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transformers
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<!-- 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. -->
# presentation_irony_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9344
- F1: 0.6745
## 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: 5.1637764704815665e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6675 | 1.0 | 90 | 0.5988 | 0.6684 |
| 0.5872 | 2.0 | 180 | 0.6039 | 0.6742 |
| 0.3953 | 3.0 | 270 | 0.8549 | 0.6557 |
| 0.0355 | 4.0 | 360 | 0.9344 | 0.6745 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "presentation_irony_42", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "irony"}, "metrics": [{"type": "f1", "value": 0.6745358521762839, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/presentation_irony_42
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
presentation\_irony\_42
=======================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9344
* F1: 0.6745
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: 5.1637764704815665e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.1637764704815665e-05\n* train\\_batch\\_size: 32\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 5.1637764704815665e-05\n* train\\_batch\\_size: 32\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
104,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 5.1637764704815665e-05\n* train\\_batch\\_size: 32\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
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. -->
# presentation_sentiment_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0860
- F1: 0.7183
## 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: 7.2792011721188e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- 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 | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3747 | 1.0 | 11404 | 0.6515 | 0.7045 |
| 0.6511 | 2.0 | 22808 | 0.7334 | 0.7188 |
| 0.0362 | 3.0 | 34212 | 0.9498 | 0.7195 |
| 1.0576 | 4.0 | 45616 | 1.0860 | 0.7183 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "presentation_sentiment_1234567", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics": [{"type": "f1", "value": 0.71829420028644, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/presentation_sentiment_1234567
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
presentation\_sentiment\_1234567
================================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0860
* F1: 0.7183
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: 7.2792011721188e-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.2792011721188e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 7.2792011721188e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
103,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 7.2792011721188e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
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. -->
# presentation_sentiment_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0860
- F1: 0.7183
## 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: 7.2792011721188e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- 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 | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3747 | 1.0 | 11404 | 0.6515 | 0.7045 |
| 0.6511 | 2.0 | 22808 | 0.7334 | 0.7188 |
| 0.0362 | 3.0 | 34212 | 0.9498 | 0.7195 |
| 1.0576 | 4.0 | 45616 | 1.0860 | 0.7183 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "presentation_sentiment_31415", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics": [{"type": "f1", "value": 0.71829420028644, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/presentation_sentiment_31415
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
presentation\_sentiment\_31415
==============================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0860
* F1: 0.7183
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: 7.2792011721188e-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.2792011721188e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 7.2792011721188e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
103,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 7.2792011721188e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
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. -->
# presentation_sentiment_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6491
- F1: 0.7176
## 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: 6.923967812567773e-06
- 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4391 | 1.0 | 2851 | 0.6591 | 0.6953 |
| 0.6288 | 2.0 | 5702 | 0.6265 | 0.7158 |
| 0.4071 | 3.0 | 8553 | 0.6401 | 0.7179 |
| 0.6532 | 4.0 | 11404 | 0.6491 | 0.7176 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "presentation_sentiment_42", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics": [{"type": "f1", "value": 0.7175864613336908, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/presentation_sentiment_42
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
presentation\_sentiment\_42
===========================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6491
* F1: 0.7176
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: 6.923967812567773e-06
* 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: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.923967812567773e-06\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: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 6.923967812567773e-06\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: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
104,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 6.923967812567773e-06\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: 4### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
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. -->
# sentiment_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2671
- F1: 0.7253
## 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: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- 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 | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6647 | 1.0 | 11404 | 0.6424 | 0.7189 |
| 0.6018 | 2.0 | 22808 | 0.7947 | 0.7170 |
| 0.5004 | 3.0 | 34212 | 1.0811 | 0.7200 |
| 0.3761 | 4.0 | 45616 | 1.2671 | 0.7253 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "sentiment_trained", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics": [{"type": "f1", "value": 0.7253452834090693, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/sentiment_trained
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
sentiment\_trained
==================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2671
* F1: 0.7253
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: 1.2140338797769864e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
104,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
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. -->
# sentiment_trained_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2854
- F1: 0.7165
## 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: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1234567
- 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 | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6603 | 1.0 | 11404 | 0.7020 | 0.6992 |
| 0.5978 | 2.0 | 22808 | 0.8024 | 0.7151 |
| 0.5495 | 3.0 | 34212 | 1.0837 | 0.7139 |
| 0.4026 | 4.0 | 45616 | 1.2854 | 0.7165 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "sentiment_trained_1234567", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics": [{"type": "f1", "value": 0.7165064254565859, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/sentiment_trained_1234567
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
sentiment\_trained\_1234567
===========================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2854
* F1: 0.7165
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: 1.2140338797769864e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 1234567
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 1234567\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 1234567\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
106,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 1234567\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
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. -->
# sentiment_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2481
- F1: 0.7188
## 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: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 31415
- 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 | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.651 | 1.0 | 11404 | 0.6669 | 0.7141 |
| 0.6066 | 2.0 | 22808 | 0.8160 | 0.7198 |
| 0.503 | 3.0 | 34212 | 1.0659 | 0.7182 |
| 0.386 | 4.0 | 45616 | 1.2481 | 0.7188 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "sentiment_trained_31415", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics": [{"type": "f1", "value": 0.7188262432133108, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/sentiment_trained_31415
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
sentiment\_trained\_31415
=========================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2481
* F1: 0.7188
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: 1.2140338797769864e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 31415
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 31415\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 31415\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
106,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 31415\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
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. -->
# sentiment_trained_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3194
- F1: 0.7132
## 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: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6405 | 1.0 | 11404 | 0.6631 | 0.7046 |
| 0.5998 | 2.0 | 22808 | 0.8429 | 0.7102 |
| 0.5118 | 3.0 | 34212 | 1.0906 | 0.7155 |
| 0.3745 | 4.0 | 45616 | 1.3194 | 0.7132 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "sentiment_trained_42", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "sentiment"}, "metrics": [{"type": "f1", "value": 0.7131935389791447, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/sentiment_trained_42
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
sentiment\_trained\_42
======================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3194
* F1: 0.7132
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: 1.2140338797769864e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
104,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 1.2140338797769864e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
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. -->
# test_emotion_trained_test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5866
- F1: 0.7015
## 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: 2.458132814624325e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 0
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 51 | 0.7877 | 0.5569 |
| No log | 2.0 | 102 | 0.6188 | 0.6937 |
| No log | 3.0 | 153 | 0.5969 | 0.7068 |
| No log | 4.0 | 204 | 0.5866 | 0.7015 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "test_emotion_trained_test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emotion"}, "metrics": [{"type": "f1", "value": 0.7014611518188594, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/test_emotion_trained_test
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
test\_emotion\_trained\_test
============================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5866
* F1: 0.7015
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: 2.458132814624325e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.458132814624325e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 2.458132814624325e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
104,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 2.458132814624325e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
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. -->
# test_hate_trained_test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1807
- F1: 0.7692
## 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: 5.257754679724796e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4362 | 1.0 | 1125 | 0.5282 | 0.7369 |
| 0.3193 | 2.0 | 2250 | 0.6364 | 0.7571 |
| 0.1834 | 3.0 | 3375 | 1.0346 | 0.7625 |
| 0.0776 | 4.0 | 4500 | 1.1807 | 0.7692 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "test_hate_trained_test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "hate"}, "metrics": [{"type": "f1", "value": 0.7691585677255204, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/test_hate_trained_test
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
test\_hate\_trained\_test
=========================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1807
* F1: 0.7692
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: 5.257754679724796e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.257754679724796e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 5.257754679724796e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
104,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 5.257754679724796e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
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. -->
# test_irony_trained_test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7674
- F1: 0.6680
## 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: 9.207906329883037e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 358 | 0.6655 | 0.5924 |
| 0.684 | 2.0 | 716 | 0.6889 | 0.6024 |
| 0.5826 | 3.0 | 1074 | 0.7085 | 0.6488 |
| 0.5826 | 4.0 | 1432 | 0.7674 | 0.6680 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tweet_eval"], "metrics": ["f1"], "model-index": [{"name": "test_irony_trained_test", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "irony"}, "metrics": [{"type": "f1", "value": 0.6680395323922843, "name": "F1"}]}]}]}
|
text-classification
|
aXhyra/test_irony_trained_test
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
test\_irony\_trained\_test
==========================
This model is a fine-tuned version of distilbert-base-uncased on the tweet\_eval dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7674
* F1: 0.6680
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: 9.207906329883037e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.207906329883037e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 9.207906329883037e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
65,
104,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-tweet_eval #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: 9.207906329883037e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
Please visit the repo for training details. https://github.com/AADeLucia/gpt2-narrative-decoding
|
{}
|
text-generation
|
aadelucia/GPT2_medium_narrative_finetuned_large
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Please visit the repo for training details. URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
50
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
Please visit the repo for training details. https://github.com/AADeLucia/gpt2-narrative-decoding
|
{}
|
text-generation
|
aadelucia/GPT2_medium_narrative_finetuned_medium
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Please visit the repo for training details. URL
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
50
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
Please visit the repo for training details. https://github.com/AADeLucia/gpt2-narrative-decoding
|
{}
|
text-generation
|
aadelucia/GPT2_small_narrative_finetuned_medium
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Please visit the repo for training details. URL
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# Chandler friends DialogGPT Modal
|
{"tags": ["conversational"]}
|
text-generation
|
aadilhassan/Chandlerbot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Chandler friends DialogGPT Modal
|
[
"# Chandler friends DialogGPT Modal"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Chandler friends DialogGPT Modal"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Chandler friends DialogGPT Modal"
] |
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] |
null | null |
transformers
|
# NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) and [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm)
# Wav2Vec2-Large-XLSR-53-Finnish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Finnish using the [Common Voice](https://huggingface.co/datasets/common_voice), [CSS10 Finnish](https://www.kaggle.com/bryanpark/finnish-single-speaker-speech-dataset) and [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) datasets.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import librosa
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "fi", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish")
model = Wav2Vec2ForCTC.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish")
resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 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(sampling_rate, speech_array).squeeze()
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 Finnish test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "fi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish")
model = Wav2Vec2ForCTC.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\...\…\–\é]'
resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(sampling_rate, speech_array).squeeze()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 32.378771 %
## Training
The Common Voice `train`, `validation` and `other` datasets were used for training as well as `CSS10 Finnish` and `Finnish parliament session 2` datasets.
The script used for training can be found from [Google Colab](https://colab.research.google.com/drive/1vnEGC9BnNRmVyIHj-0UsVulh_cUYSGWA?usp=sharing)
|
{"language": "fi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Finnish by Aapo Tanskanen", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice fi", "type": "common_voice", "args": "fi"}, "metrics": [{"type": "wer", "value": 32.378771, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
aapot/wav2vec2-large-xlsr-53-finnish
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fi",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fi"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 and Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm
# Wav2Vec2-Large-XLSR-53-Finnish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the Common Voice, CSS10 Finnish and Finnish parliament session 2 datasets.
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 Finnish test data of Common Voice.
Test Result: 32.378771 %
## Training
The Common Voice 'train', 'validation' and 'other' datasets were used for training as well as 'CSS10 Finnish' and 'Finnish parliament session 2' datasets.
The script used for training can be found from Google Colab
|
[
"# NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 and Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm",
"# Wav2Vec2-Large-XLSR-53-Finnish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the Common Voice, CSS10 Finnish and Finnish parliament session 2 datasets.\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 Finnish test data of Common Voice. \n\n\n\n\nTest Result: 32.378771 %",
"## Training\n\nThe Common Voice 'train', 'validation' and 'other' datasets were used for training as well as 'CSS10 Finnish' and 'Finnish parliament session 2' datasets.\n\nThe script used for training can be found from Google Colab"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 and Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm",
"# Wav2Vec2-Large-XLSR-53-Finnish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the Common Voice, CSS10 Finnish and Finnish parliament session 2 datasets.\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 Finnish test data of Common Voice. \n\n\n\n\nTest Result: 32.378771 %",
"## Training\n\nThe Common Voice 'train', 'validation' and 'other' datasets were used for training as well as 'CSS10 Finnish' and 'Finnish parliament session 2' datasets.\n\nThe script used for training can be found from Google Colab"
] |
[
80,
77,
80,
20,
29,
62
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# NOTE: this is an old model and should not be used anymore!! There are a lot better newer models available at our orgnization hub: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 and Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm# Wav2Vec2-Large-XLSR-53-Finnish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the Common Voice, CSS10 Finnish and Finnish parliament session 2 datasets.\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 Finnish test data of Common Voice. \n\n\n\n\nTest Result: 32.378771 %## Training\n\nThe Common Voice 'train', 'validation' and 'other' datasets were used for training as well as 'CSS10 Finnish' and 'Finnish parliament session 2' datasets.\n\nThe script used for training can be found from Google Colab"
] |
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] |
null | null |
transformers
|
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model.
**Note**: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization.
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-lm-v2/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.
## Training data
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned [Finnish Wikipedia](https://huggingface.co/datasets/wikipedia) (August 2021) dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.7778 | 0.17 | 500 | 0.2851 | 0.3572 |
| 0.5506 | 0.34 | 1000 | 0.1595 | 0.2130 |
| 0.6569 | 0.5 | 1500 | 0.1458 | 0.2046 |
| 0.5997 | 0.67 | 2000 | 0.1374 | 0.1975 |
| 0.542 | 0.84 | 2500 | 0.1390 | 0.1956 |
| 0.4815 | 1.01 | 3000 | 0.1266 | 0.1813 |
| 0.6982 | 1.17 | 3500 | 0.1441 | 0.1965 |
| 0.4522 | 1.34 | 4000 | 0.1232 | 0.1822 |
| 0.4655 | 1.51 | 4500 | 0.1209 | 0.1702 |
| 0.4069 | 1.68 | 5000 | 0.1149 | 0.1688 |
| 0.4226 | 1.84 | 5500 | 0.1121 | 0.1560 |
| 0.3993 | 2.01 | 6000 | 0.1091 | 0.1557 |
| 0.406 | 2.18 | 6500 | 0.1115 | 0.1553 |
| 0.4098 | 2.35 | 7000 | 0.1144 | 0.1560 |
| 0.3995 | 2.51 | 7500 | 0.1028 | 0.1476 |
| 0.4101 | 2.68 | 8000 | 0.1129 | 0.1511 |
| 0.3636 | 2.85 | 8500 | 0.1025 | 0.1517 |
| 0.3534 | 3.02 | 9000 | 0.1068 | 0.1480 |
| 0.3836 | 3.18 | 9500 | 0.1072 | 0.1459 |
| 0.3531 | 3.35 | 10000 | 0.0928 | 0.1367 |
| 0.3649 | 3.52 | 10500 | 0.1042 | 0.1426 |
| 0.3645 | 3.69 | 11000 | 0.0979 | 0.1433 |
| 0.3685 | 3.85 | 11500 | 0.0947 | 0.1346 |
| 0.3325 | 4.02 | 12000 | 0.0991 | 0.1352 |
| 0.3497 | 4.19 | 12500 | 0.0919 | 0.1358 |
| 0.3303 | 4.36 | 13000 | 0.0888 | 0.1272 |
| 0.3323 | 4.52 | 13500 | 0.0888 | 0.1277 |
| 0.3452 | 4.69 | 14000 | 0.0894 | 0.1279 |
| 0.337 | 4.86 | 14500 | 0.0917 | 0.1289 |
| 0.3114 | 5.03 | 15000 | 0.0942 | 0.1313 |
| 0.3099 | 5.19 | 15500 | 0.0902 | 0.1239 |
| 0.3079 | 5.36 | 16000 | 0.0871 | 0.1256 |
| 0.3293 | 5.53 | 16500 | 0.0861 | 0.1263 |
| 0.3123 | 5.7 | 17000 | 0.0876 | 0.1203 |
| 0.3093 | 5.86 | 17500 | 0.0848 | 0.1226 |
| 0.2903 | 6.03 | 18000 | 0.0914 | 0.1221 |
| 0.297 | 6.2 | 18500 | 0.0841 | 0.1185 |
| 0.2797 | 6.37 | 19000 | 0.0858 | 0.1165 |
| 0.2878 | 6.53 | 19500 | 0.0874 | 0.1161 |
| 0.2974 | 6.7 | 20000 | 0.0835 | 0.1173 |
| 0.3051 | 6.87 | 20500 | 0.0835 | 0.1178 |
| 0.2941 | 7.04 | 21000 | 0.0852 | 0.1155 |
| 0.258 | 7.21 | 21500 | 0.0832 | 0.1132 |
| 0.2778 | 7.37 | 22000 | 0.0829 | 0.1110 |
| 0.2751 | 7.54 | 22500 | 0.0822 | 0.1069 |
| 0.2887 | 7.71 | 23000 | 0.0819 | 0.1103 |
| 0.2509 | 7.88 | 23500 | 0.0787 | 0.1055 |
| 0.2501 | 8.04 | 24000 | 0.0807 | 0.1076 |
| 0.2399 | 8.21 | 24500 | 0.0784 | 0.1052 |
| 0.2539 | 8.38 | 25000 | 0.0772 | 0.1075 |
| 0.248 | 8.55 | 25500 | 0.0772 | 0.1055 |
| 0.2689 | 8.71 | 26000 | 0.0763 | 0.1027 |
| 0.2855 | 8.88 | 26500 | 0.0756 | 0.1035 |
| 0.2421 | 9.05 | 27000 | 0.0771 | 0.0998 |
| 0.2497 | 9.22 | 27500 | 0.0756 | 0.0971 |
| 0.2367 | 9.38 | 28000 | 0.0741 | 0.0974 |
| 0.2473 | 9.55 | 28500 | 0.0739 | 0.0982 |
| 0.2396 | 9.72 | 29000 | 0.0756 | 0.0991 |
| 0.2602 | 9.89 | 29500 | 0.0737 | 0.0975 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-lm-v2 --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
{"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-1b-finnish-lm-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "fi"}, "metrics": [{"type": "wer", "value": 4.09, "name": "Test WER"}, {"type": "cer", "value": 0.88, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
aapot/wav2vec2-xlsr-1b-finnish-lm-v2
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2111.09296"
] |
[
"fi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model.
Note: this model is exactly the same as the Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2 model so this model has just been copied/moved to the 'Finnish-NLP' Hugging Face organization.
Model description
-----------------
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from this blog and this paper.
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
Intended uses & limitations
---------------------------
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the URL notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.
Training data
-------------
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
Datasets were filtered to include maximum length of 20 seconds long audio samples.
Training procedure
------------------
This model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.
For the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned Finnish Wikipedia (August 2021) dataset.
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: 8-bit Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
The pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:
* attention\_dropout: 0.094
* hidden\_dropout: 0.047
* feat\_proj\_dropout: 0.04
* mask\_time\_prob: 0.082
* layerdrop: 0.041
* activation\_dropout: 0.055
* ctc\_loss\_reduction: "mean"
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
Evaluation results
------------------
Evaluation was done with the Common Voice 7.0 Finnish test split.
To evaluate this model, run the 'URL' script in this repository:
This model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
Team Members
------------
* Aapo Tanskanen, Hugging Face profile, LinkedIn profile
* Rasmus Toivanen, Hugging Face profile, LinkedIn profile
Feel free to contact us for more details
|
[
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nThe Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.\n\n\nFor the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned Finnish Wikipedia (August 2021) dataset.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nThe Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.\n\n\nFor the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned Finnish Wikipedia (August 2021) dataset.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
107,
25,
452,
238,
4,
154
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model."
] |
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null | null |
transformers
|
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model.
**Note**: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization.
**Note**: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2)
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects. It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.
## Training data
This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:----------------------------------------------------------------------------------------------------------------------------------|:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.74 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 5.94 h | 2.29 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.98 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 87.84 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 2.07 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.968 | 0.18 | 500 | 0.4870 | 0.4720 |
| 0.6557 | 0.36 | 1000 | 0.2450 | 0.2931 |
| 0.647 | 0.54 | 1500 | 0.1818 | 0.2255 |
| 0.5297 | 0.72 | 2000 | 0.1698 | 0.2354 |
| 0.5802 | 0.9 | 2500 | 0.1581 | 0.2355 |
| 0.6351 | 1.07 | 3000 | 0.1689 | 0.2336 |
| 0.4626 | 1.25 | 3500 | 0.1719 | 0.3099 |
| 0.4526 | 1.43 | 4000 | 0.1434 | 0.2069 |
| 0.4692 | 1.61 | 4500 | 0.1645 | 0.2192 |
| 0.4584 | 1.79 | 5000 | 0.1483 | 0.1987 |
| 0.4234 | 1.97 | 5500 | 0.1499 | 0.2178 |
| 0.4243 | 2.15 | 6000 | 0.1345 | 0.2070 |
| 0.4108 | 2.33 | 6500 | 0.1383 | 0.1850 |
| 0.4048 | 2.51 | 7000 | 0.1338 | 0.1811 |
| 0.4085 | 2.69 | 7500 | 0.1290 | 0.1780 |
| 0.4026 | 2.87 | 8000 | 0.1239 | 0.1650 |
| 0.4033 | 3.04 | 8500 | 0.1346 | 0.1657 |
| 0.3986 | 3.22 | 9000 | 0.1310 | 0.1850 |
| 0.3867 | 3.4 | 9500 | 0.1273 | 0.1741 |
| 0.3658 | 3.58 | 10000 | 0.1219 | 0.1672 |
| 0.382 | 3.76 | 10500 | 0.1306 | 0.1698 |
| 0.3847 | 3.94 | 11000 | 0.1230 | 0.1577 |
| 0.3691 | 4.12 | 11500 | 0.1310 | 0.1615 |
| 0.3593 | 4.3 | 12000 | 0.1296 | 0.1622 |
| 0.3619 | 4.48 | 12500 | 0.1285 | 0.1601 |
| 0.3361 | 4.66 | 13000 | 0.1261 | 0.1569 |
| 0.3603 | 4.84 | 13500 | 0.1235 | 0.1533 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
{"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-1b-finnish-lm", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "fi"}, "metrics": [{"type": "wer", "value": 5.65, "name": "Test WER"}, {"type": "cer", "value": 1.2, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
aapot/wav2vec2-xlsr-1b-finnish-lm
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2111.09296"
] |
[
"fi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model.
Note: this model is exactly the same as the Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm model so this model has just been copied/moved to the 'Finnish-NLP' Hugging Face organization.
Note: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2
Model description
-----------------
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from this blog and this paper.
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
Intended uses & limitations
---------------------------
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the URL notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects. It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.
Training data
-------------
This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:
Datasets were filtered to include maximum length of 20 seconds long audio samples.
Training procedure
------------------
This model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.
For the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data.
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: 8-bit Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
The pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:
* attention\_dropout: 0.094
* hidden\_dropout: 0.047
* feat\_proj\_dropout: 0.04
* mask\_time\_prob: 0.082
* layerdrop: 0.041
* activation\_dropout: 0.055
* ctc\_loss\_reduction: "mean"
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
Evaluation results
------------------
Evaluation was done with the Common Voice 7.0 Finnish test split.
To evaluate this model, run the 'URL' script in this repository:
This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
Team Members
------------
* Aapo Tanskanen, Hugging Face profile, LinkedIn profile
* Rasmus Toivanen, Hugging Face profile, LinkedIn profile
Feel free to contact us for more details
|
[
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nThe Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects. It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.\n\n\nFor the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nThe Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects. It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.\n\n\nFor the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
107,
25,
412,
238,
4,
154
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model."
] |
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null | null |
transformers
|
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
**Note**: there is a version with KenLM language model used in the decoding phase producing better transcriptions: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2)
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish-v2/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
## Training data
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.7778 | 0.17 | 500 | 0.2851 | 0.3572 |
| 0.5506 | 0.34 | 1000 | 0.1595 | 0.2130 |
| 0.6569 | 0.5 | 1500 | 0.1458 | 0.2046 |
| 0.5997 | 0.67 | 2000 | 0.1374 | 0.1975 |
| 0.542 | 0.84 | 2500 | 0.1390 | 0.1956 |
| 0.4815 | 1.01 | 3000 | 0.1266 | 0.1813 |
| 0.6982 | 1.17 | 3500 | 0.1441 | 0.1965 |
| 0.4522 | 1.34 | 4000 | 0.1232 | 0.1822 |
| 0.4655 | 1.51 | 4500 | 0.1209 | 0.1702 |
| 0.4069 | 1.68 | 5000 | 0.1149 | 0.1688 |
| 0.4226 | 1.84 | 5500 | 0.1121 | 0.1560 |
| 0.3993 | 2.01 | 6000 | 0.1091 | 0.1557 |
| 0.406 | 2.18 | 6500 | 0.1115 | 0.1553 |
| 0.4098 | 2.35 | 7000 | 0.1144 | 0.1560 |
| 0.3995 | 2.51 | 7500 | 0.1028 | 0.1476 |
| 0.4101 | 2.68 | 8000 | 0.1129 | 0.1511 |
| 0.3636 | 2.85 | 8500 | 0.1025 | 0.1517 |
| 0.3534 | 3.02 | 9000 | 0.1068 | 0.1480 |
| 0.3836 | 3.18 | 9500 | 0.1072 | 0.1459 |
| 0.3531 | 3.35 | 10000 | 0.0928 | 0.1367 |
| 0.3649 | 3.52 | 10500 | 0.1042 | 0.1426 |
| 0.3645 | 3.69 | 11000 | 0.0979 | 0.1433 |
| 0.3685 | 3.85 | 11500 | 0.0947 | 0.1346 |
| 0.3325 | 4.02 | 12000 | 0.0991 | 0.1352 |
| 0.3497 | 4.19 | 12500 | 0.0919 | 0.1358 |
| 0.3303 | 4.36 | 13000 | 0.0888 | 0.1272 |
| 0.3323 | 4.52 | 13500 | 0.0888 | 0.1277 |
| 0.3452 | 4.69 | 14000 | 0.0894 | 0.1279 |
| 0.337 | 4.86 | 14500 | 0.0917 | 0.1289 |
| 0.3114 | 5.03 | 15000 | 0.0942 | 0.1313 |
| 0.3099 | 5.19 | 15500 | 0.0902 | 0.1239 |
| 0.3079 | 5.36 | 16000 | 0.0871 | 0.1256 |
| 0.3293 | 5.53 | 16500 | 0.0861 | 0.1263 |
| 0.3123 | 5.7 | 17000 | 0.0876 | 0.1203 |
| 0.3093 | 5.86 | 17500 | 0.0848 | 0.1226 |
| 0.2903 | 6.03 | 18000 | 0.0914 | 0.1221 |
| 0.297 | 6.2 | 18500 | 0.0841 | 0.1185 |
| 0.2797 | 6.37 | 19000 | 0.0858 | 0.1165 |
| 0.2878 | 6.53 | 19500 | 0.0874 | 0.1161 |
| 0.2974 | 6.7 | 20000 | 0.0835 | 0.1173 |
| 0.3051 | 6.87 | 20500 | 0.0835 | 0.1178 |
| 0.2941 | 7.04 | 21000 | 0.0852 | 0.1155 |
| 0.258 | 7.21 | 21500 | 0.0832 | 0.1132 |
| 0.2778 | 7.37 | 22000 | 0.0829 | 0.1110 |
| 0.2751 | 7.54 | 22500 | 0.0822 | 0.1069 |
| 0.2887 | 7.71 | 23000 | 0.0819 | 0.1103 |
| 0.2509 | 7.88 | 23500 | 0.0787 | 0.1055 |
| 0.2501 | 8.04 | 24000 | 0.0807 | 0.1076 |
| 0.2399 | 8.21 | 24500 | 0.0784 | 0.1052 |
| 0.2539 | 8.38 | 25000 | 0.0772 | 0.1075 |
| 0.248 | 8.55 | 25500 | 0.0772 | 0.1055 |
| 0.2689 | 8.71 | 26000 | 0.0763 | 0.1027 |
| 0.2855 | 8.88 | 26500 | 0.0756 | 0.1035 |
| 0.2421 | 9.05 | 27000 | 0.0771 | 0.0998 |
| 0.2497 | 9.22 | 27500 | 0.0756 | 0.0971 |
| 0.2367 | 9.38 | 28000 | 0.0741 | 0.0974 |
| 0.2473 | 9.55 | 28500 | 0.0739 | 0.0982 |
| 0.2396 | 9.72 | 29000 | 0.0756 | 0.0991 |
| 0.2602 | 9.89 | 29500 | 0.0737 | 0.0975 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish-v2 --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
{"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-1b-finnish-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "fi"}, "metrics": [{"type": "wer", "value": 9.73, "name": "Test WER"}, {"type": "cer", "value": 1.65, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
aapot/wav2vec2-xlsr-1b-finnish-v2
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2111.09296"
] |
[
"fi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
Note: there is a version with KenLM language model used in the decoding phase producing better transcriptions: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2
Model description
-----------------
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from this blog and this paper.
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
Intended uses & limitations
---------------------------
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the URL notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
Training data
-------------
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
Datasets were filtered to include maximum length of 20 seconds long audio samples.
Training procedure
------------------
This model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: 8-bit Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
The pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:
* attention\_dropout: 0.094
* hidden\_dropout: 0.047
* feat\_proj\_dropout: 0.04
* mask\_time\_prob: 0.082
* layerdrop: 0.041
* activation\_dropout: 0.055
* ctc\_loss\_reduction: "mean"
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
Evaluation results
------------------
Evaluation was done with the Common Voice 7.0 Finnish test split.
To evaluate this model, run the 'URL' script in this repository:
This model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
Team Members
------------
* Aapo Tanskanen, Hugging Face profile, LinkedIn profile
* Rasmus Toivanen, Hugging Face profile, LinkedIn profile
Feel free to contact us for more details
|
[
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the first row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
107,
25,
287,
238,
4,
154
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets."
] |
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null | null |
transformers
|
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
**Note**: there is a version with KenLM language model used in the decoding phase producing better transcriptions: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm)
**Note**: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: [Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2)
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-1b-finnish/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
## Training data
This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:----------------------------------------------------------------------------------------------------------------------------------|:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.74 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 5.94 h | 2.29 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.98 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 87.84 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 2.07 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-1b` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.968 | 0.18 | 500 | 0.4870 | 0.4720 |
| 0.6557 | 0.36 | 1000 | 0.2450 | 0.2931 |
| 0.647 | 0.54 | 1500 | 0.1818 | 0.2255 |
| 0.5297 | 0.72 | 2000 | 0.1698 | 0.2354 |
| 0.5802 | 0.9 | 2500 | 0.1581 | 0.2355 |
| 0.6351 | 1.07 | 3000 | 0.1689 | 0.2336 |
| 0.4626 | 1.25 | 3500 | 0.1719 | 0.3099 |
| 0.4526 | 1.43 | 4000 | 0.1434 | 0.2069 |
| 0.4692 | 1.61 | 4500 | 0.1645 | 0.2192 |
| 0.4584 | 1.79 | 5000 | 0.1483 | 0.1987 |
| 0.4234 | 1.97 | 5500 | 0.1499 | 0.2178 |
| 0.4243 | 2.15 | 6000 | 0.1345 | 0.2070 |
| 0.4108 | 2.33 | 6500 | 0.1383 | 0.1850 |
| 0.4048 | 2.51 | 7000 | 0.1338 | 0.1811 |
| 0.4085 | 2.69 | 7500 | 0.1290 | 0.1780 |
| 0.4026 | 2.87 | 8000 | 0.1239 | 0.1650 |
| 0.4033 | 3.04 | 8500 | 0.1346 | 0.1657 |
| 0.3986 | 3.22 | 9000 | 0.1310 | 0.1850 |
| 0.3867 | 3.4 | 9500 | 0.1273 | 0.1741 |
| 0.3658 | 3.58 | 10000 | 0.1219 | 0.1672 |
| 0.382 | 3.76 | 10500 | 0.1306 | 0.1698 |
| 0.3847 | 3.94 | 11000 | 0.1230 | 0.1577 |
| 0.3691 | 4.12 | 11500 | 0.1310 | 0.1615 |
| 0.3593 | 4.3 | 12000 | 0.1296 | 0.1622 |
| 0.3619 | 4.48 | 12500 | 0.1285 | 0.1601 |
| 0.3361 | 4.66 | 13000 | 0.1261 | 0.1569 |
| 0.3603 | 4.84 | 13500 | 0.1235 | 0.1533 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-1b-finnish --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
{"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-1b-finnish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "fi"}, "metrics": [{"type": "wer", "value": 13.11, "name": "Test WER"}, {"type": "cer", "value": 2.23, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
aapot/wav2vec2-xlsr-1b-finnish
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2111.09296"
] |
[
"fi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-1b for Finnish ASR. The model has been fine-tuned with 259.57 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
Note: there is a version with KenLM language model used in the decoding phase producing better transcriptions: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm
Note: there is a better V2 version of this model which has been fine-tuned longer with 16 hours of more data: Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2
Model description
-----------------
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from this blog and this paper.
This model is fine-tuned version of the pretrained model (1 billion parameter variant) for Finnish ASR.
Intended uses & limitations
---------------------------
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the URL notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
Training data
-------------
This model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:
Datasets were filtered to include maximum length of 20 seconds long audio samples.
Training procedure
------------------
This model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: 8-bit Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
The pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:
* attention\_dropout: 0.094
* hidden\_dropout: 0.047
* feat\_proj\_dropout: 0.04
* mask\_time\_prob: 0.082
* layerdrop: 0.041
* activation\_dropout: 0.055
* ctc\_loss\_reduction: "mean"
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
Evaluation results
------------------
Evaluation was done with the Common Voice 7.0 Finnish test split.
To evaluate this model, run the 'URL' script in this repository:
This model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
Team Members
------------
* Aapo Tanskanen, Hugging Face profile, LinkedIn profile
* Rasmus Toivanen, Hugging Face profile, LinkedIn profile
Feel free to contact us for more details
|
[
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: 8-bit 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\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-1b' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the second row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
107,
25,
288,
238,
4,
154
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 259.57 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets."
] |
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null | null |
transformers
|
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model.
**Note**: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization.
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (300 million parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-300m-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.
## Training data
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned [Finnish Wikipedia](https://huggingface.co/datasets/wikipedia) (August 2021) dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-300m` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.973 | 0.17 | 500 | 0.5750 | 0.6844 |
| 0.713 | 0.34 | 1000 | 0.3356 | 0.4518 |
| 0.6563 | 0.5 | 1500 | 0.3007 | 0.4039 |
| 0.642 | 0.67 | 2000 | 0.2619 | 0.3674 |
| 0.6203 | 0.84 | 2500 | 0.2488 | 0.3558 |
| 0.6016 | 1.01 | 3000 | 0.2795 | 0.3835 |
| 0.5423 | 1.17 | 3500 | 0.2652 | 0.3310 |
| 0.5639 | 1.34 | 4000 | 0.2479 | 0.3462 |
| 0.586 | 1.51 | 4500 | 0.2409 | 0.3295 |
| 0.5169 | 1.68 | 5000 | 0.2728 | 0.3352 |
| 0.5176 | 1.84 | 5500 | 0.2254 | 0.3149 |
| 0.4983 | 2.01 | 6000 | 0.2169 | 0.3009 |
| 0.4982 | 2.18 | 6500 | 0.2215 | 0.3079 |
| 0.4898 | 2.35 | 7000 | 0.2174 | 0.3023 |
| 0.4922 | 2.51 | 7500 | 0.2217 | 0.3081 |
| 0.5025 | 2.68 | 8000 | 0.2002 | 0.2710 |
| 0.4745 | 2.85 | 8500 | 0.1935 | 0.2783 |
| 0.4377 | 3.02 | 9000 | 0.1859 | 0.2742 |
| 0.4511 | 3.18 | 9500 | 0.2038 | 0.2786 |
| 0.4411 | 3.35 | 10000 | 0.1863 | 0.2651 |
| 0.4501 | 3.52 | 10500 | 0.1948 | 0.2605 |
| 0.4557 | 3.69 | 11000 | 0.1872 | 0.2695 |
| 0.4493 | 3.85 | 11500 | 0.1888 | 0.2632 |
| 0.4047 | 4.02 | 12000 | 0.1818 | 0.2559 |
| 0.4319 | 4.19 | 12500 | 0.1896 | 0.2648 |
| 0.4162 | 4.36 | 13000 | 0.1953 | 0.2595 |
| 0.4046 | 4.52 | 13500 | 0.1864 | 0.2606 |
| 0.4195 | 4.69 | 14000 | 0.1843 | 0.2467 |
| 0.4146 | 4.86 | 14500 | 0.1686 | 0.2450 |
| 0.378 | 5.03 | 15000 | 0.1731 | 0.2401 |
| 0.3792 | 5.19 | 15500 | 0.1676 | 0.2325 |
| 0.3855 | 5.36 | 16000 | 0.1740 | 0.2326 |
| 0.4029 | 5.53 | 16500 | 0.1674 | 0.2345 |
| 0.386 | 5.7 | 17000 | 0.1735 | 0.2280 |
| 0.3811 | 5.86 | 17500 | 0.1692 | 0.2258 |
| 0.3607 | 6.03 | 18000 | 0.1797 | 0.2279 |
| 0.3604 | 6.2 | 18500 | 0.1651 | 0.2206 |
| 0.3362 | 6.37 | 19000 | 0.1627 | 0.2199 |
| 0.3611 | 6.53 | 19500 | 0.1652 | 0.2172 |
| 0.3671 | 6.7 | 20000 | 0.1564 | 0.2140 |
| 0.3769 | 6.87 | 20500 | 0.1525 | 0.2101 |
| 0.3539 | 7.04 | 21000 | 0.1639 | 0.2096 |
| 0.3225 | 7.21 | 21500 | 0.1611 | 0.2087 |
| 0.3323 | 7.37 | 22000 | 0.1633 | 0.2008 |
| 0.3327 | 7.54 | 22500 | 0.1692 | 0.1975 |
| 0.3456 | 7.71 | 23000 | 0.1555 | 0.1991 |
| 0.3058 | 7.88 | 23500 | 0.1590 | 0.1959 |
| 0.3034 | 8.04 | 24000 | 0.1531 | 0.1973 |
| 0.2925 | 8.21 | 24500 | 0.1583 | 0.1978 |
| 0.2967 | 8.38 | 25000 | 0.1546 | 0.1906 |
| 0.2974 | 8.55 | 25500 | 0.1540 | 0.1869 |
| 0.3131 | 8.71 | 26000 | 0.1534 | 0.1850 |
| 0.3306 | 8.88 | 26500 | 0.1482 | 0.1844 |
| 0.2842 | 9.05 | 27000 | 0.1490 | 0.1854 |
| 0.2879 | 9.22 | 27500 | 0.1463 | 0.1799 |
| 0.27 | 9.38 | 28000 | 0.1454 | 0.1798 |
| 0.2874 | 9.55 | 28500 | 0.1504 | 0.1787 |
| 0.2757 | 9.72 | 29000 | 0.1512 | 0.1784 |
| 0.3017 | 9.89 | 29500 | 0.1484 | 0.1800 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-300m-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
{"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-300m-finnish-lm", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "fi"}, "metrics": [{"type": "wer", "value": 8.16, "name": "Test WER"}, {"type": "cer", "value": 1.97, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
aapot/wav2vec2-xlsr-300m-finnish-lm
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2111.09296"
] |
[
"fi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model.
Note: this model is exactly the same as the Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm model so this model has just been copied/moved to the 'Finnish-NLP' Hugging Face organization.
Model description
-----------------
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from this blog and this paper.
This model is fine-tuned version of the pretrained model (300 million parameter variant) for Finnish ASR.
Intended uses & limitations
---------------------------
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the URL notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.
Training data
-------------
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
Datasets were filtered to include maximum length of 20 seconds long audio samples.
Training procedure
------------------
This model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.
For the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned Finnish Wikipedia (August 2021) dataset.
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-04
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: 8-bit Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
The pretrained 'facebook/wav2vec2-xls-r-300m' model was initialized with following hyperparameters:
* attention\_dropout: 0.094
* hidden\_dropout: 0.047
* feat\_proj\_dropout: 0.04
* mask\_time\_prob: 0.082
* layerdrop: 0.041
* activation\_dropout: 0.055
* ctc\_loss\_reduction: "mean"
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
Evaluation results
------------------
Evaluation was done with the Common Voice 7.0 Finnish test split.
To evaluate this model, run the 'URL' script in this repository:
This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
Team Members
------------
* Aapo Tanskanen, Hugging Face profile, LinkedIn profile
* Rasmus Toivanen, Hugging Face profile, LinkedIn profile
Feel free to contact us for more details
|
[
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nThe Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.\n\n\nFor the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned Finnish Wikipedia (August 2021) dataset.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-04\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-300m' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nThe Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.\n\n\nFor the KenLM language model training, we followed the blog post tutorial provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned Finnish Wikipedia (August 2021) dataset.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-04\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-300m' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
107,
25,
452,
239,
4,
154
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model."
] |
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] |
null | null |
transformers
|
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
**Note**: there is a version with KenLM language model used in the decoding phase producing better transcriptions: [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm)
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (300 million parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-300m-finnish/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
## Training data
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-300m` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.973 | 0.17 | 500 | 0.5750 | 0.6844 |
| 0.713 | 0.34 | 1000 | 0.3356 | 0.4518 |
| 0.6563 | 0.5 | 1500 | 0.3007 | 0.4039 |
| 0.642 | 0.67 | 2000 | 0.2619 | 0.3674 |
| 0.6203 | 0.84 | 2500 | 0.2488 | 0.3558 |
| 0.6016 | 1.01 | 3000 | 0.2795 | 0.3835 |
| 0.5423 | 1.17 | 3500 | 0.2652 | 0.3310 |
| 0.5639 | 1.34 | 4000 | 0.2479 | 0.3462 |
| 0.586 | 1.51 | 4500 | 0.2409 | 0.3295 |
| 0.5169 | 1.68 | 5000 | 0.2728 | 0.3352 |
| 0.5176 | 1.84 | 5500 | 0.2254 | 0.3149 |
| 0.4983 | 2.01 | 6000 | 0.2169 | 0.3009 |
| 0.4982 | 2.18 | 6500 | 0.2215 | 0.3079 |
| 0.4898 | 2.35 | 7000 | 0.2174 | 0.3023 |
| 0.4922 | 2.51 | 7500 | 0.2217 | 0.3081 |
| 0.5025 | 2.68 | 8000 | 0.2002 | 0.2710 |
| 0.4745 | 2.85 | 8500 | 0.1935 | 0.2783 |
| 0.4377 | 3.02 | 9000 | 0.1859 | 0.2742 |
| 0.4511 | 3.18 | 9500 | 0.2038 | 0.2786 |
| 0.4411 | 3.35 | 10000 | 0.1863 | 0.2651 |
| 0.4501 | 3.52 | 10500 | 0.1948 | 0.2605 |
| 0.4557 | 3.69 | 11000 | 0.1872 | 0.2695 |
| 0.4493 | 3.85 | 11500 | 0.1888 | 0.2632 |
| 0.4047 | 4.02 | 12000 | 0.1818 | 0.2559 |
| 0.4319 | 4.19 | 12500 | 0.1896 | 0.2648 |
| 0.4162 | 4.36 | 13000 | 0.1953 | 0.2595 |
| 0.4046 | 4.52 | 13500 | 0.1864 | 0.2606 |
| 0.4195 | 4.69 | 14000 | 0.1843 | 0.2467 |
| 0.4146 | 4.86 | 14500 | 0.1686 | 0.2450 |
| 0.378 | 5.03 | 15000 | 0.1731 | 0.2401 |
| 0.3792 | 5.19 | 15500 | 0.1676 | 0.2325 |
| 0.3855 | 5.36 | 16000 | 0.1740 | 0.2326 |
| 0.4029 | 5.53 | 16500 | 0.1674 | 0.2345 |
| 0.386 | 5.7 | 17000 | 0.1735 | 0.2280 |
| 0.3811 | 5.86 | 17500 | 0.1692 | 0.2258 |
| 0.3607 | 6.03 | 18000 | 0.1797 | 0.2279 |
| 0.3604 | 6.2 | 18500 | 0.1651 | 0.2206 |
| 0.3362 | 6.37 | 19000 | 0.1627 | 0.2199 |
| 0.3611 | 6.53 | 19500 | 0.1652 | 0.2172 |
| 0.3671 | 6.7 | 20000 | 0.1564 | 0.2140 |
| 0.3769 | 6.87 | 20500 | 0.1525 | 0.2101 |
| 0.3539 | 7.04 | 21000 | 0.1639 | 0.2096 |
| 0.3225 | 7.21 | 21500 | 0.1611 | 0.2087 |
| 0.3323 | 7.37 | 22000 | 0.1633 | 0.2008 |
| 0.3327 | 7.54 | 22500 | 0.1692 | 0.1975 |
| 0.3456 | 7.71 | 23000 | 0.1555 | 0.1991 |
| 0.3058 | 7.88 | 23500 | 0.1590 | 0.1959 |
| 0.3034 | 8.04 | 24000 | 0.1531 | 0.1973 |
| 0.2925 | 8.21 | 24500 | 0.1583 | 0.1978 |
| 0.2967 | 8.38 | 25000 | 0.1546 | 0.1906 |
| 0.2974 | 8.55 | 25500 | 0.1540 | 0.1869 |
| 0.3131 | 8.71 | 26000 | 0.1534 | 0.1850 |
| 0.3306 | 8.88 | 26500 | 0.1482 | 0.1844 |
| 0.2842 | 9.05 | 27000 | 0.1490 | 0.1854 |
| 0.2879 | 9.22 | 27500 | 0.1463 | 0.1799 |
| 0.27 | 9.38 | 28000 | 0.1454 | 0.1798 |
| 0.2874 | 9.55 | 28500 | 0.1504 | 0.1787 |
| 0.2757 | 9.72 | 29000 | 0.1512 | 0.1784 |
| 0.3017 | 9.89 | 29500 | 0.1484 | 0.1800 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-300m-finnish --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
|
{"language": "fi", "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "finnish", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "wav2vec2-xlsr-300m-finnish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "fi"}, "metrics": [{"type": "wer", "value": 17.92, "name": "Test WER"}, {"type": "cer", "value": 3.36, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
aapot/wav2vec2-xlsr-300m-finnish
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2111.09296"
] |
[
"fi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2Vec2 XLS-R for Finnish ASR
==============================
This acoustic model is a fine-tuned version of facebook/wav2vec2-xls-r-300m for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
this paper and first released at this page.
Note: there is a version with KenLM language model used in the decoding phase producing better transcriptions: Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm
Model description
-----------------
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from this blog and this paper.
This model is fine-tuned version of the pretrained model (300 million parameter variant) for Finnish ASR.
Intended uses & limitations
---------------------------
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the URL notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
Training data
-------------
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
Datasets were filtered to include maximum length of 20 seconds long audio samples.
Training procedure
------------------
This model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-04
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: 8-bit Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
The pretrained 'facebook/wav2vec2-xls-r-300m' model was initialized with following hyperparameters:
* attention\_dropout: 0.094
* hidden\_dropout: 0.047
* feat\_proj\_dropout: 0.04
* mask\_time\_prob: 0.082
* layerdrop: 0.041
* activation\_dropout: 0.055
* ctc\_loss\_reduction: "mean"
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
Evaluation results
------------------
Evaluation was done with the Common Voice 7.0 Finnish test split.
To evaluate this model, run the 'URL' script in this repository:
This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
Team Members
------------
* Aapo Tanskanen, Hugging Face profile, LinkedIn profile
* Rasmus Toivanen, Hugging Face profile, LinkedIn profile
Feel free to contact us for more details
|
[
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-04\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-300m' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.",
"### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets.",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-04\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: 8-bit 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: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nThe pretrained 'facebook/wav2vec2-xls-r-300m' model was initialized with following hyperparameters:\n\n\n* attention\\_dropout: 0.094\n* hidden\\_dropout: 0.047\n* feat\\_proj\\_dropout: 0.04\n* mask\\_time\\_prob: 0.082\n* layerdrop: 0.041\n* activation\\_dropout: 0.055\n* ctc\\_loss\\_reduction: \"mean\"",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0\n\n\nEvaluation results\n------------------\n\n\nEvaluation was done with the Common Voice 7.0 Finnish test split.\n\n\nTo evaluate this model, run the 'URL' script in this repository:\n\n\nThis model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:\n\n\n\nTeam Members\n------------\n\n\n* Aapo Tanskanen, Hugging Face profile, LinkedIn profile\n* Rasmus Toivanen, Hugging Face profile, LinkedIn profile\n\n\nFeel free to contact us for more details"
] |
[
107,
25,
287,
239,
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[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #finnish #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #arxiv-2111.09296 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### How to use\n\n\nCheck the URL notebook in this repository for an detailed example on how to use this model.### Limitations and bias\n\n\nThis model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in this blog post.\n\n\nA vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.\n\n\nTraining data\n-------------\n\n\nThis model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:\n\n\n\nDatasets were filtered to include maximum length of 20 seconds long audio samples.\n\n\nTraining procedure\n------------------\n\n\nThis model was trained during Robust Speech Challenge Event organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.\n\n\nTraining script was provided by Hugging Face and it is available here. We only modified its data loading for our custom datasets."
] |
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null | null |
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.9456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
{"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "args": "en-fr"}, "metrics": [{"type": "bleu", "value": 52.94560734092563, "name": "Bleu"}]}]}]}
|
translation
|
aaraki/marian-finetuned-kde4-en-to-fr
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.9456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
[
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8559\n- Bleu: 52.9456",
"## 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: 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- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.6"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8559\n- Bleu: 52.9456",
"## 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: 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- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.6"
] |
[
72,
73,
6,
12,
8,
3,
103,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8559\n- Bleu: 52.9456## 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: 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- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.6"
] |
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null | null |
transformers
|
---
datasets:
-
common_voice: ~
language:
-
ur: ~
library_name:
transformers: ~
license:
mit: ~
metrics:
-
wer: ~
model-index:
-
name:
wav2vec2-xls-r-300m-Urdu: ~
results:
-
task:
dataset:
args:
ur: ~
name:
: "common_voice"
: ~
type:
common_voice: ~
metrics:
-
type:
wer: ~
value:
0.2459: ~
-
type:
cer: ~
value:
0.0691: ~
type:
automatic-speech-recognition: ~
tags:
-
audio: ~
-
automatic-speech-recognition: ~
-
speech: ~
Finetuning of [Facebook's 300M model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Common Voice 8.0 Urdu dataset
|
{}
|
automatic-speech-recognition
|
aasem/wav2vec2-xls-r-300m-Urdu
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
|
---
datasets:
-
common_voice: ~
language:
-
ur: ~
library_name:
transformers: ~
license:
mit: ~
metrics:
-
wer: ~
model-index:
-
name:
wav2vec2-xls-r-300m-Urdu: ~
results:
-
task:
dataset:
args:
ur: ~
name:
: "common_voice"
: ~
type:
common_voice: ~
metrics:
-
type:
wer: ~
value:
0.2459: ~
-
type:
cer: ~
value:
0.0691: ~
type:
automatic-speech-recognition: ~
tags:
-
audio: ~
-
automatic-speech-recognition: ~
-
speech: ~
Finetuning of Facebook's 300M model on Common Voice 8.0 Urdu dataset
|
[] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n"
] |
[
41
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
aashutosh2102/DialoGPT-smalll-harrypotter
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model
|
[
"# Harry Potter DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
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null | null |
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. -->
# c4-aristo-roberta-large
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0332
- Accuracy: 0.7370
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8204 | 1.0 | 140 | 0.7246 | 0.7171 |
| 0.5512 | 2.0 | 280 | 0.7441 | 0.7312 |
| 0.3437 | 3.0 | 420 | 0.8940 | 0.7363 |
| 0.291 | 4.0 | 560 | 1.0332 | 0.7370 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.10.0.dev20210620+cu113
- Datasets 1.6.2
- Tokenizers 0.10.2
|
{"metrics": ["accuracy"]}
|
multiple-choice
|
abarbosa/c4-aristo-roberta-large
|
[
"transformers",
"pytorch",
"roberta",
"multiple-choice",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #multiple-choice #endpoints_compatible #region-us
|
c4-aristo-roberta-large
=======================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0332
* Accuracy: 0.7370
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: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.6.1
* Pytorch 1.10.0.dev20210620+cu113
* Datasets 1.6.2
* Tokenizers 0.10.2
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.10.0.dev20210620+cu113\n* Datasets 1.6.2\n* Tokenizers 0.10.2"
] |
[
"TAGS\n#transformers #pytorch #roberta #multiple-choice #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.10.0.dev20210620+cu113\n* Datasets 1.6.2\n* Tokenizers 0.10.2"
] |
[
30,
137,
4,
38
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #multiple-choice #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.10.0.dev20210620+cu113\n* Datasets 1.6.2\n* Tokenizers 0.10.2"
] |
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] |
null | null |
transformers
|
**Context**
Most of our great brilliant ideas happen in periods of relaxation, like taking a
shower, however, once we leave the shower, we forget the brilliant idea. What if
we do not forget, and collect your ideas in the shower?
**What is the Shower Ideas concept?**
This is an app that detects when someone is taking a shower (douche) and asks
“do you have any idea?”, and the person will speak while taking the shower telling
the idea. And also will ask questions after taking a shower.
**Abstract about the model**
This model was trained based on *facebook/wav2vec2-base-960h* (which is a pretrained model on 960 hours of Librispeech on 16kHz sampled speech audio.) in order to classify the audio input into shower or no_shower.
**Dataset**
The SHD-2 dataset is a labeled collection of 2260 audio recordings of shower and no shower sounds.
The dataset consists of 6-second-long recordings organized into 2 classes (with 1130 examples per class).
# Usage
In order to use the model in your Python script just copy the following code:
```python
from transformers import pipeline
audio_input = 'example.wav'
classifier = pipeline("audio-classification", model="abdelhalim/Shower_Sound_Recognition")
labels = classifier(audio_input)
labels
```
|
{"tags": ["audio", "audio-classificaiton", "shower detection"], "datasets": ["SHD-2"], "metrics": ["Accuracy"]}
|
audio-classification
|
abdelhalim/Shower_Sound_Recognition
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"audio",
"audio-classificaiton",
"shower detection",
"dataset:SHD-2",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #audio #audio-classificaiton #shower detection #dataset-SHD-2 #endpoints_compatible #region-us
|
Context
Most of our great brilliant ideas happen in periods of relaxation, like taking a
shower, however, once we leave the shower, we forget the brilliant idea. What if
we do not forget, and collect your ideas in the shower?
What is the Shower Ideas concept?
This is an app that detects when someone is taking a shower (douche) and asks
“do you have any idea?”, and the person will speak while taking the shower telling
the idea. And also will ask questions after taking a shower.
Abstract about the model
This model was trained based on *facebook/wav2vec2-base-960h* (which is a pretrained model on 960 hours of Librispeech on 16kHz sampled speech audio.) in order to classify the audio input into shower or no_shower.
Dataset
The SHD-2 dataset is a labeled collection of 2260 audio recordings of shower and no shower sounds.
The dataset consists of 6-second-long recordings organized into 2 classes (with 1130 examples per class).
# Usage
In order to use the model in your Python script just copy the following code:
|
[
"# Usage\nIn order to use the model in your Python script just copy the following code:"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #audio #audio-classificaiton #shower detection #dataset-SHD-2 #endpoints_compatible #region-us \n",
"# Usage\nIn order to use the model in your Python script just copy the following code:"
] |
[
56,
19
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #audio #audio-classificaiton #shower detection #dataset-SHD-2 #endpoints_compatible #region-us \n# Usage\nIn order to use the model in your Python script just copy the following code:"
] |
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null | null |
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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3038
- Accuracy: 0.9465
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 2.8460 | 0.7506 |
| 3.322 | 2.0 | 636 | 1.4301 | 0.8532 |
| 3.322 | 3.0 | 954 | 0.7377 | 0.9152 |
| 1.2296 | 4.0 | 1272 | 0.4784 | 0.9316 |
| 0.449 | 5.0 | 1590 | 0.3730 | 0.9390 |
| 0.449 | 6.0 | 1908 | 0.3367 | 0.9429 |
| 0.2424 | 7.0 | 2226 | 0.3163 | 0.9468 |
| 0.1741 | 8.0 | 2544 | 0.3074 | 0.9452 |
| 0.1741 | 9.0 | 2862 | 0.3054 | 0.9458 |
| 0.1501 | 10.0 | 3180 | 0.3038 | 0.9465 |
### 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": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9464516129032258, "name": "Accuracy"}]}]}]}
|
text-classification
|
abdelkader/distilbert-base-uncased-distilled-clinc
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3038
* Accuracy: 0.9465
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: 48
* eval\_batch\_size: 48
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### 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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### 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 #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### 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"
] |
[
66,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### 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"
] |
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] |
null | null |
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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7713
- Accuracy: 0.9174
## 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: 48
- eval_batch_size: 48
- 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 | 318 | 3.2831 | 0.7426 |
| 3.785 | 2.0 | 636 | 1.8739 | 0.8335 |
| 3.785 | 3.0 | 954 | 1.1525 | 0.8926 |
| 1.6894 | 4.0 | 1272 | 0.8569 | 0.91 |
| 0.897 | 5.0 | 1590 | 0.7713 | 0.9174 |
### 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": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9174193548387096, "name": "Accuracy"}]}]}]}
|
text-classification
|
abdelkader/distilbert-base-uncased-finetuned-clinc
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7713
* Accuracy: 0.9174
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: 48
* eval\_batch\_size: 48
* 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: 48\n* eval\\_batch\\_size: 48\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-clinc_oos #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: 48\n* eval\\_batch\\_size: 48\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"
] |
[
70,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-clinc_oos #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: 48\n* eval\\_batch\\_size: 48\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"
] |
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null | null |
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-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: 0.2162
- Accuracy: 0.9215
- F1: 0.9216
## 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: 64
- eval_batch_size: 64
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8007 | 1.0 | 250 | 0.3082 | 0.907 | 0.9045 |
| 0.2438 | 2.0 | 500 | 0.2162 | 0.9215 | 0.9216 |
### 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": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9215, "name": "Accuracy"}, {"type": "f1", "value": 0.9215604730468001, "name": "F1"}]}]}]}
|
text-classification
|
abdelkader/distilbert-base-uncased-finetuned-emotion
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-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: 0.2162
* Accuracy: 0.9215
* F1: 0.9216
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: 64
* eval\_batch\_size: 64
* 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 4.15.0
* 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: 64\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* num\\_epochs: 2",
"### 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-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: 2e-05\n* train\\_batch\\_size: 64\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* num\\_epochs: 2",
"### 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"
] |
[
67,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #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: 2e-05\n* train\\_batch\\_size: 64\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* num\\_epochs: 2### 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"
] |
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] |
null | null |
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6936 | 0.54 | 500 | 1.4844 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]}
|
text2text-generation
|
abdelkader/pegasus-samsum
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
|
pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4844
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
### 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: 5e-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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### 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 #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### 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"
] |
[
57,
144,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1### 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"
] |
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null | null |
transformers
|
# Soraberta: Unsupervised Language Model Pre-training for Wolof
**bert-base-wolof** is pretrained bert-base model on wolof language .
## Soraberta models
| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters |
| :------: | :---: | :---: | :---: | :---: |
| `bert-base` | 6 | 12 | 514 | 56931622 M |
## Using Soraberta with Hugging Face's Transformers
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='abdouaziiz/bert-base-wolof')
>>> unmasker("kuy yoot du [MASK].")
[{'sequence': '[CLS] kuy yoot du seqet. [SEP]',
'score': 0.09505125880241394,
'token': 13578},
{'sequence': '[CLS] kuy yoot du daw. [SEP]',
'score': 0.08882280439138412,
'token': 679},
{'sequence': '[CLS] kuy yoot du yoot. [SEP]',
'score': 0.057790059596300125,
'token': 5117},
{'sequence': '[CLS] kuy yoot du seqat. [SEP]',
'score': 0.05671025067567825,
'token': 4992},
{'sequence': '[CLS] kuy yoot du yaqu. [SEP]',
'score': 0.0469999685883522,
'token': 1735}]
```
## Training data
The data sources are [Bible OT](http://biblewolof.com/) , [WOLOF-ONLINE](http://www.wolof-online.com/)
[ALFFA_PUBLIC](https://github.com/getalp/ALFFA_PUBLIC/tree/master/ASR/WOLOF)
## Contact
Please contact [email protected] for any question, feedback or request.
|
{"language": "wo", "tags": ["bert", "language-model", "wo", "wolof"]}
|
fill-mask
|
abdouaziiz/bert-base-wolof
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"language-model",
"wo",
"wolof",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"wo"
] |
TAGS
#transformers #pytorch #bert #fill-mask #language-model #wo #wolof #autotrain_compatible #endpoints_compatible #region-us
|
Soraberta: Unsupervised Language Model Pre-training for Wolof
=============================================================
bert-base-wolof is pretrained bert-base model on wolof language .
Soraberta models
----------------
Using Soraberta with Hugging Face's Transformers
------------------------------------------------
Training data
-------------
The data sources are Bible OT , WOLOF-ONLINE
ALFFA\_PUBLIC
Contact
-------
Please contact abdouaziz@URL for any question, feedback or request.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #fill-mask #language-model #wo #wolof #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #language-model #wo #wolof #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Soraberta: Unsupervised Language Model Pre-training for Wolof
**Soraberta** is pretrained roberta-base model on wolof language . Roberta was introduced in [this paper](https://arxiv.org/abs/1907.11692)
## Soraberta models
| Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters |
| :------: | :---: | :---: | :---: | :---: |
| `soraberta-base` | 6 | 12 | 514 | 83 M |
## Using Soraberta with Hugging Face's Transformers
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='abdouaziiz/soraberta')
>>> unmasker("juroom naari jullit man nanoo boole jend aw nag walla <mask>.")
[{'sequence': 'juroom naari jullit man nanoo boole jend aw nag walla gileem.',
'score': 0.9783930778503418,
'token': 4621,
'token_str': ' gileem'},
{'sequence': 'juroom naari jullit man nanoo boole jend aw nag walla jend.',
'score': 0.009271537885069847,
'token': 2155,
'token_str': ' jend'},
{'sequence': 'juroom naari jullit man nanoo boole jend aw nag walla aw.',
'score': 0.0027585660573095083,
'token': 704,
'token_str': ' aw'},
{'sequence': 'juroom naari jullit man nanoo boole jend aw nag walla pel.',
'score': 0.001120452769100666,
'token': 1171,
'token_str': ' pel'},
{'sequence': 'juroom naari jullit man nanoo boole jend aw nag walla juum.',
'score': 0.0005133090307936072,
'token': 5820,
'token_str': ' juum'}]
```
## Training data
The data sources are [Bible OT](http://biblewolof.com/) , [WOLOF-ONLINE](http://www.wolof-online.com/)
## Contact
Please contact [email protected] for any question, feedback or request.
|
{"language": "wo", "tags": ["roberta", "language-model", "wo", "wolof"]}
|
fill-mask
|
abdouaziiz/soraberta
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"language-model",
"wo",
"wolof",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.11692"
] |
[
"wo"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #language-model #wo #wolof #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
Soraberta: Unsupervised Language Model Pre-training for Wolof
=============================================================
Soraberta is pretrained roberta-base model on wolof language . Roberta was introduced in this paper
Soraberta models
----------------
Using Soraberta with Hugging Face's Transformers
------------------------------------------------
Training data
-------------
The data sources are Bible OT , WOLOF-ONLINE
Contact
-------
Please contact abdouaziz@URL for any question, feedback or request.
|
[] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #language-model #wo #wolof #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #language-model #wo #wolof #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-wolof-lm
Wolof is a language spoken in Senegal and neighbouring countries, this language is not too well represented, there are few resources in the field of Text en speech
In this sense we aim to bring our contribution to this, it is in this sense that enters this repo.
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) ,with a language model that is fine-tuned with the largest available speech dataset of the [ALFFA_PUBLIC](https://github.com/besacier/ALFFA_PUBLIC/tree/master/ASR/WOLOF)
It achieves the following results on the evaluation set:
- Loss: 0.367826
- Wer: 0.212565
## Model description
The duration of the training data is 16.8 hours, which we have divided into 10,000 audio files for the training and 3,339 for the test.
## Training and evaluation data
We eval the model at every 1500 step , and log it . and save at every 33340 step
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-4
- train_batch_size: 3
- eval_batch_size : 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10.0
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:-------:|:-------------:|:---------------:|:------:|
| 1500 | 2.854200 |0.642243 |0.543964 |
| 3000 | 0.599200 | 0.468138 | 0.429549|
| 4500 | 0.468300 | 0.433436 | 0.405644|
| 6000 | 0.427000 | 0.384873 | 0.344150|
| 7500 | 0.377000 | 0.374003 | 0.323892|
| 9000 | 0.337000 | 0.363674 | 0.306189|
| 10500 | 0.302400 | 0.349884 |0 .283908 |
| 12000 | 0.264100 | 0.344104 |0.277120|
| 13500 |0 .254000 |0.341820 |0.271316|
| 15000 | 0.208400| 0.326502 | 0.260695|
| 16500 | 0.203500| 0.326209 | 0.250313|
| 18000 |0.159800 |0.323539 | 0.239851|
| 19500 | 0.158200 | 0.310694 | 0.230028|
| 21000 | 0.132800 | 0.338318 | 0.229283|
| 22500 | 0.112800 | 0.336765 | 0.224145|
| 24000 | 0.103600 | 0.350208 | 0.227073 |
| 25500 | 0.091400 | 0.353609 | 0.221589 |
| 27000 | 0.084400 | 0.367826 | 0.212565 |
## Usage
The model can be used directly as follows:
```python
import librosa
import warnings
from transformers import AutoProcessor, AutoModelForCTC
from datasets import Dataset, DatasetDict
from datasets import load_metric
wer_metric = load_metric("wer")
wolof = pd.read_csv('Test.csv') # wolof contains the columns of file , and transcription
wolof = DatasetDict({'test': Dataset.from_pandas(wolof)})
chars_to_ignore_regex = '[\"\?\.\!\-\;\:\(\)\,]'
def remove_special_characters(batch):
batch["transcription"] = re.sub(chars_to_ignore_regex, '', batch["transcription"]).lower() + " "
return batch
wolof = wolof.map(remove_special_characters)
processor = AutoProcessor.from_pretrained("abdouaziiz/wav2vec2-xls-r-300m-wolof-lm")
model = AutoModelForCTC.from_pretrained("abdouaziiz/wav2vec2-xls-r-300m-wolof-lm")
warnings.filterwarnings("ignore")
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["file"], sr = 16000)
batch["speech"] = speech_array.astype('float16')
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["transcription"]
return batch
wolof = wolof.map(speech_file_to_array_fn, remove_columns=wolof.column_names["test"], num_proc=1)
def map_to_result(batch):
model.to("cuda")
input_values = processor(
batch["speech"],
sampling_rate=batch["sampling_rate"],
return_tensors="pt"
).input_values.to("cuda")
with torch.no_grad():
logits = model(input_values).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_str"] = processor.batch_decode(pred_ids)[0]
return batch
results = wolof["test"].map(map_to_result)
print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["transcription"])))
```
## PS:
The results obtained can be improved by using :
- Wav2vec2 + language model .
- Build a Spellcheker from the text of the data
- Sentence Edit Distance
|
{"license": "mit", "tags": ["automatic-speech-recognition", "asr", "pytorch", "wav2vec2", "wolof", "wo"]}
|
automatic-speech-recognition
|
abdouaziiz/wav2vec2-xls-r-300m-wolof-lm
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"asr",
"wolof",
"wo",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #asr #wolof #wo #license-mit #model-index #endpoints_compatible #has_space #region-us
|
wav2vec2-xls-r-300m-wolof-lm
============================
Wolof is a language spoken in Senegal and neighbouring countries, this language is not too well represented, there are few resources in the field of Text en speech
In this sense we aim to bring our contribution to this, it is in this sense that enters this repo.
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m ,with a language model that is fine-tuned with the largest available speech dataset of the ALFFA\_PUBLIC
It achieves the following results on the evaluation set:
* Loss: 0.367826
* Wer: 0.212565
Model description
-----------------
The duration of the training data is 16.8 hours, which we have divided into 10,000 audio files for the training and 3,339 for the test.
Training and evaluation data
----------------------------
We eval the model at every 1500 step , and log it . and save at every 33340 step
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-4
* train\_batch\_size: 3
* eval\_batch\_size : 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10.0
### Training results
Usage
-----
The model can be used directly as follows:
PS:
---
The results obtained can be improved by using :
* Wav2vec2 + language model .
* Build a Spellcheker from the text of the data
* Sentence Edit Distance
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-4\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size : 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\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: 10.0",
"### Training results\n\n\n\nUsage\n-----\n\n\nThe model can be used directly as follows:\n\n\nPS:\n---\n\n\nThe results obtained can be improved by using :\n\n\n* Wav2vec2 + language model .\n* Build a Spellcheker from the text of the data\n* Sentence Edit Distance"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #asr #wolof #wo #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-4\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size : 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\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: 10.0",
"### Training results\n\n\n\nUsage\n-----\n\n\nThe model can be used directly as follows:\n\n\nPS:\n---\n\n\nThe results obtained can be improved by using :\n\n\n* Wav2vec2 + language model .\n* Build a Spellcheker from the text of the data\n* Sentence Edit Distance"
] |
[
58,
142,
63
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #asr #wolof #wo #license-mit #model-index #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-4\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size : 8\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 64\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: 10.0### Training results\n\n\n\nUsage\n-----\n\n\nThe model can be used directly as follows:\n\n\nPS:\n---\n\n\nThe results obtained can be improved by using :\n\n\n* Wav2vec2 + language model .\n* Build a Spellcheker from the text of the data\n* Sentence Edit Distance"
] |
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] |
null | null |
transformers
|
# Arabic NER
|
{"language": "ar", "tags": ["ner", "ar", "classification"], "datasets": ["wikiann"], "pipeline_tag": "token-classification", "task_ids": ["named-entity-recognition"], "widget": [{"text": "\u0643\u0631\u064a\u0633\u062a\u064a\u0627\u0646\u0648 \u0631\u0648\u0646\u0627\u0644\u062f\u0648 \u064a\u0644\u0639\u0628 \u0645\u0639 \u0646\u0627\u062f\u064a \u064a\u0648\u0641\u0646\u062a\u0648\u0633", "example_title": "Sentence 1"}, {"text": "\u062a\u062e\u0631\u062c \u0623\u062d\u0645\u062f \u0645\u0646 \u0627\u0644\u062c\u0627\u0645\u0639\u0629 \u0627\u0644\u0623\u0645\u0631\u064a\u0643\u064a\u0629 \u0641\u064a \u0627\u0644\u0634\u0627\u0631\u0642\u0629 \u0627\u0644\u0634\u0647\u0631 \u0627\u0644\u0645\u0627\u0636\u064a", "example_title": "Sentence 2"}, {"text": "\u0644\u0627 \u064a\u0632\u0627\u0644 \u062f\u064a\u0628\u0627\u0644\u0627 \u064a\u0644\u0639\u0628 \u0644\u0641\u0631\u064a\u0642 \u064a\u0648\u0641\u0646\u062a\u0648\u0633", "example_title": "Sentence 3"}]}
|
token-classification
|
abdusah/arabert-ner
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"ner",
"ar",
"classification",
"dataset:wikiann",
"doi:10.57967/hf/0271",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #bert #token-classification #ner #ar #classification #dataset-wikiann #doi-10.57967/hf/0271 #autotrain_compatible #endpoints_compatible #region-us
|
# Arabic NER
|
[
"# Arabic NER"
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #ner #ar #classification #dataset-wikiann #doi-10.57967/hf/0271 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Arabic NER"
] |
[
62,
4
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #ner #ar #classification #dataset-wikiann #doi-10.57967/hf/0271 #autotrain_compatible #endpoints_compatible #region-us \n# Arabic NER"
] |
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] |
null | null |
transformers
|
## Dataset
English Bible Translation Dataset (https://www.kaggle.com/oswinrh/bible)
*NOTE:* It is `roberta-base` fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 `.csv` files mentioned above, which consist of around 5.5M words.
## Citation
If you use this model in your work, please add the following citation -
```
@inproceedings{nandy-etal-2021-cs60075,
title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora",
author = "Nandy, Abhilash and
Adak, Sayantan and
Halder, Tanurima and
Pokala, Sai Mahesh",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.87",
doi = "10.18653/v1/2021.semeval-1.87",
pages = "678--682",
abstract = "The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).",
}
```
|
{"language": "en", "tags": ["English", "Bible"], "dataset": ["English Bible Translation Dataset", {"Link": "https://www.kaggle.com/oswinrh/bible"}], "inference": false}
|
fill-mask
|
abhi1nandy2/Bible-roberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"English",
"Bible",
"en",
"autotrain_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #roberta #fill-mask #English #Bible #en #autotrain_compatible #region-us
|
## Dataset
English Bible Translation Dataset (URL
*NOTE:* It is 'roberta-base' fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 '.csv' files mentioned above, which consist of around 5.5M words.
If you use this model in your work, please add the following citation -
|
[
"## Dataset \n\nEnglish Bible Translation Dataset (URL\n\n*NOTE:* It is 'roberta-base' fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 '.csv' files mentioned above, which consist of around 5.5M words.\n\nIf you use this model in your work, please add the following citation -"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #English #Bible #en #autotrain_compatible #region-us \n",
"## Dataset \n\nEnglish Bible Translation Dataset (URL\n\n*NOTE:* It is 'roberta-base' fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 '.csv' files mentioned above, which consist of around 5.5M words.\n\nIf you use this model in your work, please add the following citation -"
] |
[
39,
81
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #fill-mask #English #Bible #en #autotrain_compatible #region-us \n## Dataset \n\nEnglish Bible Translation Dataset (URL\n\n*NOTE:* It is 'roberta-base' fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 '.csv' files mentioned above, which consist of around 5.5M words.\n\nIf you use this model in your work, please add the following citation -"
] |
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] |
null | null |
transformers
|
Refer to https://aclanthology.org/2021.semeval-1.87/
## Citation
If you use this model in your work, please add the following citation -
```
@inproceedings{nandy-etal-2021-cs60075,
title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora",
author = "Nandy, Abhilash and
Adak, Sayantan and
Halder, Tanurima and
Pokala, Sai Mahesh",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.87",
doi = "10.18653/v1/2021.semeval-1.87",
pages = "678--682",
abstract = "The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).",
}
```
|
{"language": ["English"], "tags": ["CRAFT", "roberta"], "datasets": ["CRAFT BioNLP Corpus"]}
|
fill-mask
|
abhi1nandy2/Craft-bionlp-roberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"CRAFT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"English"
] |
TAGS
#transformers #pytorch #jax #roberta #fill-mask #CRAFT #autotrain_compatible #endpoints_compatible #region-us
|
Refer to URL
If you use this model in your work, please add the following citation -
|
[] |
[
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #CRAFT #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
43
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #fill-mask #CRAFT #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website
## Citation
Please cite the work if you would like to use it.
```
@inproceedings{nandy-etal-2021-question-answering,
title = "Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework",
author = "Nandy, Abhilash and
Sharma, Soumya and
Maddhashiya, Shubham and
Sachdeva, Kapil and
Goyal, Pawan and
Ganguly, NIloy",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.392",
doi = "10.18653/v1/2021.findings-emnlp.392",
pages = "4600--4609",
abstract = "Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40{\%} in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.",
}
```
|
{"language": ["English"], "tags": ["EManuals", "customer support", "QA", "bert"]}
|
fill-mask
|
abhi1nandy2/EManuals_BERT
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"EManuals",
"customer support",
"QA",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"English"
] |
TAGS
#transformers #pytorch #bert #fill-mask #EManuals #customer support #QA #autotrain_compatible #endpoints_compatible #region-us
|
Refer to URL for the paper and URL for the project website
Please cite the work if you would like to use it.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #fill-mask #EManuals #customer support #QA #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
48
] |
[
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #EManuals #customer support #QA #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website
## Citation
Please cite the work if you would like to use it.
```
@inproceedings{nandy-etal-2021-question-answering,
title = "Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework",
author = "Nandy, Abhilash and
Sharma, Soumya and
Maddhashiya, Shubham and
Sachdeva, Kapil and
Goyal, Pawan and
Ganguly, NIloy",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.392",
doi = "10.18653/v1/2021.findings-emnlp.392",
pages = "4600--4609",
abstract = "Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40{\%} in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.",
}
```
|
{"language": ["English"], "tags": ["EManuals", "customer support", "QA", "roberta"]}
|
feature-extraction
|
abhi1nandy2/EManuals_RoBERTa
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"EManuals",
"customer support",
"QA",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"English"
] |
TAGS
#transformers #pytorch #roberta #feature-extraction #EManuals #customer support #QA #endpoints_compatible #region-us
|
Refer to URL for the paper and URL for the project website
Please cite the work if you would like to use it.
|
[] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #EManuals #customer support #QA #endpoints_compatible #region-us \n"
] |
[
42
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #EManuals #customer support #QA #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
Refer to https://aclanthology.org/2021.semeval-1.87/
## Citation
If you use this model in your work, please add the following citation -
```
@inproceedings{nandy-etal-2021-cs60075,
title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora",
author = "Nandy, Abhilash and
Adak, Sayantan and
Halder, Tanurima and
Pokala, Sai Mahesh",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.87",
doi = "10.18653/v1/2021.semeval-1.87",
pages = "678--682",
abstract = "The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).",
}
```
|
{"language": ["English"], "tags": ["Europarl", "roberta"], "datasets": ["Europarl"]}
|
fill-mask
|
abhi1nandy2/Europarl-roberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"Europarl",
"dataset:Europarl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"English"
] |
TAGS
#transformers #pytorch #jax #roberta #fill-mask #Europarl #dataset-Europarl #autotrain_compatible #endpoints_compatible #region-us
|
Refer to URL
If you use this model in your work, please add the following citation -
|
[] |
[
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #Europarl #dataset-Europarl #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
49
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #fill-mask #Europarl #dataset-Europarl #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
spanbert-large-cased fine-tuned for <b>"Adverse drug reaction"</b> and <b>"Drug"</b> span Extraction.
<b>Details of spanbert-large-cased:</b>
https://huggingface.co/SpanBERT/spanbert-large-cased
<b>Details of the downstream task (Adverse drug reaction and Drug Extraction) - Dataset</b>
https://huggingface.co/datasets/ade_corpus_v2
|
{"language": "en", "tags": ["spanbert"], "datasets": ["ade_corpus_v2"], "widget": [{"text": "Having fever after taking paracetamol.", "example_title": "NER"}, {"text": "Birth defects associated with thalidomide.", "example_title": "NER"}, {"text": "Deafness and kidney failure associated with gentamicin (an antibiotic).", "example_title": "NER"}, {"text": "Bleeding of the intestine associated with aspirin therapy.", "example_title": "NER"}]}
|
token-classification
|
abhibisht89/spanbert-large-cased-finetuned-ade_corpus_v2
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"spanbert",
"en",
"dataset:ade_corpus_v2",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #token-classification #spanbert #en #dataset-ade_corpus_v2 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
spanbert-large-cased fine-tuned for <b>"Adverse drug reaction"</b> and <b>"Drug"</b> span Extraction.
<b>Details of spanbert-large-cased:</b>
URL
<b>Details of the downstream task (Adverse drug reaction and Drug Extraction) - Dataset</b>
URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #spanbert #en #dataset-ade_corpus_v2 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #spanbert #en #dataset-ade_corpus_v2 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Dataset
---
---
datasets:
- covid_qa_deepset
---
---
Covid 19 question answering data obtained from [covid_qa_deepset](https://huggingface.co/datasets/covid_qa_deepset).
# Original Repository
Repository for the fine tuning, inference and evaluation scripts can be found [here](https://github.com/abhijithneilabraham/Covid-QA).
# Model in action
```
import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("abhijithneilabraham/longformer_covid_qa")
model = AutoModelForQuestionAnswering.from_pretrained("abhijithneilabraham/longformer_covid_qa")
question = "In this way, what do the mRNA-destabilising RBPs constitute ?"
text =
"""
In this way, mRNA-destabilising RBPs constitute a 'brake' on the immune system, which may ultimately be toggled therapeutically. I anticipate continued efforts in this area will lead to new methods of regaining control over inflammation in autoimmunity, selectively enhancing immunity in immunotherapy, and modulating RNA synthesis and virus replication during infection.
Another mRNA under post-transcriptional regulation by Regnase-1 and Roquin is Furin, which encodes a conserved proprotein convertase crucial in human health and disease. Furin, along with other PCSK family members, is widely implicated in immune regulation, cancer and the entry, maturation or release of a broad array of evolutionarily diverse viruses including human papillomavirus (HPV), influenza (IAV), Ebola (EboV), dengue (DenV) and human immunodeficiency virus (HIV). Here, Braun and Sauter review the roles of furin in these processes, as well as the history and future of furin-targeting therapeutics. 7 They also discuss their recent work revealing how two IFN-cinducible factors exhibit broad-spectrum inhibition of IAV, measles (MV), zika (ZikV) and HIV by suppressing furin activity. 8 Over the coming decade, I expect to see an ever-finer spatiotemporal resolution of host-oriented therapies to achieve safe, effective and broad-spectrum yet costeffective therapies for clinical use.
The increasing abundance of affordable, sensitive, high-throughput genome sequencing technologies has led to a recent boom in metagenomics and the cataloguing of the microbiome of our world. The MinION nanopore sequencer is one of the latest innovations in this space, enabling direct sequencing in a miniature form factor with only minimal sample preparation and a consumer-grade laptop computer. Nakagawa and colleagues here report on their latest experiments using this system, further improving its performance for use in resource-poor contexts for meningitis diagnoses. 9 While direct sequencing of viral genomic RNA is challenging, this system was recently used to directly sequence an RNA virus genome (IAV) for the first time. 10 I anticipate further improvements in the performance of such devices over the coming decade will transform virus surveillance efforts, the importance of which was underscored by the recent EboV and novel coronavirus (nCoV / COVID-19) outbreaks, enabling rapid deployment of antiviral treatments that take resistance-conferring mutations into account.
Decades of basic immunology research have provided a near-complete picture of the main armaments in the human antiviral arsenal. Nevertheless, this focus on mammalian defences and pathologies has sidelined examination of the types and roles of viruses and antiviral defences that exist throughout our biosphere. One case in point is the CRISPR/Cas antiviral immune system of prokaryotes, which is now repurposed as a revolutionary gene-editing biotechnology in plants and animals. 11 Another is the ancient lineage of nucleocytosolic large DNA viruses (NCLDVs), which are emerging human pathogens that possess enormous genomes of up to several megabases in size encoding hundreds of proteins with unique and unknown functions. 12 Moreover, hundreds of human-and avian-infective viruses such as IAV strain H5N1 are known, but recent efforts indicate the true number may be in the millions and many harbour zoonotic potential. 13 It is increasingly clear that host-virus interactions have generated truly vast yet poorly understood and untapped biodiversity. Closing this Special Feature, Watanabe and Kawaoka elaborate on neo-virology, an emerging field engaged in cataloguing and characterising this biodiversity through a global consortium. 14 I predict these efforts will unlock a vast wealth of currently unexplored biodiversity, leading to biotechnologies and treatments that leverage the host-virus interactions developed throughout evolution.
When biomedical innovations fall into the 'Valley of Death', patients who are therefore not reached all too often fall with them. Being entrusted with the resources and expectation to conceive, deliver and communicate dividends to society is both cherished and eagerly pursued at every stage of our careers. Nevertheless, the road to research translation is winding and is built on a foundation of basic research. Supporting industry-academia collaboration and nurturing talent and skills in the Indo-Pacific region are two of the four pillars of the National Innovation and Science Agenda. 2 These frame Australia's Medical Research and Innovation Priorities, which include antimicrobial resistance, global health and health security, drug repurposing and translational research infrastructure, 15 capturing many of the key elements of this CTI Special Feature. Establishing durable international relationships that integrate diverse expertise is essential to delivering these outcomes. To this end, NHMRC has recently taken steps under the International Engagement Strategy 16 to increase cooperation with its counterparts overseas. These include the Japan Agency for Medical Research and Development (AMED), tasked with translating the biomedical research output of that country. Given the reciprocal efforts at accelerating bilateral engagement currently underway, 17 the prospects for new areas of international cooperation and mobility have never been more exciting nor urgent. With the above in mind, all contributions to this CTI Special Feature I have selected from research presented by fellow invitees to the 2018 Awaji International Forum on Infection and Immunity (AIFII) and 2017 Consortium of Biological Sciences (ConBio) conferences in Japan. Both Australia and Japan have strong traditions in immunology and related disciplines, and I predict that the quantity, quality and importance of our bilateral cooperation will accelerate rapidly over the short to medium term. By expanding and cooperatively leveraging our respective research strengths, our efforts may yet solve the many pressing disease, cost and other sustainability issues of our time.
"""
encoding = tokenizer(question, text, return_tensors="pt")
input_ids = encoding["input_ids"]
# default is local attention everywhere
# the forward method will automatically set global attention on question tokens
attention_mask = encoding["attention_mask"]
start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
# output => a 'brake' on the immune system
```
|
{}
|
question-answering
|
abhijithneilabraham/longformer_covid_qa
|
[
"transformers",
"pytorch",
"longformer",
"question-answering",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #longformer #question-answering #endpoints_compatible #region-us
|
# Dataset
---
---
datasets:
- covid_qa_deepset
---
---
Covid 19 question answering data obtained from covid_qa_deepset.
# Original Repository
Repository for the fine tuning, inference and evaluation scripts can be found here.
# Model in action
|
[
"# Dataset\n---\n---\n\ndatasets:\n- covid_qa_deepset\n---\n\n--- \nCovid 19 question answering data obtained from covid_qa_deepset.",
"# Original Repository\nRepository for the fine tuning, inference and evaluation scripts can be found here.",
"# Model in action"
] |
[
"TAGS\n#transformers #pytorch #longformer #question-answering #endpoints_compatible #region-us \n",
"# Dataset\n---\n---\n\ndatasets:\n- covid_qa_deepset\n---\n\n--- \nCovid 19 question answering data obtained from covid_qa_deepset.",
"# Original Repository\nRepository for the fine tuning, inference and evaluation scripts can be found here.",
"# Model in action"
] |
[
30,
37,
25,
4
] |
[
"passage: TAGS\n#transformers #pytorch #longformer #question-answering #endpoints_compatible #region-us \n# Dataset\n---\n---\n\ndatasets:\n- covid_qa_deepset\n---\n\n--- \nCovid 19 question answering data obtained from covid_qa_deepset.# Original Repository\nRepository for the fine tuning, inference and evaluation scripts can be found here.# Model in action"
] |
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null | null |
sentence-transformers
|
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased')
model = AutoModel.from_pretrained('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 25,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 900,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 360 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
|
[
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
[
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
[
44,
50,
38,
64,
29,
77,
5,
6
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
] |
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] |
null | null |
transformers
|
## German NER Albert Model For Token Classification
This is a trained Albert model for Token Classification in German ,[Germeval](https://sites.google.com/site/germeval2014ner/) and can be used for Inference.
## Model Specifications
- MAX_LENGTH=128
- MODEL='albert-base-v1'
- BATCH_SIZE=32
- NUM_EPOCHS=3
- SAVE_STEPS=750
- SEED=1
- SAVE_STEPS = 100
- LOGGING_STEPS = 100
- SEED = 42
### Usage Specifications
This model is trained on Tensorflow version and is compatible with the 'ner' pipeline of huggingface.
```python
from transformers import AutoTokenizer,TFAutoModelForTokenClassification
from transformers import pipeline
model=TFAutoModelForTokenClassification.from_pretrained('abhilash1910/albert-german-ner')
tokenizer=AutoTokenizer.from_pretrained('abhilash1910/albert-german-ner')
ner_model = pipeline('ner', model=model, tokenizer=tokenizer)
seq='Berlin ist die Hauptstadt von Deutschland'
ner_model(seq)
```
The Tensorflow version of Albert is used for training the model and the output for the above mentioned segment is as follows:
```
[{'entity': 'B-PERderiv',
'index': 1,
'score': 0.09580112248659134,
'word': '▁berlin'},
{'entity': 'B-ORGpart',
'index': 2,
'score': 0.08364498615264893,
'word': '▁is'},
{'entity': 'B-LOCderiv',
'index': 3,
'score': 0.07593920826911926,
'word': 't'},
{'entity': 'B-PERderiv',
'index': 4,
'score': 0.09574996680021286,
'word': '▁die'},
{'entity': 'B-LOCderiv',
'index': 5,
'score': 0.07097965478897095,
'word': '▁'},
{'entity': 'B-PERderiv',
'index': 6,
'score': 0.07122448086738586,
'word': 'haupt'},
{'entity': 'B-PERderiv',
'index': 7,
'score': 0.12397754937410355,
'word': 'stadt'},
{'entity': 'I-OTHderiv',
'index': 8,
'score': 0.0818650871515274,
'word': '▁von'},
{'entity': 'I-LOCderiv',
'index': 9,
'score': 0.08271490037441254,
'word': '▁'},
{'entity': 'B-LOCderiv',
'index': 10,
'score': 0.08616268634796143,
'word': 'deutschland'}]
```
## Resources
For all resources , please look into [huggingface](https://huggingface.com).
---
language:
- de
tags:
- Token Classification
license: apache-2.0
datasets:
- germeval_14
---
|
{}
|
fill-mask
|
abhilash1910/albert-german-ner
|
[
"transformers",
"tf",
"albert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #tf #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
## German NER Albert Model For Token Classification
This is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.
## Model Specifications
- MAX_LENGTH=128
- MODEL='albert-base-v1'
- BATCH_SIZE=32
- NUM_EPOCHS=3
- SAVE_STEPS=750
- SEED=1
- SAVE_STEPS = 100
- LOGGING_STEPS = 100
- SEED = 42
### Usage Specifications
This model is trained on Tensorflow version and is compatible with the 'ner' pipeline of huggingface.
The Tensorflow version of Albert is used for training the model and the output for the above mentioned segment is as follows:
## Resources
For all resources , please look into huggingface.
---
language:
- de
tags:
- Token Classification
license: apache-2.0
datasets:
- germeval_14
---
|
[
"## German NER Albert Model For Token Classification\n\nThis is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.",
"## Model Specifications\n\n- MAX_LENGTH=128\n- MODEL='albert-base-v1'\n- BATCH_SIZE=32\n- NUM_EPOCHS=3\n- SAVE_STEPS=750\n- SEED=1\n- SAVE_STEPS = 100 \n- LOGGING_STEPS = 100 \n- SEED = 42",
"### Usage Specifications\n\nThis model is trained on Tensorflow version and is compatible with the 'ner' pipeline of huggingface.\n\n\n\nThe Tensorflow version of Albert is used for training the model and the output for the above mentioned segment is as follows:",
"## Resources\n\nFor all resources , please look into huggingface.\n\n\n---\nlanguage:\n- de\ntags:\n- Token Classification\nlicense: apache-2.0\ndatasets:\n- germeval_14\n---"
] |
[
"TAGS\n#transformers #tf #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## German NER Albert Model For Token Classification\n\nThis is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.",
"## Model Specifications\n\n- MAX_LENGTH=128\n- MODEL='albert-base-v1'\n- BATCH_SIZE=32\n- NUM_EPOCHS=3\n- SAVE_STEPS=750\n- SEED=1\n- SAVE_STEPS = 100 \n- LOGGING_STEPS = 100 \n- SEED = 42",
"### Usage Specifications\n\nThis model is trained on Tensorflow version and is compatible with the 'ner' pipeline of huggingface.\n\n\n\nThe Tensorflow version of Albert is used for training the model and the output for the above mentioned segment is as follows:",
"## Resources\n\nFor all resources , please look into huggingface.\n\n\n---\nlanguage:\n- de\ntags:\n- Token Classification\nlicense: apache-2.0\ndatasets:\n- germeval_14\n---"
] |
[
36,
39,
74,
57,
44
] |
[
"passage: TAGS\n#transformers #tf #albert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n## German NER Albert Model For Token Classification\n\nThis is a trained Albert model for Token Classification in German ,Germeval and can be used for Inference.## Model Specifications\n\n- MAX_LENGTH=128\n- MODEL='albert-base-v1'\n- BATCH_SIZE=32\n- NUM_EPOCHS=3\n- SAVE_STEPS=750\n- SEED=1\n- SAVE_STEPS = 100 \n- LOGGING_STEPS = 100 \n- SEED = 42### Usage Specifications\n\nThis model is trained on Tensorflow version and is compatible with the 'ner' pipeline of huggingface.\n\n\n\nThe Tensorflow version of Albert is used for training the model and the output for the above mentioned segment is as follows:## Resources\n\nFor all resources , please look into huggingface.\n\n\n---\nlanguage:\n- de\ntags:\n- Token Classification\nlicense: apache-2.0\ndatasets:\n- germeval_14\n---"
] |
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] |
null | null |
transformers
|
# DistilBERT--SQuAD-v1
Training is done on the [SQuAD](https://huggingface.co/datasets/squad) dataset. The model can be accessed via [HuggingFace](https://huggingface.co/abhilash1910/distilbert-squadv1):
## Model Specifications
We have used the following parameters:
- Training Batch Size : 512
- Learning Rate : 3e-5
- Training Epochs : 0.75
- Sequence Length : 384
- Stride : 128
## Usage Specifications
```python
from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline
model=AutoModelForQuestionAnswering.from_pretrained('abhilash1910/distilbert-squadv1')
tokenizer=AutoTokenizer.from_pretrained('abhilash1910/distilbert-squadv1')
nlp_QA=pipeline('question-answering',model=model,tokenizer=tokenizer)
QA_inp={
'question': 'What is the fund price of Huggingface in NYSE?',
'context': 'Huggingface Co. has a total fund price of $19.6 million dollars'
}
result=nlp_QA(QA_inp)
result
```
The result is:
```bash
{'score': 0.38547369837760925,
'start': 42,
'end': 55,
'answer': '$19.6 million'}
```
---
language:
- en
license: apache-2.0
datasets:
- squad_v1
---
|
{}
|
question-answering
|
abhilash1910/distilbert-squadv1
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us
|
# DistilBERT--SQuAD-v1
Training is done on the SQuAD dataset. The model can be accessed via HuggingFace:
## Model Specifications
We have used the following parameters:
- Training Batch Size : 512
- Learning Rate : 3e-5
- Training Epochs : 0.75
- Sequence Length : 384
- Stride : 128
## Usage Specifications
The result is:
---
language:
- en
license: apache-2.0
datasets:
- squad_v1
---
|
[
"# DistilBERT--SQuAD-v1\n\nTraining is done on the SQuAD dataset. The model can be accessed via HuggingFace:",
"## Model Specifications\n\nWe have used the following parameters:\n\n- Training Batch Size : 512\n- Learning Rate : 3e-5\n- Training Epochs : 0.75\n- Sequence Length : 384\n- Stride : 128",
"## Usage Specifications\n\n\n\n\nThe result is:\n\n\n\n---\nlanguage:\n- en\nlicense: apache-2.0\ndatasets:\n- squad_v1\n\n---"
] |
[
"TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us \n",
"# DistilBERT--SQuAD-v1\n\nTraining is done on the SQuAD dataset. The model can be accessed via HuggingFace:",
"## Model Specifications\n\nWe have used the following parameters:\n\n- Training Batch Size : 512\n- Learning Rate : 3e-5\n- Training Epochs : 0.75\n- Sequence Length : 384\n- Stride : 128",
"## Usage Specifications\n\n\n\n\nThe result is:\n\n\n\n---\nlanguage:\n- en\nlicense: apache-2.0\ndatasets:\n- squad_v1\n\n---"
] |
[
31,
36,
49,
31
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #region-us \n# DistilBERT--SQuAD-v1\n\nTraining is done on the SQuAD dataset. The model can be accessed via HuggingFace:## Model Specifications\n\nWe have used the following parameters:\n\n- Training Batch Size : 512\n- Learning Rate : 3e-5\n- Training Epochs : 0.75\n- Sequence Length : 384\n- Stride : 128## Usage Specifications\n\n\n\n\nThe result is:\n\n\n\n---\nlanguage:\n- en\nlicense: apache-2.0\ndatasets:\n- squad_v1\n\n---"
] |
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null | null |
transformers
|
# Roberta Masked Language Model Trained On Financial Phrasebank Corpus
This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a Financial Phrasebank Corpus.
The model is built using Huggingface transformers.
The model can be found at :[Financial_Roberta](https://huggingface.co/abhilash1910/financial_roberta)
## Specifications
The corpus for training is taken from the Financial Phrasebank (Malo et al)[https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts].
## Model Specification
The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications:
1. vocab_size=56000
2. max_position_embeddings=514
3. num_attention_heads=12
4. num_hidden_layers=6
5. type_vocab_size=1
This is trained by using RobertaConfig from transformers package.
The model is trained for 10 epochs with a gpu batch size of 64 units.
## Usage Specifications
For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers
After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model.
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("abhilash1910/financial_roberta")
model = AutoModelWithLMHead.from_pretrained("abhilash1910/financial_roberta")
```
After this the model will be downloaded, it will take some time to download all the model files.
For testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:
```python
from transformers import pipeline
model_mask = pipeline('fill-mask', model='abhilash1910/inancial_roberta')
model_mask("The company had a <mask> of 20% in 2020.")
```
Some of the examples are also provided with generic financial statements:
Example 1:
```python
model_mask("The company had a <mask> of 20% in 2020.")
```
Output:
```bash
[{'sequence': '<s>The company had a profit of 20% in 2020.</s>',
'score': 0.023112965747714043,
'token': 421,
'token_str': 'Ġprofit'},
{'sequence': '<s>The company had a loss of 20% in 2020.</s>',
'score': 0.021379893645644188,
'token': 616,
'token_str': 'Ġloss'},
{'sequence': '<s>The company had a year of 20% in 2020.</s>',
'score': 0.0185744296759367,
'token': 443,
'token_str': 'Ġyear'},
{'sequence': '<s>The company had a sales of 20% in 2020.</s>',
'score': 0.018143286928534508,
'token': 428,
'token_str': 'Ġsales'},
{'sequence': '<s>The company had a value of 20% in 2020.</s>',
'score': 0.015319528989493847,
'token': 776,
'token_str': 'Ġvalue'}]
```
Example 2:
```python
model_mask("The <mask> is listed under NYSE")
```
Output:
```bash
[{'sequence': '<s>The company is listed under NYSE</s>',
'score': 0.1566661298274994,
'token': 359,
'token_str': 'Ġcompany'},
{'sequence': '<s>The total is listed under NYSE</s>',
'score': 0.05542507395148277,
'token': 522,
'token_str': 'Ġtotal'},
{'sequence': '<s>The value is listed under NYSE</s>',
'score': 0.04729423299431801,
'token': 776,
'token_str': 'Ġvalue'},
{'sequence': '<s>The order is listed under NYSE</s>',
'score': 0.02533523552119732,
'token': 798,
'token_str': 'Ġorder'},
{'sequence': '<s>The contract is listed under NYSE</s>',
'score': 0.02087237872183323,
'token': 635,
'token_str': 'Ġcontract'}]
```
## Resources
For all resources , please look into the [HuggingFace](https://huggingface.co/) Site and the [Repositories](https://github.com/huggingface).
|
{"tags": ["finance"]}
|
fill-mask
|
abhilash1910/financial_roberta
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"fill-mask",
"finance",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.11692"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #finance #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
# Roberta Masked Language Model Trained On Financial Phrasebank Corpus
This is a Masked Language Model trained with Roberta on a Financial Phrasebank Corpus.
The model is built using Huggingface transformers.
The model can be found at :Financial_Roberta
## Specifications
The corpus for training is taken from the Financial Phrasebank (Malo et al)[URL
## Model Specification
The model chosen for training is Roberta with the following specifications:
1. vocab_size=56000
2. max_position_embeddings=514
3. num_attention_heads=12
4. num_hidden_layers=6
5. type_vocab_size=1
This is trained by using RobertaConfig from transformers package.
The model is trained for 10 epochs with a gpu batch size of 64 units.
## Usage Specifications
For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers
After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model.
After this the model will be downloaded, it will take some time to download all the model files.
For testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:
Some of the examples are also provided with generic financial statements:
Example 1:
Output:
Example 2:
Output:
## Resources
For all resources , please look into the HuggingFace Site and the Repositories.
|
[
"# Roberta Masked Language Model Trained On Financial Phrasebank Corpus \n\n\nThis is a Masked Language Model trained with Roberta on a Financial Phrasebank Corpus.\nThe model is built using Huggingface transformers.\nThe model can be found at :Financial_Roberta",
"## Specifications\n\n\nThe corpus for training is taken from the Financial Phrasebank (Malo et al)[URL",
"## Model Specification\n\n\nThe model chosen for training is Roberta with the following specifications:\n 1. vocab_size=56000\n 2. max_position_embeddings=514\n 3. num_attention_heads=12\n 4. num_hidden_layers=6\n 5. type_vocab_size=1\n\n\nThis is trained by using RobertaConfig from transformers package.\nThe model is trained for 10 epochs with a gpu batch size of 64 units.",
"## Usage Specifications\n\n\nFor using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers\nAfter that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model.\n\n\n\n\n\nAfter this the model will be downloaded, it will take some time to download all the model files.\nFor testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:\n\n\n\n\n\nSome of the examples are also provided with generic financial statements:\n\nExample 1:\n\n\n\n\n\nOutput:\n\n\n\n \n Example 2:\n \n\n\nOutput:",
"## Resources\n\nFor all resources , please look into the HuggingFace Site and the Repositories."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #finance #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta Masked Language Model Trained On Financial Phrasebank Corpus \n\n\nThis is a Masked Language Model trained with Roberta on a Financial Phrasebank Corpus.\nThe model is built using Huggingface transformers.\nThe model can be found at :Financial_Roberta",
"## Specifications\n\n\nThe corpus for training is taken from the Financial Phrasebank (Malo et al)[URL",
"## Model Specification\n\n\nThe model chosen for training is Roberta with the following specifications:\n 1. vocab_size=56000\n 2. max_position_embeddings=514\n 3. num_attention_heads=12\n 4. num_hidden_layers=6\n 5. type_vocab_size=1\n\n\nThis is trained by using RobertaConfig from transformers package.\nThe model is trained for 10 epochs with a gpu batch size of 64 units.",
"## Usage Specifications\n\n\nFor using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers\nAfter that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model.\n\n\n\n\n\nAfter this the model will be downloaded, it will take some time to download all the model files.\nFor testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:\n\n\n\n\n\nSome of the examples are also provided with generic financial statements:\n\nExample 1:\n\n\n\n\n\nOutput:\n\n\n\n \n Example 2:\n \n\n\nOutput:",
"## Resources\n\nFor all resources , please look into the HuggingFace Site and the Repositories."
] |
[
59,
58,
23,
102,
150,
23
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #fill-mask #finance #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n# Roberta Masked Language Model Trained On Financial Phrasebank Corpus \n\n\nThis is a Masked Language Model trained with Roberta on a Financial Phrasebank Corpus.\nThe model is built using Huggingface transformers.\nThe model can be found at :Financial_Roberta## Specifications\n\n\nThe corpus for training is taken from the Financial Phrasebank (Malo et al)[URL## Model Specification\n\n\nThe model chosen for training is Roberta with the following specifications:\n 1. vocab_size=56000\n 2. max_position_embeddings=514\n 3. num_attention_heads=12\n 4. num_hidden_layers=6\n 5. type_vocab_size=1\n\n\nThis is trained by using RobertaConfig from transformers package.\nThe model is trained for 10 epochs with a gpu batch size of 64 units.## Usage Specifications\n\n\nFor using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers\nAfter that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model.\n\n\n\n\n\nAfter this the model will be downloaded, it will take some time to download all the model files.\nFor testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:\n\n\n\n\n\nSome of the examples are also provided with generic financial statements:\n\nExample 1:\n\n\n\n\n\nOutput:\n\n\n\n \n Example 2:\n \n\n\nOutput:## Resources\n\nFor all resources , please look into the HuggingFace Site and the Repositories."
] |
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null | null |
transformers
|
# Roberta Trained Model For Masked Language Model On French Corpus :robot:
This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a small French News Corpus(Leipzig corpora).
The model is built using Huggingface transformers.
The model can be found at :[French-Roberta](https://huggingface.co/abhilash1910/french-roberta)
## Specifications
The corpus for training is taken from Leipzig Corpora (French News) , and is trained on a small set of the corpus (300K).
## Model Specification
The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications:
1. vocab_size=32000
2. max_position_embeddings=514
3. num_attention_heads=12
4. num_hidden_layers=6
5. type_vocab_size=1
This is trained by using RobertaConfig from transformers package.The total training parameters :68124416
The model is trained for 100 epochs with a gpu batch size of 64 units.
More details for building custom models can be found at the [HuggingFace Blog](https://huggingface.co/blog/how-to-train)
## Usage Specifications
For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers
After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/french-roberta' for the tokenizers and the model.
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("abhilash1910/french-roberta")
model = AutoModelWithLMHead.from_pretrained("abhilash1910/french-roberta")
```
After this the model will be downloaded, it will take some time to download all the model files.
For testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:
```python
from transformers import pipeline
model_mask = pipeline('fill-mask', model='abhilash1910/french-roberta')
model_mask("Le tweet <mask>.")
```
Some of the examples are also provided with generic French sentences:
Example 1:
```python
model_mask("À ce jour, <mask> projet a entraîné")
```
Output:
```bash
[{'sequence': '<s>À ce jour, belles projet a entraîné</s>',
'score': 0.18685665726661682,
'token': 6504,
'token_str': 'Ġbelles'},
{'sequence': '<s>À ce jour,- projet a entraîné</s>',
'score': 0.0005200508167035878,
'token': 17,
'token_str': '-'},
{'sequence': '<s>À ce jour, de projet a entraîné</s>',
'score': 0.00045729897101409733,
'token': 268,
'token_str': 'Ġde'},
{'sequence': '<s>À ce jour, du projet a entraîné</s>',
'score': 0.0004307595663703978,
'token': 326,
'token_str': 'Ġdu'},
{'sequence': '<s>À ce jour," projet a entraîné</s>',
'score': 0.0004219160182401538,
'token': 6,
'token_str': '"'}]
```
Example 2:
```python
model_mask("C'est un <mask>")
```
Output:
```bash
[{'sequence': "<s>C'est un belles</s>",
'score': 0.16440927982330322,
'token': 6504,
'token_str': 'Ġbelles'},
{'sequence': "<s>C'est un de</s>",
'score': 0.0005495127406902611,
'token': 268,
'token_str': 'Ġde'},
{'sequence': "<s>C'est un du</s>",
'score': 0.00044988933950662613,
'token': 326,
'token_str': 'Ġdu'},
{'sequence': "<s>C'est un-</s>",
'score': 0.00044542422983795404,
'token': 17,
'token_str': '-'},
{'sequence': "<s>C'est un </s>",
'score': 0.00037563967634923756,
'token': 202,
'token_str': 'ĉ'}]
```
## Resources
For all resources , please look into the [HuggingFace](https://huggingface.co/) Site and the [Repositories](https://github.com/huggingface).
---
language:
- fr
tags:
- fill-mask
license: apache-2.0
---
|
{}
|
fill-mask
|
abhilash1910/french-roberta
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.11692"
] |
[] |
TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us
|
# Roberta Trained Model For Masked Language Model On French Corpus :robot:
This is a Masked Language Model trained with Roberta on a small French News Corpus(Leipzig corpora).
The model is built using Huggingface transformers.
The model can be found at :French-Roberta
## Specifications
The corpus for training is taken from Leipzig Corpora (French News) , and is trained on a small set of the corpus (300K).
## Model Specification
The model chosen for training is Roberta with the following specifications:
1. vocab_size=32000
2. max_position_embeddings=514
3. num_attention_heads=12
4. num_hidden_layers=6
5. type_vocab_size=1
This is trained by using RobertaConfig from transformers package.The total training parameters :68124416
The model is trained for 100 epochs with a gpu batch size of 64 units.
More details for building custom models can be found at the HuggingFace Blog
## Usage Specifications
For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers
After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/french-roberta' for the tokenizers and the model.
After this the model will be downloaded, it will take some time to download all the model files.
For testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:
Some of the examples are also provided with generic French sentences:
Example 1:
Output:
Example 2:
Output:
## Resources
For all resources , please look into the HuggingFace Site and the Repositories.
---
language:
- fr
tags:
- fill-mask
license: apache-2.0
---
|
[
"# Roberta Trained Model For Masked Language Model On French Corpus :robot:\n\n\nThis is a Masked Language Model trained with Roberta on a small French News Corpus(Leipzig corpora).\nThe model is built using Huggingface transformers.\nThe model can be found at :French-Roberta",
"## Specifications\n\n\nThe corpus for training is taken from Leipzig Corpora (French News) , and is trained on a small set of the corpus (300K).",
"## Model Specification\n\n\nThe model chosen for training is Roberta with the following specifications:\n 1. vocab_size=32000\n 2. max_position_embeddings=514\n 3. num_attention_heads=12\n 4. num_hidden_layers=6\n 5. type_vocab_size=1\n\n\nThis is trained by using RobertaConfig from transformers package.The total training parameters :68124416\nThe model is trained for 100 epochs with a gpu batch size of 64 units. \nMore details for building custom models can be found at the HuggingFace Blog",
"## Usage Specifications\n\n\nFor using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers\nAfter that we have to specify, the pre-trained model,which in this case is 'abhilash1910/french-roberta' for the tokenizers and the model.\n\n\n\n\n\nAfter this the model will be downloaded, it will take some time to download all the model files.\nFor testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:\n\n\n\n\n\nSome of the examples are also provided with generic French sentences:\n\nExample 1:\n\n\n\n\n\nOutput:\n\n\n\n \n Example 2:\n \n\n\nOutput:",
"## Resources\n\nFor all resources , please look into the HuggingFace Site and the Repositories.\n\n---\nlanguage:\n- fr\ntags:\n- fill-mask\nlicense: apache-2.0\n---"
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta Trained Model For Masked Language Model On French Corpus :robot:\n\n\nThis is a Masked Language Model trained with Roberta on a small French News Corpus(Leipzig corpora).\nThe model is built using Huggingface transformers.\nThe model can be found at :French-Roberta",
"## Specifications\n\n\nThe corpus for training is taken from Leipzig Corpora (French News) , and is trained on a small set of the corpus (300K).",
"## Model Specification\n\n\nThe model chosen for training is Roberta with the following specifications:\n 1. vocab_size=32000\n 2. max_position_embeddings=514\n 3. num_attention_heads=12\n 4. num_hidden_layers=6\n 5. type_vocab_size=1\n\n\nThis is trained by using RobertaConfig from transformers package.The total training parameters :68124416\nThe model is trained for 100 epochs with a gpu batch size of 64 units. \nMore details for building custom models can be found at the HuggingFace Blog",
"## Usage Specifications\n\n\nFor using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers\nAfter that we have to specify, the pre-trained model,which in this case is 'abhilash1910/french-roberta' for the tokenizers and the model.\n\n\n\n\n\nAfter this the model will be downloaded, it will take some time to download all the model files.\nFor testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:\n\n\n\n\n\nSome of the examples are also provided with generic French sentences:\n\nExample 1:\n\n\n\n\n\nOutput:\n\n\n\n \n Example 2:\n \n\n\nOutput:",
"## Resources\n\nFor all resources , please look into the HuggingFace Site and the Repositories.\n\n---\nlanguage:\n- fr\ntags:\n- fill-mask\nlicense: apache-2.0\n---"
] |
[
53,
65,
35,
128,
151,
42
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us \n# Roberta Trained Model For Masked Language Model On French Corpus :robot:\n\n\nThis is a Masked Language Model trained with Roberta on a small French News Corpus(Leipzig corpora).\nThe model is built using Huggingface transformers.\nThe model can be found at :French-Roberta## Specifications\n\n\nThe corpus for training is taken from Leipzig Corpora (French News) , and is trained on a small set of the corpus (300K).## Model Specification\n\n\nThe model chosen for training is Roberta with the following specifications:\n 1. vocab_size=32000\n 2. max_position_embeddings=514\n 3. num_attention_heads=12\n 4. num_hidden_layers=6\n 5. type_vocab_size=1\n\n\nThis is trained by using RobertaConfig from transformers package.The total training parameters :68124416\nThe model is trained for 100 epochs with a gpu batch size of 64 units. \nMore details for building custom models can be found at the HuggingFace Blog## Usage Specifications\n\n\nFor using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers\nAfter that we have to specify, the pre-trained model,which in this case is 'abhilash1910/french-roberta' for the tokenizers and the model.\n\n\n\n\n\nAfter this the model will be downloaded, it will take some time to download all the model files.\nFor testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows:\n\n\n\n\n\nSome of the examples are also provided with generic French sentences:\n\nExample 1:\n\n\n\n\n\nOutput:\n\n\n\n \n Example 2:\n \n\n\nOutput:## Resources\n\nFor all resources , please look into the HuggingFace Site and the Repositories.\n\n---\nlanguage:\n- fr\ntags:\n- fill-mask\nlicense: apache-2.0\n---"
] |
[
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] |
null | null |
transformers
|
This model is a fine-tuned version of Microsoft/DialoGPT-medium trained to created sarcastic responses from the dataset "Sarcasm on Reddit" located [here](https://www.kaggle.com/danofer/sarcasm).
|
{"pipeline_tag": "conversational"}
|
text-generation
|
abhiramtirumala/DialoGPT-sarcastic
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This model is a fine-tuned version of Microsoft/DialoGPT-medium trained to created sarcastic responses from the dataset "Sarcasm on Reddit" located here.
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
55
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37229289
- CO2 Emissions (in grams): 5.448567309047846
## Validation Metrics
- Loss: 0.07081354409456253
- Accuracy: 0.9867109634551495
- Macro F1: 0.9859067529980614
- Micro F1: 0.9867109634551495
- Weighted F1: 0.9866417220968429
- Macro Precision: 0.9868771404595043
- Micro Precision: 0.9867109634551495
- Weighted Precision: 0.9869289511551576
- Macro Recall: 0.9853173241852486
- Micro Recall: 0.9867109634551495
- Weighted Recall: 0.9867109634551495
## 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/abhishek/autonlp-bbc-news-classification-37229289
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-bbc-news-classification-37229289", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-bbc-news-classification-37229289", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-bbc-news-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.448567309047846}
|
text-classification
|
abhishek/autonlp-bbc-news-classification-37229289
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-bbc-news-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-bbc-news-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37229289
- CO2 Emissions (in grams): 5.448567309047846
## Validation Metrics
- Loss: 0.07081354409456253
- Accuracy: 0.9867109634551495
- Macro F1: 0.9859067529980614
- Micro F1: 0.9867109634551495
- Weighted F1: 0.9866417220968429
- Macro Precision: 0.9868771404595043
- Micro Precision: 0.9867109634551495
- Weighted Precision: 0.9869289511551576
- Macro Recall: 0.9853173241852486
- Micro Recall: 0.9867109634551495
- Weighted Recall: 0.9867109634551495
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37229289\n- CO2 Emissions (in grams): 5.448567309047846",
"## Validation Metrics\n\n- Loss: 0.07081354409456253\n- Accuracy: 0.9867109634551495\n- Macro F1: 0.9859067529980614\n- Micro F1: 0.9867109634551495\n- Weighted F1: 0.9866417220968429\n- Macro Precision: 0.9868771404595043\n- Micro Precision: 0.9867109634551495\n- Weighted Precision: 0.9869289511551576\n- Macro Recall: 0.9853173241852486\n- Micro Recall: 0.9867109634551495\n- Weighted Recall: 0.9867109634551495",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-bbc-news-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37229289\n- CO2 Emissions (in grams): 5.448567309047846",
"## Validation Metrics\n\n- Loss: 0.07081354409456253\n- Accuracy: 0.9867109634551495\n- Macro F1: 0.9859067529980614\n- Micro F1: 0.9867109634551495\n- Weighted F1: 0.9866417220968429\n- Macro Precision: 0.9868771404595043\n- Micro Precision: 0.9867109634551495\n- Weighted Precision: 0.9869289511551576\n- Macro Recall: 0.9853173241852486\n- Micro Recall: 0.9867109634551495\n- Weighted Recall: 0.9867109634551495",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
76,
42,
149,
17
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-bbc-news-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37229289\n- CO2 Emissions (in grams): 5.448567309047846## Validation Metrics\n\n- Loss: 0.07081354409456253\n- Accuracy: 0.9867109634551495\n- Macro F1: 0.9859067529980614\n- Micro F1: 0.9867109634551495\n- Weighted F1: 0.9866417220968429\n- Macro Precision: 0.9868771404595043\n- Micro Precision: 0.9867109634551495\n- Weighted Precision: 0.9869289511551576\n- Macro Recall: 0.9853173241852486\n- Micro Recall: 0.9867109634551495\n- Weighted Recall: 0.9867109634551495## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37249301
- CO2 Emissions (in grams): 1.9859980179658823
## Validation Metrics
- Loss: 0.06406362354755402
- Accuracy: 0.9833887043189369
- Macro F1: 0.9832763664701248
- Micro F1: 0.9833887043189369
- Weighted F1: 0.9833288528828136
- Macro Precision: 0.9847257743677181
- Micro Precision: 0.9833887043189369
- Weighted Precision: 0.9835392869652073
- Macro Recall: 0.982101705176067
- Micro Recall: 0.9833887043189369
- Weighted Recall: 0.9833887043189369
## 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/abhishek/autonlp-bbc-roberta-37249301
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-bbc-roberta-37249301", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-bbc-roberta-37249301", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "unk", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-bbc-roberta"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.9859980179658823}
|
text-classification
|
abhishek/autonlp-bbc-roberta-37249301
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:abhishek/autonlp-data-bbc-roberta",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"unk"
] |
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-bbc-roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 37249301
- CO2 Emissions (in grams): 1.9859980179658823
## Validation Metrics
- Loss: 0.06406362354755402
- Accuracy: 0.9833887043189369
- Macro F1: 0.9832763664701248
- Micro F1: 0.9833887043189369
- Weighted F1: 0.9833288528828136
- Macro Precision: 0.9847257743677181
- Micro Precision: 0.9833887043189369
- Weighted Precision: 0.9835392869652073
- Macro Recall: 0.982101705176067
- Micro Recall: 0.9833887043189369
- Weighted Recall: 0.9833887043189369
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37249301\n- CO2 Emissions (in grams): 1.9859980179658823",
"## Validation Metrics\n\n- Loss: 0.06406362354755402\n- Accuracy: 0.9833887043189369\n- Macro F1: 0.9832763664701248\n- Micro F1: 0.9833887043189369\n- Weighted F1: 0.9833288528828136\n- Macro Precision: 0.9847257743677181\n- Micro Precision: 0.9833887043189369\n- Weighted Precision: 0.9835392869652073\n- Macro Recall: 0.982101705176067\n- Micro Recall: 0.9833887043189369\n- Weighted Recall: 0.9833887043189369",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-bbc-roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37249301\n- CO2 Emissions (in grams): 1.9859980179658823",
"## Validation Metrics\n\n- Loss: 0.06406362354755402\n- Accuracy: 0.9833887043189369\n- Macro F1: 0.9832763664701248\n- Micro F1: 0.9833887043189369\n- Weighted F1: 0.9833288528828136\n- Macro Precision: 0.9847257743677181\n- Micro Precision: 0.9833887043189369\n- Weighted Precision: 0.9835392869652073\n- Macro Recall: 0.982101705176067\n- Micro Recall: 0.9833887043189369\n- Weighted Recall: 0.9833887043189369",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
71,
43,
151,
17
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-bbc-roberta #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 37249301\n- CO2 Emissions (in grams): 1.9859980179658823## Validation Metrics\n\n- Loss: 0.06406362354755402\n- Accuracy: 0.9833887043189369\n- Macro F1: 0.9832763664701248\n- Micro F1: 0.9833887043189369\n- Weighted F1: 0.9833288528828136\n- Macro Precision: 0.9847257743677181\n- Micro Precision: 0.9833887043189369\n- Weighted Precision: 0.9835392869652073\n- Macro Recall: 0.982101705176067\n- Micro Recall: 0.9833887043189369\n- Weighted Recall: 0.9833887043189369## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Recall: 0.8076335877862595
- AUC: 0.8946767157983608
- F1: 0.8138461538461538
## 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/abhishek/autonlp-ferd1-2652021
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-ferd1"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
|
text-classification
|
abhishek/autonlp-ferd1-2652021
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-ferd1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-ferd1 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Recall: 0.8076335877862595
- AUC: 0.8946767157983608
- F1: 0.8138461538461538
## 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: 2652021",
"## Validation Metrics\n\n- Loss: 0.3934604227542877\n- Accuracy: 0.8411030860144452\n- Precision: 0.8201550387596899\n- Recall: 0.8076335877862595\n- AUC: 0.8946767157983608\n- F1: 0.8138461538461538",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-ferd1 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2652021",
"## Validation Metrics\n\n- Loss: 0.3934604227542877\n- Accuracy: 0.8411030860144452\n- Precision: 0.8201550387596899\n- Recall: 0.8076335877862595\n- AUC: 0.8946767157983608\n- F1: 0.8138461538461538",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
60,
23,
77,
17
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-ferd1 #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2652021## Validation Metrics\n\n- Loss: 0.3934604227542877\n- Accuracy: 0.8411030860144452\n- Precision: 0.8201550387596899\n- Recall: 0.8076335877862595\n- AUC: 0.8946767157983608\n- F1: 0.8138461538461538## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2682064
## Validation Metrics
- Loss: 0.4454168379306793
- Accuracy: 0.8188976377952756
- Precision: 0.8442028985507246
- Recall: 0.7103658536585366
- AUC: 0.8699702146791053
- F1: 0.771523178807947
## 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/abhishek/autonlp-fred2-2682064
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-fred2-2682064", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-fred2-2682064", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-fred2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
|
text-classification
|
abhishek/autonlp-fred2-2682064
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-fred2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-abhishek/autonlp-data-fred2 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2682064
## Validation Metrics
- Loss: 0.4454168379306793
- Accuracy: 0.8188976377952756
- Precision: 0.8442028985507246
- Recall: 0.7103658536585366
- AUC: 0.8699702146791053
- F1: 0.771523178807947
## 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: 2682064",
"## Validation Metrics\n\n- Loss: 0.4454168379306793\n- Accuracy: 0.8188976377952756\n- Precision: 0.8442028985507246\n- Recall: 0.7103658536585366\n- AUC: 0.8699702146791053\n- F1: 0.771523178807947",
"## 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-abhishek/autonlp-data-fred2 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2682064",
"## Validation Metrics\n\n- Loss: 0.4454168379306793\n- Accuracy: 0.8188976377952756\n- Precision: 0.8442028985507246\n- Recall: 0.7103658536585366\n- AUC: 0.8699702146791053\n- F1: 0.771523178807947",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
59,
25,
81,
17
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-abhishek/autonlp-data-fred2 #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2682064## Validation Metrics\n\n- Loss: 0.4454168379306793\n- Accuracy: 0.8188976377952756\n- Precision: 0.8442028985507246\n- Recall: 0.7103658536585366\n- AUC: 0.8699702146791053\n- F1: 0.771523178807947## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Speech Recognition
|
{"language": {}, "tags": ["autonlp", "automatic-speech-recognition", "audio"]}
|
automatic-speech-recognition
|
abhishek/autonlp-hindi-asr
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"autonlp",
"audio",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #autonlp #audio #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Speech Recognition
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Speech Recognition"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #autonlp #audio #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Speech Recognition"
] |
[
51,
17
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #autonlp #audio #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Speech Recognition"
] |
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null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-hindi-question-answering-23865268
```
Or Python API:
```
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-hindi-question-answering-23865268", use_auth_token=True)
from transformers import BertTokenizer, BertForQuestionAnswering
question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
inputs = tokenizer(question, text, return_tensors='pt')
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
loss = outputs.loss
start_scores = outputs.start_logits
end_scores = outputs.end_logits
```
|
{"language": "hi", "tags": ["autonlp", "question-answering"], "datasets": ["abhishek/autonlp-data-hindi-question-answering"], "widget": [{"text": "\u00b4\u0938\u0924\u0940\u0936 \u0927\u0935\u0928 \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0915\u0947\u0902\u0926\u094d\u0930\u00b4 \u0915\u093f\u0938 \u0930\u093e\u091c\u094d\u092f \u092e\u0947\u0902 \u0938\u094d\u0925\u093f\u0924 \u0939\u0948?", "context": "\u0938\u0924\u0940\u0936 \u0927\u0935\u0928 \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0915\u0947\u0902\u0926\u094d\u0930, \u092d\u093e\u0930\u0924\u0940\u092f \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0905\u0928\u0941\u0938\u0902\u0927\u093e\u0928 \u0938\u0902\u0917\u0920\u0928 (\u0907\u0938\u0930\u094b) \u0915\u093e \u092a\u094d\u0930\u0915\u094d\u0937\u0947\u092a\u0923 \u0915\u0947\u0902\u0926\u094d\u0930 \u0939\u0948\u0964 \u092f\u0939 \u0906\u0902\u0927\u094d\u0930 \u092a\u094d\u0930\u0926\u0947\u0936 \u0915\u0947 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\u0917\u092f\u093e \u0925\u093e\u0964\n\u092a\u094d\u0930\u0925\u092e \u0932\u093e\u0902\u091a \u092a\u0948\u0921\n\u0926\u094d\u0935\u093f\u0924\u0940\u092f \u0932\u0949\u0928\u094d\u091a \u092a\u0948\u0921\n\u0924\u0943\u0924\u0940\u092f \u0932\u093e\u0902\u091a \u092a\u0948\u0921\n\u0938\u0928\u094d\u0926\u0930\u094d\u092d \u0936\u094d\u0930\u0947\u0923\u0940:\u092d\u093e\u0930\u0924\u0940\u092f \u0905\u0902\u0924\u0930\u093f\u0915\u094d\u0937 \u0905\u0928\u0941\u0938\u0902\u0927\u093e\u0928 \u0938\u0902\u0917\u0920\u0928\n\u0936\u094d\u0930\u0947\u0923\u0940:\u092d\u093e\u0930\u0924 \u0915\u0947 \u0930\u0949\u0915\u0947\u091f \u092a\u094d\u0930\u0915\u094d\u0937\u0947\u092a\u0923 \u0938\u094d\u0925\u0932"}], "co2_eq_emissions": 39.76330395590446}
|
question-answering
|
abhishek/autonlp-hindi-question-answering-23865268
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"autonlp",
"hi",
"dataset:abhishek/autonlp-data-hindi-question-answering",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hi"
] |
TAGS
#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Extractive Question Answering
- CO2 Emissions (in grams): 39.76330395590446
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- CO2 Emissions (in grams): 39.76330395590446",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- CO2 Emissions (in grams): 39.76330395590446",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
69,
36,
17
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #question-answering #autonlp #hi #dataset-abhishek/autonlp-data-hindi-question-answering #co2_eq_emissions #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Extractive Question Answering\n- CO2 Emissions (in grams): 39.76330395590446## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 3662644
- CO2 Emissions (in grams): 25.894117734124272
## Validation Metrics
- Loss: 0.20277436077594757
- Accuracy: 0.92604
- Precision: 0.9560674830864092
- Recall: 0.89312
- AUC: 0.9814625504000001
- F1: 0.9235223559581421
## 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/abhishek/autonlp-imdb-roberta-base-3662644
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "unk", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-imdb-roberta-base"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 25.894117734124272}
|
text-classification
|
abhishek/autonlp-imdb-roberta-base-3662644
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:abhishek/autonlp-data-imdb-roberta-base",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"unk"
] |
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-imdb-roberta-base #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 3662644
- CO2 Emissions (in grams): 25.894117734124272
## Validation Metrics
- Loss: 0.20277436077594757
- Accuracy: 0.92604
- Precision: 0.9560674830864092
- Recall: 0.89312
- AUC: 0.9814625504000001
- F1: 0.9235223559581421
## 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: 3662644\n- CO2 Emissions (in grams): 25.894117734124272",
"## Validation Metrics\n\n- Loss: 0.20277436077594757\n- Accuracy: 0.92604\n- Precision: 0.9560674830864092\n- Recall: 0.89312\n- AUC: 0.9814625504000001\n- F1: 0.9235223559581421",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-imdb-roberta-base #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 3662644\n- CO2 Emissions (in grams): 25.894117734124272",
"## Validation Metrics\n\n- Loss: 0.20277436077594757\n- Accuracy: 0.92604\n- Precision: 0.9560674830864092\n- Recall: 0.89312\n- AUC: 0.9814625504000001\n- F1: 0.9235223559581421",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
73,
42,
70,
17
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-abhishek/autonlp-data-imdb-roberta-base #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 3662644\n- CO2 Emissions (in grams): 25.894117734124272## Validation Metrics\n\n- Loss: 0.20277436077594757\n- Accuracy: 0.92604\n- Precision: 0.9560674830864092\n- Recall: 0.89312\n- AUC: 0.9814625504000001\n- F1: 0.9235223559581421## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 71421
## Validation Metrics
- Loss: 0.4114699363708496
- Accuracy: 0.8248248248248248
- Precision: 0.8305439330543933
- Recall: 0.8085539714867617
- AUC: 0.9088033420466026
- F1: 0.8194014447884417
## 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/abhishek/autonlp-imdb_eval-71421
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb_eval-71421", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb_eval-71421", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-imdb_eval"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
|
text-classification
|
abhishek/autonlp-imdb_eval-71421
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-imdb_eval",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-imdb_eval #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 71421
## Validation Metrics
- Loss: 0.4114699363708496
- Accuracy: 0.8248248248248248
- Precision: 0.8305439330543933
- Recall: 0.8085539714867617
- AUC: 0.9088033420466026
- F1: 0.8194014447884417
## 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: 71421",
"## Validation Metrics\n\n- Loss: 0.4114699363708496\n- Accuracy: 0.8248248248248248\n- Precision: 0.8305439330543933\n- Recall: 0.8085539714867617\n- AUC: 0.9088033420466026\n- F1: 0.8194014447884417",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-imdb_eval #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 71421",
"## Validation Metrics\n\n- Loss: 0.4114699363708496\n- Accuracy: 0.8248248248248248\n- Precision: 0.8305439330543933\n- Recall: 0.8085539714867617\n- AUC: 0.9088033420466026\n- F1: 0.8194014447884417",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
64,
24,
77,
17
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-imdb_eval #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 71421## Validation Metrics\n\n- Loss: 0.4114699363708496\n- Accuracy: 0.8248248248248248\n- Precision: 0.8305439330543933\n- Recall: 0.8085539714867617\n- AUC: 0.9088033420466026\n- F1: 0.8194014447884417## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 31154
## Validation Metrics
- Loss: 0.19292379915714264
- Accuracy: 0.9395
- Precision: 0.9569557080474111
- Recall: 0.9204
- AUC: 0.9851040399999998
- F1: 0.9383219492302988
## 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/abhishek/autonlp-imdb_sentiment_classification-31154
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb_sentiment_classification-31154", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb_sentiment_classification-31154", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
|
text-classification
|
abhishek/autonlp-imdb_sentiment_classification-31154
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autonlp",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #autonlp #en #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 31154
## Validation Metrics
- Loss: 0.19292379915714264
- Accuracy: 0.9395
- Precision: 0.9569557080474111
- Recall: 0.9204
- AUC: 0.9851040399999998
- F1: 0.9383219492302988
## 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: 31154",
"## Validation Metrics\n\n- Loss: 0.19292379915714264\n- Accuracy: 0.9395\n- Precision: 0.9569557080474111\n- Recall: 0.9204\n- AUC: 0.9851040399999998\n- F1: 0.9383219492302988",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #autonlp #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 31154",
"## Validation Metrics\n\n- Loss: 0.19292379915714264\n- Accuracy: 0.9395\n- Precision: 0.9569557080474111\n- Recall: 0.9204\n- AUC: 0.9851040399999998\n- F1: 0.9383219492302988",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
46,
23,
69,
17
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #autonlp #en #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 31154## Validation Metrics\n\n- Loss: 0.19292379915714264\n- Accuracy: 0.9395\n- Precision: 0.9569557080474111\n- Recall: 0.9204\n- AUC: 0.9851040399999998\n- F1: 0.9383219492302988## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59362
## Validation Metrics
- Loss: 0.13092292845249176
- Accuracy: 0.9527127414314258
- Precision: 0.9634070704982427
- Recall: 0.9842171959602166
- AUC: 0.9667289746092403
- F1: 0.9737009564152002
## 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/abhishek/autonlp-japanese-sentiment-59362
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-japanese-sentiment-59362", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-japanese-sentiment-59362", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "ja", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-japanese-sentiment"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
|
text-classification
|
abhishek/autonlp-japanese-sentiment-59362
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"ja",
"dataset:abhishek/autonlp-data-japanese-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ja"
] |
TAGS
#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59362
## Validation Metrics
- Loss: 0.13092292845249176
- Accuracy: 0.9527127414314258
- Precision: 0.9634070704982427
- Recall: 0.9842171959602166
- AUC: 0.9667289746092403
- F1: 0.9737009564152002
## 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: 59362",
"## Validation Metrics\n\n- Loss: 0.13092292845249176\n- Accuracy: 0.9527127414314258\n- Precision: 0.9634070704982427\n- Recall: 0.9842171959602166\n- AUC: 0.9667289746092403\n- F1: 0.9737009564152002",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59362",
"## Validation Metrics\n\n- Loss: 0.13092292845249176\n- Accuracy: 0.9527127414314258\n- Precision: 0.9634070704982427\n- Recall: 0.9842171959602166\n- AUC: 0.9667289746092403\n- F1: 0.9737009564152002",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
65,
24,
79,
17
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59362## Validation Metrics\n\n- Loss: 0.13092292845249176\n- Accuracy: 0.9527127414314258\n- Precision: 0.9634070704982427\n- Recall: 0.9842171959602166\n- AUC: 0.9667289746092403\n- F1: 0.9737009564152002## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59363
## Validation Metrics
- Loss: 0.12651239335536957
- Accuracy: 0.9532079853817648
- Precision: 0.9729688278823665
- Recall: 0.9744633462616643
- AUC: 0.9717333684823413
- F1: 0.9737155136027014
## 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/abhishek/autonlp-japanese-sentiment-59363
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-japanese-sentiment-59363", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-japanese-sentiment-59363", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "ja", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-japanese-sentiment"], "widget": [{"text": "\ud83e\udd17AutoNLP\u304c\u5927\u597d\u304d\u3067\u3059"}]}
|
text-classification
|
abhishek/autonlp-japanese-sentiment-59363
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"ja",
"dataset:abhishek/autonlp-data-japanese-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ja"
] |
TAGS
#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 59363
## Validation Metrics
- Loss: 0.12651239335536957
- Accuracy: 0.9532079853817648
- Precision: 0.9729688278823665
- Recall: 0.9744633462616643
- AUC: 0.9717333684823413
- F1: 0.9737155136027014
## 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: 59363",
"## Validation Metrics\n\n- Loss: 0.12651239335536957\n- Accuracy: 0.9532079853817648\n- Precision: 0.9729688278823665\n- Recall: 0.9744633462616643\n- AUC: 0.9717333684823413\n- F1: 0.9737155136027014",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59363",
"## Validation Metrics\n\n- Loss: 0.12651239335536957\n- Accuracy: 0.9532079853817648\n- Precision: 0.9729688278823665\n- Recall: 0.9744633462616643\n- AUC: 0.9717333684823413\n- F1: 0.9737155136027014",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
65,
23,
80,
17
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #autonlp #ja #dataset-abhishek/autonlp-data-japanese-sentiment #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 59363## Validation Metrics\n\n- Loss: 0.12651239335536957\n- Accuracy: 0.9532079853817648\n- Precision: 0.9729688278823665\n- Recall: 0.9744633462616643\n- AUC: 0.9717333684823413\n- F1: 0.9737155136027014## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 3362554
- CO2 Emissions (in grams): 5.340540212393564
## Validation Metrics
- Loss: 0.14167872071266174
- Accuracy: 0.9587076867229332
- Precision: 0.7351351351351352
- Recall: 0.7923728813559322
- F1: 0.7626816212082591
## 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/abhishek/autonlp-prodigy-10-3362554
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("abhishek/autonlp-prodigy-10-3362554", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-prodigy-10-3362554", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-prodigy-10"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 5.340540212393564}
|
token-classification
|
abhishek/autonlp-prodigy-10-3362554
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-prodigy-10",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #token-classification #autonlp #en #dataset-abhishek/autonlp-data-prodigy-10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 3362554
- CO2 Emissions (in grams): 5.340540212393564
## Validation Metrics
- Loss: 0.14167872071266174
- Accuracy: 0.9587076867229332
- Precision: 0.7351351351351352
- Recall: 0.7923728813559322
- F1: 0.7626816212082591
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 3362554\n- CO2 Emissions (in grams): 5.340540212393564",
"## Validation Metrics\n\n- Loss: 0.14167872071266174\n- Accuracy: 0.9587076867229332\n- Precision: 0.7351351351351352\n- Recall: 0.7923728813559322\n- F1: 0.7626816212082591",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #autonlp #en #dataset-abhishek/autonlp-data-prodigy-10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 3362554\n- CO2 Emissions (in grams): 5.340540212393564",
"## Validation Metrics\n\n- Loss: 0.14167872071266174\n- Accuracy: 0.9587076867229332\n- Precision: 0.7351351351351352\n- Recall: 0.7923728813559322\n- F1: 0.7626816212082591",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
70,
41,
69,
17
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #autonlp #en #dataset-abhishek/autonlp-data-prodigy-10 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Entity Extraction\n- Model ID: 3362554\n- CO2 Emissions (in grams): 5.340540212393564## Validation Metrics\n\n- Loss: 0.14167872071266174\n- Accuracy: 0.9587076867229332\n- Precision: 0.7351351351351352\n- Recall: 0.7923728813559322\n- F1: 0.7626816212082591## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 30516963
- CO2 Emissions (in grams): 30.684995819386277
## Validation Metrics
- Loss: 0.08340361714363098
- Accuracy: 0.9688222161294113
- Precision: 0.9102096627164995
- Recall: 0.7692604006163328
- AUC: 0.9859340458715813
- F1: 0.8338204592901879
## 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/abhishek/autonlp-toxic-new-30516963
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-toxic-new-30516963", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-toxic-new-30516963", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": "autonlp", "datasets": ["abhishek/autonlp-data-toxic-new"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 30.684995819386277}
|
text-classification
|
abhishek/autonlp-toxic-new-30516963
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-toxic-new",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-toxic-new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 30516963
- CO2 Emissions (in grams): 30.684995819386277
## Validation Metrics
- Loss: 0.08340361714363098
- Accuracy: 0.9688222161294113
- Precision: 0.9102096627164995
- Recall: 0.7692604006163328
- AUC: 0.9859340458715813
- F1: 0.8338204592901879
## 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: 30516963\n- CO2 Emissions (in grams): 30.684995819386277",
"## Validation Metrics\n\n- Loss: 0.08340361714363098\n- Accuracy: 0.9688222161294113\n- Precision: 0.9102096627164995\n- Recall: 0.7692604006163328\n- AUC: 0.9859340458715813\n- F1: 0.8338204592901879",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-toxic-new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 30516963\n- CO2 Emissions (in grams): 30.684995819386277",
"## Validation Metrics\n\n- Loss: 0.08340361714363098\n- Accuracy: 0.9688222161294113\n- Precision: 0.9102096627164995\n- Recall: 0.7692604006163328\n- AUC: 0.9859340458715813\n- F1: 0.8338204592901879",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
70,
42,
78,
17
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-abhishek/autonlp-data-toxic-new #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 30516963\n- CO2 Emissions (in grams): 30.684995819386277## Validation Metrics\n\n- Loss: 0.08340361714363098\n- Accuracy: 0.9688222161294113\n- Precision: 0.9102096627164995\n- Recall: 0.7692604006163328\n- AUC: 0.9859340458715813\n- F1: 0.8338204592901879## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# muril-large-chaii
This is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.
Our full solution can be found here:
|
{"language": ["hi", "ta"], "tags": ["question-answering"], "widget": [{"text": "\u0905\u092d\u093f\u0937\u0947\u0915 \u0914\u0930 \u0909\u0926\u094d\u092d\u0935 \u0915\u094b \u0915\u094c\u0928 \u0938\u093e \u0938\u094d\u0925\u093e\u0928 \u092e\u093f\u0932\u093e?", "context": "kaggle \u0926\u094d\u0935\u093e\u0930\u093e \u0906\u092f\u094b\u091c\u093f\u0924 chaii \u092a\u094d\u0930\u0924\u093f\u092f\u094b\u0917\u093f\u0924\u093e \u092e\u0947\u0902 \u0905\u092d\u093f\u0937\u0947\u0915 \u0914\u0930 \u0909\u0926\u094d\u092d\u0935 \u0928\u0947 \u092a\u093e\u0902\u091a\u0935\u093e \u0938\u094d\u0925\u093e\u0928 \u0939\u093e\u0938\u093f\u0932 \u0915\u093f\u092f\u093e \n \u0909\u0928\u094d\u0939\u094b\u0902\u0928\u0947 xlm-roberta, muril \u0914\u0930 rembert \u091c\u0948\u0938\u0947 \u092e\u0949\u0921\u0932\u094b\u0902 \u0915\u093e \u0907\u0938\u094d\u0924\u0947\u092e\u093e\u0932 \u0915\u093f\u092f\u093e."}]}
|
question-answering
|
abhishek/muril-large-chaii
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"hi",
"ta",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hi",
"ta"
] |
TAGS
#transformers #pytorch #bert #question-answering #hi #ta #endpoints_compatible #region-us
|
# muril-large-chaii
This is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.
Our full solution can be found here:
|
[
"# muril-large-chaii\n\nThis is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.\nOur full solution can be found here:"
] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #hi #ta #endpoints_compatible #region-us \n",
"# muril-large-chaii\n\nThis is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.\nOur full solution can be found here:"
] |
[
33,
48
] |
[
"passage: TAGS\n#transformers #pytorch #bert #question-answering #hi #ta #endpoints_compatible #region-us \n# muril-large-chaii\n\nThis is __one of the models__ that we used for getting 5th place in the hindi and tamil question answering competition organized by Kaggle.\nOur full solution can be found here:"
] |
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] |
null | null |
transformers
|
# Emilybot DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
abhisht/DialoGPT-medium-Emilybot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Emilybot DialoGPT Model
|
[
"# Emilybot DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Emilybot DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Emilybot DialoGPT Model"
] |
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] |
null | null |
transformers
|
# GPT2-Tamil
This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
## Setup:
To setup the project, run the following command,
```python
pip install -r requirements.txt
```
## Model:
Pretrained model on Tamil language using a causal language modeling (CLM) objective.
## Dataset Used:
The GTP-2 model is trained on [oscar dataset - ta](https://huggingface.co/datasets/oscar) and [IndicNLP dataset - ta](https://indicnlp.ai4bharat.org/corpora/)
## Intended uses & limitations:
You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
## How to pretrain the model:
To perform training, do the following steps,
- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
```python
>>> export MODEL_DIR=<model_dir>
```
- Create the config.json by running the following command,
```python
>>> python src/create_config.py
```
- Create the tokenizer by running the following command,
```python
>>> python src/train_tokenizer.py
```
- Once the config and tokenizer is created, run the following script to start training the flax model
```python
>>> python scripts/train_gpt2-oscar-tamil.sh
```
## How to use:
To perform language generation using the model, pipeline can be used directly.
- First convert the flax model to pytorch using the following command,
```python
python src/convert_flax_to_pytorch.py
```
- Use the following snippet to perform language generation,
```python
>>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
>>> model_name = 'abinayam/gpt-2-tamil'
>>> model = AutoModelWithLMHead.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> set_seed(42)
>>> input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு"
>>> max_len = 300
>>> no_seq = 5
>>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
>>> sequence = generator(input_text, max_length=max_len, num_return_sequences=no_seq)
```
|
{"language": "ta", "datasets": ["oscar", "IndicNLP"], "widget": [{"text": "\u0b92\u0bb0\u0bc1 \u0b8a\u0bb0\u0bbf\u0bb2\u0bc7 \u0b92\u0bb0\u0bc1 \u0b95\u0bbe\u0b95\u0bcd\u0b95\u0bc8\u0b95\u0bcd\u0b95\u0bc1"}]}
|
text-generation
|
abinayam/gpt-2-tamil
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"ta",
"dataset:oscar",
"dataset:IndicNLP",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ta"
] |
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #ta #dataset-oscar #dataset-IndicNLP #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2-Tamil
This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
## Setup:
To setup the project, run the following command,
## Model:
Pretrained model on Tamil language using a causal language modeling (CLM) objective.
## Dataset Used:
The GTP-2 model is trained on oscar dataset - ta and IndicNLP dataset - ta
## Intended uses & limitations:
You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
## How to pretrain the model:
To perform training, do the following steps,
- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
- Create the URL by running the following command,
- Create the tokenizer by running the following command,
- Once the config and tokenizer is created, run the following script to start training the flax model
## How to use:
To perform language generation using the model, pipeline can be used directly.
- First convert the flax model to pytorch using the following command,
- Use the following snippet to perform language generation,
|
[
"# GPT2-Tamil\n\nThis repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.",
"## Setup:\nTo setup the project, run the following command,",
"## Model:\nPretrained model on Tamil language using a causal language modeling (CLM) objective.",
"## Dataset Used:\nThe GTP-2 model is trained on oscar dataset - ta and IndicNLP dataset - ta",
"## Intended uses & limitations:\nYou can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.",
"## How to pretrain the model:\nTo perform training, do the following steps,\n\n- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)\n\n- Create the URL by running the following command,\n\n- Create the tokenizer by running the following command,\n\n- Once the config and tokenizer is created, run the following script to start training the flax model",
"## How to use:\nTo perform language generation using the model, pipeline can be used directly.\n\n- First convert the flax model to pytorch using the following command,\n\n- Use the following snippet to perform language generation,"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #ta #dataset-oscar #dataset-IndicNLP #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2-Tamil\n\nThis repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.",
"## Setup:\nTo setup the project, run the following command,",
"## Model:\nPretrained model on Tamil language using a causal language modeling (CLM) objective.",
"## Dataset Used:\nThe GTP-2 model is trained on oscar dataset - ta and IndicNLP dataset - ta",
"## Intended uses & limitations:\nYou can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.",
"## How to pretrain the model:\nTo perform training, do the following steps,\n\n- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)\n\n- Create the URL by running the following command,\n\n- Create the tokenizer by running the following command,\n\n- Once the config and tokenizer is created, run the following script to start training the flax model",
"## How to use:\nTo perform language generation using the model, pipeline can be used directly.\n\n- First convert the flax model to pytorch using the following command,\n\n- Use the following snippet to perform language generation,"
] |
[
76,
49,
14,
23,
30,
61,
90,
48
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #ta #dataset-oscar #dataset-IndicNLP #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# GPT2-Tamil\n\nThis repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.## Setup:\nTo setup the project, run the following command,## Model:\nPretrained model on Tamil language using a causal language modeling (CLM) objective.## Dataset Used:\nThe GTP-2 model is trained on oscar dataset - ta and IndicNLP dataset - ta## Intended uses & limitations:\nYou can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.## How to pretrain the model:\nTo perform training, do the following steps,\n\n- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)\n\n- Create the URL by running the following command,\n\n- Create the tokenizer by running the following command,\n\n- Once the config and tokenizer is created, run the following script to start training the flax model## How to use:\nTo perform language generation using the model, pipeline can be used directly.\n\n- First convert the flax model to pytorch using the following command,\n\n- Use the following snippet to perform language generation,"
] |
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] |
null | null |
transformers
|
# Model v2
|
{"tags": ["conversational"]}
|
text-generation
|
abjbpi/DS_small
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model v2
|
[
"# Model v2"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model v2"
] |
[
51,
4
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model v2"
] |
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] |
null | null |
transformers
|
# My Awesome Model
|
{"tags": ["conversational"]}
|
text-generation
|
abjbpi/Dwight_Schrute
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
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"
] |
[
51,
4
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model"
] |
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] |
null | null |
transformers
|
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on German using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.
As capitalization is an important part of the German language (eg. Sie vs. sie).
I trained a model using a vocab that includes both lower case and upper case letters in hopes that the model would learn the correct casing.
This removes the need to do any post-processing like truecasing.
| Reference | Prediction |
| ------------- | ------------- |
| **Die** zoologische **Einordnung** der **Spezies** ist seit **Jahrzehnten** umstritten | **Die** psoologische **Einordnung** der **Spezies** ist seit **Jahrzehnten** umstritten |
| **Hauptgeschäftsfeld** war ursprünglich der öffentliche **Sektor** in **Irland** | **Hauptgeschäftsfeld** war ursprünglich der öffentliche **Sektor** in **Irland** |
| **Er** vertrat den **Wahlkreis Donauwörth** im **Parlament** | **Er** vertrat den **Wahlkreis DonauWört** im **Parlament** |
| **Ich** bin gespannt welche **Lieder** sie wählt | **Ich** bin gespannt welche **Lieder** see wählt |
| **Eine** allgemein verbindliche **Definition** gibt es nicht | **Eine** allgemeinverbindliche **Definition** gibt es nicht |
```
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import soundfile as sf
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("abnerh/wav2vec2-xlsr-300m-german-truecase")
model = Wav2Vec2ForCTC.from_pretrained("abnerh/wav2vec2-xlsr-300m-german-truecase")
speech, sr = sf.read('audio.wav')
# tokenize
input_values = processor(speech, return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
# print transcription results
print(transcription)
```
|
{}
|
automatic-speech-recognition
|
abnerh/wav2vec2-xlsr-300m-german-truecase
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
Fine-tuned facebook/wav2vec2-xls-r-300m on German using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.
As capitalization is an important part of the German language (eg. Sie vs. sie).
I trained a model using a vocab that includes both lower case and upper case letters in hopes that the model would learn the correct casing.
This removes the need to do any post-processing like truecasing.
|
[] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
# Transferring Monolingual Model to Low-Resource Language: The Case Of Tigrinya:
## Proposed Method:
<img src="data/proposed.png" height = "330" width ="760" >
The proposed method transfers a mono-lingual Transformer model into new target language at lexical level by learning new token embeddings. All implementation in this repo uses XLNet as a source Transformer model, however, other Transformer models can also be used similarly.
## Main files:
All files are IPython Notebook files which can be excuted simply in Google Colab.
- train.ipynb : Fine-tunes XLNet (mono-lingual transformer) on new target language (Tigrinya) sentiment analysis dataset. [](https://colab.research.google.com/drive/1bSSrKE-TSphUyrNB2UWhFI-Bkoz0a5l0?usp=sharing)
- test.ipynb : Evaluates the fine-tuned model on test data. [](https://colab.research.google.com/drive/17R1lvRjxILVNk971vzZT79o2OodwaNIX?usp=sharing)
- token_embeddings.ipynb : Trains a word2vec token embeddings for Tigrinya language. [](https://colab.research.google.com/drive/1hCtetAllAjBw28EVQkJFpiKdFtXmuxV7?usp=sharing)
- process_Tigrinya_comments.ipynb : Extracts Tigrinya comments from mixed language contents. [](https://colab.research.google.com/drive/1-ndLlBV-iLZNBW3Z8OfKAqUUCjvGbdZU?usp=sharing)
- extract_YouTube_comments.ipynb : Downloads available comments from a YouTube channel ID. [](https://colab.research.google.com/drive/1b7G85wHKe18y45JIDtvDJdO5dOkRmDdp?usp=sharing)
- auto_labelling.ipynb : Automatically labels Tigrinya comments in to positive or negative sentiments based on [Emoji's sentiment](http://kt.ijs.si/data/Emoji_sentiment_ranking/). [](https://colab.research.google.com/drive/1wnZf7CBBCIr966vRUITlxKCrANsMPpV7?usp=sharing)
## Tigrinya Tokenizer:
A [sentencepiece](https://github.com/google/sentencepiece) based tokenizer for Tigrinya has been released to the public and can be accessed as in the following:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("abryee/TigXLNet")
tokenizer.tokenize("ዋዋዋው እዛ ፍሊም ካብተን ዘድንቀን ሓንቲ ኢያ ሞ ብጣዕሚ ኢና ነመስግን ሓንቲ ክብላ ደልየ ዘሎኹ ሓደራኣኹም ኣብ ጊዜኹም ተረክቡ")
## TigXLNet:
A new general purpose transformer model for low-resource language Tigrinya is also released to the public and be accessed as in the following:
from transformers import AutoConfig, AutoModel
config = AutoConfig.from_pretrained("abryee/TigXLNet")
config.d_head = 64
model = AutoModel.from_pretrained("abryee/TigXLNet", config=config)
## Evaluation:
The proposed method is evaluated using two datasets:
- A newly created sentiment analysis dataset for low-resource language (Tigriyna).
<table>
<tr>
<td> <table>
<thead>
<tr>
<th><sub>Models</sub></th>
<th><sub>Configuration</sub></th>
<th><sub>F1-Score</sub></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan=3><sub>BERT</sub></td>
<td rowspan=1><sub>+Frozen BERT weights</sub></td>
<td><sub>54.91</sub></td>
</tr>
<tr>
<td rowspan=1><sub>+Random embeddings</sub></td>
<td><sub>74.26</sub></td>
</tr>
<tr>
<td rowspan=1><sub>+Frozen token embeddings</sub></td>
<td><sub>76.35</sub></td>
</tr>
<tr>
<td rowspan=3><sub>mBERT</sub></td>
<td rowspan=1><sub>+Frozen mBERT weights</sub></td>
<td><sub>57.32</sub></td>
</tr>
<tr>
<td rowspan=1><sub>+Random embeddings</sub></td>
<td><sub>76.01</sub></td>
</tr>
<tr>
<td rowspan=1><sub>+Frozen token embeddings</sub></td>
<td><sub>77.51</sub></td>
</tr>
<tr>
<td rowspan=3><sub>XLNet</sub></td>
<td rowspan=1><sub>+Frozen XLNet weights</sub></td>
<td><strong><sub>68.14</sub></strong></td>
</tr>
<tr>
<td rowspan=1><sub>+Random embeddings</sub></td>
<td><strong><sub>77.83</sub></strong></td>
</tr>
<tr>
<td rowspan=1><sub>+Frozen token embeddings</sub></td>
<td><strong><sub>81.62</sub></strong></td>
</tr>
</tbody>
</table> </td>
<td><img src="data/effect_of_dataset_size.png" alt="3" width = 480px height = 280px></td>
</tr>
</table>
- Cross-lingual Sentiment dataset ([CLS](https://zenodo.org/record/3251672#.Xs65VzozbIU)).
<table>
<thead>
<tr>
<th rowspan=2><sub>Models</sub></th>
<th rowspan=1 colspan=3><sub>English</sub></th>
<th rowspan=1 colspan=3><sub>German</sub></th>
<th rowspan=1 colspan=3><sub>French</sub></th>
<th rowspan=1 colspan=3><sub>Japanese</sub></th>
<th rowspan=2><sub>Average</sub></th>
</tr>
<tr>
<th colspan=1><sub>Books</sub></th>
<th colspan=1><sub>DVD</sub></th>
<th colspan=1><sub>Music</sub></th>
<th colspan=1><sub>Books</sub></th>
<th colspan=1><sub>DVD</sub></th>
<th colspan=1><sub>Music</sub></th>
<th colspan=1><sub>Books</sub></th>
<th colspan=1><sub>DVD</sub></th>
<th colspan=1><sub>Music</sub></th>
<th colspan=1><sub>Books</sub></th>
<th colspan=1><sub>DVD</sub></th>
<th colspan=1><sub>Music</sub></th>
</tr>
</thead>
<tbody>
<tr>
<td colspan=1><sub>XLNet</sub></td>
<td colspan=1><sub><strong>92.90</strong></sub></td>
<td colspan=1><sub><strong>93.31</strong></sub></td>
<td colspan=1><sub><strong>92.02</strong></sub></td>
<td colspan=1><sub>85.23</sub></td>
<td colspan=1><sub>83.30</sub></td>
<td colspan=1><sub>83.89</sub></td>
<td colspan=1><sub>73.05</sub></td>
<td colspan=1><sub>69.80</sub></td>
<td colspan=1><sub>70.12</sub></td>
<td colspan=1><sub>83.20</sub></td>
<td colspan=1><sub><strong>86.07</strong></sub></td>
<td colspan=1><sub>85.24</sub></td>
<td colspan=1><sub>83.08</sub></td>
</tr>
<tr>
<td colspan=1><sub>mBERT</sub></td>
<td colspan=1><sub>92.78</sub></td>
<td colspan=1><sub>90.30</sub></td>
<td colspan=1><sub>91.88</sub></td>
<td colspan=1><sub><strong>88.65</strong></sub></td>
<td colspan=1><sub><strong>85.85</strong></sub></td>
<td colspan=1><sub><strong>90.38</strong></sub></td>
<td colspan=1><sub><strong>91.09</strong></sub></td>
<td colspan=1><sub><strong>88.57</strong></sub></td>
<td colspan=1><sub><strong>93.67</strong></sub></td>
<td colspan=1><sub><strong>84.35</strong></sub></td>
<td colspan=1><sub>81.77</sub></td>
<td colspan=1><sub><strong>87.53</strong></sub></td>
<td colspan=1><sub><strong>88.90</strong></sub></td>
</tr>
</tbody>
</table>
## Dataset used for this paper:
We have constructed new sentiment analysis dataset for Tigrinya language and it can be found in the zip file (Tigrinya Sentiment Analysis Dataset)
## Citing our paper:
Our paper can be accessed from ArXiv [link](https://arxiv.org/pdf/2006.07698.pdf), and please consider citing our work.
@misc{tela2020transferring,
title={Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya},
author={Abrhalei Tela and Abraham Woubie and Ville Hautamaki},
year={2020},
eprint={2006.07698},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
## Any questions, comments, feedback is appreciated! And can be forwarded to the following email: [email protected]
|
{}
| null |
abrhaleitela/TigXLNet
|
[
"transformers",
"pytorch",
"xlnet",
"arxiv:2006.07698",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2006.07698"
] |
[] |
TAGS
#transformers #pytorch #xlnet #arxiv-2006.07698 #endpoints_compatible #region-us
|
Transferring Monolingual Model to Low-Resource Language: The Case Of Tigrinya:
==============================================================================
Proposed Method:
----------------
The proposed method transfers a mono-lingual Transformer model into new target language at lexical level by learning new token embeddings. All implementation in this repo uses XLNet as a source Transformer model, however, other Transformer models can also be used similarly.
Main files:
-----------
All files are IPython Notebook files which can be excuted simply in Google Colab.
* URL : Fine-tunes XLNet (mono-lingual transformer) on new target language (Tigrinya) sentiment analysis dataset. ![Open In Colab](URL
* URL : Evaluates the fine-tuned model on test data. ![Open In Colab](URL
* token\_embeddings.ipynb : Trains a word2vec token embeddings for Tigrinya language. ![Open In Colab](URL
* process\_Tigrinya\_comments.ipynb : Extracts Tigrinya comments from mixed language contents. ![Open In Colab](URL
* extract\_YouTube\_comments.ipynb : Downloads available comments from a YouTube channel ID. ![Open In Colab](URL
* auto\_labelling.ipynb : Automatically labels Tigrinya comments in to positive or negative sentiments based on Emoji's sentiment. ![Open In Colab](URL
Tigrinya Tokenizer:
-------------------
A sentencepiece based tokenizer for Tigrinya has been released to the public and can be accessed as in the following:
```
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("abryee/TigXLNet")
tokenizer.tokenize("ዋዋዋው እዛ ፍሊም ካብተን ዘድንቀን ሓንቲ ኢያ ሞ ብጣዕሚ ኢና ነመስግን ሓንቲ ክብላ ደልየ ዘሎኹ ሓደራኣኹም ኣብ ጊዜኹም ተረክቡ")
```
TigXLNet:
---------
A new general purpose transformer model for low-resource language Tigrinya is also released to the public and be accessed as in the following:
```
from transformers import AutoConfig, AutoModel
config = AutoConfig.from_pretrained("abryee/TigXLNet")
config.d_head = 64
model = AutoModel.from_pretrained("abryee/TigXLNet", config=config)
```
Evaluation:
-----------
The proposed method is evaluated using two datasets:
* A newly created sentiment analysis dataset for low-resource language (Tigriyna).
3 |
* Cross-lingual Sentiment dataset (CLS).
Dataset used for this paper:
----------------------------
We have constructed new sentiment analysis dataset for Tigrinya language and it can be found in the zip file (Tigrinya Sentiment Analysis Dataset)
Citing our paper:
-----------------
Our paper can be accessed from ArXiv link, and please consider citing our work.
```
@misc{tela2020transferring,
title={Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya},
author={Abrhalei Tela and Abraham Woubie and Ville Hautamaki},
year={2020},
eprint={2006.07698},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Any questions, comments, feedback is appreciated! And can be forwarded to the following email: URL@URL
------------------------------------------------------------------------------------------------------
|
[] |
[
"TAGS\n#transformers #pytorch #xlnet #arxiv-2006.07698 #endpoints_compatible #region-us \n"
] |
[
33
] |
[
"passage: TAGS\n#transformers #pytorch #xlnet #arxiv-2006.07698 #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
ruGPT-3 fine-tuned on russian fanfiction about Bangatan Boys (BTS).
|
{}
|
text-generation
|
accelotron/rugpt3-ficbook-bts
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
ruGPT-3 fine-tuned on russian fanfiction about Bangatan Boys (BTS).
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
#3PO
|
{"tags": ["conversational"]}
|
text-generation
|
aced/DialoGPT-medium-3PO
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#3PO
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null | null |
https://www.guilded.gg/thisiscineplex/overview/news/XRz48Dr6
https://www.guilded.gg/FLIXmasGR/overview/news/7R0WorPy
https://www.guilded.gg/FLIXmasGR/overview/news/NyE5BPmy
https://www.guilded.gg/FLIXmasGR/overview/news/2l3Konal
https://www.guilded.gg/FLIXmasGR/overview/news/AykDjvVR
https://www.guilded.gg/FLIXmasGR/overview/news/16YOGQoR
https://www.guilded.gg/FLIXmasGR/overview/news/KR2ngpXR
https://www.guilded.gg/FLIXmasGR/overview/news/xypa2qZR
https://www.guilded.gg/FLIXmasGR/overview/news/A6jZGQk6
https://www.guilded.gg/FLIXmasGR/overview/news/1ROQVMe6
https://www.guilded.gg/FLIXmasGR/overview/news/4yAW0Kvl
https://www.guilded.gg/FLIXmasGR/overview/news/JlaoGQBy
https://www.guilded.gg/FLIXmasGR/overview/news/YyrPnVEl
https://www.guilded.gg/FLIXmasGR/overview/news/4lGz3aBR
https://www.guilded.gg/FLIXmasGR/overview/news/16nKkj1y
https://www.guilded.gg/FLIXmasGR/overview/news/X6QA0Ng6
https://www.guilded.gg/FLIXmasGR/overview/news/XRz4xGa6
https://www.guilded.gg/FLIXmasGR/overview/news/PlqV9826
https://www.guilded.gg/FLIXmasGR/overview/news/7R0WokWy
https://www.guilded.gg/FLIXmasGR/overview/news/qlDvK4dy
https://www.guilded.gg/FLIXmasGR/overview/news/2l3KopZl
https://www.guilded.gg/FLIXmasGR/overview/news/16YOGj4R
https://www.guilded.gg/FLIXmasGR/overview/news/4ldxGzQl
|
{}
| null |
activatepin/RC_News
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
URL
URL
URL
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URL
URL
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URL
URL
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|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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null | null |
transformers
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.
Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).
`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`.
## Instructions
Loading the post-trained weights are as simple as, e.g.,
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_laptop")
model = AutoModel.from_pretrained("activebus/BERT-DK_laptop")
```
## Evaluation Results
Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf)
## Citation
If you find this work useful, please cite as following.
```
@inproceedings{xu_bert2019,
title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
month = "jun",
year = "2019",
}
```
|
{}
|
fill-mask
|
activebus/BERT-DK_laptop
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus.
Models are post-trained from Amazon Dataset and Yelp Dataset.
'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.
## Instructions
Loading the post-trained weights are as simple as, e.g.,
## Evaluation Results
Check our NAACL paper
If you find this work useful, please cite as following.
|
[
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
44,
66,
81,
22,
22
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.## Instructions\nLoading the post-trained weights are as simple as, e.g.,## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
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] |
null | null |
transformers
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.
Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).
## Instructions
Loading the post-trained weights are as simple as, e.g.,
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_rest")
model = AutoModel.from_pretrained("activebus/BERT-DK_rest")
```
## Evaluation Results
Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf)
## Citation
If you find this work useful, please cite as following.
```
@inproceedings{xu_bert2019,
title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
month = "jun",
year = "2019",
}
```
|
{}
|
fill-mask
|
activebus/BERT-DK_rest
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus.
Models are post-trained from Amazon Dataset and Yelp Dataset.
## Instructions
Loading the post-trained weights are as simple as, e.g.,
## Evaluation Results
Check our NAACL paper
If you find this work useful, please cite as following.
|
[
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.\n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.\n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
39,
51,
44,
22,
22
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.\n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.## Instructions\nLoading the post-trained weights are as simple as, e.g.,## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
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null | null |
transformers
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`.
`BERT-PT_*` addtionally uses SQuAD 1.1.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.
Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).
## Instructions
Loading the post-trained weights are as simple as, e.g.,
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_laptop")
model = AutoModel.from_pretrained("activebus/BERT-PT_laptop")
```
## Evaluation Results
Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf)
## Citation
If you find this work useful, please cite as following.
```
@inproceedings{xu_bert2019,
title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
month = "jun",
year = "2019",
}
```
|
{}
|
fill-mask
|
activebus/BERT-PT_laptop
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'.
'BERT-PT_*' addtionally uses SQuAD 1.1.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus.
Models are post-trained from Amazon Dataset and Yelp Dataset.
## Instructions
Loading the post-trained weights are as simple as, e.g.,
## Evaluation Results
Check our NAACL paper
If you find this work useful, please cite as following.
|
[
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'. \n'BERT-PT_*' addtionally uses SQuAD 1.1.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'. \n'BERT-PT_*' addtionally uses SQuAD 1.1.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
39,
83,
44,
22,
22
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_laptop' is trained from 100MB laptop corpus under 'Electronics/Computers & Accessories/Laptops'. \n'BERT-PT_*' addtionally uses SQuAD 1.1.## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.## Instructions\nLoading the post-trained weights are as simple as, e.g.,## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
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] |
null | null |
transformers
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp.
`BERT-PT_*` addtionally uses SQuAD 1.1.
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.
Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).
## Instructions
Loading the post-trained weights are as simple as, e.g.,
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_rest")
model = AutoModel.from_pretrained("activebus/BERT-PT_rest")
```
## Evaluation Results
Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf)
## Citation
If you find this work useful, please cite as following.
```
@inproceedings{xu_bert2019,
title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
month = "jun",
year = "2019",
}
```
|
{}
|
fill-mask
|
activebus/BERT-PT_rest
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.
'BERT-PT_*' addtionally uses SQuAD 1.1.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus.
Models are post-trained from Amazon Dataset and Yelp Dataset.
## Instructions
Loading the post-trained weights are as simple as, e.g.,
## Evaluation Results
Check our NAACL paper
If you find this work useful, please cite as following.
|
[
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.\n'BERT-PT_*' addtionally uses SQuAD 1.1.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.\n'BERT-PT_*' addtionally uses SQuAD 1.1.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
[
44,
68,
44,
22,
22
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT-DK_rest' is trained from 1G (19 types) restaurants from Yelp.\n'BERT-PT_*' addtionally uses SQuAD 1.1.## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.## Instructions\nLoading the post-trained weights are as simple as, e.g.,## Evaluation Results\n\nCheck our NAACL paper \n\n\nIf you find this work useful, please cite as following."
] |
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] |
null | null |
transformers
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
Please visit https://github.com/howardhsu/BERT-for-RRC-ABSA for details.
`BERT-XD_Review` is a cross-domain (beyond just `laptop` and `restaurant`) language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`.
The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers).
## Model Description
The original model is from `BERT-base-uncased`.
Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).
## Instructions
Loading the post-trained weights are as simple as, e.g.,
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-XD_Review")
model = AutoModel.from_pretrained("activebus/BERT-XD_Review")
```
## Evaluation Results
Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf)
`BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).
## Citation
If you find this work useful, please cite as following.
```
@inproceedings{xu_bert2019,
title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
month = "jun",
year = "2019",
}
```
|
{}
| null |
activebus/BERT-XD_Review
|
[
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
Please visit URL for details.
'BERT-XD_Review' is a cross-domain (beyond just 'laptop' and 'restaurant') language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.
The preprocessing code here.
## Model Description
The original model is from 'BERT-base-uncased'.
Models are post-trained from Amazon Dataset and Yelp Dataset.
## Instructions
Loading the post-trained weights are as simple as, e.g.,
## Evaluation Results
Check our NAACL paper
'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).
If you find this work useful, please cite as following.
|
[
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \nPlease visit URL for details. \n\n'BERT-XD_Review' is a cross-domain (beyond just 'laptop' and 'restaurant') language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.\nThe preprocessing code here.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased'. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).\n\n\nIf you find this work useful, please cite as following."
] |
[
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \nPlease visit URL for details. \n\n'BERT-XD_Review' is a cross-domain (beyond just 'laptop' and 'restaurant') language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.\nThe preprocessing code here.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased'. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).\n\n\nIf you find this work useful, please cite as following."
] |
[
23,
142,
36,
22,
80
] |
[
"passage: TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \nPlease visit URL for details. \n\n'BERT-XD_Review' is a cross-domain (beyond just 'laptop' and 'restaurant') language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.\nThe preprocessing code here.## Model Description\n\nThe original model is from 'BERT-base-uncased'. \nModels are post-trained from Amazon Dataset and Yelp Dataset.## Instructions\nLoading the post-trained weights are as simple as, e.g.,## Evaluation Results\n\nCheck our NAACL paper \n'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).\n\n\nIf you find this work useful, please cite as following."
] |
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null | null |
transformers
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
`BERT_Review` is cross-domain (beyond just `laptop` and `restaurant`) language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`.
The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers).
## Model Description
The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.
Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).
## Instructions
Loading the post-trained weights are as simple as, e.g.,
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review")
model = AutoModel.from_pretrained("activebus/BERT_Review")
```
## Evaluation Results
Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf)
`BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).
## Citation
If you find this work useful, please cite as following.
```
@inproceedings{xu_bert2019,
title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
month = "jun",
year = "2019",
}
```
|
{}
|
fill-mask
|
activebus/BERT_Review
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# ReviewBERT
BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
'BERT_Review' is cross-domain (beyond just 'laptop' and 'restaurant') language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.
The preprocessing code here.
## Model Description
The original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus.
Models are post-trained from Amazon Dataset and Yelp Dataset.
## Instructions
Loading the post-trained weights are as simple as, e.g.,
## Evaluation Results
Check our NAACL paper
'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).
If you find this work useful, please cite as following.
|
[
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT_Review' is cross-domain (beyond just 'laptop' and 'restaurant') language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.\nThe preprocessing code here.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).\n\n\nIf you find this work useful, please cite as following."
] |
[
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT_Review' is cross-domain (beyond just 'laptop' and 'restaurant') language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.\nThe preprocessing code here.",
"## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.",
"## Instructions\nLoading the post-trained weights are as simple as, e.g.,",
"## Evaluation Results\n\nCheck our NAACL paper \n'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).\n\n\nIf you find this work useful, please cite as following."
] |
[
39,
128,
44,
22,
80
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# ReviewBERT\n\nBERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. \n\n'BERT_Review' is cross-domain (beyond just 'laptop' and 'restaurant') language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on 'bert-base-uncased'.\nThe preprocessing code here.## Model Description\n\nThe original model is from 'BERT-base-uncased' trained from Wikipedia+BookCorpus. \nModels are post-trained from Amazon Dataset and Yelp Dataset.## Instructions\nLoading the post-trained weights are as simple as, e.g.,## Evaluation Results\n\nCheck our NAACL paper \n'BERT_Review' is expected to have similar performance on domain-specific tasks (such as aspect extraction) as 'BERT-DK', but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).\n\n\nIf you find this work useful, please cite as following."
] |
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] |
null | null |
transformers
|
This model was distilled from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)
## Usage
```python
from transformers import AutoTokenizer # Or BertTokenizer
from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads
from transformers import AutoModel # or BertModel, for BERT without pretraining heads
model = AutoModelForPreTraining.from_pretrained('adalbertojunior/distilbert-portuguese-cased')
tokenizer = AutoTokenizer.from_pretrained('adalbertojunior/distilbert-portuguese-cased', do_lower_case=False)
```
You should fine tune it on your own data.
It can achieve accuracy up to 99% relative to the original BERTimbau in some tasks.
|
{"language": ["pt"]}
|
feature-extraction
|
adalbertojunior/distilbert-portuguese-cased
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"pt",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pt"
] |
TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #pt #endpoints_compatible #has_space #region-us
|
This model was distilled from BERTimbau
## Usage
You should fine tune it on your own data.
It can achieve accuracy up to 99% relative to the original BERTimbau in some tasks.
|
[
"## Usage\n\n\nYou should fine tune it on your own data.\n\nIt can achieve accuracy up to 99% relative to the original BERTimbau in some tasks."
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #pt #endpoints_compatible #has_space #region-us \n",
"## Usage\n\n\nYou should fine tune it on your own data.\n\nIt can achieve accuracy up to 99% relative to the original BERTimbau in some tasks."
] |
[
40,
34
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #pt #endpoints_compatible #has_space #region-us \n## Usage\n\n\nYou should fine tune it on your own data.\n\nIt can achieve accuracy up to 99% relative to the original BERTimbau in some tasks."
] |
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] |
null | null |
transformers
|
Image Captioning in Portuguese trained with ViT and GPT2
[DEMO](https://huggingface.co/spaces/adalbertojunior/image_captioning_portuguese)
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
|
{"language": ["pt"]}
| null |
adalbertojunior/image_captioning_portuguese
|
[
"transformers",
"pytorch",
"jax",
"vision-encoder-decoder",
"pt",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pt"
] |
TAGS
#transformers #pytorch #jax #vision-encoder-decoder #pt #endpoints_compatible #has_space #region-us
|
Image Captioning in Portuguese trained with ViT and GPT2
DEMO
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
|
[] |
[
"TAGS\n#transformers #pytorch #jax #vision-encoder-decoder #pt #endpoints_compatible #has_space #region-us \n"
] |
[
38
] |
[
"passage: TAGS\n#transformers #pytorch #jax #vision-encoder-decoder #pt #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets.
email : [email protected]
|
{}
|
text-classification
|
adam-chell/tweet-sentiment-analyzer
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets.
email : adam.chellaoui@URL
|
[] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: _string_
Can be either `gradio` or `streamlit`
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list.
|
{"title": "Twitter Sentiments", "emoji": "\ud83d\ude0d", "colorFrom": "yellow", "colorTo": "blue", "sdk": "streamlit", "app_file": "app.py", "pinned": false}
| null |
adam3242/test
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'sdk': _string_
Can be either 'gradio' or 'streamlit'
'app_file': _string_
Path to your main application file (which contains either 'gradio' or 'streamlit' Python code).
Path is relative to the root of the repository.
'pinned': _boolean_
Whether the Space stays on top of your list.
|
[
"# Configuration\r\n\r\n'title': _string_ \r\nDisplay title for the Space\r\n\r\n'emoji': _string_ \r\nSpace emoji (emoji-only character allowed)\r\n\r\n'colorFrom': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'colorTo': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'sdk': _string_ \r\nCan be either 'gradio' or 'streamlit'\r\n\r\n'app_file': _string_ \r\nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \r\nPath is relative to the root of the repository.\r\n\r\n'pinned': _boolean_ \r\nWhether the Space stays on top of your list."
] |
[
"TAGS\n#region-us \n",
"# Configuration\r\n\r\n'title': _string_ \r\nDisplay title for the Space\r\n\r\n'emoji': _string_ \r\nSpace emoji (emoji-only character allowed)\r\n\r\n'colorFrom': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'colorTo': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'sdk': _string_ \r\nCan be either 'gradio' or 'streamlit'\r\n\r\n'app_file': _string_ \r\nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \r\nPath is relative to the root of the repository.\r\n\r\n'pinned': _boolean_ \r\nWhether the Space stays on top of your list."
] |
[
6,
192
] |
[
"passage: TAGS\n#region-us \n# Configuration\r\n\r\n'title': _string_ \r\nDisplay title for the Space\r\n\r\n'emoji': _string_ \r\nSpace emoji (emoji-only character allowed)\r\n\r\n'colorFrom': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'colorTo': _string_ \r\nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\r\n\r\n'sdk': _string_ \r\nCan be either 'gradio' or 'streamlit'\r\n\r\n'app_file': _string_ \r\nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \r\nPath is relative to the root of the repository.\r\n\r\n'pinned': _boolean_ \r\nWhether the Space stays on top of your list."
] |
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null | null | null |
dsfregrtgr
|
{}
| null |
adam3242/testing
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
dsfregrtgr
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("addy88/T5-23-emotions-detections")
tokenizer = T5Tokenizer.from_pretrained("addy88/T5-23-emotions-detections")
text_to_summarize="emotion: i don't like it this is nonsense."
input_ids = tokenizer.encode(text_to_summarize, return_tensors="pt", add_special_tokens=True)
input_ids = input_ids.to(self.device)
generated_ids = model.generate(
input_ids=input_ids,
num_beams=2,
max_length=512,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True,
top_p=0.95,
top_k=50,
num_return_sequences=1,
)
preds = [tokenizer.decode(g,skip_special_tokens=True,clean_up_tokenization_spaces=True,)for g in generated_ids]
```
|
{}
|
text2text-generation
|
addy88/T5-23-emotions-detections
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
### How to use
Here is how to use this model in PyTorch:
|
[
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
48,
17
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\nHere is how to use this model in PyTorch:"
] |
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] |
null | null |
sentence-transformers
|
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Finetune on [ELI5](https://huggingface.co/datasets/eli5)
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('addy88/eli5-all-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('addy88/eli5-all-mpnet-base-v2')
model = AutoModel.from_pretrained('addy88/eli5-all-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=addy88/eli5-all-mpnet-base-v2)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 14393 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1439,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
addy88/eli5-all-mpnet-base-v2
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1908.10084"
] |
[] |
TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #endpoints_compatible #region-us
|
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Finetune on ELI5
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 14393 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
This model was trained by sentence-transformers.
If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:
|
[
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 14393 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] |
[
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #endpoints_compatible #region-us \n",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 14393 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] |
[
51,
38,
64,
29,
102,
5,
53
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #endpoints_compatible #region-us \n## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 14393 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors\n\nThis model was trained by sentence-transformers. \n \nIf you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks:"
] |
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] |
null | null |
transformers
|
This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: [](https://colab.research.google.com/drive/1KNf5siQdM7ILQM-pHsP6gNVPKl1SJdU1)
__The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
- large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication
- using gradient checkpoints to store one only activation per layer: using dramatically less memory at the cost of 30% slower training
- scalable fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) and [8-bit Adam](https://arxiv.org/abs/2110.02861)
In other words, all of the large weight-matrices are frozen in 8-bit, and you only train small adapters and optionally 1d tensors (layernorm scales, biases).
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. [This notebook measures wikitext test perplexity](https://colab.research.google.com/drive/1FxGeYQyE7cx9VNCBC4gUyRVZGORW7c6g) and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use **8-bit only for storage, and all computations are performed in float16 or float32**. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from [the LoRA paper](https://arxiv.org/pdf/2106.09685.pdf).
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Can I use this technique with other models?
The model was converted using [this notebook](https://colab.research.google.com/drive/1rwxh0XRdVi8VEbTx97l9xXr4JbRhZaq5#scrollTo=CX3VHn-J1Zer). It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
{}
|
text-generation
|
addy88/gpt-j-8bit
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"arxiv:2110.02861",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.09685",
"2110.02861"
] |
[] |
TAGS
#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us
|
This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: .
!img
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use 8-bit only for storage, and all computations are performed in float16 or float32. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from the LoRA paper.
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Can I use this technique with other models?
The model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
[
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Can I use this technique with other models?\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
[
"TAGS\n#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Can I use this technique with other models?\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
[
55,
78,
65
] |
[
"passage: TAGS\n#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us \n### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.### Can I use this technique with other models?\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
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] |
null | null |
transformers
|
This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: [](https://colab.research.google.com/drive/1KNf5siQdM7ILQM-pHsP6gNVPKl1SJdU1)
__The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
- large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication
- using gradient checkpoints to store one only activation per layer: using dramatically less memory at the cost of 30% slower training
- scalable fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) and [8-bit Adam](https://arxiv.org/abs/2110.02861)
In other words, all of the large weight-matrices are frozen in 8-bit, and you only train small adapters and optionally 1d tensors (layernorm scales, biases).
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. [This notebook measures wikitext test perplexity](https://colab.research.google.com/drive/1FxGeYQyE7cx9VNCBC4gUyRVZGORW7c6g) and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use **8-bit only for storage, and all computations are performed in float16 or float32**. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from [the LoRA paper](https://arxiv.org/pdf/2106.09685.pdf).
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Can I use this technique with other models?
The model was converted using [this notebook](https://colab.research.google.com/drive/1rwxh0XRdVi8VEbTx97l9xXr4JbRhZaq5#scrollTo=CX3VHn-J1Zer). It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
{}
|
text-generation
|
addy88/gptj8
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"arxiv:2110.02861",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.09685",
"2110.02861"
] |
[] |
TAGS
#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us
|
This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: .
!img
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use 8-bit only for storage, and all computations are performed in float16 or float32. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from the LoRA paper.
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Can I use this technique with other models?
The model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
[
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Can I use this technique with other models?\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
[
"TAGS\n#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Can I use this technique with other models?\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
[
55,
78,
65
] |
[
"passage: TAGS\n#transformers #pytorch #gptj #text-generation #arxiv-2106.09685 #arxiv-2110.02861 #autotrain_compatible #endpoints_compatible #region-us \n### How should I fine-tune the model?\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.### Can I use this technique with other models?\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
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] |
null | null |
transformers
|
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/hindi-wav2vec2-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/hindi-wav2vec2-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
{}
|
automatic-speech-recognition
|
addy88/hindi-wav2vec2-stt
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
## Usage
The model can be used directly (without a language model) as follows:
|
[
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
37,
20
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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] |
null | null |
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-timit-demo-colab
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1092
- Wer: 0.1728
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.4664 | 4.0 | 500 | 2.3026 | 0.9866 |
| 0.8171 | 8.0 | 1000 | 0.0980 | 0.1885 |
| 0.2983 | 12.0 | 1500 | 0.0943 | 0.1750 |
| 0.1769 | 16.0 | 2000 | 0.0990 | 0.1737 |
| 0.1823 | 20.0 | 2500 | 0.1068 | 0.1757 |
| 0.0761 | 24.0 | 3000 | 0.1041 | 0.1719 |
| 0.0993 | 28.0 | 3500 | 0.1092 | 0.1728 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-base-timit-demo-colab", "results": []}]}
|
automatic-speech-recognition
|
addy88/hubert-base-timit-demo-colab
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
hubert-base-timit-demo-colab
============================
This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1092
* Wer: 0.1728
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.13.0
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #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: 0.0001\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: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
53,
130,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #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: 0.0001\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: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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-0.04820118844509125,
-0.08443311601877213,
0.0980115607380867,
0.0712682455778122,
0.04132433980703354,
-0.049978673458099365,
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] |
null | null |
transformers
|
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverFeatureExtractor, PerceiverForImageClassificationLearned
import requests
from PIL import Image
feature_extractor = PerceiverFeatureExtractor.from_pretrained("addy88/perceiver_image_classifier")
model = PerceiverForImageClassificationLearned.from_pretrained("addy88/perceiver_image_classifier")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# prepare input
encoding = feature_extractor(image, return_tensors="pt")
inputs = encoding.pixel_values
# forward pass
outputs = model(inputs)
logits = outputs.logits
print("Predicted class:", model.config.id2label[logits.argmax(-1).item()])
>>> should print Predicted class: tabby, tabby cat
```
|
{}
|
image-classification
|
addy88/perceiver_image_classifier
|
[
"transformers",
"pytorch",
"perceiver",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #perceiver #image-classification #autotrain_compatible #endpoints_compatible #region-us
|
### How to use
Here is how to use this model in PyTorch:
|
[
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
"TAGS\n#transformers #pytorch #perceiver #image-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
39,
17
] |
[
"passage: TAGS\n#transformers #pytorch #perceiver #image-classification #autotrain_compatible #endpoints_compatible #region-us \n### How to use\nHere is how to use this model in PyTorch:"
] |
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] |
null | null |
transformers
|
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverTokenizer, PerceiverForMaskedLM
tokenizer = PerceiverTokenizer.from_pretrained("addy88/perceiver_imdb")
model = PerceiverForMaskedLM.from_pretrained("addy88/perceiver_imdb")
text = "This is an incomplete sentence where some words are missing."
# prepare input
encoding = tokenizer(text, padding="max_length", return_tensors="pt")
# mask " missing.". Note that the model performs much better if the masked span starts with a space.
encoding.input_ids[0, 52:61] = tokenizer.mask_token_id
inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device)
# forward pass
outputs = model(inputs=inputs, attention_mask=input_mask)
logits = outputs.logits
masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1)
print(tokenizer.decode(masked_tokens_predictions))
>>> should print " missing."
```
|
{}
|
text-classification
|
addy88/perceiver_imdb
|
[
"transformers",
"pytorch",
"perceiver",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #perceiver #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
### How to use
Here is how to use this model in PyTorch:
|
[
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
"TAGS\n#transformers #pytorch #perceiver #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
39,
17
] |
[
"passage: TAGS\n#transformers #pytorch #perceiver #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### How to use\nHere is how to use this model in PyTorch:"
] |
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] |
null | null |
transformers
|
This model is funetune version of Codebert in roberta. On CodeSearchNet.
###
Quick start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("addy88/programming-lang-identifier")
model = AutoModelForSequenceClassification.from_pretrained("addy88/programming-lang-identifier")
input_ids = tokenizer.encode(CODE_TO_IDENTIFY)
logits = model(input_ids)[0]
language_idx = logits.argmax() # index for the resulting label
###
|
{}
|
text-classification
|
addy88/programming-lang-identifier
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This model is funetune version of Codebert in roberta. On CodeSearchNet.
###
Quick start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("addy88/programming-lang-identifier")
model = AutoModelForSequenceClassification.from_pretrained("addy88/programming-lang-identifier")
input_ids = URL(CODE_TO_IDENTIFY)
logits = model(input_ids)[0]
language_idx = URL() # index for the resulting label
###
|
[
"# index for the resulting label"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# index for the resulting label"
] |
[
41,
7
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n# index for the resulting label"
] |
[
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] |
null | null |
transformers
|
Pretraining Dataset: debatelab/aaac
|
{}
|
text2text-generation
|
addy88/t5-argument-anlyser
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Pretraining Dataset: debatelab/aaac
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
48
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
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. -->
# t5-base-finetuned-sn-to-en
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the itihasa dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["itihasa"], "model-index": [{"name": "t5-base-finetuned-sn-to-en", "results": []}]}
|
text2text-generation
|
addy88/t5-base-finetuned-sn-to-en
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:itihasa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-itihasa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-finetuned-sn-to-en
This model is a fine-tuned version of google/t5-v1_1-base on the itihasa dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
[
"# t5-base-finetuned-sn-to-en\n\nThis model is a fine-tuned version of google/t5-v1_1-base on the itihasa 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: 1\n- mixed_precision_training: Native AMP",
"### Framework versions\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 #t5 #text2text-generation #generated_from_trainer #dataset-itihasa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-finetuned-sn-to-en\n\nThis model is a fine-tuned version of google/t5-v1_1-base on the itihasa 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: 1\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
73,
41,
6,
12,
8,
3,
103,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-itihasa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-base-finetuned-sn-to-en\n\nThis model is a fine-tuned version of google/t5-v1_1-base on the itihasa 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: 1\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("addy88/t5-grammar-correction")
model = AutoModelForSeq2SeqLM.from_pretrained("addy88/t5-grammar-correction")
input_ids = tokenizer('grammar: This sentences has has bads grammar.', return_tensors='pt').input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
{}
|
text2text-generation
|
addy88/t5-grammar-correction
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
### How to use
Here is how to use this model in PyTorch:
|
[
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\nHere is how to use this model in PyTorch:"
] |
[
48,
17
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\nHere is how to use this model in PyTorch:"
] |
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] |
null | null |
transformers
|
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-odia-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-odia-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
{}
|
automatic-speech-recognition
|
addy88/wav2vec-odia-stt
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
## Usage
The model can be used directly (without a language model) as follows:
|
[
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
37,
20
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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] |
null | null |
transformers
|
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/addy88/wav2vec2-assamese-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/addy88/wav2vec2-assamese-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
{}
|
automatic-speech-recognition
|
addy88/wav2vec2-assamese-stt
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
## Usage
The model can be used directly (without a language model) as follows:
|
[
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
37,
20
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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] |
null | null |
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: 0.1339
- Accuracy: 0.9768
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.102 | 1.0 | 399 | 1.0087 | 0.6574 |
| 0.5228 | 2.0 | 798 | 0.4266 | 0.9247 |
| 0.3222 | 3.0 | 1197 | 0.2037 | 0.9744 |
| 0.2096 | 4.0 | 1596 | 0.1444 | 0.9766 |
| 0.2003 | 5.0 | 1995 | 0.1339 | 0.9768 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]}
|
audio-classification
|
addy88/wav2vec2-base-finetuned-ks
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
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: 0.1339
* Accuracy: 0.9768
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 1.10.0+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 1.10.0+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 1.10.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
58,
144,
4,
33
] |
[
"passage: 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 1.10.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-bengali-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-bengali-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
{}
|
automatic-speech-recognition
|
addy88/wav2vec2-bengali-stt
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
## Usage
The model can be used directly (without a language model) as follows:
|
[
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
37,
20
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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] |
null | null |
transformers
|
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-bhojpuri-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-bhojpuri-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
{}
|
automatic-speech-recognition
|
addy88/wav2vec2-bhojpuri-stt
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
## Usage
The model can be used directly (without a language model) as follows:
|
[
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
37,
20
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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] |
null | null |
transformers
|
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-dogri-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-dogri-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
{}
|
automatic-speech-recognition
|
addy88/wav2vec2-dogri-stt
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
## Usage
The model can be used directly (without a language model) as follows:
|
[
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:"
] |
[
37,
20
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n## Usage\nThe model can be used directly (without a language model) as follows:"
] |
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