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18.3M
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text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-finetuned-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3741
- F1: 0.8700
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 179 | 0.4458 | 0.8516 |
| No log | 2.0 | 358 | 0.3741 | 0.8700 |
| 0.385 | 3.0 | 537 | 0.3720 | 0.8693 |
| 0.385 | 4.0 | 716 | 0.3744 | 0.8689 |
| 0.385 | 5.0 | 895 | 0.3801 | 0.8695 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["f1"], "model_index": [{"name": "bert-base-finetuned-ynat", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "klue", "type": "klue", "args": "ynat"}, "metric": {"name": "F1", "type": "f1", "value": 0.8699556378491373}}]}]}
|
eliza-dukim/bert-base-finetuned-ynat
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
question-answering
|
transformers
|
## Boostcamp AI Tech Special Mission 01, Multi-lingual BERT for KorQuAD v1
{'exact_match': 69.89954970557672, 'f1': 77.40349093437989, 'epoch': 15.0}
|
{}
|
eliza-dukim/bert-base-multilingual-cased_korquad-v1
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
{}
|
eliza-dukim/para-kqc-sim
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
eliza-dukim/roberta-large-qaconv-sds-aug
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
eliza-dukim/roberta-large-second
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
elliee123/test
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
Test model to get an idea how this thing works
|
{}
|
elliotsmith/dummy-model
| null |
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
elliotsmith/tmp_trainer
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
ellisvalentiner/layoutlmv2-finetuned-funsd-test
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
ellziez/me
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
eloquentcow69/Hh
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
elozano/bert-base-cased-clickbait-news
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
elozano/bert-base-cased-fake-news
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{}
|
elozano/bert-base-cased-news-category
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{"language": "en", "license": "mit", "datasets": ["tweet_eval"], "widget": [{"text": "Stop sharing which songs did you listen to during this year on Spotify, NOBODY CARES", "example_title": "Anger"}, {"text": "I love that joke HAHAHAHAHA", "example_title": "Joy"}, {"text": "Despite I've not studied a lot for this exam, I think I will pass \ud83d\ude1c", "example_title": "Optimism"}, {"text": "My dog died this morning...", "example_title": "Sadness"}]}
|
elozano/tweet_emotion_eval
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{"language": "en", "license": "mit", "datasets": ["tweet_eval"], "widget": [{"text": "You're a complete idiot!", "example_title": "Offensive"}, {"text": "I am tired of studying for tomorrow's exam", "example_title": "Non-Offensive"}]}
|
elozano/tweet_offensive_eval
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
{"language": "en", "license": "mit", "datasets": ["tweet_eval"], "widget": [{"text": "I love summer!", "example_title": "Positive"}, {"text": "Does anyone want to play?", "example_title": "Neutral"}, {"text": "This movie is just awful! \ud83d\ude2b", "example_title": "Negative"}]}
|
elozano/tweet_sentiment_eval
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
feature-extraction
|
transformers
|
{}
|
eltoto1219/lxmert-base-uncased
| null |
[
"transformers",
"lxmert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
question-answering
|
transformers
|
{}
|
eltoto1219/lxmert-gqa-untuned
| null |
[
"transformers",
"pytorch",
"lxmert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
fill-mask
|
transformers
|
{}
|
elusive-magnolia/dummy-model
| null |
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
elverk/elverk
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emalmi/t5-small-finetuned-jfleg0
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emalmi/t5-small-finetuned-xsum
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
# MacBERTh
This model is a Historical Language Model for English coming from the [MacBERTh project](https://macberth.netlify.app/).
The architecture is based on BERT base uncased from the original BERT pre-training codebase.
The training material comes from different sources including:
- EEBO
- ECCO
- COHA
- CLMET3.1
- EVANS
- Hansard Corpus
with a total word count of approximately 3.9B tokens.
Details and evaluation can be found in the accompanying publications:
- [MacBERTh: Development and Evaluation of a Historically Pre-trained Language Model for English (1450-1950)](https://aclanthology.org/2021.nlp4dh-1.4/)
- [Adapting vs. Pre-training Language Models for Historical Languages](https://doi.org/10.46298/jdmdh.9152)
|
{"language": ["en"], "license": "mit"}
|
emanjavacas/MacBERTh
| null |
[
"transformers",
"pytorch",
"bert",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
emanuelscaglione/performerbert-base-uncased
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emanuelscaglione/t5-small-finetuned-xsum
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emdikey/roberta-base-bne-finetuned-amazon_reviews_multi
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-classification
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 607517182
- CO2 Emissions (in grams): 3.842950628218143
## Validation Metrics
- Loss: 0.4033123552799225
- Accuracy: 0.8679706601466992
- Macro F1: 0.719846919916469
- Micro F1: 0.8679706601466993
- Weighted F1: 0.8622411469250695
- Macro Precision: 0.725309168791155
- Micro Precision: 0.8679706601466992
- Weighted Precision: 0.8604370906049568
- Macro Recall: 0.7216672806300003
- Micro Recall: 0.8679706601466992
- Weighted Recall: 0.8679706601466992
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/emekaboris/autonlp-new_tx-607517182
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-new_tx-607517182", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-new_tx-607517182", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "unk", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-new_tx"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.842950628218143}
|
emekaboris/autonlp-new_tx-607517182
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:emekaboris/autonlp-data-new_tx",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 17923124
- CO2 Emissions (in grams): 133.57087522185148
## Validation Metrics
- Loss: 0.2080804407596588
- Accuracy: 0.9325402190077058
- Macro F1: 0.7283811287183823
- Micro F1: 0.9325402190077058
- Weighted F1: 0.9315711955594153
- Macro Precision: 0.8106599661500661
- Micro Precision: 0.9325402190077058
- Weighted Precision: 0.9324644116921059
- Macro Recall: 0.7020515544343829
- Micro Recall: 0.9325402190077058
- Weighted Recall: 0.9325402190077058
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/emekaboris/autonlp-txc-17923124
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-txc-17923124", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-txc-17923124", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-txc"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 133.57087522185148}
|
emekaboris/autonlp-txc-17923124
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:emekaboris/autonlp-data-txc",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 17923129
- CO2 Emissions (in grams): 610.861733873082
## Validation Metrics
- Loss: 0.2319454699754715
- Accuracy: 0.9264228741381642
- Macro F1: 0.6730537318152493
- Micro F1: 0.9264228741381642
- Weighted F1: 0.9251493598895151
- Macro Precision: 0.7767479491141245
- Micro Precision: 0.9264228741381642
- Weighted Precision: 0.9277971545757154
- Macro Recall: 0.6617262519071917
- Micro Recall: 0.9264228741381642
- Weighted Recall: 0.9264228741381642
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/emekaboris/autonlp-txc-17923129
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("emekaboris/autonlp-txc-17923129", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("emekaboris/autonlp-txc-17923129", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["emekaboris/autonlp-data-txc"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 610.861733873082}
|
emekaboris/autonlp-txc-17923129
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:emekaboris/autonlp-data-txc",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
KcELECTRA([https://github.com/Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA))의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다.
|
{}
|
emeraldgoose/bad-korean-tokenizer
| null |
[
"transformers",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
## Data-annotation-nlp-10 (BoostCamp AI)
위키피디아(스포츠) dataset 구축을 진행하면서 얻은 문장을 통해 bert 사전학습을 진행
## How to use
```python
from transformers import AutoTokenizer, BertForMaskedLM
model = BertForMaskedLM.from_pretrained("emeraldgoose/bert-base-v1-sports")
tokenizer = AutoTokenizer.from_pretrained("emeraldgoose/bert-base-v1-sports")
text = "산악 자전거 경기는 상대적으로 새로운 [MASK] 1990년대에 활성화 되었다."
inputs = tokenizer.encode(text, return_tensors='pt')
model.eval()
outputs = model(inputs)['logits']
predict = outputs.argmax(-1)[0]
print(tokenizer.decode(predict))
```
|
{"language": "ko", "mask_token": "[MASK]", "widget": [{"text": "\uc0b0\uc545 \uc790\uc804\uac70 \uacbd\uae30\ub294 \uc0c1\ub300\uc801\uc73c\ub85c \uc0c8\ub85c\uc6b4 [MASK] 1990\ub144\ub300\uc5d0 \ud65c\uc131\ud654 \ub418\uc5c8\ub2e4."}]}
|
emeraldgoose/bert-base-v1-sports
| null |
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
emeson77/uganda_ASR
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emeson77/wav2vec2-large-xls-r-300m-lauganda-colab
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
automatic-speech-recognition
|
transformers
|
<!-- 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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7214
- Wer: 0.5555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4408 | 7.83 | 400 | 0.8109 | 0.7792 |
| 0.2469 | 15.68 | 800 | 0.6794 | 0.5975 |
| 0.0871 | 23.52 | 1200 | 0.7214 | 0.5555 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]}
|
emeson77/wav2vec2-large-xls-r-300m-turkish-colab
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# danish-bert-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3584
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.3285 |
| 0.2879 | 2.0 | 630 | 0.3288 |
| 0.2879 | 3.0 | 945 | 0.3178 |
| 0.1371 | 4.0 | 1260 | 0.3584 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "danish-bert-botxo-danish-finetuned-hatespeech", "results": []}]}
|
emfa/danish-bert-botxo-danish-finetuned-hatespeech
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# danish-roberta-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [flax-community/roberta-base-danish](https://huggingface.co/flax-community/roberta-base-danish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2849
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.3074 |
| 0.3016 | 2.0 | 630 | 0.3152 |
| 0.3016 | 3.0 | 945 | 0.2849 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "danish-roberta-botxo-danish-finetuned-hatespeech", "results": []}]}
|
emfa/danish-roberta-botxo-danish-finetuned-hatespeech
| null |
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# l-lectra-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [Maltehb/-l-ctra-danish-electra-small-uncased](https://huggingface.co/Maltehb/-l-ctra-danish-electra-small-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.2561 |
| 0.291 | 2.0 | 630 | 0.2491 |
| 0.291 | 3.0 | 945 | 0.2434 |
| 0.2089 | 4.0 | 1260 | 0.2608 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "l-lectra-danish-finetuned-hatespeech", "results": []}]}
|
emfa/l-lectra-danish-finetuned-hatespeech
| null |
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text-generation
|
transformers
|
This model aims at being a french conversational agent. This consists of a fine-tuning of Dialo-GPT for french language. The dataset used gathers 36k conversations extracted from books, movies, interviews and dialogues for learning french.
More details about the model can be found [there](https://github.com/emil2000dza/DialoGPT-fine-tuned-for-french-language)
|
{"language": ["fr"], "tags": [{}, {}]}
|
emil2000/dialogpt-for-french-language
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
## daT5-base
A smaller version of [Google's mt5-base](https://huggingface.co/google/mt5-base) model, where the original model is reduced to only include Danish embeddings.
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emillykkejensen/daT5-base")
model = AutoModel.from_pretrained("emillykkejensen/daT5-base")
```
## Further reading
[Gist](https://gist.github.com/emillykkejensen/8bf1b323495efc7252dee966e6bc1b5c) showing (in Danish) how the embeddings are extracted
[Article](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) explaining how to do it by [David Dale](https://huggingface.co/cointegrated)
## Also check out
[daT5-large](https://huggingface.co/emillykkejensen/daT5-large)
|
{"language": ["da"], "license": "apache-2.0"}
|
emillykkejensen/daT5-base
| null |
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
text2text-generation
|
transformers
|
## daT5-large
A smaller version of [Google's mt5-large](https://huggingface.co/google/mt5-base) model, where the original model is reduced to only include Danish embeddings.
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emillykkejensen/daT5-large")
model = AutoModel.from_pretrained("emillykkejensen/daT5-large")
```
## Further reading
[Gist](https://gist.github.com/emillykkejensen/8bf1b323495efc7252dee966e6bc1b5c) showing (in Danish) how the embeddings are extracted (for mt5-base)
[Article](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) explaining how to do it by [David Dale](https://huggingface.co/cointegrated)
## Also check out
[daT5-base](https://huggingface.co/emillykkejensen/daT5-base)
|
{"language": ["da"], "license": "apache-2.0"}
|
emillykkejensen/daT5-large
| null |
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"da",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# ClinicalBERT - Bio + Clinical BERT Model
The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries.
This model card describes the Bio+Clinical BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on all MIMIC notes.
## Pretraining Data
The `Bio_ClinicalBERT` model was trained on all notes from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words).
## Model Pretraining
### Note Preprocessing
Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer).
### Pretraining Procedures
The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`).
### Pretraining Hyperparameters
We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15
and max predictions per sequence = 20).
## How to use the model
Load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
```
## More Information
Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks.
## Questions?
Post a Github issue on the [clinicalBERT repo](https://github.com/EmilyAlsentzer/clinicalBERT) or email [email protected] with any questions.
|
{"language": "en", "license": "mit", "tags": ["fill-mask"]}
|
emilyalsentzer/Bio_ClinicalBERT
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"arxiv:1904.03323",
"arxiv:1901.08746",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
fill-mask
|
transformers
|
# ClinicalBERT - Bio + Discharge Summary BERT Model
The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries.
This model card describes the Bio+Discharge Summary BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on only discharge summaries from MIMIC.
## Pretraining Data
The `Bio_Discharge_Summary_BERT` model was trained on all discharge summaries from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words).
## Model Pretraining
### Note Preprocessing
Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer).
### Pretraining Procedures
The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`).
### Pretraining Hyperparameters
We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15
and max predictions per sequence = 20).
## How to use the model
Load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_Discharge_Summary_BERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_Discharge_Summary_BERT")
```
## More Information
Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks.
## Questions?
Post a Github issue on the [clinicalBERT repo](https://github.com/EmilyAlsentzer/clinicalBERT) or email [email protected] with any questions.
|
{"language": "en", "license": "mit", "tags": ["fill-mask"]}
|
emilyalsentzer/Bio_Discharge_Summary_BERT
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"en",
"arxiv:1904.03323",
"arxiv:1901.08746",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
emix111/k2t-test3
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emix111/modello
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
automatic-speech-recognition
|
espnet
|
## ESPnet2 ASR model
### `eml914/streaming_transformer_asr_librispeech`
This model was trained by Emiru Tsunoo using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 12eb132418a1f69548f7998e53273cd05d989ed9
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model eml914/streaming_transformer_asr_librispeech
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Nov 17 18:18:46 JST 2021`
- python version: `3.8.11 (default, Aug 3 2021, 15:09:35) [GCC 7.5.0]`
- espnet version: `espnet 0.10.5a1`
- pytorch version: `pytorch 1.4.0`
- Git hash: `12eb132418a1f69548f7998e53273cd05d989ed9`
- Commit date: `Tue Nov 16 10:12:21 2021 +0900`
## asr_train_asr_streaming_fbank_pitch_en_bpe5000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|54402|97.6|2.2|0.3|0.3|2.7|31.9|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|50948|93.5|5.8|0.7|0.9|7.4|50.4|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|52576|97.5|2.3|0.3|0.3|2.9|33.1|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean_dbg|2620|62|96.8|3.2|0.0|0.0|3.2|0.0|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|52343|93.5|5.7|0.8|0.9|7.4|53.7|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|288456|99.2|0.4|0.4|0.3|1.1|31.9|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|265951|97.2|1.6|1.2|0.9|3.7|50.4|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|281530|99.2|0.4|0.4|0.3|1.1|33.1|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean_dbg|2620|367|99.5|0.0|0.5|0.8|1.4|0.0|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|272758|97.3|1.5|1.3|0.9|3.6|53.7|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean|2703|68010|96.8|2.1|1.1|0.4|3.6|31.9|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other|2864|63110|91.9|5.9|2.2|1.5|9.6|50.4|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean|2620|65818|96.7|2.2|1.1|0.4|3.7|33.1|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean_dbg|2620|94|97.9|2.1|0.0|1.1|3.2|0.0|
|decode_asr_streaming_lm_lm_train_lm_adam_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other|2939|65101|91.8|5.5|2.7|1.2|9.4|53.7|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_streaming.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_streaming_fbank_pitch_en_bpe5000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 33851
dist_launcher: null
multiprocessing_distributed: true
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
unused_parameters: false
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
pretrain_path: null
init_param: []
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 16000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_fbank_pitch_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 800
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/fbank_pitch/train_960_sp/feats.scp
- speech
- kaldi_ark
- - dump/fbank_pitch/train_960_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/fbank_pitch/dev/feats.scp
- speech
- kaldi_ark
- - dump/fbank_pitch/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ▁THE
- S
- ▁AND
- ▁OF
- ▁TO
- ▁A
- ▁IN
- ▁I
- ▁HE
- ▁THAT
- ▁WAS
- ED
- ▁IT
- ''''
- ▁HIS
- ING
- ▁YOU
- ▁WITH
- ▁FOR
- ▁HAD
- T
- ▁AS
- ▁HER
- ▁IS
- ▁BE
- ▁BUT
- ▁NOT
- ▁SHE
- D
- ▁AT
- ▁ON
- LY
- ▁HIM
- ▁THEY
- ▁ALL
- ▁HAVE
- ▁BY
- ▁SO
- ▁THIS
- ▁MY
- ▁WHICH
- ▁ME
- ▁SAID
- ▁FROM
- ▁ONE
- Y
- E
- ▁WERE
- ▁WE
- ▁NO
- N
- ▁THERE
- ▁OR
- ER
- ▁AN
- ▁WHEN
- ▁ARE
- ▁THEIR
- ▁WOULD
- ▁IF
- ▁WHAT
- ▁THEM
- ▁WHO
- ▁OUT
- M
- ▁DO
- ▁WILL
- ▁UP
- ▁BEEN
- P
- R
- ▁MAN
- ▁THEN
- ▁COULD
- ▁MORE
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- ▁CRACK
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- ▁GENEROUS
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- VIE
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- ABOUT
- ▁LUXURY
- ▁MONSTROUS
- ▁TENDENCY
- ▁PARADISE
- ▁CULTURE
- ▁JULIUS
- ▁RAOUL
- ▁REMEDY
- ▁DECAY
- ▁SCOLD
- ▁SPLIT
- ▁ASSAULT
- ▁DECEMBER
- ▁MOSCOW
- ▁EXPLORE
- ▁TROUSERS
- ▁WRIST
- PIECE
- ▁MUSKET
- ▁VALENTINE
- ▁TYRANT
- ▁ABRAHAM
- ▁MEDIUM
- ▁ARTIFICIAL
- ▁FACULTY
- ▁OBLIGATION
- ▁RESEMBLANCE
- ▁INQUIRIES
- ▁DETAIN
- ▁SWARM
- ▁PLEDGE
- ▁ADMIRABLE
- ▁DEFECT
- ▁SUPERINTEND
- ▁PATRIOT
- ▁CLUNG
- ▁DISMAL
- ▁RECIT
- ▁IGNOR
- ▁AMELIA
- ▁JUSTIFY
- ▁ELEPHANT
- ▁ESTIMATE
- ▁KNELT
- ▁SERVING
- ▁WHIM
- ▁SHRILL
- ▁STUDIO
- ▁TEXT
- ▁ALEXANDER
- ▁WROUGHT
- ▁ABUNDANT
- ▁SITUATED
- ▁REGAIN
- ▁FIERY
- ▁SNEER
- ▁SWEAT
- ▁GLARE
- ▁NIGH
- ▁ESCORT
- ▁INEVITABLE
- ▁PSMITH
- ▁RELUCTANT
- ▁PRECEDING
- ▁RESORT
- ▁OUTRAGE
- ▁AMBASSADOR
- ▁CONSOLATION
- ▁RECOGNITION
- ▁REMORSE
- ▁BEHALF
- ▁FORMIDABLE
- ▁GRAVITY
- ▁DIVIDE
- ▁CONFRONT
- ▁GIGANTIC
- ▁OCTOBER
- ▁FLANK
- ▁SLEW
- ▁CLARA
- ▁FILM
- ▁BULK
- ▁POMP
- ▁ELEANOR
- ▁EMPHASIS
- ▁JAPANESE
- ▁CAVALRY
- ▁EXCLUSIVE
- ▁PERFUME
- ▁BRONZE
- ▁FEDERAL
- ▁LIQUID
- ▁RUBBING
- ▁OVEN
- DOLPH
- ▁CONVULS
- ▁DEPRIVED
- ▁RESPONSIBILITY
- ▁SIGNIFICANT
- ▁WAISTCOAT
- ▁CLUSTER
- ▁MARTHA
- ▁REVERSE
- ▁ATTORNEY
- ▁DROOP
- ▁SKILFUL
- ▁HABITUAL
- ▁PUMP
- ▁INTERVEN
- ▁OWL
- ▁CONJECTURE
- ▁FANTASTIC
- ▁RESPONSIBLE
- ▁DESTINED
- ▁DOCUMENT
- ▁THEREUPON
- ▁GODDESS
- ▁PACIFIC
- ▁WARRANT
- ▁COSTUME
- ▁BRIDLE
- ▁CALIFORNIA
- ▁DEMOCRATIC
- ▁EUSTACE
- ▁SQUIRREL
- ▁UNCOMMON
- ▁MARVELLOUS
- ▁PLOUGH
- ▁TRAGEDY
- ▁VAULT
- ▁HESITATE
- ▁REFRAIN
- ▁ADMIRING
- ▁CORPORAL
- ▁ENTITLED
- ▁SHREWD
- ▁SQUEEZ
- ▁ACCURATE
- ▁TEMPEST
- ▁MONUMENT
- ▁SIEGE
- ▁CHINESE
- ▁RAVEN
- ▁LOUNG
- ▁ASSASSIN
- ▁INFLICT
- ▁AGITATED
- ▁DESIRABLE
- ▁EARLIEST
- ▁LAUNCH
- ▁PILOT
- ▁PULSE
- ▁MUTE
- LEIGH
- ▁LIQUOR
- ▁SCARECROW
- ▁SKULL
- ▁DESOLATE
- ▁SUBLIME
- ▁SERENE
- ▁RECESS
- ▁WAKING
- ▁CHARLOTTE
- ▁CIRCULAR
- ▁INJUSTICE
- ▁PINOCCHIO
- ▁PRISCILLA
- ▁THYSELF
- ▁OCCURRENCE
- ▁CASUAL
- ▁FRANTIC
- ▁LEGEND
- ▁FERTIL
- ▁BACKGROUND
- ▁DELICACY
- ▁ESTRALLA
- ▁MANUSCRIPT
- ▁RESPONSE
- ▁UNIVERSITY
- ▁WOLVES
- ▁SCANDAL
- ▁STUMBLE
- ▁HOARSE
- ▁BODILY
- ▁CONVENT
- ▁EXAMINING
- ▁INCAPABLE
- ▁PERCEIVING
- ▁PHILADELPHIA
- ▁SUBSEQUENT
- ▁THIEVES
- ▁ACCUMULAT
- ▁DAMSEL
- ▁SCOTCH
- ▁UNDERNEATH
- ▁NOBILITY
- ▁SMASH
- ▁REVOLT
- ▁ENGAGE
- ▁CATHEDRAL
- ▁CHAMPION
- ▁DESPATCH
- ▁ETERNITY
- ▁JANUARY
- ▁PLEADED
- ▁PROBABILITY
- ▁JIMMIE
- ▁PARALLEL
- ▁FISHERMAN
- ▁JERRY
- ▁SWORE
- ▁DRAUGHT
- ▁OPPONENT
- ▁PRIMITIVE
- ▁SIGNIFICANCE
- ▁SUBSTANTIAL
- ▁AMAZED
- ▁DUNBAR
- ▁COMMEND
- ▁CONTEMPLATE
- ▁TESTIMONY
- ▁IMPERIAL
- ▁ADAPT
- ▁JUICE
- ▁CALAMIT
- CULAR
- ▁CHATEAU
- ▁PHOENIX
- ▁PRUDENT
- ▁SOLUTION
- ▁VILLEFORT
- ▁REACTION
- ▁RELAX
- ▁YU
- ▁PROHIBIT
- ▁DISTRUST
- ▁PLUNDER
- ▁WELFARE
- ▁NAVIGAT
- ▁PARLOR
- ▁LAZY
- ▁DETACH
- OMETER
- ▁PRIV
- ▁DISCOURAGE
- ▁OBSTINATE
- ▁REJOICING
- ▁SERMON
- ▁VEHICLE
- ▁FANCIES
- ▁ENLIGHTEN
- ▁ACUTE
- ▁ILLUSION
- ▁ANTHEA
- ▁MARTIAN
- ▁EXCITE
- ▁GENEROSITY
- OLOGIST
- ▁AMAZING
- ▁UNWORTHY
- ▁INTERNAL
- ▁INCENSE
- ▁VIBRAT
- ▁ADHERE
- ROACH
- ▁FEBRUARY
- ▁MEXICAN
- ▁POTATOES
- ▁INCESSANT
- ▁INTERPOSED
- ▁PARCEL
- ▁VEXED
- ▁PROMOTE
- MIDST
- ▁ARISTOCRAT
- ▁CYRIL
- ▁EMBARK
- ▁ABUNDANCE
- ▁LITERALLY
- ▁SURGEON
- ▁TERRACE
- ▁ATLANTIC
- ▁MARTYR
- ▁SPECK
- ▁SENATE
- ▁LOAF
- ▁ADMINISTER
- ▁APPREHEND
- ▁SUBDUED
- ▁TEMPORARY
- ▁DOMINION
- ▁ELABORATE
- ▁DIGNIFIED
- ▁ELIZA
- ▁SPLASH
- ▁CONSEIL
- ▁DEXTER
- ▁UNSEEN
- ▁TRAGIC
- VOCATION
- ▁GRATIFY
- ▁BACHELOR
- ▁DEFENSE
- ▁EXCURSION
- ▁FACULTIES
- ▁PROPRIETOR
- ▁SYMPATHETIC
- ▁UNNECESSARY
- ▁RADIANT
- ▁VACANT
- ▁OUNCE
- ▁SCREW
- ▁PHENOMENON
- ▁PROMINENT
- ▁WORRIED
- ▁STUDIES
- ▁CLIMATE
- ▁KEITH
- ▁ARAMIS
- ▁BLISS
- ▁CONTINUAL
- ▁SURPASS
- ▁HEBREW
- ▁IDENTITY
- ▁PROVOKE
- ▁TEMPERAMENT
- ▁CHARIOT
- ▁HARBOR
- ▁NINTH
- ▁PRIOR
- ▁DESIROUS
- ▁JERUSALEM
- ▁UNDERTAKING
- ▁EDISON
- ▁MIRTH
- ▁SCOUT
- ▁APPARATUS
- ▁ILLUSTRATION
- ▁INTELLIGIBLE
- ▁INVARIABLY
- ▁PIERCED
- ▁REVIEW
- ▁FLICKER
- ▁HAZARD
- ▁REVELATION
- ▁DIXON
- ▁EXCITING
- ▁GOSPEL
- ▁CONSTANCE
- ▁OVERTAKE
- ▁GUINEA
- ▁ALADDIN
- ▁CHICAGO
- ▁TULLIVER
- ▁HAMILTON
- ▁GARRISON
- ▁DISCIPLE
- ▁INTENSITY
- ▁TRAITOR
- ▁CHANCELLOR
- ▁PROVERB
- ▁DAGGER
- ▁FORESEE
- ▁CONFIDE
- ▁GLIMMER
- ▁CHAUVELIN
- ▁ILLUSTRATE
- ▁VOLUNTEER
- ▁JUNGLE
- ▁STREAK
- ▁SUNRISE
- ▁DISSOLV
- ▁QUEST
- ▁AWHILE
- ▁FELICITY
- ▁LEGISLATURE
- ▁LEONORA
- ▁MAGAZINE
- ▁PITIFUL
- ▁COLONY
- ▁SHAWL
- ▁ARRIVING
- ▁FUNDAMENTAL
- ▁CARPENTER
- ▁OVERFLOW
- ▁EXPAND
- ▁HARVEST
- ▁FEMININE
- ▁INNUMERABLE
- ▁SCRAMBLE
- ▁TWENTIETH
- ▁TRIFLING
- ▁GHASTL
- ▁CONQUEST
- ▁DANIEL
- ▁FACILIT
- ▁FORSAKE
- ▁BEHAVIOUR
- ▁GORGEOUS
- ▁PRODUCING
- ▁HAPPIER
- ▁PROMISING
- ▁RAINBOW
- ▁INSTINCTIVELY
- ▁DECREE
- ▁EYEBROWS
- ▁IRRESISTIBLE
- ▁PHARAOH
- ▁SCROOGE
- ▁UNNATURAL
- ▁CRUMBS
- ▁REFINED
- ▁DREARY
- ▁TRENCH
- ▁CONVINCE
- ▁FRINGE
- ▁EXTREMITY
- ▁INTIMACY
- ▁SCOUNDREL
- ▁SUFFRAGE
- ▁UNEASINESS
- ▁BARRICADE
- ▁CIRCULAT
- ▁SAMUEL
- ▁BRUCE
- ▁DARCY
- <sos/eos>
init: xavier_uniform
input_size: 83
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: false
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: null
frontend_conf: {}
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_fbank_pitch_en_bpe5000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: contextual_block_transformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d
normalize_before: true
block_size: 40
hop_size: 16
look_ahead: 16
init_average: true
ctx_pos_enc: true
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: 0.9.7
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
eml914/streaming_transformer_asr_librispeech
| null |
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
emma19/speech-recognition
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
{}
|
empushy/gpt2-alerts
| null |
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
text-generation
|
transformers
|
{}
|
empushy/gpt2-emulator
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emranmdanas/asr_model
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
emranmdanas/wav2vec2-large-xls-r-300m-tr-colab
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
summarization
|
transformers
|
# arxiv27k-t5-abst-title-gen/
This model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6002
- Rouge1: 32.8
- Rouge2: 21.9
- Rougel: 34.8
-
## Model description
Model has been trained with a colab-pro notebook in 4 hours.
## Intended uses & limitations
Can be used for generating journal titles from given abstracts
### Training args
model_args = T5Args()
model_args.max_seq_length = 256
model_args.train_batch_size = 8
model_args.eval_batch_size = 8
model_args.num_train_epochs = 6
model_args.evaluate_during_training = False
model_args.use_multiprocessing = False
model_args.fp16 = False
model_args.save_steps = 40000
model_args.save_eval_checkpoints = False
model_args.save_model_every_epoch = True
model_args.output_dir = OUTPUT_DIR
model_args.no_cache = True
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
model_args.num_return_sequences = 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
### Contact
[email protected]
Davut Emre Taşar
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "arxiv27k-t5-abst-title-gen/", "results": []}]}
|
emre/arxiv27k-t5-abst-title-gen
| null |
[
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1620
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2256 | 1.0 | 5533 | 1.1620 |
| 0.9551 | 2.0 | 11066 | 1.1237 |
| 0.7726 | 3.0 | 16599 | 1.1620 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
emre/distilbert-base-uncased-finetuned-squad
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
question-answering
|
transformers
|
# Turkish SQuAD Model : Question Answering
Fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset
* Loodos-BERT-base: https://huggingface.co/loodos/bert-base-turkish-uncased
* TQuAD dataset: https://github.com/TQuad/turkish-nlp-qa-dataset
# Training Code
```
!python3 Turkish-QA.py \
--model_type bert \
--model_name_or_path loodos/bert-base-turkish-uncased
--do_train \
--do_eval \
--train_file trainQ.json \
--predict_file dev1.json \
--per_gpu_train_batch_size 8 \
--learning_rate 5e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--output_dir "./model"
```
# Example Usage
> Load Model
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("emre/distilbert-tr-q-a")
model = AutoModelForQuestionAnswering.from_pretrained("emre/distilbert-tr-q-a")
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
```
> Apply the model
```
def ask(question,context):
temp = nlp(question=question, context=context)
start_idx = temp["start"]
end_idx = temp["end"]
return context[start_idx:end_idx]
izmir="İzmir, Türkiye'de Ege Bölgesi'nde yer alan şehir ve ülkenin 81 ilinden biridir. Ülkenin nüfus bakımından en kalabalık üçüncü şehridir. Ekonomik, tarihi ve sosyo-kültürel açıdan önde gelen şehirlerden biridir. Nüfusu 2021 itibarıyla 4.425.789 kişidir. Yüzölçümü olarak ülkenin yirmi üçüncü büyük ilidir."
soru1 = "İzmir'in nüfusu kaçtır?"
print(ask(soru1,izmir))
soru2 = "İzmir hangi bölgede bulunur?"
print(ask(soru2,izmir))
```
|
{"language": "tr", "tags": ["question-answering", "loodos-bert-base", "TQuAD", "tr"], "datasets": ["TQuAD"]}
|
emre/distilbert-tr-q-a
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"loodos-bert-base",
"TQuAD",
"tr",
"dataset:TQuAD",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null |
transformers
|
# jurisprudence-textgen-gpt-2
Pretrained model on Turkish language using a causal language modeling (CLM) objective.
## Model description of Original GPT-2
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
## Model description of jurisprudence-textgen-gpt-2
Jurisprudence-textgen-gpt-2 is a transformers model for tensorflow pretrained with 18950 Turkish court Jurisprudence text data which has been obtained from [Bilirkisi GITHUB REPO TRAIN DATA] (https://github.com/Bilirkisi/Bilirkisi/tree/main/train) with 5 epochs.
Model Training results are:
Epoch 1/5
4986/4986 - 2770s 552ms/step - loss: 4.0122 - output_1_loss: 4.0122 - output_1_accuracy: 0.4544
Epoch 2/5
4986/4986 - 2753s 552ms/step - loss: 2.7074 - output_1_loss: 2.7074 - output_1_accuracy: 0.5843
Epoch 3/5
4986/4986 - 2754s 552ms/step - loss: 2.3411 - output_1_loss: 2.3411 - output_1_accuracy: 0.6214
Epoch 4/5
4986/4986 - 2754s 552ms/step - loss: 2.1241 - output_1_loss: 2.1241 - output_1_accuracy: 0.6431
Epoch 5/5
4986/4986 - 2754s 552ms/step - loss: 1.9647 - output_1_loss: 1.9647 - output_1_accuracy: 0.6597
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a turkish law included downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation.
Here is how to use this model to get the features of a given text in Tensorflow:
```python
>>> from transformers import GPT2Tokenizer , TFGPT2LMHeadModel
>>> tokenizer = GPT2Tokenizer.from_pretrained('emre/jurisprudence-textgen-gpt-2')
>>> model = TFGPT2LMHeadModel.from_pretrained('emre/jurisprudence-textgen-gpt-2')
>>> text = "Tarafların karşılıklı iddia ve savunmalarına," #Translation: "Mutual claims and defenses of the parties,"
>>> # encoding the input text
>>> input_ids = tokenizer.encode(text, return_tensors='tf')
>>> # getting out output
>>> beam_output = model.generate(
>>> input_ids,
>>> max_length = 250,
>>> num_beams = 5,
>>> temperature = 0.7,
>>> no_repeat_ngram_size=2,
>>> num_return_sequences=5
>>> )
>>> for i in range(5):
>>> print(tokenizer.decode(beam_output[i]))
[{'generated_text': "Tarafların karşılıklı iddia ve savunmalarına, dayandıkları belgelere, temyiz olunan kararda yazılı gerekçelere göre yerinde bulunmayan temyiz sebeplerinin reddiyle usul ve kanuna uygun mahkeme kararının İİK. 366. ve HUMK. 438. maddeleri uyarınca (ONANMASINA), 13.10 YTL onama harcı temyiz edenden alındığından başkaca harç alınmasına mahal olmadığına, 25.12.2007 gününde oybirliğiyle karar verildi."},
{'generated_text': "Tarafların karşılıklı iddia ve savunmalarına, dayandıkları belgelere, temyiz olunan kararda yazılı gerekçelere göre yerinde bulunmayan temyiz itirazlarının reddiyle usul ve kanuna uygun mahkeme kararının İİK. 366. ve HUMK. 438. maddeleri uyarınca (ONANMASINA), 15,60 TL onama harcı temyiz edenden alındığından başkaca harç alınmasına mahal olmadığına, 30/12/2009 gününde oybirliğiyle karar verildi."},
{'generated_text': "Tarafların karşılıklı iddia ve savunmalarına, dayandıkları belgelere, temyiz olunan kararda yazılı gerekçelere göre yerinde bulunmayan temyiz sebeplerinin reddiyle usul ve kanuna uygun mahkeme kararının İİK. 366. ve HUMK. 438. maddeleri uyarınca (ONANMASINA), 15,60 TL onama harcı temyiz edenden alındığından başkaca harç alınmasına mahal olmadığına, 30/12/2009 gününde oybirliğiyle karar verildi."},
{'generated_text': "Tarafların karşılıklı iddia ve savunmalarına, dayandıkları belgelere, temyiz olunan kararda yazılı gerekçelere göre yerinde bulunmayan temyiz sebeplerinin reddiyle usul ve kanuna uygun mahkeme kararının İİK. 366. ve HUMK. 438. maddeleri uyarınca (ONANMASINA), 13.10 YTL onama harcı temyiz edenden alındığından başkaca harç alınmasına mahal olmadığına, 25/12/2007 gününde oybirliğiyle karar verildi."},
{'generated_text': "Tarafların karşılıklı iddia ve savunmalarına, dayandıkları belgelere, temyiz olunan kararda yazılı gerekçelere göre yerinde bulunmayan temyiz sebeplerinin reddiyle usul ve kanuna uygun mahkeme kararının İİK. 366. ve HUMK. 438. maddeleri uyarınca (ONANMASINA), 13.10 YTL onama harcı temyiz edenden alındığından başkaca harç alınmasına mahal olmadığına, 27/12/2007 gününde oybirliğiyle karar verildi."}]
```
### BibTeX entry and citation info
soon will be defined..
|
{"language": "tr", "license": "mit"}
|
emre/jurisprudence-textgen-gpt-2
| null |
[
"transformers",
"tf",
"gpt2",
"tr",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
# wav2vec-tr-lite-AG
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("emre/wav2vec-tr-lite-AG")
model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00005
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4388 | 3.7 | 400 | 1.366 | 0.9701 |
| 0.3766 | 7.4 | 800 | 0.4914 | 0.5374 |
| 0.2295 | 11.11 | 1200 | 0.3934 | 0.4125 |
| 0.1121 | 14.81 | 1600 | 0.3264 | 0.2904 |
| 0.1473 | 18.51 | 2000 | 0.3103 | 0.2671 |
| 0.1013 | 22.22 | 2400 | 0.2589 | 0.2324 |
| 0.0704 | 25.92 | 2800 | 0.2826 | 0.2339 |
| 0.0537 | 29.63 | 3200 | 0.2704 | 0.2309 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]}
|
emre/wav2vec-tr-lite-AG
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- 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-tr
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 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2224
- Wer: 0.2869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.8222 | 0.64 | 500 | 3.5026 | 1.0 |
| 3.2136 | 1.28 | 1000 | 3.0593 | 1.0000 |
| 2.8882 | 1.91 | 1500 | 2.4670 | 0.9939 |
| 2.3743 | 2.55 | 2000 | 1.1844 | 0.8657 |
| 1.9456 | 3.19 | 2500 | 0.8228 | 0.7397 |
| 1.7781 | 3.83 | 3000 | 0.6826 | 0.6753 |
| 1.6848 | 4.46 | 3500 | 0.5885 | 0.6140 |
| 1.6228 | 5.1 | 4000 | 0.5274 | 0.5789 |
| 1.5768 | 5.74 | 4500 | 0.4900 | 0.5519 |
| 1.5431 | 6.38 | 5000 | 0.4508 | 0.5238 |
| 1.5019 | 7.02 | 5500 | 0.4248 | 0.5021 |
| 1.4684 | 7.65 | 6000 | 0.4009 | 0.4827 |
| 1.4635 | 8.29 | 6500 | 0.3830 | 0.4700 |
| 1.4291 | 8.93 | 7000 | 0.3707 | 0.4595 |
| 1.4271 | 9.57 | 7500 | 0.3570 | 0.4514 |
| 1.3938 | 10.2 | 8000 | 0.3479 | 0.4378 |
| 1.3914 | 10.84 | 8500 | 0.3396 | 0.4368 |
| 1.3767 | 11.48 | 9000 | 0.3253 | 0.4262 |
| 1.3641 | 12.12 | 9500 | 0.3251 | 0.4178 |
| 1.355 | 12.76 | 10000 | 0.3138 | 0.4136 |
| 1.336 | 13.39 | 10500 | 0.3121 | 0.4069 |
| 1.3292 | 14.03 | 11000 | 0.3041 | 0.4014 |
| 1.3249 | 14.67 | 11500 | 0.3014 | 0.3931 |
| 1.3156 | 15.31 | 12000 | 0.3014 | 0.3929 |
| 1.313 | 15.94 | 12500 | 0.2969 | 0.3968 |
| 1.3068 | 16.58 | 13000 | 0.2965 | 0.3966 |
| 1.2785 | 17.22 | 13500 | 0.2943 | 0.3850 |
| 1.2867 | 17.86 | 14000 | 0.2912 | 0.3782 |
| 1.2714 | 18.49 | 14500 | 0.2819 | 0.3747 |
| 1.2844 | 19.13 | 15000 | 0.2840 | 0.3740 |
| 1.2684 | 19.77 | 15500 | 0.2913 | 0.3828 |
| 1.26 | 20.41 | 16000 | 0.2739 | 0.3674 |
| 1.2543 | 21.05 | 16500 | 0.2740 | 0.3691 |
| 1.2532 | 21.68 | 17000 | 0.2709 | 0.3756 |
| 1.2409 | 22.32 | 17500 | 0.2669 | 0.3593 |
| 1.2404 | 22.96 | 18000 | 0.2673 | 0.3576 |
| 1.2347 | 23.6 | 18500 | 0.2678 | 0.3643 |
| 1.2351 | 24.23 | 19000 | 0.2715 | 0.3650 |
| 1.2409 | 24.87 | 19500 | 0.2637 | 0.3571 |
| 1.2152 | 25.51 | 20000 | 0.2785 | 0.3609 |
| 1.2046 | 26.15 | 20500 | 0.2610 | 0.3508 |
| 1.2082 | 26.79 | 21000 | 0.2619 | 0.3461 |
| 1.2109 | 27.42 | 21500 | 0.2597 | 0.3502 |
| 1.2014 | 28.06 | 22000 | 0.2608 | 0.3468 |
| 1.1948 | 28.7 | 22500 | 0.2573 | 0.3457 |
| 1.205 | 29.34 | 23000 | 0.2619 | 0.3464 |
| 1.2019 | 29.97 | 23500 | 0.2559 | 0.3474 |
| 1.1917 | 30.61 | 24000 | 0.2601 | 0.3462 |
| 1.1939 | 31.25 | 24500 | 0.2575 | 0.3387 |
| 1.1882 | 31.89 | 25000 | 0.2535 | 0.3368 |
| 1.191 | 32.53 | 25500 | 0.2489 | 0.3365 |
| 1.1767 | 33.16 | 26000 | 0.2501 | 0.3347 |
| 1.167 | 33.8 | 26500 | 0.2504 | 0.3347 |
| 1.1678 | 34.44 | 27000 | 0.2480 | 0.3378 |
| 1.1803 | 35.08 | 27500 | 0.2487 | 0.3345 |
| 1.167 | 35.71 | 28000 | 0.2442 | 0.3319 |
| 1.1661 | 36.35 | 28500 | 0.2495 | 0.3334 |
| 1.164 | 36.99 | 29000 | 0.2472 | 0.3292 |
| 1.1578 | 37.63 | 29500 | 0.2442 | 0.3242 |
| 1.1584 | 38.27 | 30000 | 0.2431 | 0.3314 |
| 1.1526 | 38.9 | 30500 | 0.2441 | 0.3347 |
| 1.1542 | 39.54 | 31000 | 0.2437 | 0.3330 |
| 1.1508 | 40.18 | 31500 | 0.2433 | 0.3294 |
| 1.1406 | 40.82 | 32000 | 0.2434 | 0.3271 |
| 1.1514 | 41.45 | 32500 | 0.2426 | 0.3255 |
| 1.1418 | 42.09 | 33000 | 0.2432 | 0.3233 |
| 1.1365 | 42.73 | 33500 | 0.2436 | 0.3240 |
| 1.1348 | 43.37 | 34000 | 0.2483 | 0.3257 |
| 1.1301 | 44.01 | 34500 | 0.2420 | 0.3271 |
| 1.1268 | 44.64 | 35000 | 0.2472 | 0.3225 |
| 1.1224 | 45.28 | 35500 | 0.2382 | 0.3205 |
| 1.1224 | 45.92 | 36000 | 0.2388 | 0.3184 |
| 1.1198 | 46.56 | 36500 | 0.2382 | 0.3202 |
| 1.1274 | 47.19 | 37000 | 0.2404 | 0.3172 |
| 1.1147 | 47.83 | 37500 | 0.2394 | 0.3164 |
| 1.121 | 48.47 | 38000 | 0.2406 | 0.3202 |
| 1.1109 | 49.11 | 38500 | 0.2384 | 0.3154 |
| 1.1164 | 49.74 | 39000 | 0.2375 | 0.3169 |
| 1.1105 | 50.38 | 39500 | 0.2387 | 0.3173 |
| 1.1054 | 51.02 | 40000 | 0.2362 | 0.3120 |
| 1.0893 | 51.66 | 40500 | 0.2399 | 0.3130 |
| 1.0913 | 52.3 | 41000 | 0.2357 | 0.3088 |
| 1.1017 | 52.93 | 41500 | 0.2345 | 0.3084 |
| 1.0937 | 53.57 | 42000 | 0.2330 | 0.3140 |
| 1.0945 | 54.21 | 42500 | 0.2399 | 0.3107 |
| 1.0933 | 54.85 | 43000 | 0.2383 | 0.3134 |
| 1.0912 | 55.48 | 43500 | 0.2372 | 0.3077 |
| 1.0898 | 56.12 | 44000 | 0.2339 | 0.3083 |
| 1.0903 | 56.76 | 44500 | 0.2367 | 0.3065 |
| 1.0947 | 57.4 | 45000 | 0.2352 | 0.3104 |
| 1.0751 | 58.04 | 45500 | 0.2334 | 0.3084 |
| 1.09 | 58.67 | 46000 | 0.2328 | 0.3100 |
| 1.0876 | 59.31 | 46500 | 0.2276 | 0.3050 |
| 1.076 | 59.95 | 47000 | 0.2309 | 0.3047 |
| 1.086 | 60.59 | 47500 | 0.2293 | 0.3047 |
| 1.082 | 61.22 | 48000 | 0.2328 | 0.3027 |
| 1.0714 | 61.86 | 48500 | 0.2290 | 0.3020 |
| 1.0746 | 62.5 | 49000 | 0.2313 | 0.3059 |
| 1.076 | 63.14 | 49500 | 0.2342 | 0.3050 |
| 1.0648 | 63.78 | 50000 | 0.2286 | 0.3025 |
| 1.0586 | 64.41 | 50500 | 0.2338 | 0.3044 |
| 1.0753 | 65.05 | 51000 | 0.2308 | 0.3045 |
| 1.0664 | 65.69 | 51500 | 0.2273 | 0.3009 |
| 1.0739 | 66.33 | 52000 | 0.2298 | 0.3027 |
| 1.0695 | 66.96 | 52500 | 0.2247 | 0.2996 |
| 1.06 | 67.6 | 53000 | 0.2276 | 0.3015 |
| 1.0742 | 68.24 | 53500 | 0.2280 | 0.2974 |
| 1.0618 | 68.88 | 54000 | 0.2291 | 0.2989 |
| 1.062 | 69.52 | 54500 | 0.2302 | 0.2971 |
| 1.0572 | 70.15 | 55000 | 0.2280 | 0.2990 |
| 1.055 | 70.79 | 55500 | 0.2278 | 0.2983 |
| 1.0553 | 71.43 | 56000 | 0.2282 | 0.2991 |
| 1.0509 | 72.07 | 56500 | 0.2261 | 0.2959 |
| 1.0469 | 72.7 | 57000 | 0.2216 | 0.2919 |
| 1.0476 | 73.34 | 57500 | 0.2267 | 0.2989 |
| 1.0494 | 73.98 | 58000 | 0.2260 | 0.2960 |
| 1.0517 | 74.62 | 58500 | 0.2297 | 0.2989 |
| 1.0458 | 75.26 | 59000 | 0.2246 | 0.2923 |
| 1.0382 | 75.89 | 59500 | 0.2255 | 0.2922 |
| 1.0462 | 76.53 | 60000 | 0.2258 | 0.2954 |
| 1.0375 | 77.17 | 60500 | 0.2251 | 0.2929 |
| 1.0332 | 77.81 | 61000 | 0.2277 | 0.2940 |
| 1.0423 | 78.44 | 61500 | 0.2243 | 0.2896 |
| 1.0379 | 79.08 | 62000 | 0.2274 | 0.2928 |
| 1.0398 | 79.72 | 62500 | 0.2237 | 0.2928 |
| 1.0395 | 80.36 | 63000 | 0.2265 | 0.2956 |
| 1.0397 | 80.99 | 63500 | 0.2240 | 0.2920 |
| 1.0262 | 81.63 | 64000 | 0.2244 | 0.2934 |
| 1.0335 | 82.27 | 64500 | 0.2265 | 0.2936 |
| 1.0385 | 82.91 | 65000 | 0.2238 | 0.2928 |
| 1.0289 | 83.55 | 65500 | 0.2219 | 0.2912 |
| 1.0372 | 84.18 | 66000 | 0.2236 | 0.2898 |
| 1.0279 | 84.82 | 66500 | 0.2219 | 0.2902 |
| 1.0325 | 85.46 | 67000 | 0.2240 | 0.2908 |
| 1.0202 | 86.1 | 67500 | 0.2206 | 0.2886 |
| 1.0166 | 86.73 | 68000 | 0.2219 | 0.2886 |
| 1.0259 | 87.37 | 68500 | 0.2235 | 0.2897 |
| 1.0337 | 88.01 | 69000 | 0.2210 | 0.2873 |
| 1.0264 | 88.65 | 69500 | 0.2216 | 0.2882 |
| 1.0231 | 89.29 | 70000 | 0.2223 | 0.2899 |
| 1.0281 | 89.92 | 70500 | 0.2214 | 0.2872 |
| 1.0135 | 90.56 | 71000 | 0.2218 | 0.2868 |
| 1.0291 | 91.2 | 71500 | 0.2209 | 0.2863 |
| 1.0321 | 91.84 | 72000 | 0.2199 | 0.2876 |
| 1.028 | 92.47 | 72500 | 0.2214 | 0.2858 |
| 1.0213 | 93.11 | 73000 | 0.2219 | 0.2875 |
| 1.0261 | 93.75 | 73500 | 0.2232 | 0.2869 |
| 1.0197 | 94.39 | 74000 | 0.2227 | 0.2866 |
| 1.0298 | 95.03 | 74500 | 0.2228 | 0.2868 |
| 1.0192 | 95.66 | 75000 | 0.2230 | 0.2865 |
| 1.0156 | 96.3 | 75500 | 0.2220 | 0.2869 |
| 1.0075 | 96.94 | 76000 | 0.2223 | 0.2866 |
| 1.0201 | 97.58 | 76500 | 0.2219 | 0.2866 |
| 1.0159 | 98.21 | 77000 | 0.2219 | 0.2876 |
| 1.0087 | 98.85 | 77500 | 0.2219 | 0.2873 |
| 1.0159 | 99.49 | 78000 | 0.2223 | 0.2867 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
{"language": "tr", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-large-xls-r-300m-tr", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice_8_0", "args": "tr"}, "metrics": [{"type": "wer", "value": 28.69, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-large-xls-r-300m-tr
| null |
[
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"tr",
"dataset:mozilla-foundation/common_voice_8_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4714
- Wer: 0.5316
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.2446 | 1.17 | 400 | 3.2621 | 1.0 |
| 1.739 | 2.35 | 800 | 0.5832 | 0.7688 |
| 0.4718 | 3.52 | 1200 | 0.4785 | 0.6824 |
| 0.3574 | 4.69 | 1600 | 0.4814 | 0.6792 |
| 0.2946 | 5.86 | 2000 | 0.4484 | 0.6506 |
| 0.2674 | 7.04 | 2400 | 0.4612 | 0.6225 |
| 0.2349 | 8.21 | 2800 | 0.4600 | 0.6050 |
| 0.2206 | 9.38 | 3200 | 0.4772 | 0.6048 |
| 0.2072 | 10.56 | 3600 | 0.4676 | 0.6106 |
| 0.1984 | 11.73 | 4000 | 0.4816 | 0.6079 |
| 0.1793 | 12.9 | 4400 | 0.4616 | 0.5836 |
| 0.172 | 14.08 | 4800 | 0.4808 | 0.5860 |
| 0.1624 | 15.25 | 5200 | 0.4854 | 0.5820 |
| 0.156 | 16.42 | 5600 | 0.4609 | 0.5656 |
| 0.1448 | 17.59 | 6000 | 0.4926 | 0.5817 |
| 0.1406 | 18.77 | 6400 | 0.4638 | 0.5654 |
| 0.1337 | 19.94 | 6800 | 0.4731 | 0.5652 |
| 0.1317 | 21.11 | 7200 | 0.4861 | 0.5639 |
| 0.1179 | 22.29 | 7600 | 0.4766 | 0.5521 |
| 0.1197 | 23.46 | 8000 | 0.4824 | 0.5584 |
| 0.1096 | 24.63 | 8400 | 0.5006 | 0.5559 |
| 0.1038 | 25.81 | 8800 | 0.4994 | 0.5440 |
| 0.0992 | 26.98 | 9200 | 0.4867 | 0.5405 |
| 0.0984 | 28.15 | 9600 | 0.4798 | 0.5361 |
| 0.0943 | 29.33 | 10000 | 0.4714 | 0.5316 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"language": "tt", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "tt"], "datasets": ["common_voice"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "tt"}, "metrics": [{"type": "wer", "value": 53.16, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-large-xlsr-53-W2V2-TATAR-SMALL
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"tt",
"dataset:common_voice",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-W2V2-TR-MED
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4467
- Wer: 0.4598
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1343 | 4.21 | 400 | 2.3674 | 1.0372 |
| 0.8075 | 8.42 | 800 | 0.4583 | 0.6308 |
| 0.3209 | 12.63 | 1200 | 0.4291 | 0.5531 |
| 0.2273 | 16.84 | 1600 | 0.4348 | 0.5378 |
| 0.1764 | 21.05 | 2000 | 0.4550 | 0.5326 |
| 0.148 | 25.26 | 2400 | 0.4839 | 0.5319 |
| 0.1268 | 29.47 | 2800 | 0.4515 | 0.5070 |
| 0.1113 | 33.68 | 3200 | 0.4590 | 0.4930 |
| 0.1025 | 37.89 | 3600 | 0.4546 | 0.4888 |
| 0.0922 | 42.11 | 4000 | 0.4782 | 0.4852 |
| 0.082 | 46.32 | 4400 | 0.4605 | 0.4752 |
| 0.0751 | 50.53 | 4800 | 0.4358 | 0.4689 |
| 0.0699 | 54.74 | 5200 | 0.4359 | 0.4629 |
| 0.0633 | 58.95 | 5600 | 0.4467 | 0.4598 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-W2V2-TR-MED", "results": []}]}
|
emre/wav2vec2-large-xlsr-53-W2V2-TR-MED
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3966
- Wer: 0.4834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1516 | 4.21 | 400 | 2.7673 | 1.0 |
| 0.9134 | 8.42 | 800 | 0.4618 | 0.6418 |
| 0.3273 | 12.63 | 1200 | 0.4188 | 0.5535 |
| 0.2252 | 16.84 | 1600 | 0.4144 | 0.5232 |
| 0.1692 | 21.05 | 2000 | 0.3995 | 0.5030 |
| 0.1355 | 25.26 | 2400 | 0.4073 | 0.4920 |
| 0.1172 | 29.47 | 2800 | 0.3966 | 0.4834 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-demo-colab", "results": []}]}
|
emre/wav2vec2-large-xlsr-53-demo-colab
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-sah-CV8
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5089
- Wer: 0.5606
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.6849 | 16.67 | 500 | 1.1135 | 0.9344 |
| 0.8223 | 33.33 | 1000 | 0.5148 | 0.5686 |
| 0.5477 | 50.0 | 1500 | 0.5089 | 0.5606 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"language": "sah", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-sah-CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice sah", "type": "common_voice", "args": "sah"}, "metrics": [{"type": "wer", "value": 56.06, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8.0", "type": "mozilla-foundation/common_voice_8_0", "args": "sah"}, "metrics": [{"type": "wer", "value": 43.75, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-large-xlsr-53-sah-CV8
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"sah",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
# wav2vec2-xls-r-300m-Br-small
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: 1.0573
- Wer: 0.6675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.7464 | 2.79 | 400 | 1.7474 | 1.1018 |
| 1.1117 | 5.59 | 800 | 0.9434 | 0.8697 |
| 0.6481 | 8.39 | 1200 | 0.9251 | 0.7910 |
| 0.4754 | 11.19 | 1600 | 0.9208 | 0.7412 |
| 0.3602 | 13.98 | 2000 | 0.9284 | 0.7232 |
| 0.2873 | 16.78 | 2400 | 0.9299 | 0.6940 |
| 0.2386 | 19.58 | 2800 | 1.0182 | 0.6927 |
| 0.1971 | 22.38 | 3200 | 1.0456 | 0.6898 |
| 0.1749 | 25.17 | 3600 | 1.0208 | 0.6769 |
| 0.1487 | 27.97 | 4000 | 1.0573 | 0.6675 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"language": "br", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Br-small", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice br", "type": "common_voice", "args": "br"}, "metrics": [{"type": "wer", "value": 66.75, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-Br-small
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-Russian-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3514
- Wer: 0.4838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.512 | 1.32 | 400 | 3.2207 | 1.0 |
| 3.1562 | 2.65 | 800 | 3.0166 | 1.0 |
| 1.5211 | 3.97 | 1200 | 0.7134 | 0.8275 |
| 0.6724 | 5.3 | 1600 | 0.4713 | 0.6402 |
| 0.4693 | 6.62 | 2000 | 0.3904 | 0.5668 |
| 0.3693 | 7.95 | 2400 | 0.3609 | 0.5121 |
| 0.3004 | 9.27 | 2800 | 0.3514 | 0.4838 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"language": ["ru"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Russian-small", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ru", "type": "common_voice", "args": "ru"}, "metrics": [{"type": "wer", "value": 48.38, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ru"}, "metrics": [{"type": "wer", "value": 58.25, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ru"}, "metrics": [{"type": "wer", "value": 56.83, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-Russian-small
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ru",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8
This model is a fine-tuned version of [emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8](https://huggingface.co/emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2708
- Wer: 0.5010
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.0402 | 0.67 | 500 | 0.3354 | 0.5681 |
| 0.7265 | 1.33 | 1000 | 0.3181 | 0.5444 |
| 0.6858 | 2.0 | 1500 | 0.3044 | 0.5322 |
| 0.6537 | 2.66 | 2000 | 0.2911 | 0.5217 |
| 0.6337 | 3.33 | 2500 | 0.2874 | 0.5164 |
| 0.6111 | 3.99 | 3000 | 0.2758 | 0.5059 |
| 0.5815 | 4.66 | 3500 | 0.2708 | 0.5010 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8", "results": []}]}
|
emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8-Tr-med-CommonVoice8
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-Tr-med-CommonVoice8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2556
- Wer: 0.4914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4876 | 6.66 | 5000 | 0.3252 | 0.5784 |
| 0.6919 | 13.32 | 10000 | 0.2720 | 0.5172 |
| 0.5919 | 19.97 | 15000 | 0.2556 | 0.4914 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"language": "tr", "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Tr-med-CommonVoice8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 49.14, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-Tr-med-CommonVoice8
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-Turkish-Tr-med
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4727
- Wer: 0.4677
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8093 | 4.21 | 400 | 2.7831 | 1.0 |
| 0.9881 | 8.42 | 800 | 0.5088 | 0.6681 |
| 0.3519 | 12.63 | 1200 | 0.4496 | 0.6007 |
| 0.2436 | 16.84 | 1600 | 0.4993 | 0.5654 |
| 0.1874 | 21.05 | 2000 | 0.4793 | 0.5530 |
| 0.1561 | 25.26 | 2400 | 0.5187 | 0.5589 |
| 0.1336 | 29.47 | 2800 | 0.5135 | 0.5311 |
| 0.1163 | 33.68 | 3200 | 0.4960 | 0.5143 |
| 0.1056 | 37.89 | 3600 | 0.4795 | 0.5045 |
| 0.0959 | 42.11 | 4000 | 0.4883 | 0.4987 |
| 0.0819 | 46.32 | 4400 | 0.4799 | 0.4903 |
| 0.0756 | 50.53 | 4800 | 0.4822 | 0.4831 |
| 0.0692 | 54.74 | 5200 | 0.4621 | 0.4762 |
| 0.062 | 58.95 | 5600 | 0.4727 | 0.4677 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Turkish-Tr-med", "results": []}]}
|
emre/wav2vec2-xls-r-300m-Turkish-Tr-med
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4813
- Wer: 0.7207
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2 | 0.53 | 400 | 3.1949 | 0.9964 |
| 2.9387 | 1.07 | 800 | 2.5015 | 1.0337 |
| 1.5975 | 1.6 | 1200 | 1.0928 | 0.9945 |
| 1.0688 | 2.13 | 1600 | 0.8388 | 0.9390 |
| 0.8977 | 2.66 | 2000 | 0.7106 | 0.8889 |
| 0.789 | 3.2 | 2400 | 0.6051 | 0.8273 |
| 0.7116 | 3.73 | 2800 | 0.5580 | 0.7855 |
| 0.6576 | 4.26 | 3200 | 0.5033 | 0.7433 |
| 0.6002 | 4.79 | 3600 | 0.4813 | 0.7207 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8", "results": []}]}
|
emre/wav2vec2-xls-r-300m-Turkish-Tr-small-CommonVoice8
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-Turkish-Tr-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4375
- Wer: 0.5050
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8735 | 4.21 | 400 | 2.8173 | 1.0002 |
| 1.0073 | 8.42 | 800 | 0.4981 | 0.6717 |
| 0.3395 | 12.63 | 1200 | 0.4470 | 0.5866 |
| 0.2254 | 16.84 | 1600 | 0.4349 | 0.5491 |
| 0.1648 | 21.05 | 2000 | 0.4454 | 0.5284 |
| 0.1325 | 25.26 | 2400 | 0.4552 | 0.5131 |
| 0.1102 | 29.47 | 2800 | 0.4375 | 0.5050 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Turkish-Tr-small", "results": []}]}
|
emre/wav2vec2-xls-r-300m-Turkish-Tr-small
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9068
- Wer: 0.7900
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.6926 | 19.05 | 400 | 2.7538 | 1.0 |
| 0.7031 | 38.1 | 800 | 0.9068 | 0.7900 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"language": "sah", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice sah", "type": "common_voice", "args": "sah"}, "metrics": [{"type": "wer", "value": 79.0, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-W2V2-XLSR-300M-YAKUT-SMALL
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"sah",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
# wav2vec2-xls-r-300m-ab-CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2105
- Wer: 0.5474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.7729 | 0.63 | 500 | 3.0624 | 1.0021 |
| 2.7348 | 1.26 | 1000 | 1.0460 | 0.9815 |
| 1.2756 | 1.9 | 1500 | 0.4618 | 0.8309 |
| 1.0419 | 2.53 | 2000 | 0.3725 | 0.7449 |
| 0.9491 | 3.16 | 2500 | 0.3368 | 0.7345 |
| 0.9006 | 3.79 | 3000 | 0.3014 | 0.6936 |
| 0.8519 | 4.42 | 3500 | 0.2852 | 0.6767 |
| 0.8243 | 5.06 | 4000 | 0.2701 | 0.6504 |
| 0.7902 | 5.69 | 4500 | 0.2641 | 0.6221 |
| 0.7767 | 6.32 | 5000 | 0.2549 | 0.6192 |
| 0.7516 | 6.95 | 5500 | 0.2515 | 0.6179 |
| 0.737 | 7.59 | 6000 | 0.2408 | 0.5963 |
| 0.7217 | 8.22 | 6500 | 0.2429 | 0.6261 |
| 0.7101 | 8.85 | 7000 | 0.2366 | 0.5687 |
| 0.6922 | 9.48 | 7500 | 0.2277 | 0.5680 |
| 0.6866 | 10.11 | 8000 | 0.2242 | 0.5847 |
| 0.6703 | 10.75 | 8500 | 0.2222 | 0.5803 |
| 0.6649 | 11.38 | 9000 | 0.2247 | 0.5765 |
| 0.6513 | 12.01 | 9500 | 0.2182 | 0.5644 |
| 0.6369 | 12.64 | 10000 | 0.2128 | 0.5508 |
| 0.6425 | 13.27 | 10500 | 0.2132 | 0.5514 |
| 0.6399 | 13.91 | 11000 | 0.2116 | 0.5495 |
| 0.6208 | 14.54 | 11500 | 0.2105 | 0.5474 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"language": "ab", "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-ab-CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ab"}, "metrics": [{"type": "wer", "value": 44.9, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-ab-CV8
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"ab",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-as-CV8-v1
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
{"language": "as", "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-as-CV8-v1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8.0", "type": "mozilla-foundation/common_voice_8_0", "args": "as"}, "metrics": [{"type": "wer", "value": 100.0, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-as-CV8-v1
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"as",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-bas-CV8-v2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6121
- Wer: 0.5697
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 90
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.5211 | 16.13 | 500 | 1.2661 | 0.9153 |
| 0.7026 | 32.25 | 1000 | 0.6245 | 0.6516 |
| 0.3752 | 48.38 | 1500 | 0.6039 | 0.6148 |
| 0.2752 | 64.51 | 2000 | 0.6080 | 0.5808 |
| 0.2155 | 80.63 | 2500 | 0.6121 | 0.5697 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
{"language": "bas", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "bas", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-bas-CV8-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "bas"}, "metrics": [{"type": "wer", "value": 56.97, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-bas-CV8-v2
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"bas",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-gl-CV8
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2151
- Wer: 0.2080
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9427 | 4.9 | 500 | 2.8801 | 1.0 |
| 2.1594 | 9.8 | 1000 | 0.4092 | 0.4001 |
| 0.7332 | 14.71 | 1500 | 0.2151 | 0.2080 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"language": "gl", "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-gl-CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice gl", "type": "common_voice", "args": "gl"}, "metrics": [{"type": "wer", "value": 0.208, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8.0", "type": "mozilla-foundation/common_voice_8_0", "args": "gl"}, "metrics": [{"type": "wer", "value": 22.94, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "gl"}, "metrics": [{"type": "wer", "value": 47.82, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "gl"}, "metrics": [{"type": "wer", "value": 50.8, "name": "Test WER"}]}]}]}
|
emre/wav2vec2-xls-r-300m-gl-CV8
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"gl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-hy-AM-CV8-v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9145
- Wer: 0.9598
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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: 170
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 5.7132 | 83.31 | 500 | 1.9274 | 1.0523 |
| 1.017 | 166.62 | 1000 | 0.9145 | 0.9598 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-hy-AM-CV8-v1", "results": []}]}
|
emre/wav2vec2-xls-r-300m-hy-AM-CV8-v1
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
zero-shot-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased_allnli_tr
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6144
- Accuracy: 0.7662
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8623 | 0.03 | 1000 | 0.9076 | 0.5917 |
| 0.7528 | 0.07 | 2000 | 0.8587 | 0.6119 |
| 0.7074 | 0.1 | 3000 | 0.7867 | 0.6647 |
| 0.6949 | 0.14 | 4000 | 0.7474 | 0.6772 |
| 0.6681 | 0.17 | 5000 | 0.7661 | 0.6814 |
| 0.6597 | 0.2 | 6000 | 0.7264 | 0.6943 |
| 0.6495 | 0.24 | 7000 | 0.7841 | 0.6781 |
| 0.6323 | 0.27 | 8000 | 0.7256 | 0.6952 |
| 0.6308 | 0.31 | 9000 | 0.7319 | 0.6958 |
| 0.6254 | 0.34 | 10000 | 0.7054 | 0.7004 |
| 0.6233 | 0.37 | 11000 | 0.7069 | 0.7085 |
| 0.6165 | 0.41 | 12000 | 0.6880 | 0.7181 |
| 0.6033 | 0.44 | 13000 | 0.6844 | 0.7197 |
| 0.6014 | 0.48 | 14000 | 0.6753 | 0.7129 |
| 0.5947 | 0.51 | 15000 | 0.7000 | 0.7039 |
| 0.5965 | 0.54 | 16000 | 0.6708 | 0.7263 |
| 0.5979 | 0.58 | 17000 | 0.6562 | 0.7285 |
| 0.5787 | 0.61 | 18000 | 0.6554 | 0.7297 |
| 0.58 | 0.65 | 19000 | 0.6544 | 0.7315 |
| 0.574 | 0.68 | 20000 | 0.6549 | 0.7339 |
| 0.5751 | 0.71 | 21000 | 0.6545 | 0.7289 |
| 0.5659 | 0.75 | 22000 | 0.6467 | 0.7371 |
| 0.5732 | 0.78 | 23000 | 0.6448 | 0.7362 |
| 0.5637 | 0.82 | 24000 | 0.6520 | 0.7355 |
| 0.5648 | 0.85 | 25000 | 0.6412 | 0.7345 |
| 0.5622 | 0.88 | 26000 | 0.6350 | 0.7358 |
| 0.5579 | 0.92 | 27000 | 0.6347 | 0.7393 |
| 0.5518 | 0.95 | 28000 | 0.6417 | 0.7392 |
| 0.5547 | 0.99 | 29000 | 0.6321 | 0.7437 |
| 0.524 | 1.02 | 30000 | 0.6430 | 0.7412 |
| 0.4982 | 1.05 | 31000 | 0.6253 | 0.7458 |
| 0.5002 | 1.09 | 32000 | 0.6316 | 0.7418 |
| 0.4993 | 1.12 | 33000 | 0.6197 | 0.7487 |
| 0.4963 | 1.15 | 34000 | 0.6307 | 0.7462 |
| 0.504 | 1.19 | 35000 | 0.6272 | 0.7480 |
| 0.4922 | 1.22 | 36000 | 0.6410 | 0.7433 |
| 0.5016 | 1.26 | 37000 | 0.6295 | 0.7461 |
| 0.4957 | 1.29 | 38000 | 0.6183 | 0.7506 |
| 0.4883 | 1.32 | 39000 | 0.6261 | 0.7502 |
| 0.4985 | 1.36 | 40000 | 0.6315 | 0.7496 |
| 0.4885 | 1.39 | 41000 | 0.6189 | 0.7529 |
| 0.4909 | 1.43 | 42000 | 0.6189 | 0.7473 |
| 0.4894 | 1.46 | 43000 | 0.6314 | 0.7433 |
| 0.4912 | 1.49 | 44000 | 0.6184 | 0.7446 |
| 0.4851 | 1.53 | 45000 | 0.6258 | 0.7461 |
| 0.4879 | 1.56 | 46000 | 0.6286 | 0.7480 |
| 0.4907 | 1.6 | 47000 | 0.6196 | 0.7512 |
| 0.4884 | 1.63 | 48000 | 0.6157 | 0.7526 |
| 0.4755 | 1.66 | 49000 | 0.6056 | 0.7591 |
| 0.4811 | 1.7 | 50000 | 0.5977 | 0.7582 |
| 0.4787 | 1.73 | 51000 | 0.5915 | 0.7621 |
| 0.4779 | 1.77 | 52000 | 0.6014 | 0.7583 |
| 0.4767 | 1.8 | 53000 | 0.6041 | 0.7623 |
| 0.4737 | 1.83 | 54000 | 0.6093 | 0.7563 |
| 0.4836 | 1.87 | 55000 | 0.6001 | 0.7568 |
| 0.4765 | 1.9 | 56000 | 0.6109 | 0.7601 |
| 0.4776 | 1.94 | 57000 | 0.6046 | 0.7599 |
| 0.4769 | 1.97 | 58000 | 0.5970 | 0.7568 |
| 0.4654 | 2.0 | 59000 | 0.6147 | 0.7614 |
| 0.4144 | 2.04 | 60000 | 0.6439 | 0.7566 |
| 0.4101 | 2.07 | 61000 | 0.6373 | 0.7527 |
| 0.4192 | 2.11 | 62000 | 0.6136 | 0.7575 |
| 0.4128 | 2.14 | 63000 | 0.6283 | 0.7560 |
| 0.4204 | 2.17 | 64000 | 0.6187 | 0.7625 |
| 0.4114 | 2.21 | 65000 | 0.6127 | 0.7621 |
| 0.4097 | 2.24 | 66000 | 0.6188 | 0.7626 |
| 0.4129 | 2.28 | 67000 | 0.6156 | 0.7639 |
| 0.4085 | 2.31 | 68000 | 0.6232 | 0.7616 |
| 0.4074 | 2.34 | 69000 | 0.6240 | 0.7605 |
| 0.409 | 2.38 | 70000 | 0.6153 | 0.7591 |
| 0.4046 | 2.41 | 71000 | 0.6375 | 0.7587 |
| 0.4117 | 2.45 | 72000 | 0.6145 | 0.7629 |
| 0.4002 | 2.48 | 73000 | 0.6279 | 0.7610 |
| 0.4042 | 2.51 | 74000 | 0.6176 | 0.7646 |
| 0.4055 | 2.55 | 75000 | 0.6277 | 0.7643 |
| 0.4021 | 2.58 | 76000 | 0.6196 | 0.7642 |
| 0.4081 | 2.62 | 77000 | 0.6127 | 0.7659 |
| 0.408 | 2.65 | 78000 | 0.6237 | 0.7638 |
| 0.3997 | 2.68 | 79000 | 0.6190 | 0.7636 |
| 0.4093 | 2.72 | 80000 | 0.6152 | 0.7648 |
| 0.4095 | 2.75 | 81000 | 0.6155 | 0.7627 |
| 0.4088 | 2.79 | 82000 | 0.6130 | 0.7641 |
| 0.4063 | 2.82 | 83000 | 0.6072 | 0.7646 |
| 0.3978 | 2.85 | 84000 | 0.6128 | 0.7662 |
| 0.4034 | 2.89 | 85000 | 0.6157 | 0.7627 |
| 0.4044 | 2.92 | 86000 | 0.6127 | 0.7661 |
| 0.403 | 2.96 | 87000 | 0.6126 | 0.7664 |
| 0.4033 | 2.99 | 88000 | 0.6144 | 0.7662 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"language": ["tr"], "license": "mit", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/bert-base-multilingual-cased-allnli_tr
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/bert-base-multilingual-cased-multinli_tr
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/bert-base-multilingual-cased-snli_tr
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-turkish-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5771
- Accuracy: 0.7978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8559 | 0.03 | 1000 | 0.7577 | 0.6798 |
| 0.6612 | 0.07 | 2000 | 0.7263 | 0.6958 |
| 0.6115 | 0.1 | 3000 | 0.6431 | 0.7364 |
| 0.5916 | 0.14 | 4000 | 0.6347 | 0.7407 |
| 0.5719 | 0.17 | 5000 | 0.6317 | 0.7483 |
| 0.5575 | 0.2 | 6000 | 0.6034 | 0.7544 |
| 0.5521 | 0.24 | 7000 | 0.6148 | 0.7568 |
| 0.5393 | 0.27 | 8000 | 0.5931 | 0.7610 |
| 0.5382 | 0.31 | 9000 | 0.5866 | 0.7665 |
| 0.5306 | 0.34 | 10000 | 0.5881 | 0.7594 |
| 0.5295 | 0.37 | 11000 | 0.6120 | 0.7632 |
| 0.5225 | 0.41 | 12000 | 0.5620 | 0.7759 |
| 0.5112 | 0.44 | 13000 | 0.5641 | 0.7769 |
| 0.5133 | 0.48 | 14000 | 0.5571 | 0.7798 |
| 0.5023 | 0.51 | 15000 | 0.5719 | 0.7722 |
| 0.5017 | 0.54 | 16000 | 0.5482 | 0.7844 |
| 0.5111 | 0.58 | 17000 | 0.5503 | 0.7800 |
| 0.4929 | 0.61 | 18000 | 0.5502 | 0.7836 |
| 0.4923 | 0.65 | 19000 | 0.5424 | 0.7843 |
| 0.4894 | 0.68 | 20000 | 0.5417 | 0.7851 |
| 0.4877 | 0.71 | 21000 | 0.5514 | 0.7841 |
| 0.4818 | 0.75 | 22000 | 0.5494 | 0.7848 |
| 0.4898 | 0.78 | 23000 | 0.5450 | 0.7859 |
| 0.4823 | 0.82 | 24000 | 0.5417 | 0.7878 |
| 0.4806 | 0.85 | 25000 | 0.5354 | 0.7875 |
| 0.4779 | 0.88 | 26000 | 0.5338 | 0.7848 |
| 0.4744 | 0.92 | 27000 | 0.5277 | 0.7934 |
| 0.4678 | 0.95 | 28000 | 0.5507 | 0.7871 |
| 0.4727 | 0.99 | 29000 | 0.5603 | 0.7789 |
| 0.4243 | 1.02 | 30000 | 0.5626 | 0.7894 |
| 0.3955 | 1.05 | 31000 | 0.5324 | 0.7939 |
| 0.4022 | 1.09 | 32000 | 0.5322 | 0.7925 |
| 0.3976 | 1.12 | 33000 | 0.5450 | 0.7920 |
| 0.3913 | 1.15 | 34000 | 0.5464 | 0.7948 |
| 0.406 | 1.19 | 35000 | 0.5406 | 0.7958 |
| 0.3875 | 1.22 | 36000 | 0.5489 | 0.7878 |
| 0.4024 | 1.26 | 37000 | 0.5427 | 0.7925 |
| 0.3988 | 1.29 | 38000 | 0.5335 | 0.7904 |
| 0.393 | 1.32 | 39000 | 0.5415 | 0.7923 |
| 0.3988 | 1.36 | 40000 | 0.5385 | 0.7962 |
| 0.3912 | 1.39 | 41000 | 0.5383 | 0.7950 |
| 0.3949 | 1.43 | 42000 | 0.5415 | 0.7931 |
| 0.3902 | 1.46 | 43000 | 0.5438 | 0.7893 |
| 0.3948 | 1.49 | 44000 | 0.5348 | 0.7906 |
| 0.3921 | 1.53 | 45000 | 0.5361 | 0.7890 |
| 0.3944 | 1.56 | 46000 | 0.5419 | 0.7953 |
| 0.3959 | 1.6 | 47000 | 0.5402 | 0.7967 |
| 0.3926 | 1.63 | 48000 | 0.5429 | 0.7925 |
| 0.3854 | 1.66 | 49000 | 0.5346 | 0.7959 |
| 0.3864 | 1.7 | 50000 | 0.5241 | 0.7979 |
| 0.385 | 1.73 | 51000 | 0.5149 | 0.8002 |
| 0.3871 | 1.77 | 52000 | 0.5325 | 0.8002 |
| 0.3819 | 1.8 | 53000 | 0.5332 | 0.8022 |
| 0.384 | 1.83 | 54000 | 0.5419 | 0.7873 |
| 0.3899 | 1.87 | 55000 | 0.5225 | 0.7974 |
| 0.3894 | 1.9 | 56000 | 0.5358 | 0.7977 |
| 0.3838 | 1.94 | 57000 | 0.5264 | 0.7988 |
| 0.3881 | 1.97 | 58000 | 0.5280 | 0.7956 |
| 0.3756 | 2.0 | 59000 | 0.5601 | 0.7969 |
| 0.3156 | 2.04 | 60000 | 0.5936 | 0.7925 |
| 0.3125 | 2.07 | 61000 | 0.5898 | 0.7938 |
| 0.3179 | 2.11 | 62000 | 0.5591 | 0.7981 |
| 0.315 | 2.14 | 63000 | 0.5853 | 0.7970 |
| 0.3122 | 2.17 | 64000 | 0.5802 | 0.7979 |
| 0.3105 | 2.21 | 65000 | 0.5758 | 0.7979 |
| 0.3076 | 2.24 | 66000 | 0.5685 | 0.7980 |
| 0.3117 | 2.28 | 67000 | 0.5799 | 0.7944 |
| 0.3108 | 2.31 | 68000 | 0.5742 | 0.7988 |
| 0.3047 | 2.34 | 69000 | 0.5907 | 0.7921 |
| 0.3114 | 2.38 | 70000 | 0.5723 | 0.7937 |
| 0.3035 | 2.41 | 71000 | 0.5944 | 0.7955 |
| 0.3129 | 2.45 | 72000 | 0.5838 | 0.7928 |
| 0.3071 | 2.48 | 73000 | 0.5929 | 0.7949 |
| 0.3061 | 2.51 | 74000 | 0.5794 | 0.7967 |
| 0.3068 | 2.55 | 75000 | 0.5892 | 0.7954 |
| 0.3053 | 2.58 | 76000 | 0.5796 | 0.7962 |
| 0.3117 | 2.62 | 77000 | 0.5763 | 0.7981 |
| 0.3062 | 2.65 | 78000 | 0.5852 | 0.7964 |
| 0.3004 | 2.68 | 79000 | 0.5793 | 0.7966 |
| 0.3146 | 2.72 | 80000 | 0.5693 | 0.7985 |
| 0.3146 | 2.75 | 81000 | 0.5788 | 0.7982 |
| 0.3079 | 2.79 | 82000 | 0.5726 | 0.7978 |
| 0.3058 | 2.82 | 83000 | 0.5677 | 0.7988 |
| 0.3055 | 2.85 | 84000 | 0.5701 | 0.7982 |
| 0.3049 | 2.89 | 85000 | 0.5809 | 0.7970 |
| 0.3044 | 2.92 | 86000 | 0.5741 | 0.7986 |
| 0.3057 | 2.96 | 87000 | 0.5743 | 0.7980 |
| 0.3081 | 2.99 | 88000 | 0.5771 | 0.7978 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"language": ["tr"], "license": "mit", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/bert-base-turkish-cased-allnli_tr
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
sentence-similarity
|
sentence-transformers
|
# emrecan/bert-base-turkish-cased-mean-nli-stsb-tr
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine translated versions of [NLI](https://huggingface.co/datasets/nli_tr) and [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) datasets, using example [training scripts]( https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) from sentence-transformers GitHub repository.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]
model = SentenceTransformer('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
model = AutoModel.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
Evaluation results on test and development sets are given below:
| Split | Epoch | cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman |
|------------|-------|----------------|-----------------|-------------------|--------------------|-------------------|--------------------|-------------|--------------|
| test | - | 0.834 | 0.830 | 0.820 | 0.819 | 0.819 | 0.818 | 0.799 | 0.789 |
| validation | 1 | 0.850 | 0.848 | 0.831 | 0.835 | 0.83 | 0.83 | 0.80 | 0.806 |
| validation | 2 | 0.857 | 0.857 | 0.844 | 0.848 | 0.844 | 0.848 | 0.813 | 0.810 |
| validation | 3 | 0.860 | 0.859 | 0.846 | 0.851 | 0.846 | 0.850 | 0.825 | 0.822 |
| validation | 4 | 0.859 | 0.860 | 0.846 | 0.851 | 0.846 | 0.851 | 0.825 | 0.823 |
## Training
Training scripts [`training_nli_v2.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli_v2.py) and [`training_stsbenchmark_continue_training.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) were used to train the model.
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 200,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["nli_tr", "emrecan/stsb-mt-turkish"], "pipeline_tag": "sentence-similarity", "widget": {"source_sentence": "Bu \u00e7ok mutlu bir ki\u015fi", "sentences": ["Bu mutlu bir k\u00f6pek", "Bu sevincinden havalara u\u00e7an bir insan", "\u00c7ok kar ya\u011f\u0131yor"]}}
|
emrecan/bert-base-turkish-cased-mean-nli-stsb-tr
| null |
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"tr",
"dataset:nli_tr",
"dataset:emrecan/stsb-mt-turkish",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
null | null |
{}
|
emrecan/bert-base-turkish-cased-mean-nli
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/bert-base-turkish-cased-multinli_tr
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/bert-base-turkish-cased-snli_tr
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
<!-- 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. -->
# convbert-base-turkish-mc4-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/convbert-base-turkish-mc4-cased](https://huggingface.co/dbmdz/convbert-base-turkish-mc4-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5541
- Accuracy: 0.8111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7338 | 0.03 | 1000 | 0.6722 | 0.7236 |
| 0.603 | 0.07 | 2000 | 0.6465 | 0.7399 |
| 0.5605 | 0.1 | 3000 | 0.5801 | 0.7728 |
| 0.55 | 0.14 | 4000 | 0.5994 | 0.7626 |
| 0.529 | 0.17 | 5000 | 0.5720 | 0.7697 |
| 0.5196 | 0.2 | 6000 | 0.5692 | 0.7769 |
| 0.5117 | 0.24 | 7000 | 0.5725 | 0.7785 |
| 0.5044 | 0.27 | 8000 | 0.5532 | 0.7787 |
| 0.5016 | 0.31 | 9000 | 0.5546 | 0.7812 |
| 0.5031 | 0.34 | 10000 | 0.5461 | 0.7870 |
| 0.4949 | 0.37 | 11000 | 0.5725 | 0.7826 |
| 0.4894 | 0.41 | 12000 | 0.5419 | 0.7933 |
| 0.4796 | 0.44 | 13000 | 0.5278 | 0.7914 |
| 0.4795 | 0.48 | 14000 | 0.5193 | 0.7953 |
| 0.4713 | 0.51 | 15000 | 0.5534 | 0.7771 |
| 0.4738 | 0.54 | 16000 | 0.5098 | 0.8039 |
| 0.481 | 0.58 | 17000 | 0.5244 | 0.7958 |
| 0.4634 | 0.61 | 18000 | 0.5215 | 0.7972 |
| 0.465 | 0.65 | 19000 | 0.5129 | 0.7985 |
| 0.4624 | 0.68 | 20000 | 0.5062 | 0.8047 |
| 0.4597 | 0.71 | 21000 | 0.5114 | 0.8029 |
| 0.4571 | 0.75 | 22000 | 0.5070 | 0.8073 |
| 0.4602 | 0.78 | 23000 | 0.5115 | 0.7993 |
| 0.4552 | 0.82 | 24000 | 0.5085 | 0.8052 |
| 0.4538 | 0.85 | 25000 | 0.5118 | 0.7974 |
| 0.4517 | 0.88 | 26000 | 0.5036 | 0.8044 |
| 0.4517 | 0.92 | 27000 | 0.4930 | 0.8062 |
| 0.4413 | 0.95 | 28000 | 0.5307 | 0.7964 |
| 0.4483 | 0.99 | 29000 | 0.5195 | 0.7938 |
| 0.4036 | 1.02 | 30000 | 0.5238 | 0.8029 |
| 0.3724 | 1.05 | 31000 | 0.5125 | 0.8082 |
| 0.3777 | 1.09 | 32000 | 0.5099 | 0.8075 |
| 0.3753 | 1.12 | 33000 | 0.5172 | 0.8053 |
| 0.367 | 1.15 | 34000 | 0.5188 | 0.8053 |
| 0.3819 | 1.19 | 35000 | 0.5218 | 0.8046 |
| 0.363 | 1.22 | 36000 | 0.5202 | 0.7993 |
| 0.3794 | 1.26 | 37000 | 0.5240 | 0.8048 |
| 0.3749 | 1.29 | 38000 | 0.5026 | 0.8054 |
| 0.367 | 1.32 | 39000 | 0.5198 | 0.8075 |
| 0.3759 | 1.36 | 40000 | 0.5298 | 0.7993 |
| 0.3701 | 1.39 | 41000 | 0.5072 | 0.8091 |
| 0.3742 | 1.43 | 42000 | 0.5071 | 0.8098 |
| 0.3706 | 1.46 | 43000 | 0.5317 | 0.8037 |
| 0.3716 | 1.49 | 44000 | 0.5034 | 0.8052 |
| 0.3717 | 1.53 | 45000 | 0.5258 | 0.8012 |
| 0.3714 | 1.56 | 46000 | 0.5195 | 0.8050 |
| 0.3781 | 1.6 | 47000 | 0.5004 | 0.8104 |
| 0.3725 | 1.63 | 48000 | 0.5124 | 0.8113 |
| 0.3624 | 1.66 | 49000 | 0.5040 | 0.8094 |
| 0.3657 | 1.7 | 50000 | 0.4979 | 0.8111 |
| 0.3669 | 1.73 | 51000 | 0.4968 | 0.8100 |
| 0.3636 | 1.77 | 52000 | 0.5075 | 0.8079 |
| 0.36 | 1.8 | 53000 | 0.4985 | 0.8110 |
| 0.3624 | 1.83 | 54000 | 0.5125 | 0.8070 |
| 0.366 | 1.87 | 55000 | 0.4918 | 0.8117 |
| 0.3655 | 1.9 | 56000 | 0.5051 | 0.8109 |
| 0.3609 | 1.94 | 57000 | 0.5083 | 0.8105 |
| 0.3672 | 1.97 | 58000 | 0.5129 | 0.8085 |
| 0.3545 | 2.0 | 59000 | 0.5467 | 0.8109 |
| 0.2938 | 2.04 | 60000 | 0.5635 | 0.8049 |
| 0.29 | 2.07 | 61000 | 0.5781 | 0.8041 |
| 0.2992 | 2.11 | 62000 | 0.5470 | 0.8077 |
| 0.2957 | 2.14 | 63000 | 0.5765 | 0.8073 |
| 0.292 | 2.17 | 64000 | 0.5472 | 0.8106 |
| 0.2893 | 2.21 | 65000 | 0.5590 | 0.8085 |
| 0.2883 | 2.24 | 66000 | 0.5535 | 0.8064 |
| 0.2923 | 2.28 | 67000 | 0.5508 | 0.8095 |
| 0.2868 | 2.31 | 68000 | 0.5679 | 0.8098 |
| 0.2892 | 2.34 | 69000 | 0.5660 | 0.8057 |
| 0.292 | 2.38 | 70000 | 0.5494 | 0.8088 |
| 0.286 | 2.41 | 71000 | 0.5653 | 0.8085 |
| 0.2939 | 2.45 | 72000 | 0.5673 | 0.8070 |
| 0.286 | 2.48 | 73000 | 0.5600 | 0.8092 |
| 0.2844 | 2.51 | 74000 | 0.5508 | 0.8095 |
| 0.2913 | 2.55 | 75000 | 0.5645 | 0.8088 |
| 0.2859 | 2.58 | 76000 | 0.5677 | 0.8095 |
| 0.2892 | 2.62 | 77000 | 0.5598 | 0.8113 |
| 0.2898 | 2.65 | 78000 | 0.5618 | 0.8096 |
| 0.2814 | 2.68 | 79000 | 0.5664 | 0.8103 |
| 0.2917 | 2.72 | 80000 | 0.5484 | 0.8122 |
| 0.2907 | 2.75 | 81000 | 0.5522 | 0.8116 |
| 0.2896 | 2.79 | 82000 | 0.5540 | 0.8093 |
| 0.2907 | 2.82 | 83000 | 0.5469 | 0.8104 |
| 0.2882 | 2.85 | 84000 | 0.5471 | 0.8122 |
| 0.2878 | 2.89 | 85000 | 0.5532 | 0.8108 |
| 0.2858 | 2.92 | 86000 | 0.5511 | 0.8115 |
| 0.288 | 2.96 | 87000 | 0.5491 | 0.8111 |
| 0.2834 | 2.99 | 88000 | 0.5541 | 0.8111 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/convbert-base-turkish-mc4-cased-allnli_tr
| null |
[
"transformers",
"pytorch",
"convbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/convbert-base-turkish-mc4-cased-multinli_tr
| null |
[
"transformers",
"pytorch",
"convbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/convbert-base-turkish-mc4-cased-snli_tr
| null |
[
"transformers",
"pytorch",
"convbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-turkish-cased_allnli_tr
This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6481
- Accuracy: 0.7381
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.94 | 0.03 | 1000 | 0.9074 | 0.5813 |
| 0.8102 | 0.07 | 2000 | 0.8802 | 0.5949 |
| 0.7737 | 0.1 | 3000 | 0.8491 | 0.6155 |
| 0.7576 | 0.14 | 4000 | 0.8283 | 0.6261 |
| 0.7286 | 0.17 | 5000 | 0.8150 | 0.6362 |
| 0.7162 | 0.2 | 6000 | 0.7998 | 0.6400 |
| 0.7092 | 0.24 | 7000 | 0.7830 | 0.6565 |
| 0.6962 | 0.27 | 8000 | 0.7653 | 0.6629 |
| 0.6876 | 0.31 | 9000 | 0.7630 | 0.6687 |
| 0.6778 | 0.34 | 10000 | 0.7475 | 0.6739 |
| 0.6737 | 0.37 | 11000 | 0.7495 | 0.6781 |
| 0.6712 | 0.41 | 12000 | 0.7350 | 0.6826 |
| 0.6559 | 0.44 | 13000 | 0.7274 | 0.6897 |
| 0.6493 | 0.48 | 14000 | 0.7248 | 0.6902 |
| 0.6483 | 0.51 | 15000 | 0.7263 | 0.6858 |
| 0.6445 | 0.54 | 16000 | 0.7070 | 0.6978 |
| 0.6467 | 0.58 | 17000 | 0.7083 | 0.6981 |
| 0.6332 | 0.61 | 18000 | 0.6996 | 0.7004 |
| 0.6288 | 0.65 | 19000 | 0.6979 | 0.6978 |
| 0.6308 | 0.68 | 20000 | 0.6912 | 0.7040 |
| 0.622 | 0.71 | 21000 | 0.6904 | 0.7092 |
| 0.615 | 0.75 | 22000 | 0.6872 | 0.7094 |
| 0.6186 | 0.78 | 23000 | 0.6877 | 0.7075 |
| 0.6183 | 0.82 | 24000 | 0.6818 | 0.7111 |
| 0.6115 | 0.85 | 25000 | 0.6856 | 0.7122 |
| 0.608 | 0.88 | 26000 | 0.6697 | 0.7179 |
| 0.6071 | 0.92 | 27000 | 0.6727 | 0.7181 |
| 0.601 | 0.95 | 28000 | 0.6798 | 0.7118 |
| 0.6018 | 0.99 | 29000 | 0.6854 | 0.7071 |
| 0.5762 | 1.02 | 30000 | 0.6697 | 0.7214 |
| 0.5507 | 1.05 | 31000 | 0.6710 | 0.7185 |
| 0.5575 | 1.09 | 32000 | 0.6709 | 0.7226 |
| 0.5493 | 1.12 | 33000 | 0.6659 | 0.7191 |
| 0.5464 | 1.15 | 34000 | 0.6709 | 0.7232 |
| 0.5595 | 1.19 | 35000 | 0.6642 | 0.7220 |
| 0.5446 | 1.22 | 36000 | 0.6709 | 0.7202 |
| 0.5524 | 1.26 | 37000 | 0.6751 | 0.7148 |
| 0.5473 | 1.29 | 38000 | 0.6642 | 0.7209 |
| 0.5477 | 1.32 | 39000 | 0.6662 | 0.7223 |
| 0.5522 | 1.36 | 40000 | 0.6586 | 0.7227 |
| 0.5406 | 1.39 | 41000 | 0.6602 | 0.7258 |
| 0.54 | 1.43 | 42000 | 0.6564 | 0.7273 |
| 0.5458 | 1.46 | 43000 | 0.6780 | 0.7213 |
| 0.5448 | 1.49 | 44000 | 0.6561 | 0.7235 |
| 0.5418 | 1.53 | 45000 | 0.6600 | 0.7253 |
| 0.5408 | 1.56 | 46000 | 0.6616 | 0.7274 |
| 0.5451 | 1.6 | 47000 | 0.6557 | 0.7283 |
| 0.5385 | 1.63 | 48000 | 0.6583 | 0.7295 |
| 0.5261 | 1.66 | 49000 | 0.6468 | 0.7325 |
| 0.5364 | 1.7 | 50000 | 0.6447 | 0.7329 |
| 0.5294 | 1.73 | 51000 | 0.6429 | 0.7320 |
| 0.5332 | 1.77 | 52000 | 0.6508 | 0.7272 |
| 0.5274 | 1.8 | 53000 | 0.6492 | 0.7326 |
| 0.5286 | 1.83 | 54000 | 0.6470 | 0.7318 |
| 0.5359 | 1.87 | 55000 | 0.6393 | 0.7354 |
| 0.5366 | 1.9 | 56000 | 0.6445 | 0.7367 |
| 0.5296 | 1.94 | 57000 | 0.6413 | 0.7313 |
| 0.5346 | 1.97 | 58000 | 0.6393 | 0.7315 |
| 0.5264 | 2.0 | 59000 | 0.6448 | 0.7357 |
| 0.4857 | 2.04 | 60000 | 0.6640 | 0.7335 |
| 0.4888 | 2.07 | 61000 | 0.6612 | 0.7318 |
| 0.4964 | 2.11 | 62000 | 0.6516 | 0.7337 |
| 0.493 | 2.14 | 63000 | 0.6503 | 0.7356 |
| 0.4961 | 2.17 | 64000 | 0.6519 | 0.7348 |
| 0.4847 | 2.21 | 65000 | 0.6517 | 0.7327 |
| 0.483 | 2.24 | 66000 | 0.6555 | 0.7310 |
| 0.4857 | 2.28 | 67000 | 0.6525 | 0.7312 |
| 0.484 | 2.31 | 68000 | 0.6444 | 0.7342 |
| 0.4792 | 2.34 | 69000 | 0.6508 | 0.7330 |
| 0.488 | 2.38 | 70000 | 0.6513 | 0.7344 |
| 0.472 | 2.41 | 71000 | 0.6547 | 0.7346 |
| 0.4872 | 2.45 | 72000 | 0.6500 | 0.7342 |
| 0.4782 | 2.48 | 73000 | 0.6585 | 0.7358 |
| 0.481 | 2.51 | 74000 | 0.6477 | 0.7356 |
| 0.4822 | 2.55 | 75000 | 0.6587 | 0.7346 |
| 0.4728 | 2.58 | 76000 | 0.6572 | 0.7340 |
| 0.4841 | 2.62 | 77000 | 0.6443 | 0.7374 |
| 0.4885 | 2.65 | 78000 | 0.6494 | 0.7362 |
| 0.4752 | 2.68 | 79000 | 0.6509 | 0.7382 |
| 0.4883 | 2.72 | 80000 | 0.6457 | 0.7371 |
| 0.4888 | 2.75 | 81000 | 0.6497 | 0.7364 |
| 0.4844 | 2.79 | 82000 | 0.6481 | 0.7376 |
| 0.4833 | 2.82 | 83000 | 0.6451 | 0.7389 |
| 0.48 | 2.85 | 84000 | 0.6423 | 0.7373 |
| 0.4832 | 2.89 | 85000 | 0.6477 | 0.7357 |
| 0.4805 | 2.92 | 86000 | 0.6464 | 0.7379 |
| 0.4775 | 2.96 | 87000 | 0.6477 | 0.7380 |
| 0.4843 | 2.99 | 88000 | 0.6481 | 0.7381 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/distilbert-base-turkish-cased-allnli_tr
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/distilbert-base-turkish-cased-multinli_tr
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
zero-shot-classification
|
transformers
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["nli_tr"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Dolar y\u00fckselmeye devam ediyor.", "candidate_labels": "ekonomi, siyaset, spor"}, {"text": "Senaryo \u00e7ok sa\u00e7mayd\u0131, be\u011fendim diyemem.", "candidate_labels": "olumlu, olumsuz"}]}
|
emrecan/distilbert-base-turkish-cased-snli_tr
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"zero-shot-classification",
"nli",
"tr",
"dataset:nli_tr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
en/bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1453
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2065 | 1.0 | 5577 | 1.1289 |
| 0.9226 | 2.0 | 11154 | 1.1019 |
| 0.7411 | 3.0 | 16731 | 1.1453 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
en/distilbert-base-uncased-finetuned-squad
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
question-answering
|
transformers
|
{}
|
okanvk/bert-question-answering-cased-squadv2_tr
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
question-answering
|
transformers
|
{}
|
okanvk/bert-question-answering-uncased-squadv2_tr
| null |
[
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
question-answering
|
transformers
|
{}
|
okanvk/electra-base-discriminator-finetuned_squadv1_tr
| null |
[
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
question-answering
|
transformers
|
{}
|
okanvk/electra-base-discriminator-finetuned_squadv2_tr
| null |
[
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
question-answering
|
transformers
|
{}
|
okanvk/electra-tr-enelpi-squad-qa
| null |
[
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null | null |
{}
|
okanvk/example
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
okanvk/med-electra-small-30k-discriminator
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
okanvk/med-electra-small-64k-discriminator
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
null |
transformers
|
{}
|
okanvk/med-electra-small-discriminator
| null |
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
|
feature-extraction
|
transformers
|
# Model description
The model was created for selective question answering in Polish. I.e. it is used to find passages containing the answers to the given question.
It is used to encode the contexts (aka passages) in the DPR bi-encoder architecture. The architecture requires two separate models.
The question part has to be encoded with the corresponding [question encoder](https://huggingface.co/enelpol/czywiesz-question).
The model was created by fine-tuning [Herbert base cased](https://huggingface.co/allegro/herbert-base-cased) on "Czywiesz" dataset.
[Czywiesz](https://clarin-pl.eu/dspace/handle/11321/39) dataset contains questions and Wikipedia articles extracted from the Polish Wikipedia.
# Usage
It is the easiest to use the model with the [Haystack framework](https://haystack.deepset.ai/overview/intro).
```python
from haystack.document_stores import FAISSDocumentStore
from haystack.retriever import DensePassageRetriever
document_store = FAISSDocumentStore(faiss_index_factory_str="Flat")
retriever = DensePassageRetriever(
document_store=document_store,
query_embedding_model="enelpol/czywiesz-question",
passage_embedding_model="enelpol/czywiesz-context"
)
for document in documents:
document_store.write_documents([document])
document_store.update_embeddings(retriever)
document_store.save("contexts.faiss")
```
|
{"language": "pl", "datasets": ["enelpol/czywiesz"]}
|
enelpol/czywiesz-context
| null |
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"pl",
"dataset:enelpol/czywiesz",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
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