```python
from transformers import pipeline

fill_mask = pipeline(
    "fill-mask",
    model="julien-c/EsperBERTo-small",
    tokenizer="julien-c/EsperBERTo-small"
)

fill_mask("Jen la komenco de bela <mask>.")

# This is the beginning of a beautiful <mask>.
# =>

# {
#     'score':0.06502299010753632
#     'sequence':'<s> Jen la komenco de bela vivo.</s>'
#     'token':1099
# }
# {
#     'score':0.0421181358397007
#     'sequence':'<s> Jen la komenco de bela vespero.</s>'
#     'token':5100
# }
# {
#     'score':0.024884626269340515
#     'sequence':'<s> Jen la komenco de bela laboro.</s>'
#     'token':1570
# }
# {
#     'score':0.02324388362467289
#     'sequence':'<s> Jen la komenco de bela tago.</s>'
#     'token':1688
# }
# {
#     'score':0.020378097891807556
#     'sequence':'<s> Jen la komenco de bela festo.</s>'
#     'token':4580
# }
```


model name: EsperBERTo-small


model author: julien-c


task: fill-mask


tags: pytorch, jax, roberta, fill-mask, eo, transformers, autonlp_compatible, infinity_compatible


in the editor i only change this line


Example of a hf.co repo containing signed commits.


hello tabs


model name: dummy-for-flat


tags:


language: multilingual
tags:


- fasttext
datasets:
- wikipedia
- tatoeba
- setimes
license: cc-by-sa-4.0
library_name: fasttext
inference: false


## FastText model for language identification


#### ♻ Imported from https://fasttext.cc/docs/en/language-identification.html


> [1] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification


```bibtex
@article{joulin2016bag,
  title={Bag of Tricks for Efficient Text Classification},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.01759},
  year={2016}
}
```


> [2] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models


```bibtex
@article{joulin2016fasttext,
  title={FastText.zip: Compressing text classification models},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1612.03651},
  year={2016}
}
```


model name: fasttext-language-id


model author: julien-c


tags: multilingual, dataset:wikipedia, dataset:tatoeba, dataset:setimes, fasttext, license:cc-by-sa-4.0


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
inference: false


## Example ESPnet2 TTS model


### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave`


♻ Imported from https://zenodo.org/record/4381098/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Training


![](./exp/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent/images/attn_loss.png)


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi-jsut_tts_train_tacotron2


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- ci


## Dummy model used for unit testing and CI


```python
import json
import os
from transformers import RobertaConfig, RobertaForMaskedLM, TFRobertaForMaskedLM

DIRNAME = "./dummy-unknown"

config = RobertaConfig(10, 20, 1, 1, 40)

model = RobertaForMaskedLM(config)
model.save_pretrained(DIRNAME)

tf_model = TFRobertaForMaskedLM.from_pretrained(DIRNAME, from_pt=True)
tf_model.save_pretrained(DIRNAME)

# Tokenizer:

vocab = [
    "l",
    "o",
    "w",
    "e",
    "r",
    "s",
    "t",
    "i",
    "d",
    "n",
    "\u0120",
    "\u0120l",
    "\u0120n",
    "\u0120lo",
    "\u0120low",
    "er",
    "\u0120lowest",
    "\u0120newer",
    "\u0120wider",
    "<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]

vocab_file = os.path.join(DIRNAME, "vocab.json")
merges_file = os.path.join(DIRNAME, "merges.txt")
with open(vocab_file, "w", encoding="utf-8") as fp:
    fp.write(json.dumps(vocab_tokens) + "\n")
with open(merges_file, "w", encoding="utf-8") as fp:
    fp.write("\n".join(merges))
```


model name: dummy-unknown


model author: julien-c


task: fill-mask


tags: pytorch, tf, jax, roberta, fill-mask, transformers, ci, autonlp_compatible, infinity_compatible


## license: apache-2.0


model name: Hello


tags: license:apache-2.0


- image-classification
- huggingpics
metrics:
- accuracy


model-index:


- name: hotdog-not-hotdog
results:
  - task:
name: Image Classification
type: image-classification
metrics:
    - name: Accuracy
type: accuracy
value: 0.824999988079071


# hotdog-not-hotdog


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


#### hot dog


![hot dog](images/hot_dog.jpg)


#### not hot dog


![miscellaneous](images/miscellaneous.jpg)


model name: hotdog-not-hotdog


model author: julien-c


task: image-classification


tags: pytorch, tensorboard, vit, image-classification, transformers, huggingpics, model-index


tags:


- flair
- token-classification
- sequence-tagger-model
language: de
datasets:
- conll2003
inference: false


## Flair NER model `de-ner-conll03-v0.4.pt`


Imported from https://nlp.informatik.hu-berlin.de/resources/models/de-ner/


### Demo: How to use in Flair


```python
from flair.data import Sentence
from flair.models import SequenceTagger

sentence = Sentence(
	"Mein Name ist Julien, ich lebe zurzeit in Paris, ich arbeite bei Hugging Face, Inc."
)

tagger = SequenceTagger.load("julien-c/flair-de-ner")

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence.to_tagged_string())

```


yields the following output:


> `Mein Name ist Julien <S-PER> , ich lebe zurzeit in Paris <S-LOC> , ich arbeite bei Hugging <B-ORG> Face <E-ORG> , Inc <S-ORG> .`


### Thanks [@stefan-it](https://huggingface.co/stefan-it) for the Flair integration ❤ 🔥


model name: flair-de-ner


task: token-classification


tags: pytorch, de, dataset:conll2003, flair, token-classification, sequence-tagger-model


- espnet
- audio
- text-to-speech
language: ja
datasets:
- jsut
license: cc-by-4.0
inference: false


## Example ESPnet2 TTS model


♻ Imported from https://zenodo.org/record/3963886/


This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


Model id:
`kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best`


### Citing ESPnet


```BibTex
@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
  title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
  author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
  booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7654--7658},
  year={2020},
  organization={IEEE}
}
```


or arXiv:


```bibtex
@misc{watanabe2018espnet,
      title={ESPnet: End-to-End Speech Processing Toolkit},
      author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
      year={2018},
      eprint={1804.00015},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: kan-bayashi-jsut_tts_train_tacotron2_ja


model author: julien-c


task: text-to-speech


tags: ja, dataset:jsut, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


language: eo
thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
widget:


- text: "Mi estas viro kej estas tago varma."


# EsperBERTo: RoBERTa-like Language model trained on Esperanto


**Companion model to blog post https://huggingface.co/blog/how-to-train** 🔥


## Training Details


- current checkpoint: 566000
- machine name: `galinette`


## Example pipeline


```python
from transformers import TokenClassificationPipeline, pipeline

MODEL_PATH = "./models/EsperBERTo-small-pos/"

nlp = pipeline(
    "ner",
    model=MODEL_PATH,
    tokenizer=MODEL_PATH,
)
# or instantiate a TokenClassificationPipeline directly.

nlp("Mi estas viro kej estas tago varma.")

# {'entity': 'PRON', 'score': 0.9979867339134216, 'word': ' Mi'}
# {'entity': 'VERB', 'score': 0.9683094620704651, 'word': ' estas'}
# {'entity': 'VERB', 'score': 0.9797462821006775, 'word': ' estas'}
# {'entity': 'NOUN', 'score': 0.8509314060211182, 'word': ' tago'}
# {'entity': 'ADJ', 'score': 0.9996201395988464, 'word': ' varma'}
```


model name: EsperBERTo-small-pos


task: token-classification


tags: pytorch, jax, roberta, token-classification, eo, transformers, autonlp_compatible, infinity_compatible


tags:


- espnet
- audio
- text-to-speech
language: en
datasets:
- ljspeech
license: cc-by-4.0
widget:
- text: "Hello, how are you doing?"


## Example ESPnet2 TTS model


### `kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best`


♻ Imported from https://zenodo.org/record/3989498#.X90RlOlKjkM


This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).


### Demo: How to use in ESPnet2


```python
# coming soon
```


### Citing ESPnet


or arXiv:


### Training config


See full config in [`config.yaml`](./config.yaml)


```yaml
config: conf/tuning/train_tacotron2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_tacotron2_raw
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
```


model name: ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train


model author: julien-c


task: text-to-speech


tags: en, dataset:ljspeech, arxiv:1804.00015, espnet, audio, text-to-speech, license:cc-by-4.0


tags:


- image-classification
- timm
- dpn
license: apache-2.0
datasets:
- imagenet


# `dpn92` from `rwightman/pytorch-image-models`


From [`rwightman/pytorch-image-models`](https://github.com/rwightman/pytorch-image-models):


```
""" PyTorch implementation of DualPathNetworks
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.

This implementation is compatible with the pretrained weights from cypw's MXNet implementation.

Hacked together by / Copyright 2020 Ross Wightman
"""
```


## Model description


[Dual Path Networks](https://arxiv.org/abs/1707.01629)


## Intended uses & limitations


You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few).


### How to use


You can use this model with the usual factory method in `timm`:


```python
import PIL
import timm
import torch

model = timm.create_model("julien-c/timm-dpn92")
img = PIL.Image.open(path_to_an_image)
img = img.convert("RGB")

config = model.default_cfg

if isinstance(config["input_size"], tuple):
    img_size = config["input_size"][-2:]
else:
    img_size = config["input_size"]

transform = timm.data.transforms_factory.transforms_imagenet_eval(
    img_size=img_size,
    interpolation=config["interpolation"],
    mean=config["mean"],
    std=config["std"],
)

input_tensor = transform(cat_img)
input_tensor = input_tensor.unsqueeze(0)
# ^ batch size = 1
with torch.no_grad():
    output = model(input_tensor)

probs = output.squeeze(0).softmax(dim=0)
```


### Limitations and bias


The training images in the dataset are usually photos clearly representing one of the 1,000 labels. The model will
probably not generalize well on drawings or images containing multiple objects with different labels.


The training images in the dataset come mostly from the US (45.4%) and Great Britain (7.6%). As such the model or
models created by fine-tuning this model will work better on images picturing scenes from these countries (see
[this paper](https://arxiv.org/abs/1906.02659) for examples).


More generally, [recent research](https://arxiv.org/abs/2010.15052) has shown that even models trained in an
unsupervised fashion on ImageNet (i.e. without using the labels) will pick up racial and gender bias represented in
the training images.


## Training data


This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 millions of
hand-annotated images with 1,000 categories.


## Training procedure


To be completed


### Preprocessing


## Evaluation results


### BibTeX entry and citation info


```bibtex
@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
```


and


```bibtex
@misc{chen2017dual,
      title={Dual Path Networks},
      author={Yunpeng Chen and Jianan Li and Huaxin Xiao and Xiaojie Jin and Shuicheng Yan and Jiashi Feng},
      year={2017},
      eprint={1707.01629},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```


model name: timm-dpn92


model author: julien-c


task: image-classification


tags: pytorch, dataset:imagenet, arxiv:1707.01629, arxiv:1906.02659, arxiv:2010.15052, timm, image-classification, dpn, license:apache-2.0


tags:


- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
inference: false


## Flair NER model `en-ner-conll03-v0.4.pt`


Imported from https://nlp.informatik.hu-berlin.de/resources/models/ner/


### Demo: How to use in Flair


```python
from flair.data import Sentence
from flair.models import SequenceTagger

sentence = Sentence(
	"My name is Julien, I currently live in Paris, I work at Hugging Face, Inc."
)

tagger = SequenceTagger.load("julien-c/flair-ner")

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence.to_tagged_string())

```


yields the following output:


> `My name is Julien <S-PER> , I currently live in Paris <S-LOC> , I work at Hugging <B-LOC> Face <E-LOC> .`


### Thanks [@stefan-it](https://huggingface.co/stefan-it) for the Flair integration ❤ 🔥


model name: flair-ner


task: token-classification


tags: pytorch, en, dataset:conll2003, flair, token-classification, sequence-tagger-model


language:


- ab
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - AB dataset.
It achieves the following results on the evaluation set:


- Loss: 133.2596
- Wer: 19.1571


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP


### Training results


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


model name: xls-r-ab-test


model author: pablouribe


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, ab, dataset:common_voice, transformers, common_voice, generated_from_trainer, model-index


language:


- es
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- robust-speech-event
datasets:
- common_voice
model-index:
- name: xls-r-spanish-test
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: es
metrics:
    - name: Test WER
type: wer
value: 13.89
    - name: Test CER
type: cer
value: 3.85
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
    - name: Test WER
type: wer
value: 37.66
    - name: Test CER
type: cer
value: 15.32


This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ES dataset.
It achieves the following results on the evaluation set:


- Loss: 0.1461
- Wer: 1.0063


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.953         | 0.15  | 1000  | 2.9528          | 1.0    |
| 1.1519        | 0.3   | 2000  | 0.3735          | 1.0357 |
| 1.0278        | 0.45  | 3000  | 0.2529          | 1.0390 |
| 0.9922        | 0.61  | 4000  | 0.2208          | 1.0270 |
| 0.9618        | 0.76  | 5000  | 0.2088          | 1.0294 |
| 0.9364        | 0.91  | 6000  | 0.2019          | 1.0214 |
| 0.9179        | 1.06  | 7000  | 0.1940          | 1.0294 |
| 0.9154        | 1.21  | 8000  | 0.1915          | 1.0290 |
| 0.8985        | 1.36  | 9000  | 0.1837          | 1.0211 |
| 0.9055        | 1.51  | 10000 | 0.1838          | 1.0273 |
| 0.8861        | 1.67  | 11000 | 0.1765          | 1.0139 |
| 0.892         | 1.82  | 12000 | 0.1723          | 1.0188 |
| 0.8778        | 1.97  | 13000 | 0.1735          | 1.0092 |
| 0.8645        | 2.12  | 14000 | 0.1707          | 1.0106 |
| 0.8595        | 2.27  | 15000 | 0.1713          | 1.0186 |
| 0.8392        | 2.42  | 16000 | 0.1686          | 1.0053 |
| 0.8436        | 2.57  | 17000 | 0.1653          | 1.0096 |
| 0.8405        | 2.73  | 18000 | 0.1689          | 1.0077 |
| 0.8382        | 2.88  | 19000 | 0.1645          | 1.0114 |
| 0.8247        | 3.03  | 20000 | 0.1647          | 1.0078 |
| 0.8219        | 3.18  | 21000 | 0.1611          | 1.0026 |
| 0.8024        | 3.33  | 22000 | 0.1580          | 1.0062 |
| 0.8087        | 3.48  | 23000 | 0.1578          | 1.0038 |
| 0.8097        | 3.63  | 24000 | 0.1556          | 1.0057 |
| 0.8094        | 3.79  | 25000 | 0.1552          | 1.0035 |
| 0.7836        | 3.94  | 26000 | 0.1516          | 1.0052 |
| 0.8042        | 4.09  | 27000 | 0.1515          | 1.0054 |
| 0.7925        | 4.24  | 28000 | 0.1499          | 1.0031 |
| 0.7855        | 4.39  | 29000 | 0.1490          | 1.0041 |
| 0.7814        | 4.54  | 30000 | 0.1482          | 1.0068 |
| 0.7859        | 4.69  | 31000 | 0.1460          | 1.0066 |
| 0.7819        | 4.85  | 32000 | 0.1464          | 1.0062 |
| 0.7784        | 5.0   | 33000 | 0.1460          | 1.0063 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0


model name: xls-r-spanish-test


model author: pablouribe


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, es, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_7_0, generated_from_trainer, robust-speech-event, model-index


# BERT-STEM


BERT model fine-tuned on Science Technology Engineering and Mathematics (STEM) lessons.


## Install:


To install from pip:


```
pip install bertstem
```


## Quickstart


To encode sentences and get embedding matrix for embedding layers:


```python
from BERT_STEM.BertSTEM import *
bert = BertSTEM()

# Example dataframe with text in spanish
data = {'col_1': [3, 2, 1],
'col_2': ['hola como estan', 'alumnos queridos', 'vamos a hablar de matematicas']}

df = pd.DataFrame.from_dict(data)

# Encode sentences using BertSTEM:
bert._encode_df(df, column='col_2', encoding='sum')

# Get embedding matrix:

embedding_matrix = bert.get_embedding_matrix()
```


To use it from HuggingFace:


```python
from BERT_STEM.Encode import *
import pandas as pd
import transformers

# Download spanish BERTSTEM:
model = transformers.BertModel.from_pretrained("pablouribe/bertstem")

# Download spanish tokenizer:
tokenizer = transformers.BertTokenizerFast.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased",
                                                            do_lower_case=True,
                                                            add_special_tokens = False)

# Example dataframe with text in spanish
data = {'col_1': [3, 2, 1],
        'col_2': ['hola como estan', 'alumnos queridos', 'vamos a hablar de matematicas']}

df = pd.DataFrame.from_dict(data)

# Encode sentences using BertSTEM:
sentence_encoder(df, model, tokenizer, column = 'col_2', encoding = 'sum')
```


model name: bertstem


tags: pytorch, bert, pretraining, transformers, infinity_compatible


language:


- en
tags:
- text-classification
- hate-speech
- counterspeech
- irt
- arxiv:2009.10277
datasets:
- ucberkeley-dlab/measuring-hate-speech


# Measuring hate speech: RoBERTa-Large


This model predicts a continuous hate speech score as described in Kennedy et al. (2020).


## Citation


```
@article{kennedy2020constructing,
  title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application},
  author={Kennedy, Chris J and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia},
  journal={arXiv preprint arXiv:2009.10277},
  year={2020}
}
```


## References


Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). [Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application](https://arxiv.org/abs/2009.10277). arXiv preprint arXiv:2009.10277.


model name: hate-measure-roberta-large


model author: ucberkeley-dlab


task: text-classification


tags: keras, en, dataset:ucberkeley-dlab/measuring-hate-speech, arxiv:2009.10277, text-classification, hate-speech, counterspeech, irt


tags:


- conversational


# Phoenix DialoGPT model


model name: DialoGPT-small-phoenix


model author: kripanshudixit


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


# The Manipulator


model name: TheManipulator


model author: disdamoe


# Moe DialoGPT Model


model name: TheGreatManipulator


model name: DialoGPT-small-moe


license: cc-by-4.0
tags:


- generated_from_trainer
model-index:
- name: herbert-base-cased-finetuned-squad
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# herbert-base-cased-finetuned-squad


This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 1.2071


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3


### Training results


| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 233  | 1.2474          |
| No log        | 2.0   | 466  | 1.1951          |
| 1.3459        | 3.0   | 699  | 1.2071          |


### Framework versions


- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: herbert-base-cased-finetuned-squad


model author: Aleksandra


task: question-answering


tags: pytorch, tensorboard, bert, question-answering, transformers, license:cc-by-4.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


tags:


- glove
- gensim
license: PDDL


# Glove Twitter


Pre-trained glove vectors based on 2B tweets, 27B tokens, 1.2M vocab, uncased.


Read more:


* https://nlp.stanford.edu/projects/glove/
* https://nlp.stanford.edu/pubs/glove.pdf


## Example Usage


```python
import gensim.downloader as api
model = api.load("glove-twitter-25", from_hf=True)
model.most_similar(positive=['fruit', 'flower'], topn=1)

"""
Output:

[('cherry', 0.9183273911476135)]
"""
```


model name: glove-twitter-25


model author: Gensim


tags: glove, gensim, license:pddl


- conversational


# Rick DialoGPT Model


model name: DialogGPT-small-Rickk


model author: arifbhrn


task: conversational


language: en
datasets:


- quora


# T5-small for paraphrase generation


Google's T5-small fine-tuned on [Quora Question Pairs](https://huggingface.co/datasets/quora) dataset for paraphrasing.


## Model in Action 🚀


```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-quora")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-quora")

def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
        text = prefix + sentence + " </s>"
        encoding = tokenizer.encode_plus(
            text, pad_to_max_length=True, return_tensors="pt"
        )
        input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
            "attention_mask"
        ].to(device)

        model_output = model.generate(
            input_ids=input_ids,
            attention_mask=attention_masks,
            do_sample=True,
            max_length=max_length,
            top_k=top_k,
            top_p=0.98,
            early_stopping=True,
            num_return_sequences=n_predictions,
        )

        outputs = []
        for output in model_output:
            generated_sent = tokenizer.decode(
                output, skip_special_tokens=True, clean_up_tokenization_spaces=True
            )
            if (
                generated_sent.lower() != sentence.lower()
                and generated_sent not in outputs
            ):
                outputs.append(generated_sent)
        return outputs

paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")

for sent in paraphrases:
  print(sent)
```


## Output


```
My house is up clean on Saturday morning. Thank you for this email. I'm introducing a new name and name. I'm running my house at home. I'm a taller myself. I'm gonna go with it on Monday. (the house will be up cleaned).
Is there anything that will be cleaned every Saturday morning?
The house is clean and will be cleaned each Saturday by my wife.
I will clean the house for almost a week. I have to clean it all the weekend. I will be able to do it. My house is new.
If I clean my house every Monday, I can call it clean.
```


Created by [Het Pandya/@hetpandya](https://github.com/hetpandya) | [LinkedIn](https://www.linkedin.com/in/het-pandya)


Made with ♥ in India


model name: t5-small-quora


model author: hetpandya


task: text2text-generation


tags: pytorch, t5, text2text-generation, en, dataset:quora, transformers, autonlp_compatible


- tapaco


Google's T5 small fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.


```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-tapaco")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-tapaco")

def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
        text = prefix + sentence + " </s>"
        encoding = tokenizer.encode_plus(
            text, pad_to_max_length=True, return_tensors="pt"
        )
        input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
            "attention_mask"
        ].to(device)

        model_output = model.generate(
            input_ids=input_ids,
            attention_mask=attention_masks,
            do_sample=True,
            max_length=max_length,
            top_k=top_k,
            top_p=0.98,
            early_stopping=True,
            num_return_sequences=n_predictions,
        )

        outputs = []
        for output in model_output:
            generated_sent = tokenizer.decode(
                output, skip_special_tokens=True, clean_up_tokenization_spaces=True
            )
            if (
                generated_sent.lower() != sentence.lower()
                and generated_sent not in outputs
            ):
                outputs.append(generated_sent)
        return outputs

paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")

for sent in paraphrases:
  print(sent)
```


```
The house is cleaned every Saturday by me.
The house will be cleaned on Saturday.
I will clean the house every Saturday.
I get the house cleaned every Saturday.
I will clean this house every Saturday.
```


## Model fine-tuning


Please find my guide on fine-tuning the model here:


model name: t5-small-tapaco


tags: pytorch, t5, text2text-generation, en, dataset:tapaco, transformers, autonlp_compatible


language: en
datasets:


# T5-base for paraphrase generation


Google's T5-base fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.


<!-- ## Model fine-tuning -->


<!-- The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him! -->


## Model in Action 🚀


```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-base-tapaco")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-base-tapaco")

def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
        text = prefix + sentence + " </s>"
        encoding = tokenizer.encode_plus(
            text, pad_to_max_length=True, return_tensors="pt"
        )
        input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
            "attention_mask"
        ].to(device)

        model_output = model.generate(
            input_ids=input_ids,
            attention_mask=attention_masks,
            do_sample=True,
            max_length=max_length,
            top_k=top_k,
            top_p=0.98,
            early_stopping=True,
            num_return_sequences=n_predictions,
        )

        outputs = []
        for output in model_output:
            generated_sent = tokenizer.decode(
                output, skip_special_tokens=True, clean_up_tokenization_spaces=True
            )
            if (
                generated_sent.lower() != sentence.lower()
                and generated_sent not in outputs
            ):
                outputs.append(generated_sent)
        return outputs

paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")

for sent in paraphrases:
  print(sent)
```


## Output


```
The house will get cleaned for a whole week.
The house is cleaning by me every weekend.
What was going to do not get do with the house from me every Thursday.
The house should be cleaned on Sunday--durse.
It's time that I would be cleaning her house in tomorrow.
```


Made with ♥ in India


model name: t5-base-tapaco


model author: hetpandya


task: text2text-generation


## Albert xxlarge version 1 language model fine-tuned on SQuAD2.0


### (updated 30Sept2020) with the following results:


```
exact: 86.11134506864315
f1: 89.35371214945009
total': 11873
HasAns_exact': 83.56950067476383
HasAns_f1': 90.06353312254078
HasAns_total': 5928
NoAns_exact': 88.64592094196804
NoAns_f1': 88.64592094196804
NoAns_total': 5945
best_exact': 86.11134506864315
best_exact_thresh': 0.0
best_f1': 89.35371214944985
best_f1_thresh': 0.0
```


### from script:


```
python ${EXAMPLES}/run_squad.py \
  --model_type albert \
  --model_name_or_path albert-xxlarge-v1 \
  --do_train \
  --do_eval \
  --train_file ${SQUAD}/train-v2.0.json \
  --predict_file ${SQUAD}/dev-v2.0.json \
  --version_2_with_negative \
  --do_lower_case \
  --num_train_epochs 3 \
  --max_steps 8144 \
  --warmup_steps 814 \
  --learning_rate 3e-5 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --per_gpu_train_batch_size 6 \
  --gradient_accumulation_steps 8 \
  --per_gpu_eval_batch_size 48 \
  --fp16 \
  --fp16_opt_level O1 \
  --threads 12 \
  --logging_steps 50 \
  --save_steps 3000 \
  --overwrite_output_dir \
  --output_dir ${MODEL_PATH}
```


### using the following software & system:


```
Transformers: 3.1.0
PyTorch: 1.6.0
TensorFlow: 2.3.1
Python: 3.8.1
OS: Linux-5.4.0-48-generic-x86_64-with-glibc2.10
CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
```


model name: albert_xxlargev1_squad2_512


model author: ahotrod


task: question-answering


tags: pytorch, tf, albert, question-answering, transformers, autonlp_compatible, infinity_compatible


## RoBERTa-large language model fine-tuned on SQuAD2.0


### with the following results:


```
  "exact": 84.46896319380106,
  "f1": 87.85388093408943,
  "total": 11873,
  "HasAns_exact": 81.37651821862349,
  "HasAns_f1": 88.1560607844881,
  "HasAns_total": 5928,
  "NoAns_exact": 87.55256518082422,
  "NoAns_f1": 87.55256518082422,
  "NoAns_total": 5945,
  "best_exact": 84.46896319380106,
  "best_exact_thresh": 0.0,
  "best_f1": 87.85388093408929,
  "best_f1_thresh": 0.0
```


```
python ${EXAMPLES}/run_squad.py \
  --model_type roberta \
  --model_name_or_path roberta-large \
  --do_train \
  --do_eval \
  --train_file ${SQUAD}/train-v2.0.json \
  --predict_file ${SQUAD}/dev-v2.0.json \
  --version_2_with_negative \
  --do_lower_case \
  --num_train_epochs 3 \
  --warmup_steps 1642 \
  --weight_decay 0.01 \
  --learning_rate 3e-5 \
  --adam_epsilon 1e-6 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --per_gpu_train_batch_size 8 \
  --gradient_accumulation_steps 6 \
  --per_gpu_eval_batch_size 48 \
  --threads 12 \
  --logging_steps 50 \
  --save_steps 2000 \
  --overwrite_output_dir \
  --output_dir ${MODEL_PATH}
$@
```


### using the following system & software:


```
Transformers: 2.7.0
PyTorch: 1.4.0
TensorFlow: 2.1.0
Python: 3.7.7
OS/Platform: Linux-5.3.0-46-generic-x86_64-with-debian-buster-sid
CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
```


model name: roberta_large_squad2


tags: pytorch, tf, jax, roberta, question-answering, transformers, autonlp_compatible, infinity_compatible


## ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0


```
  "exact": 87.09677419354838,
  "f1": 89.98343832723452,
  "total": 11873,
  "HasAns_exact": 84.66599190283401,
  "HasAns_f1": 90.44759839056285,
  "HasAns_total": 5928,
  "NoAns_exact": 89.52060555088309,
  "NoAns_f1": 89.52060555088309,
  "NoAns_total": 5945,
  "best_exact": 87.09677419354838,
  "best_exact_thresh": 0.0,
  "best_f1": 89.98343832723432,
  "best_f1_thresh": 0.0
```


### from script:


```
python ${EXAMPLES}/run_squad.py \
  --model_type electra \
  --model_name_or_path google/electra-large-discriminator \
  --do_train \
  --do_eval \
  --train_file ${SQUAD}/train-v2.0.json \
  --predict_file ${SQUAD}/dev-v2.0.json \
  --version_2_with_negative \
  --do_lower_case \
  --num_train_epochs 3 \
  --warmup_steps 306 \
  --weight_decay 0.01 \
  --learning_rate 3e-5 \
  --max_grad_norm 0.5 \
  --adam_epsilon 1e-6 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --per_gpu_train_batch_size 8 \
  --gradient_accumulation_steps 16 \
  --per_gpu_eval_batch_size 128 \
  --fp16 \
  --fp16_opt_level O1 \
  --threads 12 \
  --logging_steps 50 \
  --save_steps 1000 \
  --overwrite_output_dir \
  --output_dir ${MODEL_PATH}
```


```
Transformers: 2.11.0
PyTorch: 1.5.0
TensorFlow: 2.2.0
Python: 3.8.1
OS/Platform: Linux-5.3.0-59-generic-x86_64-with-glibc2.10
CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
```


model name: electra_large_discriminator_squad2_512


model author: ahotrod


task: question-answering


tags: pytorch, tf, electra, question-answering, transformers, autonlp_compatible, infinity_compatible


# Albumin-15s


## Model description


This is a version of [Albert-base-v2](https://huggingface.co/albert-base-v2) for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.


The Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences.


More information can be found in our [github]() and our iGEMs [wiki]().


## Intended uses & limitations


You can use the fine-tuned model for either masked aptamer pair sequence classification, which one is more affine for target protein Albumin, prediction, but it's mostly intended to be fine-tuned again on a different length aptamer or simply expanded datasets.


#### How to use


This model can be used to predict compared affinity with dataset preprocessing function which encodes the specific type of data (Sequence1, Sequence2, Label) where Label indicates binary if Sequence1 is more affine to target protein Albumin.


```python
from transformers import AutoTokenizer, BertModel
mname = "Vilnius-Lithuania-iGEM/Albumin"
model = BertModel.from_pretrained(mname)
```


To predict batches of sequences you have to employ custom functions shown in [git/prediction.ipynb]()


#### Limitations and bias


It seems that fine-tuned Albert model for this kind of task has limition of 90 % accuracy predicting which aptamer is more suitable for a target protein, also Albert-large or immense dataset of 15s aptamer could increase accuracy few %, however extrapolation case is not studied and we cannot confirm this model is state-of-The-art when one of aptamers is SUPER good (has almost maximum entropy to the Albumin).


## Eval results


accuracy :  0.8601


precision:  0.8515


recall   :  0.8725


f1       :  0.8618


roc_auc  :  0.9388


The score was calculated using sklearn.metrics.


model name: Albumin


model author: Vilnius-Lithuania-iGEM


task: fill-mask


tags: pytorch, bert, fill-mask, transformers, autonlp_compatible, infinity_compatible


## license: apache-2.0


## Overview


This model was trained with data from https://registry.opendata.aws/helpful-sentences-from-reviews/ to predict how "helpful" a review is.


The model was fine-tuned from the `distilbert-base-uncased` model


### Labels


LABEL_0 - Not helpful\
LABEL_1 - Helpful


### How to use


The following code shows how to make a prediction with this model


```python
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    TextClassificationPipeline,
)

tokenizer = AutoTokenizer.from_pretrained("banjtheman/distilbert-base-uncased-helpful-amazon")

model = AutoModelForSequenceClassification.from_pretrained(
    "banjtheman/distilbert-base-uncased-helpful-amazon"
)

pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)

result = pipe("This was a Christmas gift for my grandson.")

print(result)

#[{'label': 'LABEL_0', 'score': 0.998775064945221}]
# This is NOT A HELPFUL comment

```


model name: distilbert-base-uncased-helpful-amazon


model author: banjtheman


task: text-classification


tags: pytorch, distilbert, text-classification, transformers, license:apache-2.0, infinity_compatible


tags:


- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: camembert-base-wikipedia-4gb-finetuned-job-ner
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# camembert-base-wikipedia-4gb-finetuned-job-ner


This model is a fine-tuned version of [camembert/camembert-base-wikipedia-4gb](https://huggingface.co/camembert/camembert-base-wikipedia-4gb) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0435
- Precision: 0.9134
- Recall: 0.9197
- F1: 0.9165
- Accuracy: 0.9873


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## 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


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0463        | 1.0   | 7543 | 0.0435          | 0.9134    | 0.9197 | 0.9165 | 0.9873   |


### Framework versions


- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: camembert-base-wikipedia-4gb-finetuned-job-ner


model author: SergeiGKS


task: token-classification


tags: pytorch, tensorboard, camembert, token-classification, transformers, generated_from_trainer, model-index, autonlp_compatible


tags:


- conversational


# Neosh Bot1


This is a simplified version. Hopefully will train a more complex model in the future.


model name: neosh-bot1


model author: milayue


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


language:


- sat
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- sat
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-sat-a3
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: sat
metrics:
    - name: Test WER
type: wer
value: 0.357429718875502
    - name: Test CER
type: cer
value: 0.14203730272596843


# wav2vec2-large-xls-r-300m-sat-a3


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SAT dataset.
It achieves the following results on the evaluation set:


- Loss: 0.8961
- Wer: 0.3976


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0004
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 200
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 11.1266       | 33.29  | 100  | 2.8577          | 1.0    |
| 2.1549        | 66.57  | 200  | 1.0799          | 0.5542 |
| 0.5628        | 99.86  | 300  | 0.7973          | 0.4016 |
| 0.0779        | 133.29 | 400  | 0.8424          | 0.4177 |
| 0.0404        | 166.57 | 500  | 0.9048          | 0.4137 |
| 0.0212        | 199.86 | 600  | 0.8961          | 0.3976 |


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-sat-a3


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, sat, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- pa-IN
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset.
It achieves the following results on the evaluation set:


- Loss: 1.1508
- Wer: 0.4908


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 100.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5841        | 9.26  | 500  | 3.2514          | 0.9941 |
| 0.3992        | 18.52 | 1000 | 0.8790          | 0.6107 |
| 0.2409        | 27.78 | 1500 | 1.0012          | 0.6366 |
| 0.1447        | 37.04 | 2000 | 1.0167          | 0.6276 |
| 0.1109        | 46.3  | 2500 | 1.0638          | 0.5653 |
| 0.0797        | 55.56 | 3000 | 1.1447          | 0.5715 |
| 0.0636        | 64.81 | 3500 | 1.1503          | 0.5316 |
| 0.0466        | 74.07 | 4000 | 1.2227          | 0.5386 |
| 0.0372        | 83.33 | 4500 | 1.1214          | 0.5225 |
| 0.0239        | 92.59 | 5000 | 1.1375          | 0.4998 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


model name: wav2vec2-xls-r-pa-IN-a1


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, pa-IN, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, model-index


language:


- as
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- as
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-as-v9
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hsb
metrics:
    - name: Test WER
type: wer
value: []
    - name: Test CER
type: cer
value: []


# wav2vec2-large-xls-r-300m-as-v9


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:


- Loss: 1.1679
- Wer: 0.5761


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.000111
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 200
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.3852        | 10.51  | 200  | 3.6402          | 1.0    |
| 3.5374        | 21.05  | 400  | 3.3894          | 1.0    |
| 2.8645        | 31.56  | 600  | 1.3143          | 0.8303 |
| 1.1784        | 42.1   | 800  | 0.9417          | 0.6661 |
| 0.7805        | 52.62  | 1000 | 0.9292          | 0.6237 |
| 0.5973        | 63.15  | 1200 | 0.9489          | 0.6014 |
| 0.4784        | 73.67  | 1400 | 0.9916          | 0.5962 |
| 0.4138        | 84.21  | 1600 | 1.0272          | 0.6121 |
| 0.3491        | 94.72  | 1800 | 1.0412          | 0.5984 |
| 0.3062        | 105.26 | 2000 | 1.0769          | 0.6005 |
| 0.2707        | 115.77 | 2200 | 1.0708          | 0.5752 |
| 0.2459        | 126.31 | 2400 | 1.1285          | 0.6009 |
| 0.2234        | 136.82 | 2600 | 1.1209          | 0.5949 |
| 0.2035        | 147.36 | 2800 | 1.1348          | 0.5842 |
| 0.1876        | 157.87 | 3000 | 1.1480          | 0.5872 |
| 0.1669        | 168.41 | 3200 | 1.1496          | 0.5838 |
| 0.1595        | 178.92 | 3400 | 1.1721          | 0.5778 |
| 0.1505        | 189.46 | 3600 | 1.1654          | 0.5744 |
| 0.1486        | 199.97 | 3800 | 1.1679          | 0.5761 |


### Framework versions


- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-as-v9


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, as, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- hi
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hi-d3
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: hi
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition


# wav2vec2-large-xls-r-300m-hi-d3


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset.
It achieves the following results on the evaluation set:


- Loss: 0.7988
- Wer: 0.3713


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.000388
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 750
- num_epochs: 50
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.2826        | 1.36  | 200  | 3.5253          | 1.0    |
| 2.7019        | 2.72  | 400  | 1.1744          | 0.7360 |
| 0.7358        | 4.08  | 600  | 0.7781          | 0.5501 |
| 0.4942        | 5.44  | 800  | 0.7590          | 0.5345 |
| 0.4056        | 6.8   | 1000 | 0.6885          | 0.4776 |
| 0.3243        | 8.16  | 1200 | 0.7195          | 0.4861 |
| 0.2785        | 9.52  | 1400 | 0.7473          | 0.4930 |
| 0.2448        | 10.88 | 1600 | 0.7201          | 0.4574 |
| 0.2155        | 12.24 | 1800 | 0.7686          | 0.4648 |
| 0.2039        | 13.6  | 2000 | 0.7440          | 0.4624 |
| 0.1792        | 14.96 | 2200 | 0.7815          | 0.4658 |
| 0.1695        | 16.33 | 2400 | 0.7678          | 0.4557 |
| 0.1598        | 17.68 | 2600 | 0.7468          | 0.4393 |
| 0.1568        | 19.05 | 2800 | 0.7440          | 0.4422 |
| 0.1391        | 20.41 | 3000 | 0.7656          | 0.4317 |
| 0.1283        | 21.77 | 3200 | 0.7892          | 0.4299 |
| 0.1194        | 23.13 | 3400 | 0.7646          | 0.4192 |
| 0.1116        | 24.49 | 3600 | 0.8156          | 0.4330 |
| 0.1111        | 25.85 | 3800 | 0.7661          | 0.4322 |
| 0.1023        | 27.21 | 4000 | 0.7419          | 0.4276 |
| 0.1007        | 28.57 | 4200 | 0.8488          | 0.4245 |
| 0.0925        | 29.93 | 4400 | 0.8062          | 0.4070 |
| 0.0918        | 31.29 | 4600 | 0.8412          | 0.4218 |
| 0.0813        | 32.65 | 4800 | 0.8045          | 0.4087 |
| 0.0805        | 34.01 | 5000 | 0.8411          | 0.4113 |
| 0.0774        | 35.37 | 5200 | 0.7664          | 0.3943 |
| 0.0666        | 36.73 | 5400 | 0.8082          | 0.3939 |
| 0.0655        | 38.09 | 5600 | 0.7948          | 0.4000 |
| 0.0617        | 39.45 | 5800 | 0.8084          | 0.3932 |
| 0.0606        | 40.81 | 6000 | 0.8223          | 0.3841 |
| 0.0569        | 42.18 | 6200 | 0.7892          | 0.3832 |
| 0.0544        | 43.54 | 6400 | 0.8326          | 0.3834 |
| 0.0508        | 44.89 | 6600 | 0.7952          | 0.3774 |
| 0.0492        | 46.26 | 6800 | 0.7923          | 0.3756 |
| 0.0459        | 47.62 | 7000 | 0.7925          | 0.3701 |
| 0.0423        | 48.98 | 7200 | 0.7988          | 0.3713 |


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-hi-d3


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, hi, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_7_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- ab
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:


- Loss: 0.6178
- Wer: 0.5794


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.00025
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 70.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2793        | 27.27 | 300  | 3.0737          | 1.0    |
| 1.5348        | 54.55 | 600  | 0.6312          | 0.6334 |


### Framework versions


- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-ab-v4


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, ab, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_7_0, generated_from_trainer, model-index


language:


- bg
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- bg
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-bg-v1
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bg
metrics:
    - name: Test WER
type: wer
value: 0.4709579127785184
    - name: Test CER
type: cer
value: 0.10205125354383235


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset.
It achieves the following results on the evaluation set:


- Loss: 0.5197
- Wer: 0.4689


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 7e-05
- train_batch_size: 32
- 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3711        | 2.61  | 300  | 4.3122          | 1.0    |
| 3.1653        | 5.22  | 600  | 3.1156          | 1.0    |
| 2.8904        | 7.83  | 900  | 2.8421          | 0.9918 |
| 0.9207        | 10.43 | 1200 | 0.9895          | 0.8689 |
| 0.6384        | 13.04 | 1500 | 0.6994          | 0.7700 |
| 0.5215        | 15.65 | 1800 | 0.5628          | 0.6443 |
| 0.4573        | 18.26 | 2100 | 0.5316          | 0.6174 |
| 0.3875        | 20.87 | 2400 | 0.4932          | 0.5779 |
| 0.3562        | 23.48 | 2700 | 0.4972          | 0.5475 |
| 0.3218        | 26.09 | 3000 | 0.4895          | 0.5219 |
| 0.2954        | 28.7  | 3300 | 0.5226          | 0.5192 |
| 0.287         | 31.3  | 3600 | 0.4957          | 0.5146 |
| 0.2587        | 33.91 | 3900 | 0.4944          | 0.4893 |
| 0.2496        | 36.52 | 4200 | 0.4976          | 0.4895 |
| 0.2365        | 39.13 | 4500 | 0.5185          | 0.4819 |
| 0.2264        | 41.74 | 4800 | 0.5152          | 0.4776 |
| 0.2224        | 44.35 | 5100 | 0.5031          | 0.4746 |
| 0.2096        | 46.96 | 5400 | 0.5062          | 0.4708 |
| 0.2038        | 49.57 | 5700 | 0.5217          | 0.4698 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


### Evaluation Script


!python eval.py \
--model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1 \
--dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs


model name: wav2vec2-large-xls-r-300m-bg-v1


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, bg, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- kk
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- kk
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-kk-n2
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: tt
metrics:
    - name: Test WER
type: wer
value: 0.4355
    - name: Test CER
type: cer
value: 0.10469915859660263


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - KK dataset.
It achieves the following results on the evaluation set:


- Loss: 0.7149
- Wer: 0.451


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.000222
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 150.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 9.6799        | 9.09   | 200  | 3.6119          | 1.0    |
| 3.1332        | 18.18  | 400  | 2.5352          | 1.005  |
| 1.0465        | 27.27  | 600  | 0.6169          | 0.682  |
| 0.3452        | 36.36  | 800  | 0.6572          | 0.607  |
| 0.2575        | 45.44  | 1000 | 0.6527          | 0.578  |
| 0.2088        | 54.53  | 1200 | 0.6828          | 0.551  |
| 0.158         | 63.62  | 1400 | 0.7074          | 0.5575 |
| 0.1309        | 72.71  | 1600 | 0.6523          | 0.5595 |
| 0.1074        | 81.8   | 1800 | 0.7262          | 0.5415 |
| 0.087         | 90.89  | 2000 | 0.7199          | 0.521  |
| 0.0711        | 99.98  | 2200 | 0.7113          | 0.523  |
| 0.0601        | 109.09 | 2400 | 0.6863          | 0.496  |
| 0.0451        | 118.18 | 2600 | 0.6998          | 0.483  |
| 0.0378        | 127.27 | 2800 | 0.6971          | 0.4615 |
| 0.0319        | 136.36 | 3000 | 0.7119          | 0.4475 |
| 0.0305        | 145.44 | 3200 | 0.7181          | 0.459  |


### Framework versions


### Evaluation Command


!python eval.py \
--model_id DrishtiSharma/wav2vec2-xls-r-300m-kk-n2 \
--dataset mozilla-foundation/common_voice_8_0 --config kk --split test --log_outputs


model name: wav2vec2-xls-r-300m-kk-n2


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, kk, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- rm-vallader
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- rm-vallader
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-rm-vallader-d1
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: rm-vallader
metrics:
    - name: Test WER
type: wer
value: 0.26472007722007723
    - name: Test CER
type: cer
value: 0.05860608074430969


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RM-VALLADER dataset.
It achieves the following results on the evaluation set:


- Loss: 0.2754
- Wer: 0.2831


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 7.5e-05
- train_batch_size: 32
- 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: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.927         | 15.15 | 500  | 2.9196          | 1.0    |
| 1.3835        | 30.3  | 1000 | 0.5879          | 0.5866 |
| 0.7415        | 45.45 | 1500 | 0.3077          | 0.3316 |
| 0.5575        | 60.61 | 2000 | 0.2735          | 0.2954 |
| 0.4581        | 75.76 | 2500 | 0.2707          | 0.2802 |
| 0.3977        | 90.91 | 3000 | 0.2785          | 0.2809 |


### Framework versions


model name: wav2vec2-xls-r-300m-rm-vallader-d1


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, rm-vallader, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- sr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- sr
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-sr-v4
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: tt
metrics:
    - name: Test WER
type: wer
value: 0.303313
    - name: Test CER
type: cer
value: 0.1048951


# wav2vec2-large-xls-r-300m-sr-v4


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SR dataset.
It achieves the following results on the evaluation set:


- Loss: 0.5570
- Wer: 0.3038


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 200
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.2934        | 7.5   | 300  | 2.9777          | 0.9995 |
| 1.5049        | 15.0  | 600  | 0.5036          | 0.4806 |
| 0.3263        | 22.5  | 900  | 0.5822          | 0.4055 |
| 0.2008        | 30.0  | 1200 | 0.5609          | 0.4032 |
| 0.1543        | 37.5  | 1500 | 0.5203          | 0.3710 |
| 0.1158        | 45.0  | 1800 | 0.6458          | 0.3985 |
| 0.0997        | 52.5  | 2100 | 0.6227          | 0.4013 |
| 0.0834        | 60.0  | 2400 | 0.6048          | 0.3836 |
| 0.0665        | 67.5  | 2700 | 0.6197          | 0.3686 |
| 0.0602        | 75.0  | 3000 | 0.5418          | 0.3453 |
| 0.0524        | 82.5  | 3300 | 0.5310          | 0.3486 |
| 0.0445        | 90.0  | 3600 | 0.5599          | 0.3374 |
| 0.0406        | 97.5  | 3900 | 0.5958          | 0.3327 |
| 0.0358        | 105.0 | 4200 | 0.6017          | 0.3262 |
| 0.0302        | 112.5 | 4500 | 0.5613          | 0.3248 |
| 0.0285        | 120.0 | 4800 | 0.5659          | 0.3462 |
| 0.0213        | 127.5 | 5100 | 0.5568          | 0.3206 |
| 0.0215        | 135.0 | 5400 | 0.6524          | 0.3472 |
| 0.0162        | 142.5 | 5700 | 0.6223          | 0.3458 |
| 0.0137        | 150.0 | 6000 | 0.6625          | 0.3313 |
| 0.0114        | 157.5 | 6300 | 0.5739          | 0.3336 |
| 0.0101        | 165.0 | 6600 | 0.5906          | 0.3285 |
| 0.008         | 172.5 | 6900 | 0.5982          | 0.3112 |
| 0.0076        | 180.0 | 7200 | 0.5399          | 0.3094 |
| 0.0071        | 187.5 | 7500 | 0.5387          | 0.2991 |
| 0.0057        | 195.0 | 7800 | 0.5570          | 0.3038 |


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0


### Evaluation Script


!python eval.py \
--model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 \
--dataset mozilla-foundation/common_voice_8_0 --config sr --split test --log_outputs


model name: wav2vec2-large-xls-r-300m-sr-v4


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, sr, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- pa-IN
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset.
It achieves the following results on the evaluation set:


- Loss: 1.0855
- Wer: 0.4755


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1200
- num_epochs: 100.0
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4607        | 9.26  | 500  | 2.7746          | 1.0416 |
| 0.3442        | 18.52 | 1000 | 0.9114          | 0.5911 |
| 0.2213        | 27.78 | 1500 | 0.9687          | 0.5751 |
| 0.1242        | 37.04 | 2000 | 1.0204          | 0.5461 |
| 0.0998        | 46.3  | 2500 | 1.0250          | 0.5233 |
| 0.0727        | 55.56 | 3000 | 1.1072          | 0.5382 |
| 0.0605        | 64.81 | 3500 | 1.0588          | 0.5073 |
| 0.0458        | 74.07 | 4000 | 1.0818          | 0.5069 |
| 0.0338        | 83.33 | 4500 | 1.0948          | 0.5108 |
| 0.0223        | 92.59 | 5000 | 1.0986          | 0.4775 |


### Framework versions


- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-pa-IN-dx1


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, pa-IN, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, model-index


language:


- hsb
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- hsb
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hsb-v1
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: hsb
metrics:
    - name: Test WER
type: wer
value: 0.4393
    - name: Test CER
type: cer
value: 0.1036


# wav2vec2-large-xls-r-300m-hsb-v1


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HSB dataset.
It achieves the following results on the evaluation set:


- Loss: 0.5684
- Wer: 0.4402


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.00045
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.972         | 3.23  | 100  | 3.7498          | 1.0    |
| 3.3401        | 6.45  | 200  | 3.2320          | 1.0    |
| 3.2046        | 9.68  | 300  | 3.1741          | 0.9806 |
| 2.4031        | 12.9  | 400  | 1.0579          | 0.8996 |
| 1.0427        | 16.13 | 500  | 0.7989          | 0.7557 |
| 0.741         | 19.35 | 600  | 0.6405          | 0.6299 |
| 0.5699        | 22.58 | 700  | 0.6129          | 0.5928 |
| 0.4607        | 25.81 | 800  | 0.6548          | 0.5695 |
| 0.3827        | 29.03 | 900  | 0.6268          | 0.5190 |
| 0.3282        | 32.26 | 1000 | 0.5919          | 0.5016 |
| 0.2764        | 35.48 | 1100 | 0.5953          | 0.4805 |
| 0.2335        | 38.71 | 1200 | 0.5717          | 0.4728 |
| 0.2106        | 41.94 | 1300 | 0.5674          | 0.4569 |
| 0.1859        | 45.16 | 1400 | 0.5685          | 0.4502 |
| 0.1592        | 48.39 | 1500 | 0.5684          | 0.4402 |


### Framework versions


- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-hsb-v1


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, hsb, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- or
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- or
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-or-d5
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: or
metrics:
    - name: Test WER
type: wer
value: []
    - name: Test CER
type: cer
value: []


# wav2vec2-large-xls-r-300m-or-d5


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - OR dataset.
It achieves the following results on the evaluation set:


- Loss: 0.9571
- Wer: 0.5450


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.000111
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 200
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.2958        | 12.5  | 300  | 4.9014          | 1.0    |
| 3.4065        | 25.0  | 600  | 3.5150          | 1.0    |
| 1.5402        | 37.5  | 900  | 0.8356          | 0.7249 |
| 0.6049        | 50.0  | 1200 | 0.7754          | 0.6349 |
| 0.4074        | 62.5  | 1500 | 0.7994          | 0.6217 |
| 0.3097        | 75.0  | 1800 | 0.8815          | 0.5985 |
| 0.2593        | 87.5  | 2100 | 0.8532          | 0.5754 |
| 0.2097        | 100.0 | 2400 | 0.9077          | 0.5648 |
| 0.1784        | 112.5 | 2700 | 0.9047          | 0.5668 |
| 0.1567        | 125.0 | 3000 | 0.9019          | 0.5728 |
| 0.1315        | 137.5 | 3300 | 0.9295          | 0.5827 |
| 0.1125        | 150.0 | 3600 | 0.9256          | 0.5681 |
| 0.1035        | 162.5 | 3900 | 0.9148          | 0.5496 |
| 0.0901        | 175.0 | 4200 | 0.9480          | 0.5483 |
| 0.0817        | 187.5 | 4500 | 0.9799          | 0.5516 |
| 0.079         | 200.0 | 4800 | 0.9571          | 0.5450 |


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-or-d5


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, or, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- br
license: apache-2.0
tags:
- generated_from_trainer
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-br-d2
results:
  - task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 8
args: br
metrics:
    - type: wer\
value: []
name: Test WER
    - name: Test CER
type: cer
value: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# wav2vec2-large-xls-r-300m-br-d2


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset.
It achieves the following results on the evaluation set:


- Loss: 1.1257
- Wer: 0.4631


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.00034
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 750
- num_epochs: 50
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 14.0379       | 0.68  | 100  | 5.6808          | 1.0    |
| 3.9145        | 1.35  | 200  | 3.1970          | 1.0    |
| 3.0293        | 2.03  | 300  | 2.9513          | 1.0    |
| 2.0927        | 2.7   | 400  | 1.4545          | 0.8887 |
| 1.1556        | 3.38  | 500  | 1.0966          | 0.7564 |
| 0.9628        | 4.05  | 600  | 0.9808          | 0.7364 |
| 0.7869        | 4.73  | 700  | 1.0488          | 0.7355 |
| 0.703         | 5.41  | 800  | 0.9500          | 0.6881 |
| 0.6657        | 6.08  | 900  | 0.9309          | 0.6259 |
| 0.5663        | 6.76  | 1000 | 0.9133          | 0.6357 |
| 0.496         | 7.43  | 1100 | 0.9890          | 0.6028 |
| 0.4748        | 8.11  | 1200 | 0.9469          | 0.5894 |
| 0.4135        | 8.78  | 1300 | 0.9270          | 0.6045 |
| 0.3579        | 9.46  | 1400 | 0.8818          | 0.5708 |
| 0.353         | 10.14 | 1500 | 0.9244          | 0.5781 |
| 0.334         | 10.81 | 1600 | 0.9009          | 0.5638 |
| 0.2917        | 11.49 | 1700 | 1.0132          | 0.5828 |
| 0.29          | 12.16 | 1800 | 0.9696          | 0.5668 |
| 0.2691        | 12.84 | 1900 | 0.9811          | 0.5455 |
| 0.25          | 13.51 | 2000 | 0.9951          | 0.5624 |
| 0.2467        | 14.19 | 2100 | 0.9653          | 0.5573 |
| 0.2242        | 14.86 | 2200 | 0.9714          | 0.5378 |
| 0.2066        | 15.54 | 2300 | 0.9829          | 0.5394 |
| 0.2075        | 16.22 | 2400 | 1.0547          | 0.5520 |
| 0.1923        | 16.89 | 2500 | 1.0014          | 0.5397 |
| 0.1919        | 17.57 | 2600 | 0.9978          | 0.5477 |
| 0.1908        | 18.24 | 2700 | 1.1064          | 0.5397 |
| 0.157         | 18.92 | 2800 | 1.0629          | 0.5238 |
| 0.159         | 19.59 | 2900 | 1.0642          | 0.5321 |
| 0.1652        | 20.27 | 3000 | 1.0207          | 0.5328 |
| 0.141         | 20.95 | 3100 | 0.9948          | 0.5312 |
| 0.1417        | 21.62 | 3200 | 1.0338          | 0.5328 |
| 0.1514        | 22.3  | 3300 | 1.0513          | 0.5313 |
| 0.1365        | 22.97 | 3400 | 1.0357          | 0.5291 |
| 0.1319        | 23.65 | 3500 | 1.0587          | 0.5167 |
| 0.1298        | 24.32 | 3600 | 1.0636          | 0.5236 |
| 0.1245        | 25.0  | 3700 | 1.1367          | 0.5280 |
| 0.1114        | 25.68 | 3800 | 1.0633          | 0.5200 |
| 0.1088        | 26.35 | 3900 | 1.0495          | 0.5210 |
| 0.1175        | 27.03 | 4000 | 1.0897          | 0.5095 |
| 0.1043        | 27.7  | 4100 | 1.0580          | 0.5309 |
| 0.0951        | 28.38 | 4200 | 1.0448          | 0.5067 |
| 0.1011        | 29.05 | 4300 | 1.0665          | 0.5137 |
| 0.0889        | 29.73 | 4400 | 1.0579          | 0.5026 |
| 0.0833        | 30.41 | 4500 | 1.0740          | 0.5037 |
| 0.0889        | 31.08 | 4600 | 1.0933          | 0.5083 |
| 0.0784        | 31.76 | 4700 | 1.0715          | 0.5089 |
| 0.0767        | 32.43 | 4800 | 1.0658          | 0.5049 |
| 0.0769        | 33.11 | 4900 | 1.1118          | 0.4979 |
| 0.0722        | 33.78 | 5000 | 1.1413          | 0.4986 |
| 0.0709        | 34.46 | 5100 | 1.0706          | 0.4885 |
| 0.0664        | 35.14 | 5200 | 1.1217          | 0.4884 |
| 0.0648        | 35.81 | 5300 | 1.1298          | 0.4941 |
| 0.0657        | 36.49 | 5400 | 1.1330          | 0.4920 |
| 0.0582        | 37.16 | 5500 | 1.0598          | 0.4835 |
| 0.0602        | 37.84 | 5600 | 1.1097          | 0.4943 |
| 0.0598        | 38.51 | 5700 | 1.0976          | 0.4876 |
| 0.0547        | 39.19 | 5800 | 1.0734          | 0.4825 |
| 0.0561        | 39.86 | 5900 | 1.0926          | 0.4850 |
| 0.0516        | 40.54 | 6000 | 1.1579          | 0.4751 |
| 0.0478        | 41.22 | 6100 | 1.1384          | 0.4706 |
| 0.0396        | 41.89 | 6200 | 1.1462          | 0.4739 |
| 0.0472        | 42.57 | 6300 | 1.1277          | 0.4732 |
| 0.0447        | 43.24 | 6400 | 1.1517          | 0.4752 |
| 0.0423        | 43.92 | 6500 | 1.1219          | 0.4784 |
| 0.0426        | 44.59 | 6600 | 1.1311          | 0.4724 |
| 0.0391        | 45.27 | 6700 | 1.1135          | 0.4692 |
| 0.0362        | 45.95 | 6800 | 1.0878          | 0.4645 |
| 0.0329        | 46.62 | 6900 | 1.1137          | 0.4668 |
| 0.0356        | 47.3  | 7000 | 1.1233          | 0.4687 |
| 0.0328        | 47.97 | 7100 | 1.1238          | 0.4653 |
| 0.0323        | 48.65 | 7200 | 1.1307          | 0.4646 |
| 0.0325        | 49.32 | 7300 | 1.1242          | 0.4645 |
| 0.03          | 50.0  | 7400 | 1.1257          | 0.4631 |


### Framework versions


model name: wav2vec2-large-xls-r-300m-br-d2


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, br, dataset:mozilla-foundation/common_voice_8_0, transformers, license:apache-2.0, generated_from_trainer, robust-speech-event, model-index


language:


- bas
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- bas
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-bas-v1
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: bas
metrics:
    - name: Test WER
type: wer
value: 0.3566497929130234
    - name: Test CER
type: cer
value: 0.1102657634184471


# wav2vec2-large-xls-r-300m-bas-v1


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BAS dataset.
It achieves the following results on the evaluation set:


- Loss: 0.5997
- Wer: 0.3870


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.000111
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 12.7076       | 5.26  | 200  | 3.6361          | 1.0    |
| 3.1657        | 10.52 | 400  | 3.0101          | 1.0    |
| 2.3987        | 15.78 | 600  | 0.9125          | 0.6774 |
| 1.0079        | 21.05 | 800  | 0.6477          | 0.5352 |
| 0.7392        | 26.31 | 1000 | 0.5432          | 0.4929 |
| 0.6114        | 31.57 | 1200 | 0.5498          | 0.4639 |
| 0.5222        | 36.83 | 1400 | 0.5220          | 0.4561 |
| 0.4648        | 42.1  | 1600 | 0.5586          | 0.4289 |
| 0.4103        | 47.36 | 1800 | 0.5337          | 0.4082 |
| 0.3692        | 52.62 | 2000 | 0.5421          | 0.3861 |
| 0.3403        | 57.88 | 2200 | 0.5549          | 0.4096 |
| 0.3011        | 63.16 | 2400 | 0.5833          | 0.3925 |
| 0.2932        | 68.42 | 2600 | 0.5674          | 0.3815 |
| 0.2696        | 73.68 | 2800 | 0.5734          | 0.3889 |
| 0.2496        | 78.94 | 3000 | 0.5968          | 0.3985 |
| 0.2289        | 84.21 | 3200 | 0.5888          | 0.3893 |
| 0.2091        | 89.47 | 3400 | 0.5849          | 0.3852 |
| 0.2005        | 94.73 | 3600 | 0.5938          | 0.3875 |
| 0.1876        | 99.99 | 3800 | 0.5997          | 0.3870 |


### Framework versions


- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0


### Evaluation Script


!python eval.py \
--model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bas-v1 \
--dataset mozilla-foundation/common_voice_8_0 --config bas --split test --log_outputs


model name: wav2vec2-large-xls-r-300m-bas-v1


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, bas, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-hi-cv8-b2
results:
  - task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 7
args: hi
metrics:
    - type: wer\
value: []\
name: Test WER
    - name: Test CER
type: cer
value: []


# wav2vec2-large-xls-r-300m-hi-cv8-b2


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset.
It achieves the following results on the evaluation set:


- Loss: 0.7322
- Wer: 0.3469


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 35
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.6226        | 1.04  | 200  | 3.8855          | 1.0    |
| 3.4678        | 2.07  | 400  | 3.4283          | 1.0    |
| 2.3668        | 3.11  | 600  | 1.0743          | 0.7175 |
| 0.7308        | 4.15  | 800  | 0.7663          | 0.5498 |
| 0.4985        | 5.18  | 1000 | 0.6957          | 0.5001 |
| 0.3817        | 6.22  | 1200 | 0.6932          | 0.4866 |
| 0.3281        | 7.25  | 1400 | 0.7034          | 0.4983 |
| 0.2752        | 8.29  | 1600 | 0.6588          | 0.4606 |
| 0.2475        | 9.33  | 1800 | 0.6514          | 0.4328 |
| 0.219         | 10.36 | 2000 | 0.6396          | 0.4176 |
| 0.2036        | 11.4  | 2200 | 0.6867          | 0.4162 |
| 0.1793        | 12.44 | 2400 | 0.6943          | 0.4196 |
| 0.1724        | 13.47 | 2600 | 0.6862          | 0.4260 |
| 0.1554        | 14.51 | 2800 | 0.7615          | 0.4222 |
| 0.151         | 15.54 | 3000 | 0.7058          | 0.4110 |
| 0.1335        | 16.58 | 3200 | 0.7172          | 0.3986 |
| 0.1326        | 17.62 | 3400 | 0.7182          | 0.3923 |
| 0.1225        | 18.65 | 3600 | 0.6995          | 0.3910 |
| 0.1146        | 19.69 | 3800 | 0.7075          | 0.3875 |
| 0.108         | 20.73 | 4000 | 0.7297          | 0.3858 |
| 0.1048        | 21.76 | 4200 | 0.7413          | 0.3850 |
| 0.0979        | 22.8  | 4400 | 0.7452          | 0.3793 |
| 0.0946        | 23.83 | 4600 | 0.7436          | 0.3759 |
| 0.0897        | 24.87 | 4800 | 0.7289          | 0.3754 |
| 0.0854        | 25.91 | 5000 | 0.7271          | 0.3667 |
| 0.0803        | 26.94 | 5200 | 0.7378          | 0.3656 |
| 0.0752        | 27.98 | 5400 | 0.7488          | 0.3680 |
| 0.0718        | 29.02 | 5600 | 0.7185          | 0.3619 |
| 0.0702        | 30.05 | 5800 | 0.7428          | 0.3554 |
| 0.0653        | 31.09 | 6000 | 0.7447          | 0.3559 |
| 0.0638        | 32.12 | 6200 | 0.7327          | 0.3523 |
| 0.058         | 33.16 | 6400 | 0.7339          | 0.3488 |
| 0.0594        | 34.2  | 6600 | 0.7322          | 0.3469 |


### Framework versions


- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: wav2vec2-large-xls-r-300m-hi-cv8-b2


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, hi, dataset:mozilla-foundation/common_voice_8_0, transformers, license:apache-2.0, robust-speech-event, model-index


language:


- or
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- or
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-or-dx12
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: or
metrics:
    - name: Test WER
type: wer
value: []
    - name: Test CER
type: cer
value: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# wav2vec2-large-xls-r-300m-or-dx12


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:


- Loss: 1.4638
- Wer: 0.5602


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 0.0004
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200
- mixed_precision_training: Native AMP


### Training results


| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 13.5059       | 4.17   | 100  | 10.3789         | 1.0    |
| 4.5964        | 8.33   | 200  | 4.3294          | 1.0    |
| 3.4448        | 12.5   | 300  | 3.7903          | 1.0    |
| 3.3683        | 16.67  | 400  | 3.5289          | 1.0    |
| 2.042         | 20.83  | 500  | 1.1531          | 0.7857 |
| 0.5721        | 25.0   | 600  | 1.0267          | 0.7646 |
| 0.3274        | 29.17  | 700  | 1.0773          | 0.6938 |
| 0.2466        | 33.33  | 800  | 1.0323          | 0.6647 |
| 0.2047        | 37.5   | 900  | 1.1255          | 0.6733 |
| 0.1847        | 41.67  | 1000 | 1.1194          | 0.6515 |
| 0.1453        | 45.83  | 1100 | 1.1215          | 0.6601 |
| 0.1367        | 50.0   | 1200 | 1.1898          | 0.6627 |
| 0.1334        | 54.17  | 1300 | 1.3082          | 0.6687 |
| 0.1041        | 58.33  | 1400 | 1.2514          | 0.6177 |
| 0.1024        | 62.5   | 1500 | 1.2055          | 0.6528 |
| 0.0919        | 66.67  | 1600 | 1.4125          | 0.6369 |
| 0.074         | 70.83  | 1700 | 1.4006          | 0.6634 |
| 0.0681        | 75.0   | 1800 | 1.3943          | 0.6131 |
| 0.0709        | 79.17  | 1900 | 1.3545          | 0.6296 |
| 0.064         | 83.33  | 2000 | 1.2437          | 0.6237 |
| 0.0552        | 87.5   | 2100 | 1.3762          | 0.6190 |
| 0.056         | 91.67  | 2200 | 1.3763          | 0.6323 |
| 0.0514        | 95.83  | 2300 | 1.2897          | 0.6164 |
| 0.0409        | 100.0  | 2400 | 1.4257          | 0.6104 |
| 0.0379        | 104.17 | 2500 | 1.4219          | 0.5853 |
| 0.0367        | 108.33 | 2600 | 1.4361          | 0.6032 |
| 0.0412        | 112.5  | 2700 | 1.4713          | 0.6098 |
| 0.0353        | 116.67 | 2800 | 1.4132          | 0.6369 |
| 0.0336        | 120.83 | 2900 | 1.5210          | 0.6098 |
| 0.0302        | 125.0  | 3000 | 1.4686          | 0.5939 |
| 0.0398        | 129.17 | 3100 | 1.5456          | 0.6204 |
| 0.0291        | 133.33 | 3200 | 1.4111          | 0.5827 |
| 0.0247        | 137.5  | 3300 | 1.3866          | 0.6151 |
| 0.0196        | 141.67 | 3400 | 1.4513          | 0.5880 |
| 0.0218        | 145.83 | 3500 | 1.5100          | 0.5899 |
| 0.0196        | 150.0  | 3600 | 1.4936          | 0.5999 |
| 0.0164        | 154.17 | 3700 | 1.5012          | 0.5701 |
| 0.0168        | 158.33 | 3800 | 1.5601          | 0.5919 |
| 0.0151        | 162.5  | 3900 | 1.4891          | 0.5761 |
| 0.0137        | 166.67 | 4000 | 1.4839          | 0.5800 |
| 0.0143        | 170.83 | 4100 | 1.4826          | 0.5754 |
| 0.0114        | 175.0  | 4200 | 1.4950          | 0.5708 |
| 0.0092        | 179.17 | 4300 | 1.5008          | 0.5694 |
| 0.0104        | 183.33 | 4400 | 1.4774          | 0.5728 |
| 0.0096        | 187.5  | 4500 | 1.4948          | 0.5767 |
| 0.0105        | 191.67 | 4600 | 1.4557          | 0.5694 |
| 0.009         | 195.83 | 4700 | 1.4615          | 0.5628 |
| 0.0081        | 200.0  | 4800 | 1.4638          | 0.5602 |


### Framework versions


model name: wav2vec2-large-xls-r-300m-or-dx12


model author: DrishtiSharma


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, or, dataset:common_voice, transformers, license:apache-2.0, mozilla-foundation/common_voice_8_0, generated_from_trainer, robust-speech-event, model_for_talk, model-index


language:


- kk
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- kk
- robust-speech-event
- model_for_talk
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-kk-with-LM
results:
  - task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: tt
metrics:
    - name: Test WER
type: wer
value: 0.4355
    - name: Test CER
type: cer
value: 0.10469915859660263


This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - KK dataset.
It achieves the following results on the evaluation set:


- Loss: 0.7149
- Wer: 0.451


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 0.000222
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 150.0
- mixed_precision_training: Native AMP


### Training results