wav2Vec2-Large-XLSR-53-German
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using 3% of the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "de", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
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 {language} 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", "de", split="test[:10%]")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
model = Wav2Vec2ForCTC.from_pretrained("marcel/wav2vec2-large-xlsr-german-demo")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\”\�\カ\æ\無\ན\カ\臣\ѹ\…\«\»\ð\ı\„\幺\א\ב\比\ш\ע\)\ứ\в\œ\ч\+\—\ш\‚\נ\м\ń\乡\$\=\ש\ф\支\(\°\и\к\̇]'
substitutions = {
'e' : '[\ə\é\ě\ę\ê\ế\ế\ë\ė\е]',
'o' : '[\ō\ô\ô\ó\ò\ø\ọ\ŏ\õ\ő\о]',
'a' : '[\á\ā\ā\ă\ã\å\â\à\ą\а]',
'c' : '[\č\ć\ç\с]',
'l' : '[\ł]',
'u' : '[\ú\ū\ứ\ů]',
'und' : '[\&]',
'r' : '[\ř]',
'y' : '[\ý]',
's' : '[\ś\š\ș\ş]',
'i' : '[\ī\ǐ\í\ï\î\ï]',
'z' : '[\ź\ž\ź\ż]',
'n' : '[\ñ\ń\ņ]',
'g' : '[\ğ]',
'ss' : '[\ß]',
't' : '[\ț\ť]',
'd' : '[\ď\đ]',
"'": '[\ʿ\་\’\`\´\ʻ\`\‘]',
'p': '\р'
}
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()
for x in substitutions:
batch["sentence"] = re.sub(substitutions[x], x, batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
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**: 29.35 %
## Training
The first 3% of the Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found TODO
|
manandey/wav2vec2-large-xlsr-breton | manandey | 2021-07-06T11:29:55Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: br
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Breton by Manan Dey
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice br
type: common_voice
args: br
metrics:
- name: Test WER
type: wer
value: 54.04
---
# Wav2Vec2-Large-XLSR-53-Breton
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton 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", "br", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
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 {language} 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", "br", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-breton")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)\/\«\»\½\…]'
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**: 54.04%
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
m3hrdadfi/wav2vec2-large-xlsr-turkish | m3hrdadfi | 2021-07-06T11:07:44Z | 211 | 8 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: tr
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- label: Common Voice sample 1378
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-turkish/resolve/main/sample1378.flac
- label: Common Voice sample 1589
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-turkish/resolve/main/sample1589.flac
model-index:
- name: XLSR Wav2Vec2 Turkish by Mehrdad Farahani
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: 27.51
---
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Turkish using [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:
**Requirements**
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!pip install jiwer
```
**Prediction**
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import numpy as np
import re
import string
import IPython.display as ipd
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"',
"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
]
chars_to_mapping = {
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = text.replace("\u0307", " ").strip()
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-turkish")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-turkish").to(device)
dataset = load_dataset("common_voice", "et", split="test[:1%]")
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
max_items = np.random.randint(0, len(result), 10).tolist()
for i in max_items:
reference, predicted = result["sentence"][i], result["predicted"][i]
print("reference:", reference)
print("predicted:", predicted)
print('---')
```
**Output:**
```text
reference: ülke şu anda iki federasyona üye
predicted: ülke şu anda iki federasyona üye
---
reference: foruma dört yüzde fazla kişi katıldı
predicted: soruma dört yüzden fazla kişi katıldı
---
reference: mobi altmış üç çalışanları da mutsuz
predicted: mobia haltmış üç çalışanları da mutsur
---
reference: kentin mali esnekliğinin düşük olduğu bildirildi
predicted: kentin mali esnekleğinin düşük olduğu bildirildi
---
reference: fouere iki ülkeyi sorunu abartmamaya çağırdı
predicted: foor iki ülkeyi soruna abartmamaya çanayordı
---
reference: o ülkeden herhangi bir tepki geldi mi
predicted: o ülkeden herhayın bir tepki geldi mi
---
reference: bunlara asla sırtımızı dönmeyeceğiz
predicted: bunlara asla sırtımızı dönmeyeceğiz
---
reference: sizi ayakta tutan nedir
predicted: sizi ayakta tutan nedir
---
reference: artık insanlar daha bireysel yaşıyor
predicted: artık insanlar daha bir eyselli yaşıyor
---
reference: her ikisi de diyaloga hazır olduğunu söylüyor
predicted: her ikisi de diyaloğa hazır olduğunu söylüyor
---
reference: merkez bankasının başlıca amacı düşük enflasyon
predicted: merkez bankasının başlrıca anatı güşükyen flasyon
---
reference: firefox
predicted: fair foks
---
reference: ülke halkı çok misafirsever ve dışa dönük
predicted: ülke halktı çok isatirtever ve dışa dönük
---
reference: ancak kamuoyu bu durumu pek de affetmiyor
predicted: ancak kamuonyulgukirmu pek deafıf etmiyor
---
reference: i ki madende iki bin beş yüzden fazla kişi çalışıyor
predicted: i ki madende iki bin beş yüzden fazla kişi çalışıyor
---
reference: sunnyside park dışarıdan oldukça iyi görünüyor
predicted: sani sahip park dışarıdan oldukça iyi görünüyor
---
reference: büyük ödül on beş bin avro
predicted: büyük ödül on beş bin avro
---
reference: köyümdeki camiler depoya dönüştürüldü
predicted: küyümdeki camiler depoya dönüştürüldü
---
reference: maç oldukça diplomatik bir sonuçla birbir bitti
predicted: maç oldukça diplomatik bir sonuçla bir birbitti
---
reference: kuşların ikisi de karantinada öldüler
predicted: kuşların ikiste karantinada özdüler
---
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
import numpy as np
import re
import string
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"',
"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
]
chars_to_mapping = {
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
"\u0307": " "
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = text.replace("\u0307", " ").strip()
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
text = re.sub(" +", " ", text)
text = text.strip() + " "
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-turkish")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-turkish").to(device)
dataset = load_dataset("common_voice", "tr", split="test")
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
wer = load_metric("wer")
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
```
]
**Test Result**:
- WER: 27.51%
## Training & Report
The Common Voice `train`, `validation` datasets were used for training.
You can see the training states [here](https://wandb.ai/m3hrdadfi/finetuned_wav2vec_xlsr_turkish/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Turkish--Vmlldzo1Njc1MDc?accessToken=02vm5cwbi7d342vyt7h9w9859zex0enltdmjoreyjt3bd5qwv0vs0g3u93iv92q0)
The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) |
m3hrdadfi/wav2vec2-large-xlsr-persian-shemo | m3hrdadfi | 2021-07-06T10:48:23Z | 46 | 3 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fa",
"dataset:shemo",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: fa
datasets:
- shemo
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- label: ShEMO sample 250
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-shemo/resolve/main/sample250.flac
- label: ShEMO sample 52
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-shemo/resolve/main/sample52.flac
model-index:
- name: XLSR Wav2Vec2 Persian (Farsi) ShEMO by Mehrdad Farahani
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: ShEMO fa
type: shemo
args: fa
metrics:
- name: Test WER
type: wer
value: 30.00
---
# Wav2Vec2-Large-XLSR-53-Persian ShEMO
Fine-tuned [Wav2Vec2-Large-XLSR-53-Persian V2](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v2) in Persian (Farsi) using [ShEMO](https://www.kaggle.com/mansourehk/shemo-persian-speech-emotion-detection-database). 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:
**Requirements**
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!pip install jiwer
!pip install hazm
!pip install num2fawords
```
**Prediction**
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
from num2fawords import words, ordinal_words
import numpy as np
import hazm
import re
import string
import IPython.display as ipd
_normalizer = hazm.Normalizer()
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "؟", "?", "«", "»", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?",
".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„',
'ā', 'š',
# "ء",
]
# In case of farsi
chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
chars_to_mapping = {
'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی',
'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی",
"ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع",
"ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", 'ﺍ': "ا", 'ة': "ه",
'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
# "ها": " ها", "ئ": "ی",
"a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ",
"g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ",
"m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ",
"s": " اس ", "t": " تی ", "u": " یو ", "v": " وی ", "w": " دبلیو ", "x": " اکس ",
"y": " وای ", "z": " زد ",
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = _normalizer.normalize(text)
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
text = re.sub(" +", " ", text)
_text = []
for word in text.split():
try:
word = int(word)
_text.append(words(word))
except:
_text.append(word)
text = " ".join(_text) + " "
text = text.strip() + " "
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-shemo")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-shemo").to(device)
dataset = load_dataset("csv", data_files={"test": "/content/fa/dataset/test.csv"}, delimiter="\t")["test"]
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
max_items = np.random.randint(0, len(result), 20).tolist()
for i in max_items:
reference, predicted = result["sentence"][i], result["predicted"][i]
print("reference:", reference)
print("predicted:", predicted)
print('---')
```
**Output:**
```text
reference: همون شبی که قسم خوردی منو از جونت بیشتر دوست داری و تا آخر عمر کنار من می مونی همون شبی که به من وعده دادی بزرگترین جشن های ازدواج رو برام بگیری
predicted: همون شبی که قسم خوردی منو از جونت بیشتر دوستاری و تا آخر عمر کنار من می مونیمو یبی که به من وعض دادین بزرگترین جشن های ازدواج و برام بگیری
---
reference: خودتون دم به ساعت فحشش می دین کتکش می زنین بس نیست
predicted: خودتون دم به ساعت فشش می دیم کتاکش می زنیم بس نیست
---
reference: خونه
predicted: خونه
---
reference: شلوغش نکن
predicted: شلوغش نکن
---
reference: برای بقیه سوییت هایی در نظر گرفتم
predicted: برای بقی سویید هایی در نظر گرفتم
---
reference: برو گمشو برو گمشو برو بیرون
predicted: برو گمشو برو گمشو برو بیرون
---
reference: فقط یک سال بعد از خاتمه جنگ بود که حقیقت رو فهمیدی
predicted: فقط یک سال بعد از خاتمه جنگ بود که حقیقت و فهمیدید
---
reference: غیر از اون دو نفری که اینجا خوابیدند کسان دیگه ای از دوستانشو به تو معرفی نکرده
predicted: غیر از اون دو نفری که اینجا خوابیدند کسانه دیگه ای از دوستانشو به تو معرفی نکرده
---
reference: من می دونم اینجایی درو واز کن کویی کوئک
predicted: من می دونم این جایی د رو واز کن کوری فکر
---
reference: نویسنده باید چهار تا چشم داشته باشه چهار تا گوش
predicted: نویسند باید چهار تا چشم داشته باشه و چهار تا گوش
---
reference: غیر از اون دو نفری که اینجا خوابیدند کسان دیگه ای از دوستانشو به تو معرفی نکرده
predicted: غیر از اون دو نفری که اینجا خوابیدند کسانه دیگه ای از دوستانشو به تو معرفی نکرده
---
reference: پس همراهان من چه می کنن چه می کنن که این سرکرده کولی ها تونسته خودشو اینجا برسونه
predicted: پس همرا حال من چه می کنن چه می کنن که این سرکرده کلی ها تونسته خودش رو اینجا برسونه
---
reference: گوش بدید مادمازل حقیقت اینه که من دلم می خواد به شما کمک کنم زیبایی و جوانی شما دل منو به رحم میاره به من اعتماد کنید دلم می خواد بتونم شما رو از مرگ نجات بدم
predicted: هوش بدید مادماز حقیقت اینه که من دلم می خواد به شما کمک کنم زیبای و جوانی شما دل منو به رحم می آره به من اعتماد کنید دلم می خواد بتونم شما رو از مرگ نجات بدم
---
reference: قربان به نظر می رسه شما نه تنها به مرگ رونالد دریو بلکه به مرگ خانم مونرو هم مشکوکید
predicted: قربان به نظر می رسه شما نه تن ها به مرگ رونال گریو بلکه به مرگ خانم مونرا مشکوکین
---
reference: برای اینکه شما رو دوست دارم
predicted: برای اینکه شما رو دوست دارم
---
reference: مرتبه اول دنبال جسدی می گشتن که انداخته بودن کنار خیابون
predicted: حر تبه اول دنبال جسدی می گشتند که انداخته بودن کنار خیابون
---
reference: خونه
predicted: خونه
---
reference: کدبانوی جدید این طبقه هستم
predicted: کدبانوی جدید این طبقه هستم
---
reference: و این برات خیلی گرون تموم شد
predicted: و این برات خیلی گرون تموم شد
---
reference: خب چرا نمی دین به خودشون
predicted: خبچرا نمی تون به خودشون
```
## Evaluation
The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
from num2fawords import words, ordinal_words
import numpy as np
import hazm
import re
import string
_normalizer = hazm.Normalizer()
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "؟", "?", "«", "»", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?",
".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„',
'ā', 'š',
# "ء",
]
# In case of farsi
chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
chars_to_mapping = {
'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی',
'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی",
"ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع",
"ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", 'ﺍ': "ا", 'ة': "ه",
'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
# "ها": " ها", "ئ": "ی",
"a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ",
"g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ",
"m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ",
"s": " اس ", "t": " تی ", "u": " یو ", "v": " وی ", "w": " دبلیو ", "x": " اکس ",
"y": " وای ", "z": " زد ",
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = _normalizer.normalize(text)
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
text = re.sub(" +", " ", text)
_text = []
for word in text.split():
try:
word = int(word)
_text.append(words(word))
except:
_text.append(word)
text = " ".join(_text) + " "
text = text.strip() + " "
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-shemo")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian-shemo").to(device)
dataset = load_dataset("csv", data_files={"test": "/content/fa/dataset/test.csv"}, delimiter="\t")["test"]
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
wer = load_metric("wer")
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
```
**Test Result:**
- WER: 31.00%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Persian_ShEMO_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) |
m3hrdadfi/wav2vec2-large-xlsr-estonian | m3hrdadfi | 2021-07-06T10:28:26Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"et",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: et
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- label: Common Voice sample 1123
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-estonian/resolve/main/sample1123.flac
- label: Common Voice sample 910
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-estonian/resolve/main/sample910.flac
model-index:
- name: XLSR Wav2Vec2 Estonian by Mehrdad Farahani
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice et
type: common_voice
args: et
metrics:
- name: Test WER
type: wer
value: 33.93
---
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Estonian using [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:
**Requirements**
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!pip install jiwer
```
**Prediction**
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import numpy as np
import re
import string
import IPython.display as ipd
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"',
"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
]
chars_to_mapping = {
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = text.replace("\u0307", " ").strip()
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian").to(device)
dataset = load_dataset("common_voice", "et", split="test[:1%]")
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
max_items = np.random.randint(0, len(result), 10).tolist()
for i in max_items:
reference, predicted = result["sentence"][i], result["predicted"][i]
print("reference:", reference)
print("predicted:", predicted)
print('---')
```
**Output:**
```text
reference: õhulossid lagunevad ning ees ootab maapind
predicted: õhulassid lagunevad ning ees ootab maapind
---
reference: milliseks kiievisse pääsemise nimel võistlev muusik soome muusikamaastiku hetkeseisu hindab ning kas ta ka ennast sellel tulevikus tegutsemas näeb kuuled videost
predicted: milliseks gievisse pääsemise nimel võitlev muusiks soome muusikama aastiku hetke seisu hindab ning kas ta ennast selle tulevikus tegutsemast näeb kuulad videost
---
reference: näiteks kui pool seina on tehtud tekib tunne et tahaks tegelikult natuke teistsugust ja hakkame otsast peale
predicted: näiteks kui pool seine on tehtud tekib tunnetahaks tegelikult matuka teistsugust jahappanna otsast peane
---
reference: neuroesteetilised katsed näitavad et just nägude vaatlemine aktiveerib inimese aju esteetilist keskust
predicted: neuroaisteetiliselt katsed näitaval et just nägude vaatlemine aptiveerid inimese aju est eedilist keskust
---
reference: paljud inimesed kindlasti kadestavad teid kuid ei julge samamoodi vabalt võtta
predicted: paljud inimesed kindlasti kadestavadteid kuid ei julge sama moodi vabalt võtta
---
reference: parem on otsida pileteid inkognito veebi kaudu
predicted: parem on otsida pileteid ning kognitu veebikaudu
---
reference: ja vot siin ma jäin vaikseks
predicted: ja vat siisma ja invaikseks
---
reference: mida sa iseendale juubeli puhul soovid
predicted: mida saise endale jubeli puhul soovid
---
reference: kuumuse ja kõrge temperatuuri tõttu kuivas tühjadel karjamaadel rohi mis muutus kergesti süttivaks
predicted: kuumuse ja kõrge temperatuuri tõttu kuivast ühjadal karjamaadel rohi mis muutus kergesti süttivaks
---
reference: ilmselt on inimesi kelle jaoks on see hea lahendus
predicted: ilmselt on inimesi kelle jaoks on see hea lahendus
---
```
## Evaluation
The model can be evaluated as follows on the Estonian test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
import numpy as np
import re
import string
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"',
"“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
]
chars_to_mapping = {
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = text.replace("\u0307", " ").strip()
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-estonian").to(device)
dataset = load_dataset("common_voice", "et", split="test")
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
wer = load_metric("wer")
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
```
**Test Result**:
- WER: 33.93%
## Training & Report
The Common Voice `train`, `validation` datasets were used for training.
You can see the training states [here](https://wandb.ai/m3hrdadfi/finetuned_wav2vec_xlsr_estonian/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Estonian--Vmlldzo1NjA1MTI?accessToken=k2b2g3a2i12m1sdwf13q8b226pplmmyw12joxo6vk38eb4djellfzmn9fp2725fw)
The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Estonian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb) |
m3hrdadfi/wav2vec2-base-100k-gtzan-music-genres | m3hrdadfi | 2021-07-06T10:26:27Z | 54 | 20 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"audio-classification",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- audio
- automatic-speech-recognition
- audio-classification
---
# Music Genre Classification using Wav2Vec 2.0
## How to use
### Requirements
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
```
### Prediction
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
```
```python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = "m3hrdadfi/wav2vec2-base-100k-voxpopuli-gtzan-music"
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
```
```python
def speech_file_to_array_fn(path, sampling_rate):
speech_array, _sampling_rate = torchaudio.load(path)
resampler = torchaudio.transforms.Resample(_sampling_rate)
speech = resampler(speech_array).squeeze().numpy()
return speech
def predict(path, sampling_rate):
speech = speech_file_to_array_fn(path, sampling_rate)
inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits = model(**inputs).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs
```
```python
path = "genres_original/disco/disco.00067.wav"
outputs = predict(path, sampling_rate)
```
```bash
[
{'Label': 'blues', 'Score': '0.0%'},
{'Label': 'classical', 'Score': '0.0%'},
{'Label': 'country', 'Score': '0.0%'},
{'Label': 'disco', 'Score': '99.8%'},
{'Label': 'hiphop', 'Score': '0.0%'},
{'Label': 'jazz', 'Score': '0.0%'},
{'Label': 'metal', 'Score': '0.0%'},
{'Label': 'pop', 'Score': '0.0%'},
{'Label': 'reggae', 'Score': '0.0%'},
{'Label': 'rock', 'Score': '0.0%'}
]
```
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
| label | precision | recall | f1-score | support |
|:------------:|:---------:|:------:|:--------:|:-------:|
| blues | 0.792 | 0.950 | 0.864 | 20 |
| classical | 0.864 | 0.950 | 0.905 | 20 |
| country | 0.812 | 0.650 | 0.722 | 20 |
| disco | 0.778 | 0.700 | 0.737 | 20 |
| hiphop | 0.933 | 0.700 | 0.800 | 20 |
| jazz | 1.000 | 0.850 | 0.919 | 20 |
| metal | 0.783 | 0.900 | 0.837 | 20 |
| pop | 0.917 | 0.550 | 0.687 | 20 |
| reggae | 0.543 | 0.950 | 0.691 | 20 |
| rock | 0.611 | 0.550 | 0.579 | 20 |
| accuracy | 0.775 | 0.775 | 0.775 | 0 |
| macro avg | 0.803 | 0.775 | 0.774 | 200 |
| weighted avg | 0.803 | 0.775 | 0.774 | 200 |
## Questions?
Post a Github issue from [HERE](https://github.com/m3hrdadfi/soxan/issues). |
kmfoda/wav2vec2-large-xlsr-arabic | kmfoda | 2021-07-06T09:45:10Z | 23 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ar",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: ar
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Arabic by Othmane Rifki
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ar
type: common_voice
args: ar
metrics:
- name: Test WER
type: wer
value: 46.77
---
# Wav2Vec2-Large-XLSR-53-Arabic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic 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 librosa
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ar", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("kmfoda/wav2vec2-large-xlsr-arabic")
model = Wav2Vec2ForCTC.from_pretrained("kmfoda/wav2vec2-large-xlsr-arabic")
resamplers = { # all three sampling rates exist in test split
48000: torchaudio.transforms.Resample(48000, 16000),
44100: torchaudio.transforms.Resample(44100, 16000),
32000: torchaudio.transforms.Resample(32000, 16000),
}
def prepare_example(example):
speech, sampling_rate = torchaudio.load(example["path"])
example["speech"] = resamplers[sampling_rate](speech).squeeze().numpy()
return example
test_dataset = test_dataset.map(prepare_example)
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 Arabic test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ar", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("kmfoda/wav2vec2-large-xlsr-arabic")
model = Wav2Vec2ForCTC.from_pretrained("kmfoda/wav2vec2-large-xlsr-arabic")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\؟\_\؛\ـ\—]'
resamplers = { # all three sampling rates exist in test split
48000: torchaudio.transforms.Resample(48000, 16000),
44100: torchaudio.transforms.Resample(44100, 16000),
32000: torchaudio.transforms.Resample(32000, 16000),
}
def prepare_example(example):
speech, sampling_rate = torchaudio.load(example["path"])
example["speech"] = resamplers[sampling_rate](speech).squeeze().numpy()
return example
test_dataset = test_dataset.map(prepare_example)
# 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**: 52.53
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://huggingface.co/kmfoda/wav2vec2-large-xlsr-arabic/tree/main) |
joaoalvarenga/wav2vec2-large-xlsr-portuguese | joaoalvarenga | 2021-07-06T09:30:27Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"apache-2.0",
"portuguese-speech-corpus",
"xlsr-fine-tuning-week",
"PyTorch",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: pt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 13.766801%
---
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## 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", "pt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese")
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 Portuguese test data of Common Voice.
You need to install Enelvo, an open-source spell correction trained with Twitter user posts
`pip install enelvo`
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from enelvo import normaliser
import re
test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
norm = normaliser.Normaliser()
# 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"] = [norm.normalise(i) for i in 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 (wer)**: 13.766801%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py
|
joaoalvarenga/wav2vec2-large-xlsr-portuguese-a | joaoalvarenga | 2021-07-06T09:23:08Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"apache-2.0",
"portuguese-speech-corpus",
"xlsr-fine-tuning-week",
"PyTorch",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: pt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese A
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 15.037146%
---
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## 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", "pt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
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 Portuguese 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", "pt", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
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 (wer)**: 15.037146%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py
|
joaoalvarenga/wav2vec2-large-xlsr-53-spanish | joaoalvarenga | 2021-07-06T09:14:19Z | 0 | 0 | null | [
"audio",
"speech",
"wav2vec2",
"es",
"apache-2.0",
"spanish-speech-corpus",
"automatic-speech-recognition",
"xlsr-fine-tuning-week",
"PyTorch",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: es
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- es
- apache-2.0
- spanish-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Spanish
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ES
type: common_voice
args: es
metrics:
- name: Test WER
type: wer
value: Training
---
# Wav2Vec2-Large-XLSR-53-Spanish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## 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", "es", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")
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):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn 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 the Portuguese 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", "es", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")
model.to("cuda")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' # TODO: adapt this list to include all special characters you removed from the data
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):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn 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):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn 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 (wer) **: Training
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-spanish/blob/main/fine-tuning.py |
joaoalvarenga/wav2vec2-large-100k-voxpopuli-pt | joaoalvarenga | 2021-07-06T09:11:37Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"apache-2.0",
"portuguese-speech-corpus",
"PyTorch",
"voxpopuli",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: pt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- voxpopuli
license: apache-2.0
model-index:
- name: JoaoAlvarenga Wav2Vec2 Large 100k VoxPopuli Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 19.735723%
---
# Wav2Vec2-Large-100k-VoxPopuli-Portuguese
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## 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", "pt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")
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 Portuguese test data of Common Voice.
You need to install Enelvo, an open-source spell correction trained with Twitter user posts
`pip install enelvo`
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from enelvo import normaliser
import re
test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
norm = normaliser.Normaliser()
# 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"] = [norm.normalise(i) for i in 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 (wer)**: 19.735723%
## Training
The Common Voice `train`, `validation` datasets were used for training.
|
joaoalvarenga/wav2vec2-cv-coral-30ep | joaoalvarenga | 2021-07-06T09:07:11Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"apache-2.0",
"portuguese-speech-corpus",
"xlsr-fine-tuning-week",
"PyTorch",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: pt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese A
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 15.037146%
---
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## 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", "pt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
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 Portuguese 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", "pt", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
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 (wer)**: 15.037146%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py |
joaoalvarenga/model-sid-voxforge-cv-cetuc-0 | joaoalvarenga | 2021-07-06T08:50:10Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"apache-2.0",
"portuguese-speech-corpus",
"xlsr-fine-tuning-week",
"PyTorch",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: pt
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese A
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 15.037146%
---
# Wav2Vec2-Large-XLSR-53-Portuguese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
## 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", "pt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
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 Portuguese 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", "pt", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
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 (wer)**: 15.037146%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py |
infinitejoy/Wav2Vec2-Large-XLSR-53-Tamil | infinitejoy | 2021-07-06T06:33:19Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ta",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language: ta
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Joydeep Bhattacharjee XLSR Wav2Vec2 Large 53 Tamil
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ta
type: common_voice
args: ta
metrics:
- name: Test WER
type: wer
value: 71.29
---
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tamil 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", "ta", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Tamil")
model = Wav2Vec2ForCTC.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Tamil")
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 Tamil 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", "ta", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Tamil")
model = Wav2Vec2ForCTC.from_pretrained("infinitejoy/Wav2Vec2-Large-XLSR-53-Tamil")
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('’ ',' ',batch["sentence"])
batch["sentence"] = re.sub(' ‘',' ',batch["sentence"])
batch["sentence"] = re.sub('’|‘','\'',batch["sentence"])
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**: 71.29 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
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