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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.
## 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
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
anasaqsme/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
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
[
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.17.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.4\n- Tokenizers 0.11.6"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.17.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.4\n- Tokenizers 0.11.6"
] |
question-answering
|
transformers
|
# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)
## Overview
- Language model: xlm-roberta-large
- Fine-tune: [deepset/xlm-roberta-large-squad2](https://huggingface.co/deepset/xlm-roberta-large-squad2)
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: [mailong25/bert-vietnamese-question-answering](https://github.com/mailong25/bert-vietnamese-question-answering/tree/master/dataset)
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab)
## Performance
Evaluated on dev-v2.0.json
```
exact: 136 / 141
f1: 0.9692671394799054
```
Evaluated on Vietnamese XQuAD: [xquad.vi.json](https://github.com/deepmind/xquad/blob/master/xquad.vi.json)
```
exact: 604 / 1190
f1: 0.7224454217571596
```
## Author
An Pham (ancs21.ps [at] gmail.com)
## License
MIT
|
{"language": "vi", "license": "mit", "tags": ["vi", "xlm-roberta"], "metrics": ["f1", "em"], "widget": [{"text": "To\u00e0 nh\u00e0 n\u00e0o cao nh\u1ea5t Vi\u1ec7t Nam?", "context": "Landmark 81 l\u00e0 m\u1ed9t to\u00e0 nh\u00e0 ch\u1ecdc tr\u1eddi trong t\u1ed5 h\u1ee3p d\u1ef1 \u00e1n Vinhomes T\u00e2n C\u1ea3ng, m\u1ed9t d\u1ef1 \u00e1n c\u00f3 t\u1ed5ng m\u1ee9c \u0111\u1ea7u t\u01b0 40.000 t\u1ef7 \u0111\u1ed3ng, do C\u00f4ng ty C\u1ed5 ph\u1ea7n \u0110\u1ea7u t\u01b0 x\u00e2y d\u1ef1ng T\u00e2n Li\u00ean Ph\u00e1t thu\u1ed9c Vingroup l\u00e0m ch\u1ee7 \u0111\u1ea7u t\u01b0. To\u00e0 th\u00e1p cao 81 t\u1ea7ng, hi\u1ec7n t\u1ea1i l\u00e0 to\u00e0 nh\u00e0 cao nh\u1ea5t Vi\u1ec7t Nam v\u00e0 l\u00e0 to\u00e0 nh\u00e0 cao nh\u1ea5t \u0110\u00f4ng Nam \u00c1 t\u1eeb th\u00e1ng 3 n\u0103m 2018."}]}
|
ancs21/xlm-roberta-large-vi-qa
| null |
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"vi",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"vi"
] |
TAGS
#transformers #pytorch #xlm-roberta #question-answering #vi #license-mit #endpoints_compatible #region-us
|
# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)
## Overview
- Language model: xlm-roberta-large
- Fine-tune: deepset/xlm-roberta-large-squad2
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: mailong25/bert-vietnamese-question-answering
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab)
## Performance
Evaluated on dev-v2.0.json
Evaluated on Vietnamese XQuAD: URL
## Author
An Pham (URL [at] URL)
## License
MIT
|
[
"# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)",
"## Overview\n\n- Language model: xlm-roberta-large\n- Fine-tune: deepset/xlm-roberta-large-squad2\n- Language: Vietnamese\n- Downstream-task: Extractive QA\n- Dataset: mailong25/bert-vietnamese-question-answering\n- Training data: train-v2.0.json (SQuAD 2.0 format)\n- Eval data: dev-v2.0.json (SQuAD 2.0 format)\n- Infrastructure: 1x Tesla P100 (Google Colab)",
"## Performance\n\nEvaluated on dev-v2.0.json\n\n\nEvaluated on Vietnamese XQuAD: URL",
"## Author\n\nAn Pham (URL [at] URL)",
"## License\n\nMIT"
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #vi #license-mit #endpoints_compatible #region-us \n",
"# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)",
"## Overview\n\n- Language model: xlm-roberta-large\n- Fine-tune: deepset/xlm-roberta-large-squad2\n- Language: Vietnamese\n- Downstream-task: Extractive QA\n- Dataset: mailong25/bert-vietnamese-question-answering\n- Training data: train-v2.0.json (SQuAD 2.0 format)\n- Eval data: dev-v2.0.json (SQuAD 2.0 format)\n- Infrastructure: 1x Tesla P100 (Google Colab)",
"## Performance\n\nEvaluated on dev-v2.0.json\n\n\nEvaluated on Vietnamese XQuAD: URL",
"## Author\n\nAn Pham (URL [at] URL)",
"## License\n\nMIT"
] |
token-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-cased-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0620
- Precision: 0.9406
- Recall: 0.9463
- F1: 0.9434
- Accuracy: 0.9861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5855 | 1.0 | 878 | 0.0848 | 0.8965 | 0.8980 | 0.8973 | 0.9760 |
| 0.058 | 2.0 | 1756 | 0.0607 | 0.9357 | 0.9379 | 0.9368 | 0.9840 |
| 0.0282 | 3.0 | 2634 | 0.0604 | 0.9354 | 0.9420 | 0.9387 | 0.9852 |
| 0.0148 | 4.0 | 3512 | 0.0606 | 0.9386 | 0.9485 | 0.9435 | 0.9861 |
| 0.0101 | 5.0 | 4390 | 0.0620 | 0.9406 | 0.9463 | 0.9434 | 0.9861 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-base-cased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9860628716077}}]}]}
|
andi611/bert-base-cased-ner-conll2003
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-cased-ner
===================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0620
* Precision: 0.9406
* Recall: 0.9463
* F1: 0.9434
* Accuracy: 0.9861
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
token-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-uncased-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1258
- Precision: 0.0269
- Recall: 0.1379
- F1: 0.0451
- Accuracy: 0.1988
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 4 | 2.1296 | 0.0270 | 0.1389 | 0.0452 | 0.1942 |
| No log | 2.0 | 8 | 2.1258 | 0.0269 | 0.1379 | 0.0451 | 0.1988 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-base-uncased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.19881805328292054}}]}]}
|
andi611/bert-base-uncased-ner-conll2003
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-ner
=====================
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1258
* Precision: 0.0269
* Recall: 0.1379
* F1: 0.0451
* Accuracy: 0.1988
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
token-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-large-uncased-ner
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0591
- Precision: 0.9465
- Recall: 0.9568
- F1: 0.9517
- Accuracy: 0.9877
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1702 | 1.0 | 878 | 0.0578 | 0.9202 | 0.9347 | 0.9274 | 0.9836 |
| 0.0392 | 2.0 | 1756 | 0.0601 | 0.9306 | 0.9448 | 0.9377 | 0.9851 |
| 0.0157 | 3.0 | 2634 | 0.0517 | 0.9405 | 0.9544 | 0.9474 | 0.9875 |
| 0.0057 | 4.0 | 3512 | 0.0591 | 0.9465 | 0.9568 | 0.9517 | 0.9877 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-large-uncased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9877039414110284}}]}]}
|
andi611/bert-large-uncased-ner-conll2003
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-large-uncased-ner
======================
This model is a fine-tuned version of bert-large-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0591
* Precision: 0.9465
* Recall: 0.9568
* F1: 0.9517
* Accuracy: 0.9877
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
token-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-large-uncased-whole-word-masking-ner-conll2003
This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0592
- Precision: 0.9527
- Recall: 0.9569
- F1: 0.9548
- Accuracy: 0.9887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4071 | 1.0 | 877 | 0.0584 | 0.9306 | 0.9418 | 0.9362 | 0.9851 |
| 0.0482 | 2.0 | 1754 | 0.0594 | 0.9362 | 0.9491 | 0.9426 | 0.9863 |
| 0.0217 | 3.0 | 2631 | 0.0550 | 0.9479 | 0.9584 | 0.9531 | 0.9885 |
| 0.0103 | 4.0 | 3508 | 0.0592 | 0.9527 | 0.9569 | 0.9548 | 0.9887 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-ner-conll2003", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9886888970085945}}]}]}
|
andi611/bert-large-uncased-whole-word-masking-ner-conll2003
| null |
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-large-uncased-whole-word-masking-ner-conll2003
===================================================
This model is a fine-tuned version of bert-large-uncased-whole-word-masking on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0592
* Precision: 0.9527
* Recall: 0.9569
* F1: 0.9548
* Accuracy: 0.9887
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_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: 4
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
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. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat
This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "conll2003"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "args": "conll2003"}}, {"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003"}}]}]}
|
andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:conll2003",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat
This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "conll2003"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "args": "conll2003"}}, {"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003"}}]}]}
|
andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:conll2003",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "conll2003"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v2", "type": "squad_v2", "args": "conll2003"}}, {"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003"}}]}]}
|
andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:conll2003",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-conll2003 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the conll2003 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat
This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the mit_movie datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "mit_movie"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v2", "type": "squad_v2"}}, {"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "mit_movie", "type": "mit_movie"}}]}]}
|
andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:mit_movie",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_movie #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_movie datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_movie datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_movie #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_movie datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the mit_restaurant datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "mit_restaurant"], "model_index": [{"name": "bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v2", "type": "squad_v2"}}, {"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "mit_restaurant", "type": "mit_restaurant"}}]}]}
|
andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:mit_restaurant",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_restaurant datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_restaurant datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version of deepset/bert-large-uncased-whole-word-masking-squad2 on the squad_v2 and the mit_restaurant datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-agnews
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1652
- Accuracy: 0.9474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1916 | 1.0 | 3375 | 0.1741 | 0.9412 |
| 0.123 | 2.0 | 6750 | 0.1631 | 0.9483 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ag_news"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-agnews", "results": [{"dataset": {"name": "ag_news", "type": "ag_news", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9473684210526315}}]}]}
|
andi611/distilbert-base-uncased-ner-agnews
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:ag_news",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-ag_news #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-agnews
==============================
This model is a fine-tuned version of distilbert-base-uncased on the ag\_news dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1652
* Accuracy: 0.9474
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 2.0
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 2.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-ag_news #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 2.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
token-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-uncased-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0664
- Precision: 0.9332
- Recall: 0.9423
- F1: 0.9377
- Accuracy: 0.9852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2042 | 1.0 | 878 | 0.0636 | 0.9230 | 0.9253 | 0.9241 | 0.9822 |
| 0.0428 | 2.0 | 1756 | 0.0577 | 0.9286 | 0.9370 | 0.9328 | 0.9841 |
| 0.0199 | 3.0 | 2634 | 0.0606 | 0.9364 | 0.9401 | 0.9383 | 0.9851 |
| 0.0121 | 4.0 | 3512 | 0.0641 | 0.9339 | 0.9380 | 0.9360 | 0.9847 |
| 0.0079 | 5.0 | 4390 | 0.0664 | 0.9332 | 0.9423 | 0.9377 | 0.9852 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.985193893275295}}]}]}
|
andi611/distilbert-base-uncased-ner-conll2003
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-ner
===========================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0664
* Precision: 0.9332
* Recall: 0.9423
* F1: 0.9377
* Accuracy: 0.9852
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
token-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-uncased-ner-mit-restaurant
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3097
- Precision: 0.7874
- Recall: 0.8104
- F1: 0.7988
- Accuracy: 0.9119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 431 | 0.4575 | 0.6220 | 0.6856 | 0.6523 | 0.8650 |
| 1.1705 | 2.0 | 862 | 0.3183 | 0.7747 | 0.7953 | 0.7848 | 0.9071 |
| 0.3254 | 3.0 | 1293 | 0.3163 | 0.7668 | 0.8021 | 0.7841 | 0.9058 |
| 0.2287 | 4.0 | 1724 | 0.3097 | 0.7874 | 0.8104 | 0.7988 | 0.9119 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mit_restaurant"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-ner-mit-restaurant", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "mit_restaurant", "type": "mit_restaurant"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9118988661540467}}]}]}
|
andi611/distilbert-base-uncased-ner-mit-restaurant
| null |
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:mit_restaurant",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #en #dataset-mit_restaurant #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-ner-mit-restaurant
==========================================
This model is a fine-tuned version of distilbert-base-uncased on the mit\_restaurant dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3097
* Precision: 0.7874
* Recall: 0.8104
* F1: 0.7988
* Accuracy: 0.9119
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #en #dataset-mit_restaurant #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-boolq
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2071
- Accuracy: 0.7315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6506 | 1.0 | 531 | 0.6075 | 0.6681 |
| 0.575 | 2.0 | 1062 | 0.5816 | 0.6978 |
| 0.4397 | 3.0 | 1593 | 0.6137 | 0.7253 |
| 0.2524 | 4.0 | 2124 | 0.8124 | 0.7466 |
| 0.126 | 5.0 | 2655 | 1.1437 | 0.7370 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["boolq"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-boolq", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "boolq", "type": "boolq", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.7314984709480122}}]}]}
|
andi611/distilbert-base-uncased-qa-boolq
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:boolq",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-boolq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-boolq
=============================
This model is a fine-tuned version of distilbert-base-uncased on the boolq dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2071
* Accuracy: 0.7315
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.8.2
* Pytorch 1.8.1+cu111
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #en #dataset-boolq #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.8.0\n* Tokenizers 0.10.3"
] |
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-qa-with-ner
This model is a fine-tuned version of [andi611/distilbert-base-uncased-qa](https://huggingface.co/andi611/distilbert-base-uncased-qa) on the conll2003 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-qa-with-ner", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
|
andi611/distilbert-base-uncased-qa-with-ner
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-qa-with-ner
This model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-qa-with-ner\n\nThis model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-qa-with-ner\n\nThis model is a fine-tuned version of andi611/distilbert-base-uncased-qa on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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-qa
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: 1.1925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "distilbert-base-uncased-qa", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]}
|
andi611/distilbert-base-uncased-squad
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-qa
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-qa\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1925",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-qa\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1925",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the squad_v2 and the mit_restaurant datasets.
## 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: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["squad_v2", "mit_restaurant"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "squad_v2", "type": "squad_v2"}}, {"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "mit_restaurant", "type": "mit_restaurant"}}]}]}
|
andi611/distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"en",
"dataset:squad_v2",
"dataset:mit_restaurant",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the squad_v2 and the mit_restaurant datasets.
## 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: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the squad_v2 and the mit_restaurant datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #en #dataset-squad_v2 #dataset-mit_restaurant #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the squad_v2 and the mit_restaurant datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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-squad2-with-ner-with-neg-with-multi-with-repeat
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
|
andi611/distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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-squad2-with-ner-with-neg-with-multi
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg-with-multi", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
|
andi611/distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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-squad2-with-ner-with-neg-with-repeat
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
|
andi611/distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg-with-repeat\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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-squad2-with-ner-with-neg
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner-with-neg", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
|
andi611/distilbert-base-uncased-squad2-with-ner-with-neg
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner-with-neg
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-squad2-with-ner-with-neg\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner-with-neg\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
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-squad2-with-ner
This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-squad2-with-ner", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
|
andi611/distilbert-base-uncased-squad2-with-ner
| null |
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:conll2003",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad2-with-ner
This model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-squad2-with-ner\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-conll2003 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad2-with-ner\n\nThis model is a fine-tuned version of twmkn9/distilbert-base-uncased-squad2 on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
token-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. -->
# roberta-base-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0814
- eval_precision: 0.9101
- eval_recall: 0.9336
- eval_f1: 0.9217
- eval_accuracy: 0.9799
- eval_runtime: 10.2964
- eval_samples_per_second: 315.646
- eval_steps_per_second: 39.529
- epoch: 1.14
- step: 500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "roberta-base-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]}
|
andi611/roberta-base-ner-conll2003
| null |
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-ner
This model is a fine-tuned version of roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0814
- eval_precision: 0.9101
- eval_recall: 0.9336
- eval_f1: 0.9217
- eval_accuracy: 0.9799
- eval_runtime: 10.2964
- eval_samples_per_second: 315.646
- eval_steps_per_second: 39.529
- epoch: 1.14
- step: 500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
[
"# roberta-base-ner\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0814\n- eval_precision: 0.9101\n- eval_recall: 0.9336\n- eval_f1: 0.9217\n- eval_accuracy: 0.9799\n- eval_runtime: 10.2964\n- eval_samples_per_second: 315.646\n- eval_steps_per_second: 39.529\n- epoch: 1.14\n- step: 500",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-ner\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0814\n- eval_precision: 0.9101\n- eval_recall: 0.9336\n- eval_f1: 0.9217\n- eval_accuracy: 0.9799\n- eval_runtime: 10.2964\n- eval_samples_per_second: 315.646\n- eval_steps_per_second: 39.529\n- epoch: 1.14\n- step: 500",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4.0",
"### Framework versions\n\n- Transformers 4.8.2\n- Pytorch 1.8.1+cu111\n- Datasets 1.8.0\n- Tokenizers 0.10.3"
] |
text-generation
|
transformers
|
# My Awesome Model
|
{"tags": ["conversational"]}
|
andikarachman/DialoGPT-small-sheldon
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
|
[
"# My Awesome Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8885
- Mae: 0.4390
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1089 | 1.0 | 235 | 0.9027 | 0.4756 |
| 0.9674 | 2.0 | 470 | 0.8885 | 0.4390 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]}
|
anditya/xlm-roberta-base-finetuned-marc-en
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8885
* Mae: 0.4390
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0423
- Train Accuracy: 0.9869
- Validation Loss: 0.0303
- Validation Accuracy: 0.9913
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 43750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.0423 | 0.9869 | 0.0303 | 0.9913 | 0 |
### Framework versions
- Transformers 4.15.0.dev0
- TensorFlow 2.6.2
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola", "results": []}]}
|
andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola
| null |
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola
===========================================================
This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0423
* Train Accuracy: 0.9869
* Validation Loss: 0.0303
* Validation Accuracy: 0.9913
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 43750, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.15.0.dev0
* TensorFlow 2.6.2
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 43750, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0.dev0\n* TensorFlow 2.6.2\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 43750, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0.dev0\n* TensorFlow 2.6.2\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andreiliphdpr/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0015
- Train Accuracy: 0.9995
- Validation Loss: 0.0570
- Validation Accuracy: 0.9915
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 43750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.0399 | 0.9870 | 0.0281 | 0.9908 | 0 |
| 0.0182 | 0.9944 | 0.0326 | 0.9901 | 1 |
| 0.0089 | 0.9971 | 0.0396 | 0.9912 | 2 |
| 0.0040 | 0.9987 | 0.0486 | 0.9918 | 3 |
| 0.0015 | 0.9995 | 0.0570 | 0.9915 | 4 |
### Framework versions
- Transformers 4.15.0.dev0
- TensorFlow 2.6.2
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "andreiliphdpr/distilbert-base-uncased-finetuned-cola", "results": []}]}
|
andreiliphdpr/distilbert-base-uncased-finetuned-cola
| null |
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
andreiliphdpr/distilbert-base-uncased-finetuned-cola
====================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0015
* Train Accuracy: 0.9995
* Validation Loss: 0.0570
* Validation Accuracy: 0.9915
* Epoch: 4
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 43750, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.15.0.dev0
* TensorFlow 2.6.2
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 43750, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0.dev0\n* TensorFlow 2.6.2\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 43750, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0.dev0\n* TensorFlow 2.6.2\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
feature-extraction
|
transformers
|
# SimCLS
SimCLS is a framework for abstractive summarization presented in [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://arxiv.org/abs/2106.01890).
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *generator*) is used to generate candidate summaries, whereas, in the second stage, the *scorer* assigns a score to each candidate given the source document. The final summary is the highest-scoring candidate.
This model is the *scorer* trained for summarization of BillSum ([paper](https://arxiv.org/abs/1910.00523), [datasets](https://huggingface.co/datasets/billsum)). It should be used in conjunction with [google/pegasus-billsum](https://huggingface.co/google/pegasus-billsum). See [our Github repository](https://github.com/andrejmiscic/simcls-pytorch) for details on training, evaluation, and usage.
## Usage
```bash
git clone https://github.com/andrejmiscic/simcls-pytorch.git
cd simcls-pytorch
pip3 install torch torchvision torchaudio transformers sentencepiece
```
```python
from src.model import SimCLS, GeneratorType
summarizer = SimCLS(generator_type=GeneratorType.Pegasus,
generator_path="google/pegasus-billsum",
scorer_path="andrejmiscic/simcls-scorer-billsum")
document = "This is a legal document."
summary = summarizer(document)
print(summary)
```
### Results
All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See [SimCLS paper](https://arxiv.org/abs/2106.01890) for a description of baselines.
We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric. Namely, we use a summary level Rouge-L score.
| System | Rouge-1 | Rouge-2 | Rouge-L\* |
|-----------------|----------------------:|----------------------:|----------------------:|
| Pegasus | 57.31 | 40.19 | 45.82 |
| **Our results** | --- | --- | --- |
| Origin | 56.24, [55.74, 56.74] | 37.46, [36.89, 38.03] | 50.71, [50.19, 51.22] |
| Min | 44.37, [43.85, 44.89] | 25.75, [25.30, 26.22] | 38.68, [38.18, 39.16] |
| Max | 62.88, [62.42, 63.33] | 43.96, [43.39, 44.54] | 57.50, [57.01, 58.00] |
| Random | 54.93, [54.43, 55.43] | 35.42, [34.85, 35.97] | 49.19, [48.68, 49.70] |
| **SimCLS** | 57.49, [57.01, 58.00] | 38.54, [37.98, 39.10] | 51.91, [51.39, 52.43] |
### Citation of the original work
```bibtex
@inproceedings{liu-liu-2021-simcls,
title = "{S}im{CLS}: A Simple Framework for Contrastive Learning of Abstractive Summarization",
author = "Liu, Yixin and
Liu, Pengfei",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.135",
doi = "10.18653/v1/2021.acl-short.135",
pages = "1065--1072",
}
```
|
{"language": ["en"], "tags": ["simcls"], "datasets": ["billsum"]}
|
andrejmiscic/simcls-scorer-billsum
| null |
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"simcls",
"en",
"dataset:billsum",
"arxiv:2106.01890",
"arxiv:1910.00523",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2106.01890",
"1910.00523"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-billsum #arxiv-2106.01890 #arxiv-1910.00523 #endpoints_compatible #region-us
|
SimCLS
======
SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization.
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *generator*) is used to generate candidate summaries, whereas, in the second stage, the *scorer* assigns a score to each candidate given the source document. The final summary is the highest-scoring candidate.
This model is the *scorer* trained for summarization of BillSum (paper, datasets). It should be used in conjunction with google/pegasus-billsum. See our Github repository for details on training, evaluation, and usage.
Usage
-----
### Results
All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.
We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric. Namely, we use a summary level Rouge-L score.
of the original work
|
[
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\nWe believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric. Namely, we use a summary level Rouge-L score.\n\n\n\nof the original work"
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-billsum #arxiv-2106.01890 #arxiv-1910.00523 #endpoints_compatible #region-us \n",
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\nWe believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric. Namely, we use a summary level Rouge-L score.\n\n\n\nof the original work"
] |
feature-extraction
|
transformers
|
# SimCLS
SimCLS is a framework for abstractive summarization presented in [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://arxiv.org/abs/2106.01890).
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *generator*) is used to generate candidate summaries, whereas, in the second stage, the *scorer* assigns a score to each candidate given the source document. The final summary is the highest-scoring candidate.
This model is the *scorer* trained for summarization of CNN/DailyMail ([paper](https://arxiv.org/abs/1602.06023), [datasets](https://huggingface.co/datasets/cnn_dailymail)). It should be used in conjunction with [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn). See [our Github repository](https://github.com/andrejmiscic/simcls-pytorch) for details on training, evaluation, and usage.
## Usage
```bash
git clone https://github.com/andrejmiscic/simcls-pytorch.git
cd simcls-pytorch
pip3 install torch torchvision torchaudio transformers sentencepiece
```
```python
from src.model import SimCLS, GeneratorType
summarizer = SimCLS(generator_type=GeneratorType.Bart,
generator_path="facebook/bart-large-cnn",
scorer_path="andrejmiscic/simcls-scorer-cnndm")
article = "This is a news article."
summary = summarizer(article)
print(summary)
```
### Results
All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See [SimCLS paper](https://arxiv.org/abs/2106.01890) for a description of baselines.
| System | Rouge-1 | Rouge-2 | Rouge-L |
|------------------|----------------------:|----------------------:|----------------------:|
| BART | 44.16 | 21.28 | 40.90 |
| **SimCLS paper** | --- | --- | --- |
| Origin | 44.39 | 21.21 | 41.28 |
| Min | 33.17 | 11.67 | 30.77 |
| Max | 54.36 | 28.73 | 50.77 |
| Random | 43.98 | 20.06 | 40.94 |
| **SimCLS** | 46.67 | 22.15 | 43.54 |
| **Our results** | --- | --- | --- |
| Origin | 44.41, [44.18, 44.63] | 21.05, [20.80, 21.29] | 41.53, [41.30, 41.75] |
| Min | 33.43, [33.25, 33.62] | 10.97, [10.82, 11.12] | 30.57, [30.40, 30.74] |
| Max | 53.87, [53.67, 54.08] | 29.72, [29.47, 29.98] | 51.13, [50.92, 51.34] |
| Random | 43.94, [43.73, 44.16] | 20.09, [19.86, 20.31] | 41.06, [40.85, 41.27] |
| **SimCLS** | 46.53, [46.32, 46.75] | 22.14, [21.91, 22.37] | 43.56, [43.34, 43.78] |
### Citation of the original work
```bibtex
@inproceedings{liu-liu-2021-simcls,
title = "{S}im{CLS}: A Simple Framework for Contrastive Learning of Abstractive Summarization",
author = "Liu, Yixin and
Liu, Pengfei",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.135",
doi = "10.18653/v1/2021.acl-short.135",
pages = "1065--1072",
}
```
|
{"language": ["en"], "tags": ["simcls"], "datasets": ["cnn_dailymail"]}
|
andrejmiscic/simcls-scorer-cnndm
| null |
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"simcls",
"en",
"dataset:cnn_dailymail",
"arxiv:2106.01890",
"arxiv:1602.06023",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2106.01890",
"1602.06023"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-cnn_dailymail #arxiv-2106.01890 #arxiv-1602.06023 #endpoints_compatible #region-us
|
SimCLS
======
SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization.
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *generator*) is used to generate candidate summaries, whereas, in the second stage, the *scorer* assigns a score to each candidate given the source document. The final summary is the highest-scoring candidate.
This model is the *scorer* trained for summarization of CNN/DailyMail (paper, datasets). It should be used in conjunction with facebook/bart-large-cnn. See our Github repository for details on training, evaluation, and usage.
Usage
-----
### Results
All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.
of the original work
|
[
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\n\n\n\nof the original work"
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-cnn_dailymail #arxiv-2106.01890 #arxiv-1602.06023 #endpoints_compatible #region-us \n",
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\n\n\n\nof the original work"
] |
feature-extraction
|
transformers
|
# SimCLS
SimCLS is a framework for abstractive summarization presented in [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://arxiv.org/abs/2106.01890).
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *generator*) is used to generate candidate summaries, whereas, in the second stage, the *scorer* assigns a score to each candidate given the source document. The final summary is the highest-scoring candidate.
This model is the *scorer* trained for summarization of XSum ([paper](https://arxiv.org/abs/1808.08745), [datasets](https://huggingface.co/datasets/xsum)). It should be used in conjunction with [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum). See [our Github repository](https://github.com/andrejmiscic/simcls-pytorch) for details on training, evaluation, and usage.
## Usage
```bash
git clone https://github.com/andrejmiscic/simcls-pytorch.git
cd simcls-pytorch
pip3 install torch torchvision torchaudio transformers sentencepiece
```
```python
from src.model import SimCLS, GeneratorType
summarizer = SimCLS(generator_type=GeneratorType.Pegasus,
generator_path="google/pegasus-xsum",
scorer_path="andrejmiscic/simcls-scorer-xsum")
article = "This is a news article."
summary = summarizer(article)
print(summary)
```
### Results
All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See [SimCLS paper](https://arxiv.org/abs/2106.01890) for a description of baselines.
| System | Rouge-1 | Rouge-2 | Rouge-L |
|------------------|----------------------:|----------------------:|----------------------:|
| Pegasus | 47.21 | 24.56 | 39.25 |
| **SimCLS paper** | --- | --- | --- |
| Origin | 47.10 | 24.53 | 39.23 |
| Min | 40.97 | 19.18 | 33.68 |
| Max | 52.45 | 28.28 | 43.36 |
| Random | 46.72 | 23.64 | 38.55 |
| **SimCLS** | 47.61 | 24.57 | 39.44 |
| **Our results** | --- | --- | --- |
| Origin | 47.16, [46.85, 47.48] | 24.59, [24.25, 24.92] | 39.30, [38.96, 39.62] |
| Min | 41.06, [40.76, 41.34] | 18.30, [18.03, 18.56] | 32.70, [32.42, 32.97] |
| Max | 51.83, [51.53, 52.14] | 28.92, [28.57, 29.26] | 44.02, [43.69, 44.36] |
| Random | 46.47, [46.17, 46.78] | 23.45, [23.13, 23.77] | 38.28, [37.96, 38.60] |
| **SimCLS** | 47.17, [46.87, 47.46] | 23.90, [23.59, 24.23] | 38.96, [38.64, 39.29] |
### Citation of the original work
```bibtex
@inproceedings{liu-liu-2021-simcls,
title = "{S}im{CLS}: A Simple Framework for Contrastive Learning of Abstractive Summarization",
author = "Liu, Yixin and
Liu, Pengfei",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.135",
doi = "10.18653/v1/2021.acl-short.135",
pages = "1065--1072",
}
```
|
{"language": ["en"], "tags": ["simcls"], "datasets": ["xsum"]}
|
andrejmiscic/simcls-scorer-xsum
| null |
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"simcls",
"en",
"dataset:xsum",
"arxiv:2106.01890",
"arxiv:1808.08745",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2106.01890",
"1808.08745"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-xsum #arxiv-2106.01890 #arxiv-1808.08745 #endpoints_compatible #region-us
|
SimCLS
======
SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization.
It is a two-stage approach consisting of a *generator* and a *scorer*. In the first stage, a large pre-trained model for abstractive summarization (the *generator*) is used to generate candidate summaries, whereas, in the second stage, the *scorer* assigns a score to each candidate given the source document. The final summary is the highest-scoring candidate.
This model is the *scorer* trained for summarization of XSum (paper, datasets). It should be used in conjunction with google/pegasus-xsum. See our Github repository for details on training, evaluation, and usage.
Usage
-----
### Results
All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.
of the original work
|
[
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\n\n\n\nof the original work"
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #simcls #en #dataset-xsum #arxiv-2106.01890 #arxiv-1808.08745 #endpoints_compatible #region-us \n",
"### Results\n\n\nAll of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.\n\n\n\nof the original work"
] |
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. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-cased-finetuned-squad", "results": []}]}
|
andresestevez/bert-base-cased-finetuned-squad
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of bert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2
- Datasets 1.13.3
- Tokenizers 0.10.3
|
[
"# bert-base-cased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.2\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-cased-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.2\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
text-generation
|
transformers
|
# Rick and Morty DialoGPT Model
|
{"tags": ["conversational"]}
|
anduush/DialoGPT-small-Rick
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model
|
[
"# Rick and Morty DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
text-generation
|
transformers
|
# Medical History Model based on ruGPT2 by @sberbank-ai
A simple model for helping medical staff to complete patient's medical histories.
Model used pretrained [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2)
|
{"language": ["ru"], "license": "mit", "tags": ["PyTorch", "Transformers"]}
|
anechaev/ru_med_gpt3sm_based_on_gpt2
| null |
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Medical History Model based on ruGPT2 by @sberbank-ai
A simple model for helping medical staff to complete patient's medical histories.
Model used pretrained sberbank-ai/rugpt3small_based_on_gpt2
|
[
"# Medical History Model based on ruGPT2 by @sberbank-ai\n\nA simple model for helping medical staff to complete patient's medical histories.\nModel used pretrained sberbank-ai/rugpt3small_based_on_gpt2"
] |
[
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Medical History Model based on ruGPT2 by @sberbank-ai\n\nA simple model for helping medical staff to complete patient's medical histories.\nModel used pretrained sberbank-ai/rugpt3small_based_on_gpt2"
] |
text2text-generation
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 583416409
- CO2 Emissions (in grams): 72.26141764997115
## Validation Metrics
- Loss: 1.4701834917068481
- Rouge1: 47.7785
- Rouge2: 24.8518
- RougeL: 40.2231
- RougeLsum: 43.9487
- Gen Len: 18.8029
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/anegi/autonlp-dialogue-summariztion-583416409
```
|
{"language": "en", "tags": "autonlp", "datasets": ["anegi/autonlp-data-dialogue-summariztion"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 72.26141764997115}
|
anegi/autonlp-dialogue-summariztion-583416409
| null |
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autonlp",
"en",
"dataset:anegi/autonlp-data-dialogue-summariztion",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #text2text-generation #autonlp #en #dataset-anegi/autonlp-data-dialogue-summariztion #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 583416409
- CO2 Emissions (in grams): 72.26141764997115
## Validation Metrics
- Loss: 1.4701834917068481
- Rouge1: 47.7785
- Rouge2: 24.8518
- RougeL: 40.2231
- RougeLsum: 43.9487
- Gen Len: 18.8029
## Usage
You can use cURL to access this model:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 583416409\n- CO2 Emissions (in grams): 72.26141764997115",
"## Validation Metrics\n\n- Loss: 1.4701834917068481\n- Rouge1: 47.7785\n- Rouge2: 24.8518\n- RougeL: 40.2231\n- RougeLsum: 43.9487\n- Gen Len: 18.8029",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #autonlp #en #dataset-anegi/autonlp-data-dialogue-summariztion #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 583416409\n- CO2 Emissions (in grams): 72.26141764997115",
"## Validation Metrics\n\n- Loss: 1.4701834917068481\n- Rouge1: 47.7785\n- Rouge2: 24.8518\n- RougeL: 40.2231\n- RougeLsum: 43.9487\n- Gen Len: 18.8029",
"## Usage\n\nYou can use cURL to access this model:"
] |
text-classification
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 412010597
- CO2 Emissions (in grams): 10.411685187181709
## Validation Metrics
- Loss: 0.12585781514644623
- Accuracy: 0.9475446428571429
- Precision: 0.9454660748256183
- Recall: 0.964424320827943
- AUC: 0.990229573862156
- F1: 0.9548511047070125
## 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/anel/autonlp-cml-412010597
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("anel/autonlp-cml-412010597", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("anel/autonlp-cml-412010597", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["anel/autonlp-data-cml"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 10.411685187181709}
|
anel/autonlp-cml-412010597
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:anel/autonlp-data-cml",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-anel/autonlp-data-cml #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 412010597
- CO2 Emissions (in grams): 10.411685187181709
## Validation Metrics
- Loss: 0.12585781514644623
- Accuracy: 0.9475446428571429
- Precision: 0.9454660748256183
- Recall: 0.964424320827943
- AUC: 0.990229573862156
- F1: 0.9548511047070125
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 412010597\n- CO2 Emissions (in grams): 10.411685187181709",
"## Validation Metrics\n\n- Loss: 0.12585781514644623\n- Accuracy: 0.9475446428571429\n- Precision: 0.9454660748256183\n- Recall: 0.964424320827943\n- AUC: 0.990229573862156\n- F1: 0.9548511047070125",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-anel/autonlp-data-cml #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 412010597\n- CO2 Emissions (in grams): 10.411685187181709",
"## Validation Metrics\n\n- Loss: 0.12585781514644623\n- Accuracy: 0.9475446428571429\n- Precision: 0.9454660748256183\n- Recall: 0.964424320827943\n- AUC: 0.990229573862156\n- F1: 0.9548511047070125",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
text-classification
|
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 432211280
- CO2 Emissions (in grams): 8.898145050355591
## Validation Metrics
- Loss: 0.12489336729049683
- Accuracy: 0.9520089285714286
- Precision: 0.9436443331246086
- Recall: 0.9747736093143596
- AUC: 0.9910066767410616
- F1: 0.958956411072224
## 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/anelnurkayeva/autonlp-covid-432211280
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("anelnurkayeva/autonlp-covid-432211280", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("anelnurkayeva/autonlp-covid-432211280", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["anelnurkayeva/autonlp-data-covid"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 8.898145050355591}
|
anelnurkayeva/autonlp-covid-432211280
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:anelnurkayeva/autonlp-data-covid",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-anelnurkayeva/autonlp-data-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 432211280
- CO2 Emissions (in grams): 8.898145050355591
## Validation Metrics
- Loss: 0.12489336729049683
- Accuracy: 0.9520089285714286
- Precision: 0.9436443331246086
- Recall: 0.9747736093143596
- AUC: 0.9910066767410616
- F1: 0.958956411072224
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 432211280\n- CO2 Emissions (in grams): 8.898145050355591",
"## Validation Metrics\n\n- Loss: 0.12489336729049683\n- Accuracy: 0.9520089285714286\n- Precision: 0.9436443331246086\n- Recall: 0.9747736093143596\n- AUC: 0.9910066767410616\n- F1: 0.958956411072224",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-anelnurkayeva/autonlp-data-covid #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 432211280\n- CO2 Emissions (in grams): 8.898145050355591",
"## Validation Metrics\n\n- Loss: 0.12489336729049683\n- Accuracy: 0.9520089285714286\n- Precision: 0.9436443331246086\n- Recall: 0.9747736093143596\n- AUC: 0.9910066767410616\n- F1: 0.958956411072224",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
fill-mask
|
transformers
|
# BERT for Patents
BERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.
If you want to learn more about the model, check out the [blog post](https://cloud.google.com/blog/products/ai-machine-learning/how-ai-improves-patent-analysis), [white paper](https://services.google.com/fh/files/blogs/bert_for_patents_white_paper.pdf) and [GitHub page](https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md) containing the original TensorFlow checkpoint.
---
### Projects using this model (or variants of it):
- [Patents4IPPC](https://github.com/ec-jrc/Patents4IPPC) (carried out by [Pi School](https://picampus-school.com/) and commissioned by the [Joint Research Centre (JRC)](https://ec.europa.eu/jrc/en) of the European Commission)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["masked-lm", "pytorch"], "metrics": ["perplexity"], "pipeline-tag": "fill-mask", "mask-token": "[MASK]", "widget": [{"text": "The present [MASK] provides a torque sensor that is small and highly rigid and for which high production efficiency is possible."}, {"text": "The present invention relates to [MASK] accessories and pertains particularly to a brake light unit for bicycles."}, {"text": "The present invention discloses a space-bound-free [MASK] and its coordinate determining circuit for determining a coordinate of a stylus pen."}, {"text": "The illuminated [MASK] includes a substantially translucent canopy supported by a plurality of ribs pivotally swingable towards and away from a shaft."}]}
|
anferico/bert-for-patents
| null |
[
"transformers",
"pytorch",
"tf",
"safetensors",
"fill-mask",
"masked-lm",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #safetensors #fill-mask #masked-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BERT for Patents
BERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.
If you want to learn more about the model, check out the blog post, white paper and GitHub page containing the original TensorFlow checkpoint.
---
### Projects using this model (or variants of it):
- Patents4IPPC (carried out by Pi School and commissioned by the Joint Research Centre (JRC) of the European Commission)
|
[
"# BERT for Patents\n\nBERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.\n\nIf you want to learn more about the model, check out the blog post, white paper and GitHub page containing the original TensorFlow checkpoint.\n\n---",
"### Projects using this model (or variants of it):\n- Patents4IPPC (carried out by Pi School and commissioned by the Joint Research Centre (JRC) of the European Commission)"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #fill-mask #masked-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT for Patents\n\nBERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT<sub>LARGE</sub>.\n\nIf you want to learn more about the model, check out the blog post, white paper and GitHub page containing the original TensorFlow checkpoint.\n\n---",
"### Projects using this model (or variants of it):\n- Patents4IPPC (carried out by Pi School and commissioned by the Joint Research Centre (JRC) of the European Commission)"
] |
text-generation
|
transformers
|
#Monke Messenger DialoGPT Model
|
{"tags": ["conversational"]}
|
ange/DialoGPT-medium-Monke
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Monke Messenger DialoGPT Model
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from unicode_tr import unicode_tr
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("aniltrkkn/wav2vec2-large-xlsr-53-turkish")
model.to("cuda")
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tbatch["sentence"] = str(unicode_tr(re.sub(chars_to_ignore_regex, "", batch["sentence"])).lower())
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
\tpred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 17.46 %
## Training
unicode_tr package is used for converting sentences to lower case since regular lower() does not work well with Turkish.
Since training data is very limited for Turkish, all data is employed with a K-Fold (k=5) training approach. Best model out of the 5 trainings is uploaded. Training arguments:
--num_train_epochs="30" \\
--per_device_train_batch_size="32" \\
--evaluation_strategy="steps" \\
--activation_dropout="0.055" \\
--attention_dropout="0.094" \\
--feat_proj_dropout="0.04" \\
--hidden_dropout="0.047" \\
--layerdrop="0.041" \\
--learning_rate="2.34e-4" \\
--mask_time_prob="0.082" \\
--warmup_steps="250" \\
All trainings took ~20 hours with a GeForce RTX 3090 Graphics Card.
|
{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "results": [{"task": {"name": "Speech Recognition", "type": "automatic-speech-recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"name": "Test WER", "type": "wer", "value": 17.46}]}]}
|
aniltrkkn/wav2vec2-large-xlsr-53-turkish
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
Test Result: 17.46 %
## Training
unicode_tr package is used for converting sentences to lower case since regular lower() does not work well with Turkish.
Since training data is very limited for Turkish, all data is employed with a K-Fold (k=5) training approach. Best model out of the 5 trainings is uploaded. Training arguments:
--num_train_epochs="30" \\
--per_device_train_batch_size="32" \\
--evaluation_strategy="steps" \\
--activation_dropout="0.055" \\
--attention_dropout="0.094" \\
--feat_proj_dropout="0.04" \\
--hidden_dropout="0.047" \\
--layerdrop="0.041" \\
--learning_rate="2.34e-4" \\
--mask_time_prob="0.082" \\
--warmup_steps="250" \\
All trainings took ~20 hours with a GeForce RTX 3090 Graphics Card.
|
[
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice. \n\n\n\n\nTest Result: 17.46 %",
"## Training\nunicode_tr package is used for converting sentences to lower case since regular lower() does not work well with Turkish.\n\nSince training data is very limited for Turkish, all data is employed with a K-Fold (k=5) training approach. Best model out of the 5 trainings is uploaded. Training arguments:\n --num_train_epochs=\"30\" \\\\\n --per_device_train_batch_size=\"32\" \\\\\n --evaluation_strategy=\"steps\" \\\\\n --activation_dropout=\"0.055\" \\\\\n --attention_dropout=\"0.094\" \\\\\n --feat_proj_dropout=\"0.04\" \\\\\n --hidden_dropout=\"0.047\" \\\\\n --layerdrop=\"0.041\" \\\\\n --learning_rate=\"2.34e-4\" \\\\\n --mask_time_prob=\"0.082\" \\\\\n --warmup_steps=\"250\" \\\\\n\nAll trainings took ~20 hours with a GeForce RTX 3090 Graphics Card."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice. \n\n\n\n\nTest Result: 17.46 %",
"## Training\nunicode_tr package is used for converting sentences to lower case since regular lower() does not work well with Turkish.\n\nSince training data is very limited for Turkish, all data is employed with a K-Fold (k=5) training approach. Best model out of the 5 trainings is uploaded. Training arguments:\n --num_train_epochs=\"30\" \\\\\n --per_device_train_batch_size=\"32\" \\\\\n --evaluation_strategy=\"steps\" \\\\\n --activation_dropout=\"0.055\" \\\\\n --attention_dropout=\"0.094\" \\\\\n --feat_proj_dropout=\"0.04\" \\\\\n --hidden_dropout=\"0.047\" \\\\\n --layerdrop=\"0.041\" \\\\\n --learning_rate=\"2.34e-4\" \\\\\n --mask_time_prob=\"0.082\" \\\\\n --warmup_steps=\"250\" \\\\\n\nAll trainings took ~20 hours with a GeForce RTX 3090 Graphics Card."
] |
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. -->
# sagemaker-BioclinicalBERT-ADR
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the ade_corpus_v2 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 171 | 0.9441 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["ade_corpus_v2"], "model-index": [{"name": "sagemaker-BioclinicalBERT-ADR", "results": []}]}
|
anindabitm/sagemaker-BioclinicalBERT-ADR
| null |
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:ade_corpus_v2",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-ade_corpus_v2 #endpoints_compatible #has_space #region-us
|
sagemaker-BioclinicalBERT-ADR
=============================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the ade\_corpus\_v2 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: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.1
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-ade_corpus_v2 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2434
- Accuracy: 0.9165
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9423 | 1.0 | 500 | 0.2434 | 0.9165 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9165, "name": "Accuracy"}]}]}]}
|
anindabitm/sagemaker-distilbert-emotion
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
sagemaker-distilbert-emotion
============================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2434
* Accuracy: 0.9165
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.1
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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. -->
# albert-base-v2-finetuned-qnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3194
- Accuracy: 0.9112
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3116 | 1.0 | 6547 | 0.2818 | 0.8849 |
| 0.2467 | 2.0 | 13094 | 0.2532 | 0.9001 |
| 0.1858 | 3.0 | 19641 | 0.3194 | 0.9112 |
| 0.1449 | 4.0 | 26188 | 0.4338 | 0.9103 |
| 0.0584 | 5.0 | 32735 | 0.5752 | 0.9052 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "qnli"}, "metrics": [{"type": "accuracy", "value": 0.9112209408749771, "name": "Accuracy"}]}]}]}
|
anirudh21/albert-base-v2-finetuned-qnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-base-v2-finetuned-qnli
=============================
This model is a fine-tuned version of albert-base-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3194
* Accuracy: 0.9112
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# albert-base-v2-finetuned-rte
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2496
- Accuracy: 0.7581
## 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: 10
- eval_batch_size: 10
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 249 | 0.5914 | 0.6751 |
| No log | 2.0 | 498 | 0.5843 | 0.7184 |
| 0.5873 | 3.0 | 747 | 0.6925 | 0.7220 |
| 0.5873 | 4.0 | 996 | 1.1613 | 0.7545 |
| 0.2149 | 5.0 | 1245 | 1.2496 | 0.7581 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.7581227436823105, "name": "Accuracy"}]}]}]}
|
anirudh21/albert-base-v2-finetuned-rte
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-base-v2-finetuned-rte
============================
This model is a fine-tuned version of albert-base-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2496
* Accuracy: 0.7581
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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# albert-base-v2-finetuned-wnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6878
- Accuracy: 0.5634
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6878 | 0.5634 |
| No log | 2.0 | 80 | 0.6919 | 0.5634 |
| No log | 3.0 | 120 | 0.6877 | 0.5634 |
| No log | 4.0 | 160 | 0.6984 | 0.4085 |
| No log | 5.0 | 200 | 0.6957 | 0.5211 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
|
anirudh21/albert-base-v2-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-base-v2-finetuned-wnli
=============================
This model is a fine-tuned version of albert-base-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6878
* Accuracy: 0.5634
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# albert-large-v2-finetuned-rte
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6827
- Accuracy: 0.5487
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 18 | 0.6954 | 0.5271 |
| No log | 2.0 | 36 | 0.6860 | 0.5379 |
| No log | 3.0 | 54 | 0.6827 | 0.5487 |
| No log | 4.0 | 72 | 0.7179 | 0.5235 |
| No log | 5.0 | 90 | 0.7504 | 0.5379 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-large-v2-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.5487364620938628, "name": "Accuracy"}]}]}]}
|
anirudh21/albert-large-v2-finetuned-rte
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-large-v2-finetuned-rte
=============================
This model is a fine-tuned version of albert-large-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6827
* Accuracy: 0.5487
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
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. -->
# albert-large-v2-finetuned-wnli
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6919
- Accuracy: 0.5352
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 17 | 0.7292 | 0.4366 |
| No log | 2.0 | 34 | 0.6919 | 0.5352 |
| No log | 3.0 | 51 | 0.7084 | 0.4648 |
| No log | 4.0 | 68 | 0.7152 | 0.5352 |
| No log | 5.0 | 85 | 0.7343 | 0.5211 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-large-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5352112676056338, "name": "Accuracy"}]}]}]}
|
anirudh21/albert-large-v2-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-large-v2-finetuned-wnli
==============================
This model is a fine-tuned version of albert-large-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6919
* Accuracy: 0.5352
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
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. -->
# albert-xlarge-v2-finetuned-mrpc
This model is a fine-tuned version of [albert-xlarge-v2](https://huggingface.co/albert-xlarge-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5563
- Accuracy: 0.7132
- F1: 0.8146
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 63 | 0.6898 | 0.5221 | 0.6123 |
| No log | 2.0 | 126 | 0.6298 | 0.6838 | 0.8122 |
| No log | 3.0 | 189 | 0.6043 | 0.7010 | 0.8185 |
| No log | 4.0 | 252 | 0.5834 | 0.7010 | 0.8146 |
| No log | 5.0 | 315 | 0.5563 | 0.7132 | 0.8146 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "albert-xlarge-v2-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.7132352941176471, "name": "Accuracy"}, {"type": "f1", "value": 0.8145800316957211, "name": "F1"}]}]}]}
|
anirudh21/albert-xlarge-v2-finetuned-mrpc
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-xlarge-v2-finetuned-mrpc
===============================
This model is a fine-tuned version of albert-xlarge-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5563
* Accuracy: 0.7132
* F1: 0.8146
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# albert-xlarge-v2-finetuned-wnli
This model is a fine-tuned version of [albert-xlarge-v2](https://huggingface.co/albert-xlarge-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6869
- Accuracy: 0.5634
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6906 | 0.5070 |
| No log | 2.0 | 80 | 0.6869 | 0.5634 |
| No log | 3.0 | 120 | 0.6905 | 0.5352 |
| No log | 4.0 | 160 | 0.6960 | 0.4225 |
| No log | 5.0 | 200 | 0.7011 | 0.3803 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-xlarge-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
|
anirudh21/albert-xlarge-v2-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-xlarge-v2-finetuned-wnli
===============================
This model is a fine-tuned version of albert-xlarge-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6869
* Accuracy: 0.5634
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# albert-xxlarge-v2-finetuned-wnli
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6970
- Accuracy: 0.5070
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 13 | 0.8066 | 0.4366 |
| No log | 2.0 | 26 | 0.6970 | 0.5070 |
| No log | 3.0 | 39 | 0.7977 | 0.4507 |
| No log | 4.0 | 52 | 0.7906 | 0.4930 |
| No log | 5.0 | 65 | 0.8459 | 0.4366 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "albert-xxlarge-v2-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5070422535211268, "name": "Accuracy"}]}]}]}
|
anirudh21/albert-xxlarge-v2-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-xxlarge-v2-finetuned-wnli
================================
This model is a fine-tuned version of albert-xxlarge-v2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6970
* Accuracy: 0.5070
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #albert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
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-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9664
- Matthews Correlation: 0.5797
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5017 | 1.0 | 535 | 0.5252 | 0.4841 |
| 0.2903 | 2.0 | 1070 | 0.5550 | 0.4967 |
| 0.1839 | 3.0 | 1605 | 0.7295 | 0.5634 |
| 0.1132 | 4.0 | 2140 | 0.7762 | 0.5702 |
| 0.08 | 5.0 | 2675 | 0.9664 | 0.5797 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5796941781913538, "name": "Matthews Correlation"}]}]}]}
|
anirudh21/bert-base-uncased-finetuned-cola
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-cola
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9664
* Matthews Correlation: 0.5797
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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-uncased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6645
- Accuracy: 0.7917
- F1: 0.8590
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 63 | 0.5387 | 0.7402 | 0.8349 |
| No log | 2.0 | 126 | 0.5770 | 0.7696 | 0.8513 |
| No log | 3.0 | 189 | 0.5357 | 0.7574 | 0.8223 |
| No log | 4.0 | 252 | 0.6645 | 0.7917 | 0.8590 |
| No log | 5.0 | 315 | 0.6977 | 0.7721 | 0.8426 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.7916666666666666, "name": "Accuracy"}, {"type": "f1", "value": 0.8590381426202321, "name": "F1"}]}]}]}
|
anirudh21/bert-base-uncased-finetuned-mrpc
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-mrpc
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6645
* Accuracy: 0.7917
* F1: 0.8590
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
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-uncased-finetuned-qnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6268
- Accuracy: 0.7917
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 63 | 0.5339 | 0.7620 |
| No log | 2.0 | 126 | 0.4728 | 0.7866 |
| No log | 3.0 | 189 | 0.5386 | 0.7847 |
| No log | 4.0 | 252 | 0.6096 | 0.7904 |
| No log | 5.0 | 315 | 0.6268 | 0.7917 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "qnli"}, "metrics": [{"type": "accuracy", "value": 0.791689547867472, "name": "Accuracy"}]}]}]}
|
anirudh21/bert-base-uncased-finetuned-qnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-qnli
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6268
* Accuracy: 0.7917
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
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-uncased-finetuned-rte
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8075
- Accuracy: 0.6643
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 63 | 0.6777 | 0.5668 |
| No log | 2.0 | 126 | 0.6723 | 0.6282 |
| No log | 3.0 | 189 | 0.7238 | 0.6318 |
| No log | 4.0 | 252 | 0.7993 | 0.6354 |
| No log | 5.0 | 315 | 0.8075 | 0.6643 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.6642599277978339, "name": "Accuracy"}]}]}]}
|
anirudh21/bert-base-uncased-finetuned-rte
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-rte
===============================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8075
* Accuracy: 0.6643
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.1\n* Tokenizers 0.10.3"
] |
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-uncased-finetuned-wnli
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6854
- Accuracy: 0.5634
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6854 | 0.5634 |
| No log | 2.0 | 80 | 0.6983 | 0.3239 |
| No log | 3.0 | 120 | 0.6995 | 0.5352 |
| No log | 4.0 | 160 | 0.6986 | 0.5634 |
| No log | 5.0 | 200 | 0.6996 | 0.5634 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
|
anirudh21/bert-base-uncased-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-wnli
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6854
* Accuracy: 0.5634
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8623
- Matthews Correlation: 0.5224
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5278 | 1.0 | 535 | 0.5223 | 0.4007 |
| 0.3515 | 2.0 | 1070 | 0.5150 | 0.4993 |
| 0.2391 | 3.0 | 1605 | 0.6471 | 0.5103 |
| 0.1841 | 4.0 | 2140 | 0.7640 | 0.5153 |
| 0.1312 | 5.0 | 2675 | 0.8623 | 0.5224 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5224154837835395, "name": "Matthews Correlation"}]}]}]}
|
anirudh21/distilbert-base-uncased-finetuned-cola
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8623
* Matthews Correlation: 0.5224
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3830
- Accuracy: 0.8456
- F1: 0.8959
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.3826 | 0.8186 | 0.8683 |
| No log | 2.0 | 460 | 0.3830 | 0.8456 | 0.8959 |
| 0.4408 | 3.0 | 690 | 0.3835 | 0.8382 | 0.8866 |
| 0.4408 | 4.0 | 920 | 0.5036 | 0.8431 | 0.8919 |
| 0.1941 | 5.0 | 1150 | 0.5783 | 0.8431 | 0.8930 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8455882352941176, "name": "Accuracy"}, {"type": "f1", "value": 0.8958677685950412, "name": "F1"}]}]}]}
|
anirudh21/distilbert-base-uncased-finetuned-mrpc
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-mrpc
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3830
* Accuracy: 0.8456
* F1: 0.8959
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-finetuned-qnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8121
- Accuracy: 0.6065
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.6949 | 0.4874 |
| No log | 2.0 | 312 | 0.6596 | 0.5957 |
| No log | 3.0 | 468 | 0.7186 | 0.5812 |
| 0.6026 | 4.0 | 624 | 0.7727 | 0.6029 |
| 0.6026 | 5.0 | 780 | 0.8121 | 0.6065 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.6064981949458483, "name": "Accuracy"}]}]}]}
|
anirudh21/distilbert-base-uncased-finetuned-qnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-qnli
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8121
* Accuracy: 0.6065
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-finetuned-rte
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6661
- Accuracy: 0.6173
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.6921 | 0.5162 |
| No log | 2.0 | 312 | 0.6661 | 0.6173 |
| No log | 3.0 | 468 | 0.7794 | 0.5632 |
| 0.5903 | 4.0 | 624 | 0.8832 | 0.5921 |
| 0.5903 | 5.0 | 780 | 0.9376 | 0.5921 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.6173285198555957, "name": "Accuracy"}]}]}]}
|
anirudh21/distilbert-base-uncased-finetuned-rte
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-rte
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6661
* Accuracy: 0.6173
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4028
- Accuracy: 0.9083
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.188 | 1.0 | 4210 | 0.3127 | 0.9037 |
| 0.1299 | 2.0 | 8420 | 0.3887 | 0.9048 |
| 0.0845 | 3.0 | 12630 | 0.4028 | 0.9083 |
| 0.0691 | 4.0 | 16840 | 0.3924 | 0.9071 |
| 0.052 | 5.0 | 21050 | 0.5047 | 0.9002 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.908256880733945, "name": "Accuracy"}]}]}]}
|
anirudh21/distilbert-base-uncased-finetuned-sst2
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-sst2
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4028
* Accuracy: 0.9083
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# distilbert-base-uncased-finetuned-wnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6883
- Accuracy: 0.5634
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6883 | 0.5634 |
| No log | 2.0 | 80 | 0.6934 | 0.5634 |
| No log | 3.0 | 120 | 0.6960 | 0.5211 |
| No log | 4.0 | 160 | 0.6958 | 0.5634 |
| No log | 5.0 | 200 | 0.6964 | 0.5634 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
|
anirudh21/distilbert-base-uncased-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-wnli
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6883
* Accuracy: 0.5634
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# electra-base-discriminator-finetuned-rte
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4793
- Accuracy: 0.8231
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.6076 | 0.6570 |
| No log | 2.0 | 312 | 0.4824 | 0.7762 |
| No log | 3.0 | 468 | 0.4793 | 0.8231 |
| 0.4411 | 4.0 | 624 | 0.7056 | 0.7906 |
| 0.4411 | 5.0 | 780 | 0.6849 | 0.8159 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "electra-base-discriminator-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.8231046931407943, "name": "Accuracy"}]}]}]}
|
anirudh21/electra-base-discriminator-finetuned-rte
| null |
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
electra-base-discriminator-finetuned-rte
========================================
This model is a fine-tuned version of google/electra-base-discriminator on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4793
* Accuracy: 0.8231
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# electra-base-discriminator-finetuned-wnli
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6893
- Accuracy: 0.5634
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6893 | 0.5634 |
| No log | 2.0 | 80 | 0.7042 | 0.4225 |
| No log | 3.0 | 120 | 0.7008 | 0.3803 |
| No log | 4.0 | 160 | 0.6998 | 0.5634 |
| No log | 5.0 | 200 | 0.7016 | 0.5352 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "electra-base-discriminator-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
|
anirudh21/electra-base-discriminator-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
electra-base-discriminator-finetuned-wnli
=========================================
This model is a fine-tuned version of google/electra-base-discriminator on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6893
* Accuracy: 0.5634
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# xlnet-base-cased-finetuned-rte
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0656
- Accuracy: 0.6895
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 156 | 0.7007 | 0.4874 |
| No log | 2.0 | 312 | 0.6289 | 0.6751 |
| No log | 3.0 | 468 | 0.7020 | 0.6606 |
| 0.6146 | 4.0 | 624 | 1.0573 | 0.6570 |
| 0.6146 | 5.0 | 780 | 1.0656 | 0.6895 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "xlnet-base-cased-finetuned-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.6895306859205776, "name": "Accuracy"}]}]}]}
|
anirudh21/xlnet-base-cased-finetuned-rte
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
xlnet-base-cased-finetuned-rte
==============================
This model is a fine-tuned version of xlnet-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0656
* Accuracy: 0.6895
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# xlnet-base-cased-finetuned-wnli
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6874
- Accuracy: 0.5634
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.7209 | 0.5352 |
| No log | 2.0 | 80 | 0.6874 | 0.5634 |
| No log | 3.0 | 120 | 0.6908 | 0.5634 |
| No log | 4.0 | 160 | 0.6987 | 0.4930 |
| No log | 5.0 | 200 | 0.6952 | 0.5634 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "xlnet-base-cased-finetuned-wnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
|
anirudh21/xlnet-base-cased-finetuned-wnli
| null |
[
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
xlnet-base-cased-finetuned-wnli
===============================
This model is a fine-tuned version of xlnet-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6874
* Accuracy: 0.5634
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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlnet #text-classification #generated_from_trainer #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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. -->
# wavlm-base-english
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the english_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0955
- Wer: 0.0773
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8664 | 0.17 | 300 | 2.8439 | 1.0 |
| 0.5009 | 0.34 | 600 | 0.2709 | 0.2162 |
| 0.2056 | 0.5 | 900 | 0.1934 | 0.1602 |
| 0.1648 | 0.67 | 1200 | 0.1576 | 0.1306 |
| 0.1922 | 0.84 | 1500 | 0.1358 | 0.1114 |
| 0.093 | 1.01 | 1800 | 0.1277 | 0.1035 |
| 0.0652 | 1.18 | 2100 | 0.1251 | 0.1005 |
| 0.0848 | 1.35 | 2400 | 0.1188 | 0.0964 |
| 0.0706 | 1.51 | 2700 | 0.1091 | 0.0905 |
| 0.0846 | 1.68 | 3000 | 0.1018 | 0.0840 |
| 0.0684 | 1.85 | 3300 | 0.0978 | 0.0809 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["automatic-speech-recognition", "english_asr", "generated_from_trainer"], "model-index": [{"name": "wavlm-base-english", "results": []}]}
|
anjulRajendraSharma/WavLm-base-en
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"english_asr",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #english_asr #generated_from_trainer #endpoints_compatible #region-us
|
wavlm-base-english
==================
This model is a fine-tuned version of microsoft/wavlm-base on the english\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0955
* Wer: 0.0773
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: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.9.1
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #english_asr #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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. -->
# wavlm-libri-clean-100h-base
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0955
- Wer: 0.0773
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8664 | 0.17 | 300 | 2.8439 | 1.0 |
| 0.5009 | 0.34 | 600 | 0.2709 | 0.2162 |
| 0.2056 | 0.5 | 900 | 0.1934 | 0.1602 |
| 0.1648 | 0.67 | 1200 | 0.1576 | 0.1306 |
| 0.1922 | 0.84 | 1500 | 0.1358 | 0.1114 |
| 0.093 | 1.01 | 1800 | 0.1277 | 0.1035 |
| 0.0652 | 1.18 | 2100 | 0.1251 | 0.1005 |
| 0.0848 | 1.35 | 2400 | 0.1188 | 0.0964 |
| 0.0706 | 1.51 | 2700 | 0.1091 | 0.0905 |
| 0.0846 | 1.68 | 3000 | 0.1018 | 0.0840 |
| 0.0684 | 1.85 | 3300 | 0.0978 | 0.0809 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["automatic-speech-recognition", "librispeech_asr", "generated_from_trainer"], "model-index": [{"name": "wavlm-libri-clean-100h-base", "results": []}]}
|
anjulRajendraSharma/wavlm-base-libri-clean-100
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #endpoints_compatible #region-us
|
wavlm-libri-clean-100h-base
===========================
This model is a fine-tuned version of microsoft/wavlm-base on the LIBRISPEECH\_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0955
* Wer: 0.0773
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: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.9.1
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wavlm #automatic-speech-recognition #librispeech_asr #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.9.1\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
text2text-generation
|
transformers
|
Model to summarize the meeting transcripts.
|
{}
|
ankitkhowal/minutes-of-meeting
| null |
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
Model to summarize the meeting transcripts.
|
[] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering
|
transformers
|
# Open Domain Question Answering
A core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. Furthermore, open-domain question answering is a benchmark task in the development of Artificial Intelligence, since understanding text and being able to answer questions about it is something that we generally associate with intelligence.
# The Natural Questions Dataset
To help spur development in open-domain question answering, we have created the Natural Questions (NQ) corpus, along with a challenge website based on this data. The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.
|
{"tags": ["small answer"], "datasets": ["natural_questions"]}
|
ankur310794/bert-large-uncased-nq-small-answer
| null |
[
"transformers",
"tf",
"bert",
"question-answering",
"small answer",
"dataset:natural_questions",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #tf #bert #question-answering #small answer #dataset-natural_questions #endpoints_compatible #region-us
|
# Open Domain Question Answering
A core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. Furthermore, open-domain question answering is a benchmark task in the development of Artificial Intelligence, since understanding text and being able to answer questions about it is something that we generally associate with intelligence.
# The Natural Questions Dataset
To help spur development in open-domain question answering, we have created the Natural Questions (NQ) corpus, along with a challenge website based on this data. The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets.
|
[
"# Open Domain Question Answering\nA core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. Furthermore, open-domain question answering is a benchmark task in the development of Artificial Intelligence, since understanding text and being able to answer questions about it is something that we generally associate with intelligence.",
"# The Natural Questions Dataset\nTo help spur development in open-domain question answering, we have created the Natural Questions (NQ) corpus, along with a challenge website based on this data. The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets."
] |
[
"TAGS\n#transformers #tf #bert #question-answering #small answer #dataset-natural_questions #endpoints_compatible #region-us \n",
"# Open Domain Question Answering\nA core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. Furthermore, open-domain question answering is a benchmark task in the development of Artificial Intelligence, since understanding text and being able to answer questions about it is something that we generally associate with intelligence.",
"# The Natural Questions Dataset\nTo help spur development in open-domain question answering, we have created the Natural Questions (NQ) corpus, along with a challenge website based on this data. The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets."
] |
text-generation
|
transformers
|
# My Awesome Model
|
{"tags": ["conversational"]}
|
ann101020/le2sbot-hp
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
|
[
"# My Awesome Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
token-classification
|
transformers
|
A POS-tagger for Old Church Slavonic trained on the Old Church Slavonic UD treebank (https://github.com/UniversalDependencies/UD_Old_Church_Slavonic-PROIEL). GitHub with api: https://github.com/annadmitrieva/chu-api
|
{"language": ["chu"], "license": "mit", "tags": ["Old Church Slavonic", "POS-tagging"], "widget": [{"text": "\u041d\u0435 \u043e\u0441\u046b\u0436\u0434\u0430\u0438\u0442\u0435 \u0434\u0430 \u043d\u0435 \u043e\u0441\u046b\u0436\u0434\u0435\u043d\u0438 \u0431\u046b\u0434\u0435\u0442\u0435"}]}
|
annadmitrieva/old-church-slavonic-pos
| null |
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"Old Church Slavonic",
"POS-tagging",
"chu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"chu"
] |
TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #Old Church Slavonic #POS-tagging #chu #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
A POS-tagger for Old Church Slavonic trained on the Old Church Slavonic UD treebank (URL GitHub with api: URL
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #Old Church Slavonic #POS-tagging #chu #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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-uncased-finetuned-addresso
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.8.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-addresso", "results": []}]}
|
annafavaro/bert-base-uncased-finetuned-addresso
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-addresso
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.8.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
[
"# bert-base-uncased-finetuned-addresso\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 5\n- eval_batch_size: 5\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.8.1\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-addresso\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 5\n- eval_batch_size: 5\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.8.1\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
null | null |
ktrain predictor for NER of ADR in patient forum discussions. Created in ktrain 0.29 with transformers 4.10. See requirements.txt to run model.
|
{}
|
annedirkson/ADR_extraction_patient_forum
| null |
[
"tf",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#tf #region-us
|
ktrain predictor for NER of ADR in patient forum discussions. Created in ktrain 0.29 with transformers 4.10. See URL to run model.
|
[] |
[
"TAGS\n#tf #region-us \n"
] |
text-generation
|
transformers
|
# German GPT-2 model
**Note**: This model was de-anonymized and now lives at:
https://huggingface.co/dbmdz/german-gpt2
Please use the new model name instead!
|
{"language": "de", "license": "mit", "widget": [{"text": "Heute ist sehr sch\u00f6nes Wetter in"}]}
|
anonymous-german-nlp/german-gpt2
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"de"
] |
TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# German GPT-2 model
Note: This model was de-anonymized and now lives at:
URL
Please use the new model name instead!
|
[
"# German GPT-2 model\n\nNote: This model was de-anonymized and now lives at:\n\nURL\n\nPlease use the new model name instead!"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# German GPT-2 model\n\nNote: This model was de-anonymized and now lives at:\n\nURL\n\nPlease use the new model name instead!"
] |
text-classification
|
transformers
|
# Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.
# Vaccinating COVID tweets
A fine-tuned model for fact-classification task on English tweets about COVID-19/vaccine.
## Intended uses & limitations
You can classify if the input tweet (or any others statement) about COVID-19/vaccine is `true`, `false` or `misleading`.
Note that since this model was trained with data up to May 2020, the most recent information may not be reflected.
#### How to use
You can use this model directly on this page or using `transformers` in python.
- Load pipeline and implement with input sequence
```python
from transformers import pipeline
pipe = pipeline("sentiment-analysis", model = "ans/vaccinating-covid-tweets")
seq = "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."
pipe(seq)
```
- Expected output
```python
[
{
"label": "false",
"score": 0.07972867041826248
},
{
"label": "misleading",
"score": 0.019911376759409904
},
{
"label": "true",
"score": 0.9003599882125854
}
]
```
- `true` examples
```python
"By the end of 2020, several vaccines had become available for use in different parts of the world."
"Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."
"RNA vaccines were the first vaccines for SARS-CoV-2 to be produced and represent an entirely new vaccine approach."
```
- `false` examples
```python
"COVID-19 vaccine caused new strain in UK."
```
#### Limitations and bias
To conservatively classify whether an input sequence is true or not, the model may have predictions biased toward `false` or `misleading`.
## Training data & Procedure
#### Pre-trained baseline model
- Pre-trained model: [BERTweet](https://github.com/VinAIResearch/BERTweet)
- trained based on the RoBERTa pre-training procedure
- 850M General English Tweets (Jan 2012 to Aug 2019)
- 23M COVID-19 English Tweets
- Size of the model: >134M parameters
- Further training
- Pre-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification
#### 1) Pre-training language model
- The model was pre-trained on COVID-19/vaccined related tweets using a masked language modeling (MLM) objective starting from BERTweet.
- Following datasets on English tweets were used:
- Tweets with trending #CovidVaccine hashtag, 207,000 tweets uploaded across Aug 2020 to Apr 2021 ([kaggle](https://www.kaggle.com/kaushiksuresh147/covidvaccine-tweets))
- Tweets about all COVID-19 vaccines, 78,000 tweets uploaded across Dec 2020 to May 2021 ([kaggle](https://www.kaggle.com/gpreda/all-covid19-vaccines-tweets))
- COVID-19 Twitter chatter dataset, 590,000 tweets uploaded across Mar 2021 to May 2021 ([github](https://github.com/thepanacealab/covid19_twitter))
#### 2) Fine-tuning for fact classification
- A fine-tuned model from pre-trained language model (1) for fact-classification task on COVID-19/vaccine.
- COVID-19/vaccine-related statements were collected from [Poynter](https://www.poynter.org/ifcn-covid-19-misinformation/) and [Snopes](https://www.snopes.com/) using Selenium resulting in over 14,000 fact-checked statements from Jan 2020 to May 2021.
- Original labels were divided within following three categories:
- `False`: includes false, no evidence, manipulated, fake, not true, unproven and unverified
- `Misleading`: includes misleading, exaggerated, out of context and needs context
- `True`: includes true and correct
## Evaluation results
| Training loss | Validation loss | Training accuracy | Validation accuracy |
| --- | --- | --- | --- |
| 0.1062 | 0.1006 | 96.3% | 94.5% |
# Contributors
- This model is a part of final team project from MLDL for DS class at SNU.
- Team BIBI - Vaccinating COVID-NineTweets
- Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom
- Advisor: Prof. Wen-Syan Li
<a href="https://gsds.snu.ac.kr/"><img src="https://gsds.snu.ac.kr/wp-content/uploads/sites/50/2021/04/GSDS_logo2-e1619068952717.png" width="200" height="80"></a>
|
{"language": "en", "license": "apache-2.0", "datasets": ["tweets"], "widget": [{"text": "Vaccines to prevent SARS-CoV-2 infection are considered the most promising approach for curbing the pandemic."}]}
|
ans/vaccinating-covid-tweets
| null |
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:tweets",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-tweets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.
=========================================================================================================================
Vaccinating COVID tweets
========================
A fine-tuned model for fact-classification task on English tweets about COVID-19/vaccine.
Intended uses & limitations
---------------------------
You can classify if the input tweet (or any others statement) about COVID-19/vaccine is 'true', 'false' or 'misleading'.
Note that since this model was trained with data up to May 2020, the most recent information may not be reflected.
#### How to use
You can use this model directly on this page or using 'transformers' in python.
* Load pipeline and implement with input sequence
* Expected output
* 'true' examples
* 'false' examples
#### Limitations and bias
To conservatively classify whether an input sequence is true or not, the model may have predictions biased toward 'false' or 'misleading'.
Training data & Procedure
-------------------------
#### Pre-trained baseline model
* Pre-trained model: BERTweet
+ trained based on the RoBERTa pre-training procedure
+ 850M General English Tweets (Jan 2012 to Aug 2019)
+ 23M COVID-19 English Tweets
+ Size of the model: >134M parameters
* Further training
+ Pre-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification
#### 1) Pre-training language model
* The model was pre-trained on COVID-19/vaccined related tweets using a masked language modeling (MLM) objective starting from BERTweet.
* Following datasets on English tweets were used:
+ Tweets with trending #CovidVaccine hashtag, 207,000 tweets uploaded across Aug 2020 to Apr 2021 (kaggle)
+ Tweets about all COVID-19 vaccines, 78,000 tweets uploaded across Dec 2020 to May 2021 (kaggle)
+ COVID-19 Twitter chatter dataset, 590,000 tweets uploaded across Mar 2021 to May 2021 (github)
#### 2) Fine-tuning for fact classification
* A fine-tuned model from pre-trained language model (1) for fact-classification task on COVID-19/vaccine.
* COVID-19/vaccine-related statements were collected from Poynter and Snopes using Selenium resulting in over 14,000 fact-checked statements from Jan 2020 to May 2021.
* Original labels were divided within following three categories:
+ 'False': includes false, no evidence, manipulated, fake, not true, unproven and unverified
+ 'Misleading': includes misleading, exaggerated, out of context and needs context
+ 'True': includes true and correct
Evaluation results
------------------
Contributors
============
* This model is a part of final team project from MLDL for DS class at SNU.
+ Team BIBI - Vaccinating COVID-NineTweets
+ Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom
+ Advisor: Prof. Wen-Syan Li
<a href="URL src="URL width="200" height="80">
|
[
"#### How to use\n\n\nYou can use this model directly on this page or using 'transformers' in python.\n\n\n* Load pipeline and implement with input sequence\n* Expected output\n* 'true' examples\n* 'false' examples",
"#### Limitations and bias\n\n\nTo conservatively classify whether an input sequence is true or not, the model may have predictions biased toward 'false' or 'misleading'.\n\n\nTraining data & Procedure\n-------------------------",
"#### Pre-trained baseline model\n\n\n* Pre-trained model: BERTweet\n\t+ trained based on the RoBERTa pre-training procedure\n\t+ 850M General English Tweets (Jan 2012 to Aug 2019)\n\t+ 23M COVID-19 English Tweets\n\t+ Size of the model: >134M parameters\n* Further training\n\t+ Pre-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification",
"#### 1) Pre-training language model\n\n\n* The model was pre-trained on COVID-19/vaccined related tweets using a masked language modeling (MLM) objective starting from BERTweet.\n* Following datasets on English tweets were used:\n\t+ Tweets with trending #CovidVaccine hashtag, 207,000 tweets uploaded across Aug 2020 to Apr 2021 (kaggle)\n\t+ Tweets about all COVID-19 vaccines, 78,000 tweets uploaded across Dec 2020 to May 2021 (kaggle)\n\t+ COVID-19 Twitter chatter dataset, 590,000 tweets uploaded across Mar 2021 to May 2021 (github)",
"#### 2) Fine-tuning for fact classification\n\n\n* A fine-tuned model from pre-trained language model (1) for fact-classification task on COVID-19/vaccine.\n* COVID-19/vaccine-related statements were collected from Poynter and Snopes using Selenium resulting in over 14,000 fact-checked statements from Jan 2020 to May 2021.\n* Original labels were divided within following three categories:\n\t+ 'False': includes false, no evidence, manipulated, fake, not true, unproven and unverified\n\t+ 'Misleading': includes misleading, exaggerated, out of context and needs context\n\t+ 'True': includes true and correct\n\n\nEvaluation results\n------------------\n\n\n\nContributors\n============\n\n\n* This model is a part of final team project from MLDL for DS class at SNU.\n\t+ Team BIBI - Vaccinating COVID-NineTweets\n\t+ Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom\n\t+ Advisor: Prof. Wen-Syan Li\n\n\n<a href=\"URL src=\"URL width=\"200\" height=\"80\">"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #en #dataset-tweets #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model directly on this page or using 'transformers' in python.\n\n\n* Load pipeline and implement with input sequence\n* Expected output\n* 'true' examples\n* 'false' examples",
"#### Limitations and bias\n\n\nTo conservatively classify whether an input sequence is true or not, the model may have predictions biased toward 'false' or 'misleading'.\n\n\nTraining data & Procedure\n-------------------------",
"#### Pre-trained baseline model\n\n\n* Pre-trained model: BERTweet\n\t+ trained based on the RoBERTa pre-training procedure\n\t+ 850M General English Tweets (Jan 2012 to Aug 2019)\n\t+ 23M COVID-19 English Tweets\n\t+ Size of the model: >134M parameters\n* Further training\n\t+ Pre-training with recent COVID-19/vaccine tweets and fine-tuning for fact classification",
"#### 1) Pre-training language model\n\n\n* The model was pre-trained on COVID-19/vaccined related tweets using a masked language modeling (MLM) objective starting from BERTweet.\n* Following datasets on English tweets were used:\n\t+ Tweets with trending #CovidVaccine hashtag, 207,000 tweets uploaded across Aug 2020 to Apr 2021 (kaggle)\n\t+ Tweets about all COVID-19 vaccines, 78,000 tweets uploaded across Dec 2020 to May 2021 (kaggle)\n\t+ COVID-19 Twitter chatter dataset, 590,000 tweets uploaded across Mar 2021 to May 2021 (github)",
"#### 2) Fine-tuning for fact classification\n\n\n* A fine-tuned model from pre-trained language model (1) for fact-classification task on COVID-19/vaccine.\n* COVID-19/vaccine-related statements were collected from Poynter and Snopes using Selenium resulting in over 14,000 fact-checked statements from Jan 2020 to May 2021.\n* Original labels were divided within following three categories:\n\t+ 'False': includes false, no evidence, manipulated, fake, not true, unproven and unverified\n\t+ 'Misleading': includes misleading, exaggerated, out of context and needs context\n\t+ 'True': includes true and correct\n\n\nEvaluation results\n------------------\n\n\n\nContributors\n============\n\n\n* This model is a part of final team project from MLDL for DS class at SNU.\n\t+ Team BIBI - Vaccinating COVID-NineTweets\n\t+ Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom\n\t+ Advisor: Prof. Wen-Syan Li\n\n\n<a href=\"URL src=\"URL width=\"200\" height=\"80\">"
] |
null | null |
This repository doesn't contain a model, but only a tokenizer that can be used with the
`tokenizers` library.
This tokenizer is just a copy of `bert-base-uncased`.
```python
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_pretrained("anthony/tokenizers-test")
```
|
{}
|
anthony/tokenizers-test
| null |
[
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
This repository doesn't contain a model, but only a tokenizer that can be used with the
'tokenizers' library.
This tokenizer is just a copy of 'bert-base-uncased'.
|
[] |
[
"TAGS\n#region-us \n"
] |
text-generation
|
transformers
|
# Belgian GPT-2 🇧🇪
**A GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).**
## Usage
You can use BelGPT-2 with [🤗 transformers](https://github.com/huggingface/transformers):
```python
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Load pretrained model and tokenizer
model = GPT2LMHeadModel.from_pretrained("antoiloui/belgpt2")
tokenizer = GPT2Tokenizer.from_pretrained("antoiloui/belgpt2")
# Generate a sample of text
model.eval()
output = model.generate(
bos_token_id=random.randint(1,50000),
do_sample=True,
top_k=50,
max_length=100,
top_p=0.95,
num_return_sequences=1
)
# Decode it
decoded_output = []
for sample in output:
decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output)
```
## Data
Below is the list of all French copora used to pre-trained the model:
| Dataset | `$corpus_name` | Raw size | Cleaned size |
| :------| :--- | :---: | :---: |
| CommonCrawl | `common_crawl` | 200.2 GB | 40.4 GB |
| NewsCrawl | `news_crawl` | 10.4 GB | 9.8 GB |
| Wikipedia | `wiki` | 19.4 GB | 4.1 GB |
| Wikisource | `wikisource` | 4.6 GB | 2.3 GB |
| Project Gutenberg | `gutenberg` | 1.3 GB | 1.1 GB |
| EuroParl | `europarl` | 289.9 MB | 278.7 MB |
| NewsCommentary | `news_commentary` | 61.4 MB | 58.1 MB |
| **Total** | | **236.3 GB** | **57.9 GB** |
## Documentation
Detailed documentation on the pre-trained model, its implementation, and the data can be found [here](https://github.com/antoiloui/belgpt2/blob/master/docs/index.md).
## Citation
For attribution in academic contexts, please cite this work as:
```
@misc{louis2020belgpt2,
author = {Louis, Antoine},
title = {{BelGPT-2: a GPT-2 model pre-trained on French corpora.}},
year = {2020},
howpublished = {\url{https://github.com/antoiloui/belgpt2}},
}
```
|
{"language": ["fr"], "license": ["mit"], "widget": [{"text": "Hier, Elon Musk a"}, {"text": "Pourquoi a-t-il"}, {"text": "Tout \u00e0 coup, elle"}]}
|
antoinelouis/belgpt2
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #fr #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Belgian GPT-2 🇧🇪
================
A GPT-2 model pre-trained on a very large and heterogeneous French corpus (~60Gb).
Usage
-----
You can use BelGPT-2 with transformers:
Data
----
Below is the list of all French copora used to pre-trained the model:
Documentation
-------------
Detailed documentation on the pre-trained model, its implementation, and the data can be found here.
For attribution in academic contexts, please cite this work as:
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #fr #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask
|
transformers
|
# NetBERT 📶
<img align="left" src="illustration.jpg" width="150"/>
<br><br><br>
NetBERT is a [BERT-base](https://huggingface.co/bert-base-cased) model further pre-trained on a huge corpus of computer networking text (~23Gb).
<br><br>
## Usage
You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.
You can use this model directly with a pipeline for [masked language modeling](https://huggingface.co/tasks/fill-mask):
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='antoinelouis/netbert')
unmasker("The nodes of a computer network may include [MASK].")
```
You can also use this model to [extract the features](https://huggingface.co/tasks/feature-extraction) of a given text:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/netbert')
model = AutoModel.from_pretrained('antoinelouis/netbert')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Documentation
Detailed documentation on the pre-trained model, its implementation, and the data can be found on [Github](https://github.com/antoiloui/netbert/blob/master/docs/index.md).
## Citation
For attribution in academic contexts, please cite this work as:
```
@mastersthesis{louis2020netbert,
title={NetBERT: A Pre-trained Language Representation Model for Computer Networking},
author={Louis, Antoine},
year={2020},
school={University of Liege}
}
```
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "widget": [{"text": "The nodes of a computer network may include [MASK]."}]}
|
antoinelouis/netbert
| null |
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# NetBERT
<img align="left" src="URL" width="150"/>
<br><br><br>
NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).
<br><br>
## Usage
You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.
You can use this model directly with a pipeline for masked language modeling:
You can also use this model to extract the features of a given text:
## Documentation
Detailed documentation on the pre-trained model, its implementation, and the data can be found on Github.
For attribution in academic contexts, please cite this work as:
|
[
"# NetBERT \n\n<img align=\"left\" src=\"URL\" width=\"150\"/>\n<br><br><br>\n\n NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).\n\n<br><br>",
"## Usage\n\nYou can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nYou can also use this model to extract the features of a given text:",
"## Documentation\n\nDetailed documentation on the pre-trained model, its implementation, and the data can be found on Github.\n\nFor attribution in academic contexts, please cite this work as:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# NetBERT \n\n<img align=\"left\" src=\"URL\" width=\"150\"/>\n<br><br><br>\n\n NetBERT is a BERT-base model further pre-trained on a huge corpus of computer networking text (~23Gb).\n\n<br><br>",
"## Usage\n\nYou can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nYou can also use this model to extract the features of a given text:",
"## Documentation\n\nDetailed documentation on the pre-trained model, its implementation, and the data can be found on Github.\n\nFor attribution in academic contexts, please cite this work as:"
] |
audio-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. -->
# distilhubert-ft-common-language
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7214
- Accuracy: 0.2797
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6543 | 1.0 | 173 | 3.7611 | 0.0491 |
| 3.2221 | 2.0 | 346 | 3.4868 | 0.1352 |
| 2.9332 | 3.0 | 519 | 3.2732 | 0.1861 |
| 2.7299 | 4.0 | 692 | 3.0944 | 0.2172 |
| 2.5638 | 5.0 | 865 | 2.9790 | 0.2400 |
| 2.3871 | 6.0 | 1038 | 2.8668 | 0.2590 |
| 2.3384 | 7.0 | 1211 | 2.7972 | 0.2653 |
| 2.2648 | 8.0 | 1384 | 2.7625 | 0.2695 |
| 2.2162 | 9.0 | 1557 | 2.7405 | 0.2782 |
| 2.1915 | 10.0 | 1730 | 2.7214 | 0.2797 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "distilhubert-ft-common-language", "results": []}]}
|
anton-l/distilhubert-ft-common-language
| null |
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us
|
distilhubert-ft-common-language
===============================
This model is a fine-tuned version of ntu-spml/distilhubert on the common\_language dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7214
* Accuracy: 0.2797
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 4
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
audio-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. -->
# distilhubert-ft-keyword-spotting
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1163
- Accuracy: 0.9706
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 256
- eval_batch_size: 32
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8176 | 1.0 | 200 | 0.7718 | 0.8116 |
| 0.2364 | 2.0 | 400 | 0.2107 | 0.9662 |
| 0.1198 | 3.0 | 600 | 0.1374 | 0.9678 |
| 0.0891 | 4.0 | 800 | 0.1163 | 0.9706 |
| 0.085 | 5.0 | 1000 | 0.1180 | 0.9690 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "distilhubert-ft-keyword-spotting", "results": []}]}
|
anton-l/distilhubert-ft-keyword-spotting
| null |
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
|
distilhubert-ft-keyword-spotting
================================
This model is a fine-tuned version of ntu-spml/distilhubert on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1163
* Accuracy: 0.9706
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 256
* eval\_batch\_size: 32
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 32\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 32\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
audio-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. -->
# hubert-base-ft-keyword-spotting
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0774
- Accuracy: 0.9819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0422 | 1.0 | 399 | 0.8999 | 0.6918 |
| 0.3296 | 2.0 | 798 | 0.1505 | 0.9778 |
| 0.2088 | 3.0 | 1197 | 0.0901 | 0.9816 |
| 0.202 | 4.0 | 1596 | 0.0848 | 0.9813 |
| 0.1535 | 5.0 | 1995 | 0.0774 | 0.9819 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "hubert-base-ft-keyword-spotting", "results": []}]}
|
anton-l/hubert-base-ft-keyword-spotting
| null |
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
hubert-base-ft-keyword-spotting
===============================
This model is a fine-tuned version of facebook/hubert-base-ls960 on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0774
* Accuracy: 0.9819
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #hubert #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
audio-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. -->
# sew-mid-100k-ft-common-language
This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1189
- Accuracy: 0.3842
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.608 | 1.0 | 173 | 3.7266 | 0.0540 |
| 3.1298 | 2.0 | 346 | 3.2180 | 0.1654 |
| 2.8481 | 3.0 | 519 | 2.9270 | 0.2019 |
| 2.648 | 4.0 | 692 | 2.6991 | 0.2619 |
| 2.5 | 5.0 | 865 | 2.5236 | 0.3004 |
| 2.2578 | 6.0 | 1038 | 2.4019 | 0.3212 |
| 2.2782 | 7.0 | 1211 | 2.1698 | 0.3658 |
| 2.1665 | 8.0 | 1384 | 2.1976 | 0.3631 |
| 2.1626 | 9.0 | 1557 | 2.1473 | 0.3791 |
| 2.1514 | 10.0 | 1730 | 2.1189 | 0.3842 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "sew-mid-100k-ft-common-language", "results": []}]}
|
anton-l/sew-mid-100k-ft-common-language
| null |
[
"transformers",
"pytorch",
"tensorboard",
"sew",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us
|
sew-mid-100k-ft-common-language
===============================
This model is a fine-tuned version of asapp/sew-mid-100k on the common\_language dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1189
* Accuracy: 0.3842
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 4
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
audio-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. -->
# sew-mid-100k-ft-keyword-spotting
This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0975
- Accuracy: 0.9757
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5999 | 1.0 | 399 | 0.2262 | 0.9635 |
| 0.4271 | 2.0 | 798 | 0.1230 | 0.9697 |
| 0.3778 | 3.0 | 1197 | 0.1052 | 0.9731 |
| 0.3227 | 4.0 | 1596 | 0.0975 | 0.9757 |
| 0.3081 | 5.0 | 1995 | 0.0962 | 0.9753 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "sew-mid-100k-ft-keyword-spotting", "results": []}]}
|
anton-l/sew-mid-100k-ft-keyword-spotting
| null |
[
"transformers",
"pytorch",
"tensorboard",
"sew",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
|
sew-mid-100k-ft-keyword-spotting
================================
This model is a fine-tuned version of asapp/sew-mid-100k on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0975
* Accuracy: 0.9757
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #sew #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
audio-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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Accuracy: 0.9823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7908 | 1.0 | 399 | 0.6776 | 0.9009 |
| 0.3202 | 2.0 | 798 | 0.2061 | 0.9763 |
| 0.221 | 3.0 | 1197 | 0.1257 | 0.9785 |
| 0.1773 | 4.0 | 1596 | 0.0990 | 0.9813 |
| 0.1729 | 5.0 | 1995 | 0.0952 | 0.9823 |
### 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": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]}
|
anton-l/wav2vec2-base-finetuned-ks
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0952
* Accuracy: 0.9823
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
audio-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. -->
# wav2vec2-base-ft-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0824
- Accuracy: 0.9826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8972 | 1.0 | 399 | 0.7023 | 0.8174 |
| 0.3274 | 2.0 | 798 | 0.1634 | 0.9773 |
| 0.1993 | 3.0 | 1197 | 0.1048 | 0.9788 |
| 0.1777 | 4.0 | 1596 | 0.0824 | 0.9826 |
| 0.1527 | 5.0 | 1995 | 0.0812 | 0.9810 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ft-keyword-spotting", "results": []}]}
|
anton-l/wav2vec2-base-ft-keyword-spotting
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-ft-keyword-spotting
=================================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0824
* Accuracy: 0.9826
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
audio-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. -->
# wav2vec2-base-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0746
- Accuracy: 0.9843
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8279 | 1.0 | 399 | 0.6792 | 0.8558 |
| 0.2961 | 2.0 | 798 | 0.1383 | 0.9798 |
| 0.2069 | 3.0 | 1197 | 0.0972 | 0.9809 |
| 0.1757 | 4.0 | 1596 | 0.0843 | 0.9825 |
| 0.1607 | 5.0 | 1995 | 0.0746 | 0.9843 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-keyword-spotting", "results": []}]}
|
anton-l/wav2vec2-base-keyword-spotting
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-keyword-spotting
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0746
* Accuracy: 0.9843
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
audio-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. -->
# wav2vec2-base-lang-id
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the anton-l/common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9836
- Accuracy: 0.7945
## 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: 32
- eval_batch_size: 4
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9568 | 1.0 | 173 | 3.2866 | 0.1146 |
| 1.9243 | 2.0 | 346 | 2.1241 | 0.3840 |
| 1.2923 | 3.0 | 519 | 1.5498 | 0.5489 |
| 0.8659 | 4.0 | 692 | 1.4953 | 0.6126 |
| 0.5539 | 5.0 | 865 | 1.2431 | 0.6926 |
| 0.4101 | 6.0 | 1038 | 1.1443 | 0.7232 |
| 0.2945 | 7.0 | 1211 | 1.0870 | 0.7544 |
| 0.1552 | 8.0 | 1384 | 1.1080 | 0.7661 |
| 0.0968 | 9.0 | 1557 | 0.9836 | 0.7945 |
| 0.0623 | 10.0 | 1730 | 1.0252 | 0.7993 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-lang-id", "results": []}]}
|
anton-l/wav2vec2-base-lang-id
| null |
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-lang-id
=====================
This model is a fine-tuned version of facebook/wav2vec2-base on the anton-l/common\_language dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9836
* Accuracy: 0.7945
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: 32
* eval\_batch\_size: 4
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.0.dev0\n* Pytorch 1.9.1+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
audio-classification
|
transformers
|
# Model Card for wav2vec2-base-superb-sv
# Model Details
## Model Description
- **Developed by:** Shu-wen Yang et al.
- **Shared by:** Anton Lozhkov
- **Model type:** Wav2Vec2 with an XVector head
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Related Models:**
- **Parent Model:** wav2vec2-large-lv60
- **Resources for more information:**
- [GitHub Repo](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/sv_voxceleb1)
- [Associated Paper](https://arxiv.org/abs/2105.010517)
# Uses
## Direct Use
This is a ported version of
[S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/sv_voxceleb1).
The base model is [wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60), which is pretrained on 16kHz
sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
See the [superb dataset card](https://huggingface.co/datasets/superb)
## Training Procedure
### Preprocessing
More information needed
### Speeds, Sizes, Times
More information needed
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
See the [superb dataset card](https://huggingface.co/datasets/superb)
### Factors
### Metrics
More information needed
## Results
More information needed
# Model Examination
More information needed
# Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
More information needed
# Citation
**BibTeX:**
```
@misc{https://doi.org/10.48550/arxiv.2006.11477,
doi = {10.48550/ARXIV.2006.11477},
url = {https://arxiv.org/abs/2006.11477},
author = {Baevski, Alexei and Zhou, Henry and Mohamed, Abdelrahman and Auli, Michael},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations},
publisher = {arXiv},
@misc{https://doi.org/10.48550/arxiv.2105.01051,
doi = {10.48550/ARXIV.2105.01051},
url = {https://arxiv.org/abs/2105.01051},
author = {Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y. and Liu, Andy T. and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and Huang, Tzu-Hsien and Tseng, Wei-Cheng and Lee, Ko-tik and Liu, Da-Rong and Huang, Zili and Dong, Shuyan and Li, Shang-Wen and Watanabe, Shinji and Mohamed, Abdelrahman and Lee, Hung-yi},
keywords = {Computation and Language (cs.CL), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {SUPERB: Speech processing Universal PERformance Benchmark},
publisher = {arXiv},
year = {2021},
}
```
# Glossary [optional]
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
Anton Lozhkov in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoProcessor, AutoModelForAudioXVector
processor = AutoProcessor.from_pretrained("anton-l/wav2vec2-base-superb-sv")
model = AutoModelForAudioXVector.from_pretrained("anton-l/wav2vec2-base-superb-sv")
```
</details>
|
{"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "wav2vec2", "audio-classification"], "datasets": ["superb"]}
|
anton-l/wav2vec2-base-superb-sv
| null |
[
"transformers",
"pytorch",
"wav2vec2",
"audio-xvector",
"speech",
"audio",
"audio-classification",
"en",
"dataset:superb",
"arxiv:2105.01051",
"arxiv:1910.09700",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[
"2105.01051",
"1910.09700",
"2006.11477"
] |
[
"en"
] |
TAGS
#transformers #pytorch #wav2vec2 #audio-xvector #speech #audio #audio-classification #en #dataset-superb #arxiv-2105.01051 #arxiv-1910.09700 #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Model Card for wav2vec2-base-superb-sv
# Model Details
## Model Description
- Developed by: Shu-wen Yang et al.
- Shared by: Anton Lozhkov
- Model type: Wav2Vec2 with an XVector head
- Language(s) (NLP): English
- License: Apache 2.0
- Related Models:
- Parent Model: wav2vec2-large-lv60
- Resources for more information:
- GitHub Repo
- Associated Paper
# Uses
## Direct Use
This is a ported version of
S3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.
The base model is wav2vec2-large-lv60, which is pretrained on 16kHz
sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
For more information refer to SUPERB: Speech processing Universal PERformance Benchmark
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
See the superb dataset card
## Training Procedure
### Preprocessing
More information needed
### Speeds, Sizes, Times
More information needed
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
See the superb dataset card
### Factors
### Metrics
More information needed
## Results
More information needed
# Model Examination
More information needed
# Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
# Technical Specifications [optional]
## Model Architecture and Objective
More information needed
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
More information needed
BibTeX:
# Glossary [optional]
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
Anton Lozhkov in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
</details>
|
[
"# Model Card for wav2vec2-base-superb-sv",
"# Model Details",
"## Model Description\n \n \n- Developed by: Shu-wen Yang et al.\n- Shared by: Anton Lozhkov\n- Model type: Wav2Vec2 with an XVector head\n- Language(s) (NLP): English\n- License: Apache 2.0\n- Related Models:\n - Parent Model: wav2vec2-large-lv60\n- Resources for more information: \n - GitHub Repo\n - Associated Paper",
"# Uses",
"## Direct Use\n \nThis is a ported version of \nS3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.\n\nThe base model is wav2vec2-large-lv60, which is pretrained on 16kHz \nsampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nFor more information refer to SUPERB: Speech processing Universal PERformance Benchmark",
"## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.",
"# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.",
"## Recommendations\n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"# Training Details",
"## Training Data\n \nSee the superb dataset card",
"## Training Procedure",
"### Preprocessing\n \nMore information needed",
"### Speeds, Sizes, Times\n \nMore information needed",
"# Evaluation",
"## Testing Data, Factors & Metrics",
"### Testing Data\n \nSee the superb dataset card",
"### Factors",
"### Metrics\n \nMore information needed",
"## Results \n \nMore information needed",
"# Model Examination\n \nMore information needed",
"# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed",
"# Technical Specifications [optional]",
"## Model Architecture and Objective\n \nMore information needed",
"## Compute Infrastructure\n \nMore information needed",
"### Hardware\n \nMore information needed",
"### Software\nMore information needed\n \nBibTeX:",
"# Glossary [optional]\nMore information needed",
"# More Information [optional]\n \nMore information needed",
"# Model Card Authors [optional]\n \n \nAnton Lozhkov in collaboration with Ezi Ozoani and the Hugging Face team",
"# Model Card Contact\n \nMore information needed",
"# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #audio-xvector #speech #audio #audio-classification #en #dataset-superb #arxiv-2105.01051 #arxiv-1910.09700 #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Model Card for wav2vec2-base-superb-sv",
"# Model Details",
"## Model Description\n \n \n- Developed by: Shu-wen Yang et al.\n- Shared by: Anton Lozhkov\n- Model type: Wav2Vec2 with an XVector head\n- Language(s) (NLP): English\n- License: Apache 2.0\n- Related Models:\n - Parent Model: wav2vec2-large-lv60\n- Resources for more information: \n - GitHub Repo\n - Associated Paper",
"# Uses",
"## Direct Use\n \nThis is a ported version of \nS3PRL's Wav2Vec2 for the SUPERB Speaker Verification task.\n\nThe base model is wav2vec2-large-lv60, which is pretrained on 16kHz \nsampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. \n\nFor more information refer to SUPERB: Speech processing Universal PERformance Benchmark",
"## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.",
"# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.",
"## Recommendations\n \nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"# Training Details",
"## Training Data\n \nSee the superb dataset card",
"## Training Procedure",
"### Preprocessing\n \nMore information needed",
"### Speeds, Sizes, Times\n \nMore information needed",
"# Evaluation",
"## Testing Data, Factors & Metrics",
"### Testing Data\n \nSee the superb dataset card",
"### Factors",
"### Metrics\n \nMore information needed",
"## Results \n \nMore information needed",
"# Model Examination\n \nMore information needed",
"# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed",
"# Technical Specifications [optional]",
"## Model Architecture and Objective\n \nMore information needed",
"## Compute Infrastructure\n \nMore information needed",
"### Hardware\n \nMore information needed",
"### Software\nMore information needed\n \nBibTeX:",
"# Glossary [optional]\nMore information needed",
"# More Information [optional]\n \nMore information needed",
"# Model Card Authors [optional]\n \n \nAnton Lozhkov in collaboration with Ezi Ozoani and the Hugging Face team",
"# Model Card Contact\n \nMore information needed",
"# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>"
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chuvash using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "cv", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Chuvash test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/cv.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-chuvash")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/cv/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/cv/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 40.01 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
The script used for training can be found [here](github.com)
|
{"language": "cv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Chuvash XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice cv", "type": "common_voice", "args": "cv"}, "metrics": [{"type": "wer", "value": 40.01, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-chuvash
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"cv",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"cv"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Chuvash
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Chuvash test data of Common Voice.
Test Result: 40.01 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-Chuvash\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Chuvash test data of Common Voice.\n\n\n\nTest Result: 40.01 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training.\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #cv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Chuvash\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Chuvash using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Chuvash test data of Common Voice.\n\n\n\nTest Result: 40.01 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training.\n\nThe script used for training can be found here"
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Estonian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "et", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Estonian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/et.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-estonian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/et/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/et/clips/"
def clean_sentence(sent):
sent = sent.lower()
# normalize apostrophes
sent = sent.replace("’", "'")
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() or ch == "'" else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 30.74 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
The script used for training can be found [here](github.com)
|
{"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Estonian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice et", "type": "common_voice", "args": "et"}, "metrics": [{"type": "wer", "value": 30.74, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-estonian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"et",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"et"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #et #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Estonian test data of Common Voice.
Test Result: 30.74 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Estonian test data of Common Voice.\n\n\n\nTest Result: 30.74 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training.\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #et #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Estonian test data of Common Voice.\n\n\n\nTest Result: 30.74 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training.\n\nThe script used for training can be found here"
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "hu", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-hungarian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-hungarian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Hungarian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/hu.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-hungarian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-hungarian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/hu/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/hu/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 42.26 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "hu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Hungarian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice hu", "type": "common_voice", "args": "hu"}, "metrics": [{"type": "wer", "value": 42.26, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-hungarian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hu",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"hu"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Hungarian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Hungarian test data of Common Voice.
Test Result: 42.26 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Hungarian test data of Common Voice.\n\n\n\nTest Result: 42.26 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Hungarian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Hungarian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Hungarian test data of Common Voice.\n\n\n\nTest Result: 42.26 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kyrgyz using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ky", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Kyrgyz test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ky.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-kyrgyz")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ky/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/ky/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 31.88 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "ky", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Kyrgyz XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ky", "type": "common_voice", "args": "ky"}, "metrics": [{"type": "wer", "value": 31.88, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-kyrgyz
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ky",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"ky"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ky #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Kyrgyz
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Kyrgyz test data of Common Voice.
Test Result: 31.88 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Kyrgyz test data of Common Voice.\n\n\n\nTest Result: 31.88 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ky #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Kyrgyz\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Kyrgyz using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Kyrgyz test data of Common Voice.\n\n\n\nTest Result: 31.88 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Latvian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Latvian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "lv", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Latvian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/lv.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-latvian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/lv/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/lv/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 26.89 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "lv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Latvian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice lv", "type": "common_voice", "args": "lv"}, "metrics": [{"type": "wer", "value": 26.89, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-latvian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lv",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"lv"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Latvian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Latvian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Latvian test data of Common Voice.
Test Result: 26.89 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Latvian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Latvian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Latvian test data of Common Voice.\n\n\n\nTest Result: 26.89 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Latvian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Latvian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Latvian test data of Common Voice.\n\n\n\nTest Result: 26.89 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Lithuanian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "lt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-lithuanian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Lithuanian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/lt.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-lithuanian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/lt/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/lt/clips/"
def clean_sentence(sent):
sent = sent.lower()
# normalize apostrophes
sent = sent.replace("’", "'")
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() or ch == "'" else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 49.00 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Lithuanian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice lt", "type": "common_voice", "args": "lt"}, "metrics": [{"type": "wer", "value": 49.0, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-lithuanian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"lt"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Lithuanian test data of Common Voice.
Test Result: 49.00 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Lithuanian test data of Common Voice.\n\n\n\nTest Result: 49.00 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Lithuanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Lithuanian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Lithuanian test data of Common Voice.\n\n\n\nTest Result: 49.00 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Mongolian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "mn", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Mongolian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/mn.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/mn/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/mn/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 38.53 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "mn", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Mongolian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice mn", "type": "common_voice", "args": "mn"}, "metrics": [{"type": "wer", "value": 38.53, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-mongolian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mn",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"mn"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Mongolian test data of Common Voice.
Test Result: 38.53 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Mongolian test data of Common Voice.\n\n\n\nTest Result: 38.53 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Mongolian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Mongolian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Mongolian test data of Common Voice.\n\n\n\nTest Result: 38.53 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Romanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romanian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ro", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Romanian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ro.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ro/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/ro/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 24.84 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "ro", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Romanian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ro", "type": "common_voice", "args": "ro"}, "metrics": [{"type": "wer", "value": 24.84, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-romanian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ro",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"ro"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ro #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Romanian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Romanian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Romanian test data of Common Voice.
Test Result: 24.84 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Romanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romanian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Romanian test data of Common Voice.\n\n\n\nTest Result: 24.84 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ro #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Romanian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Romanian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Romanian test data of Common Voice.\n\n\n\nTest Result: 24.84 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Russian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Russian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ru", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Russian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ru.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ru/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/ru/clips/"
def clean_sentence(sent):
sent = sent.lower()
# these letters are considered equivalent in written Russian
sent = sent.replace('ё', 'е')
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
# free up some memory
del model
del processor
del cv_test
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 17.39 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "ru", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Russian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ru", "type": "common_voice", "args": "ru"}, "metrics": [{"type": "wer", "value": 17.39, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-russian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ru",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Russian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Russian test data of Common Voice.
Test Result: 17.39 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Russian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Russian test data of Common Voice.\n\n\n\n\nTest Result: 17.39 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Russian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Russian test data of Common Voice.\n\n\n\n\nTest Result: 17.39 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Sakha
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Sakha using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "sah", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-sakha")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-sakha")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Sakha test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/sah.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-sakha")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-sakha")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/sah/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/sah/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 32.23 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "sah", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Sakha XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice sah", "type": "common_voice", "args": "sah"}, "metrics": [{"type": "wer", "value": 32.23, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-sakha
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sah",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"sah"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Sakha
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Sakha test data of Common Voice.
Test Result: 32.23 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Sakha\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Sakha test data of Common Voice.\n\n\n\nTest Result: 32.23 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sah #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Sakha\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Sakha using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Sakha test data of Common Voice.\n\n\n\nTest Result: 32.23 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Slovenian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Slovenian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "sl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-slovenian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-slovenian")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Slovenian test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/sl.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-slovenian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-slovenian")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/sl/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/sl/clips/"
def clean_sentence(sent):
sent = sent.lower()
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 36.04 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "sl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Slovenian XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice sl", "type": "common_voice", "args": "sl"}, "metrics": [{"type": "wer", "value": 36.04, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-slovenian
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"sl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"sl"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Slovenian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Slovenian using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Slovenian test data of Common Voice.
Test Result: 36.04 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Slovenian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Slovenian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Slovenian test data of Common Voice.\n\n\n\nTest Result: 36.04 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Slovenian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Slovenian using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Slovenian test data of Common Voice.\n\n\n\nTest Result: 36.04 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
automatic-speech-recognition
|
transformers
|
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Tatar using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tt", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Tatar test data of Common Voice.
```python
import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# Download the raw data instead of using HF datasets to save disk space
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/tt.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-tatar")
model.to("cuda")
cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/tt/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/tt/clips/"
def clean_sentence(sent):
sent = sent.lower()
# 'ё' is equivalent to 'е'
sent = sent.replace('ё', 'е')
# replace non-alpha characters with space
sent = "".join(ch if ch.isalpha() else " " for ch in sent)
# remove repeated spaces
sent = " ".join(sent.split())
return sent
targets = []
preds = []
for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
row["sentence"] = clean_sentence(row["sentence"])
speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
row["speech"] = resampler(speech_array).squeeze().numpy()
inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
targets.append(row["sentence"])
preds.append(processor.batch_decode(pred_ids)[0])
print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
```
**Test Result**: 26.76 %
## Training
The Common Voice `train` and `validation` datasets were used for training.
|
{"language": "tt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Tatar XLSR Wav2Vec2 Large 53 by Anton Lozhkov", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tt", "type": "common_voice", "args": "tt"}, "metrics": [{"type": "wer", "value": 26.76, "name": "Test WER"}]}]}]}
|
anton-l/wav2vec2-large-xlsr-53-tatar
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[
"tt"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Tatar
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Tatar test data of Common Voice.
Test Result: 26.76 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training.
|
[
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Tatar test data of Common Voice.\n\n\n\nTest Result: 26.76 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Tatar\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Tatar using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Tatar test data of Common Voice.\n\n\n\nTest Result: 26.76 %",
"## Training\n\nThe Common Voice 'train' and 'validation' datasets were used for training."
] |
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