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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. -->
# twitter_RoBERTa_base_sentence_itr0_1e-05_all_01_03_2022-13_53_11
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4118
- Accuracy: 0.8446
- F1: 0.8968
- Precision: 0.8740
- Recall: 0.9207
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 390 | 0.3532 | 0.8451 | 0.8990 | 0.8997 | 0.8983 |
| 0.4111 | 2.0 | 780 | 0.3381 | 0.8561 | 0.9080 | 0.8913 | 0.9253 |
| 0.3031 | 3.0 | 1170 | 0.3490 | 0.8537 | 0.9034 | 0.9152 | 0.8919 |
| 0.2408 | 4.0 | 1560 | 0.3562 | 0.8671 | 0.9148 | 0.9 | 0.9300 |
| 0.2408 | 5.0 | 1950 | 0.3725 | 0.8659 | 0.9131 | 0.9074 | 0.9189 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "twitter_RoBERTa_base_sentence_itr0_1e-05_all_01_03_2022-13_53_11", "results": []}]}
|
text-classification
|
ali2066/twitter_RoBERTa_base_sentence_itr0_1e-05_all_01_03_2022-13_53_11
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_base\_sentence\_itr0\_1e-05\_all\_01\_03\_2022-13\_53\_11
===========================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4118
* Accuracy: 0.8446
* F1: 0.8968
* Precision: 0.8740
* Recall: 0.9207
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #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: 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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
48,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_0.0001_all_01_03_2022-14_26_43
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2591
- Precision: 0.4174
- Recall: 0.5678
- F1: 0.4811
- Accuracy: 0.8852
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.4690 | 0.3732 | 0.1830 | 0.2456 | 0.7509 |
| No log | 2.0 | 60 | 0.3936 | 0.2067 | 0.3559 | 0.2615 | 0.7851 |
| No log | 3.0 | 90 | 0.3019 | 0.3658 | 0.4904 | 0.4190 | 0.8703 |
| No log | 4.0 | 120 | 0.2510 | 0.4387 | 0.5137 | 0.4732 | 0.8889 |
| No log | 5.0 | 150 | 0.2481 | 0.4196 | 0.5511 | 0.4764 | 0.8881 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_0.0001_all_01_03_2022-14_26_43", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_0.0001_all_01_03_2022-14_26_43
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_0.0001\_all\_01\_03\_2022-14\_26\_43
===================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2591
* Precision: 0.4174
* Recall: 0.5678
* F1: 0.4811
* Accuracy: 0.8852
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: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
49,
97,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-14_37_35
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3190
- Precision: 0.1194
- Recall: 0.2563
- F1: 0.1629
- Accuracy: 0.8546
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.4963 | 0.0223 | 0.0562 | 0.0319 | 0.7461 |
| No log | 2.0 | 60 | 0.4089 | 0.0617 | 0.1359 | 0.0849 | 0.8093 |
| No log | 3.0 | 90 | 0.3919 | 0.1053 | 0.2101 | 0.1403 | 0.8219 |
| No log | 4.0 | 120 | 0.3787 | 0.1202 | 0.2482 | 0.1619 | 0.8270 |
| No log | 5.0 | 150 | 0.3745 | 0.1171 | 0.2391 | 0.1572 | 0.8311 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-14_37_35", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-14_37_35
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_all\_01\_03\_2022-14\_37\_35
==================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3190
* Precision: 0.1194
* Recall: 0.2563
* F1: 0.1629
* Accuracy: 0.8546
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
58,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_02_39
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2903
- Precision: 0.2440
- Recall: 0.4465
- F1: 0.3155
- Accuracy: 0.8706
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.4378 | 0.0463 | 0.1136 | 0.0658 | 0.7742 |
| No log | 2.0 | 60 | 0.3739 | 0.1472 | 0.3756 | 0.2115 | 0.8284 |
| No log | 3.0 | 90 | 0.3422 | 0.1865 | 0.4330 | 0.2607 | 0.8374 |
| No log | 4.0 | 120 | 0.3286 | 0.2243 | 0.4833 | 0.3064 | 0.8438 |
| No log | 5.0 | 150 | 0.3239 | 0.2356 | 0.4809 | 0.3163 | 0.8490 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_02_39", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_02_39
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_all\_01\_03\_2022-15\_02\_39
==================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2903
* Precision: 0.2440
* Recall: 0.4465
* F1: 0.3155
* Accuracy: 0.8706
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
49,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-14_43_21
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1212
- Precision: 0.0637
- Recall: 0.0080
- F1: 0.0141
- Accuracy: 0.9707
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 15 | 0.1113 | 0.0 | 0.0 | 0.0 | 0.9752 |
| No log | 2.0 | 30 | 0.1069 | 0.0 | 0.0 | 0.0 | 0.9752 |
| No log | 3.0 | 45 | 0.0992 | 0.0 | 0.0 | 0.0 | 0.9752 |
| No log | 4.0 | 60 | 0.0938 | 0.0 | 0.0 | 0.0 | 0.9752 |
| No log | 5.0 | 75 | 0.0920 | 0.0 | 0.0 | 0.0 | 0.9752 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-14_43_21", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-14_43_21
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_editorials\_01\_03\_2022-14\_43\_21
=========================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1212
* Precision: 0.0637
* Recall: 0.0080
* F1: 0.0141
* Accuracy: 0.9707
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
58,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-15_00_35
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1155
- Precision: 0.5720
- Recall: 0.4705
- F1: 0.5163
- Accuracy: 0.9687
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 15 | 0.1256 | 0.04 | 0.0021 | 0.0039 | 0.9624 |
| No log | 2.0 | 30 | 0.0963 | 0.7121 | 0.5711 | 0.6339 | 0.9794 |
| No log | 3.0 | 45 | 0.0844 | 0.6205 | 0.5732 | 0.5959 | 0.9778 |
| No log | 4.0 | 60 | 0.0770 | 0.6201 | 0.5856 | 0.6023 | 0.9778 |
| No log | 5.0 | 75 | 0.0750 | 0.6174 | 0.5856 | 0.6011 | 0.9777 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-15_00_35", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-15_00_35
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_editorials\_01\_03\_2022-15\_00\_35
=========================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1155
* Precision: 0.5720
* Recall: 0.4705
* F1: 0.5163
* Accuracy: 0.9687
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
49,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_40_24
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3067
- Precision: 0.2871
- Recall: 0.4433
- F1: 0.3485
- Accuracy: 0.8906
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 11 | 0.4768 | 0.0 | 0.0 | 0.0 | 0.7546 |
| No log | 2.0 | 22 | 0.3665 | 0.1610 | 0.3211 | 0.2145 | 0.8487 |
| No log | 3.0 | 33 | 0.3010 | 0.1994 | 0.3690 | 0.2589 | 0.8868 |
| No log | 4.0 | 44 | 0.2748 | 0.2839 | 0.4479 | 0.3475 | 0.9037 |
| No log | 5.0 | 55 | 0.2670 | 0.3104 | 0.4704 | 0.3740 | 0.9083 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_40_24", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_40_24
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_essays\_01\_03\_2022-14\_40\_24
=====================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3067
* Precision: 0.2871
* Recall: 0.4433
* F1: 0.3485
* Accuracy: 0.8906
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
58,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_58_58
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2698
- Precision: 0.3554
- Recall: 0.4884
- F1: 0.4114
- Accuracy: 0.8973
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 11 | 0.4423 | 0.0261 | 0.0184 | 0.0216 | 0.7728 |
| No log | 2.0 | 22 | 0.3220 | 0.1256 | 0.3129 | 0.1793 | 0.8735 |
| No log | 3.0 | 33 | 0.2561 | 0.2633 | 0.4264 | 0.3255 | 0.9103 |
| No log | 4.0 | 44 | 0.2535 | 0.3303 | 0.4509 | 0.3813 | 0.9115 |
| No log | 5.0 | 55 | 0.2414 | 0.3696 | 0.4693 | 0.4135 | 0.9181 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_58_58", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_58_58
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_essays\_01\_03\_2022-14\_58\_58
=====================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2698
* Precision: 0.3554
* Recall: 0.4884
* F1: 0.4114
* Accuracy: 0.8973
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
49,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6113
- Precision: 0.0097
- Recall: 0.0145
- F1: 0.0116
- Accuracy: 0.6780
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 10 | 0.6399 | 0.0 | 0.0 | 0.0 | 0.6603 |
| No log | 2.0 | 20 | 0.6192 | 0.0 | 0.0 | 0.0 | 0.6603 |
| No log | 3.0 | 30 | 0.6133 | 0.0 | 0.0 | 0.0 | 0.6605 |
| No log | 4.0 | 40 | 0.6142 | 0.0 | 0.0 | 0.0 | 0.6617 |
| No log | 5.0 | 50 | 0.6129 | 0.0 | 0.0 | 0.0 | 0.6632 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_webDiscourse\_01\_03\_2022-14\_45\_20
===========================================================================
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6113
* Precision: 0.0097
* Recall: 0.0145
* F1: 0.0116
* Accuracy: 0.6780
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
58,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\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. -->
# twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5905
- Precision: 0.0024
- Recall: 0.0143
- F1: 0.0041
- Accuracy: 0.6867
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 10 | 0.6081 | 0.0 | 0.0 | 0.0 | 0.6904 |
| No log | 2.0 | 20 | 0.6014 | 0.0025 | 0.0130 | 0.0042 | 0.6934 |
| No log | 3.0 | 30 | 0.5953 | 0.0 | 0.0 | 0.0 | 0.6930 |
| No log | 4.0 | 40 | 0.5858 | 0.0 | 0.0 | 0.0 | 0.6941 |
| No log | 5.0 | 50 | 0.5815 | 0.0 | 0.0 | 0.0 | 0.6947 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21", "results": []}]}
|
token-classification
|
ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
twitter\_RoBERTa\_token\_itr0\_1e-05\_webDiscourse\_01\_03\_2022-14\_57\_21
===========================================================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5905
* Precision: 0.0024
* Recall: 0.0143
* F1: 0.0041
* Accuracy: 0.6867
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: 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: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu113
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
49,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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] |
null | null | null |
# Testing
This Be A Test
|
{}
| null |
aliaafee/test
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Testing
This Be A Test
|
[
"# Testing\n\nThis Be A Test"
] |
[
"TAGS\n#region-us \n",
"# Testing\n\nThis Be A Test"
] |
[
6,
7
] |
[
"passage: TAGS\n#region-us \n# Testing\n\nThis Be A Test"
] |
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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. -->
# model_output_en_de
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1298
- Bleu: 33.9121
- Gen Len: 76.8132
## 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: 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: 3.0
### Training results
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"language": ["en", "de"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "model_output_en_de", "results": []}]}
|
text2text-generation
|
alina1997/MarianMT
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"en",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en",
"de"
] |
TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #en #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# model_output_en_de
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1298
- Bleu: 33.9121
- Gen Len: 76.8132
## 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: 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: 3.0
### Training results
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
[
"# model_output_en_de\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1298\n- Bleu: 33.9121\n- Gen Len: 76.8132",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 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: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #en #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# model_output_en_de\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1298\n- Bleu: 33.9121\n- Gen Len: 76.8132",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 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: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
58,
72,
6,
12,
8,
3,
90,
4,
36
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #en #de #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# model_output_en_de\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.1298\n- Bleu: 33.9121\n- Gen Len: 76.8132## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 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: 3.0### Training results### Framework versions\n\n- Transformers 4.13.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.15.1\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. -->
# trained_model
This model is a fine-tuned version of [opus-mt-en-de](https://huggingface.co/opus-mt-en-de) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4519
- Bleu: 27.6198
- Gen Len: 106.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 3 | 1.4519 | 27.6198 | 106.0 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.8.0
- Datasets 1.18.3
- Tokenizers 0.10.3
|
{"language": ["en", "de"], "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "trained_model", "results": []}]}
|
text2text-generation
|
alina1997/marian_en_de_test
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"en",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en",
"de"
] |
TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #en #de #autotrain_compatible #endpoints_compatible #region-us
|
trained\_model
==============
This model is a fine-tuned version of opus-mt-en-de on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4519
* Bleu: 27.6198
* Gen Len: 106.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0
### Training results
### Framework versions
* Transformers 4.13.0.dev0
* Pytorch 1.8.0
* Datasets 1.18.3
* 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.8.0\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #en #de #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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.8.0\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
[
50,
98,
4,
36
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #generated_from_trainer #en #de #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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.8.0\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# ComVE-distilgpt2
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the [distilgpt2](https://github.com/huggingface/transformers/blob/master/model_cards/distilgpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.imgur.com/xKbrwBC.png">
</center>
## Eval results
The model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
```bibtex
@article{fadel2020justers,
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
year={2020}
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-distilgpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "mit", "tags": ["exbert", "commonsense", "semeval2020", "comve"], "datasets": ["ComVE"], "metrics": ["bleu"], "widget": [{"text": "Chicken can swim in water. <|continue|>"}]}
|
text-generation
|
aliosm/ComVE-distilgpt2
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"dataset:ComVE",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ComVE-distilgpt2
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.
*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the distilgpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.URL
</center>
## Eval results
The model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ComVE-distilgpt2",
"## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.",
"## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.",
"#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.",
"## Training data\n\nThe model is initialized from the distilgpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.",
"## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>",
"## Eval results\n\nThe model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ComVE-distilgpt2",
"## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.",
"## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.",
"#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.",
"## Training data\n\nThe model is initialized from the distilgpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.",
"## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>",
"## Eval results\n\nThe model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
78,
9,
65,
31,
75,
28,
47,
103,
43,
34
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ComVE-distilgpt2## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.## Training data\n\nThe model is initialized from the distilgpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>## Eval results\n\nThe model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset."
] |
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null | null |
transformers
|
# ComVE-gpt2-large
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the [gpt2-large](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with `<|conteniue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.imgur.com/xKbrwBC.png">
</center>
## Eval results
The model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
```bibtex
@article{fadel2020justers,
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
year={2020}
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-gpt2-large">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "mit", "tags": ["gpt2", "exbert", "commonsense", "semeval2020", "comve"], "datasets": ["https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation"], "metrics": ["bleu"], "widget": [{"text": "Chicken can swim in water. <|continue|>"}]}
|
text-generation
|
aliosm/ComVE-gpt2-large
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ComVE-gpt2-large
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.
*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the gpt2-large model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with '<|conteniue|>' as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.URL
</center>
## Eval results
The model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ComVE-gpt2-large",
"## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.",
"## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.",
"#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.",
"## Training data\n\nThe model is initialized from the gpt2-large model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.",
"## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|conteniue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>",
"## Eval results\n\nThe model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ComVE-gpt2-large",
"## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.",
"## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.",
"#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.",
"## Training data\n\nThe model is initialized from the gpt2-large model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.",
"## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|conteniue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>",
"## Eval results\n\nThe model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
72,
9,
65,
31,
75,
28,
47,
105,
43,
34
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ComVE-gpt2-large## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.## Training data\n\nThe model is initialized from the gpt2-large model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|conteniue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>## Eval results\n\nThe model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset."
] |
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] |
null | null |
transformers
|
# ComVE-gpt2-medium
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the [gpt2-medium](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.imgur.com/xKbrwBC.png">
</center>
## Eval results
The model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
These are some examples generated by the model:
| Against Commonsense Statement | Generated Reason |
|:-----------------------------------------------------:|:--------------------------------------------:|
| Chicken can swim in water. | Chicken can't swim. |
| shoes can fly | Shoes are not able to fly. |
| Chocolate can be used to make a coffee pot | Chocolate is not used to make coffee pots. |
| you can also buy tickets online with an identity card | You can't buy tickets with an identity card. |
| a ball is square and can roll | A ball is round and cannot roll. |
| You can use detergent to dye your hair. | Detergent is used to wash clothes. |
| you can eat mercury | mercury is poisonous |
| A gardener can follow a suspect | gardener is not a police officer |
| cars can float in the ocean just like a boat | Cars are too heavy to float in the ocean. |
| I am going to work so I can lose money. | Working is not a way to lose money. |
### BibTeX entry and citation info
```bibtex
@article{fadel2020justers,
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
year={2020}
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-gpt2-medium">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "mit", "tags": ["gpt2", "exbert", "commonsense", "semeval2020", "comve"], "datasets": ["ComVE"], "metrics": ["bleu"], "widget": [{"text": "Chicken can swim in water. <|continue|>"}]}
|
feature-extraction
|
aliosm/ComVE-gpt2-medium
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"feature-extraction",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"dataset:ComVE",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #feature-extraction #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #endpoints_compatible #text-generation-inference #region-us
|
ComVE-gpt2-medium
=================
Model description
-----------------
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
Intended uses & limitations
---------------------------
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.
*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
Training data
-------------
The model is initialized from the gpt2-medium model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
Training procedure
------------------
Each natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
<img src="https://i.URL
</center>
Eval results
------------
The model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
These are some examples generated by the model:
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
|
[
"#### How to use\n\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.\n\n\nTraining data\n-------------\n\n\nThe model is initialized from the gpt2-medium model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.\n\n\nTraining procedure\n------------------\n\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n\n\n <img src=\"https://i.URL\n</center>\nEval results\n------------\n\n\nThe model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.\n\n\nThese are some examples generated by the model:",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #feature-extraction #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #endpoints_compatible #text-generation-inference #region-us \n",
"#### How to use\n\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.\n\n\nTraining data\n-------------\n\n\nThe model is initialized from the gpt2-medium model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.\n\n\nTraining procedure\n------------------\n\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n\n\n <img src=\"https://i.URL\n</center>\nEval results\n------------\n\n\nThe model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.\n\n\nThese are some examples generated by the model:",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
71,
75,
247,
30
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #feature-extraction #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #endpoints_compatible #text-generation-inference #region-us \n#### How to use\n\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.#### Limitations and bias\n\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.\n\n\nTraining data\n-------------\n\n\nThe model is initialized from the gpt2-medium model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.\n\n\nTraining procedure\n------------------\n\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n\n\n <img src=\"https://i.URL\n</center>\nEval results\n------------\n\n\nThe model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.\n\n\nThese are some examples generated by the model:### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
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null | null |
transformers
|
# ComVE-gpt2
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the [gpt2](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.imgur.com/xKbrwBC.png">
</center>
## Eval results
The model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
```bibtex
@article{fadel2020justers,
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
year={2020}
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "mit", "tags": ["exbert", "commonsense", "semeval2020", "comve"], "datasets": ["ComVE"], "metrics": ["bleu"], "widget": [{"text": "Chicken can swim in water. <|continue|>"}]}
|
text-generation
|
aliosm/ComVE-gpt2
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"dataset:ComVE",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ComVE-gpt2
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.
*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the gpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.URL
</center>
## Eval results
The model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ComVE-gpt2",
"## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.",
"## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.",
"#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.",
"## Training data\n\nThe model is initialized from the gpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.",
"## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>",
"## Eval results\n\nThe model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ComVE-gpt2",
"## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.",
"## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.",
"#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.",
"#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.",
"## Training data\n\nThe model is initialized from the gpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.",
"## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>",
"## Eval results\n\nThe model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
78,
7,
65,
31,
75,
28,
45,
103,
43,
34
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #exbert #commonsense #semeval2020 #comve #en #dataset-ComVE #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ComVE-gpt2## Model description\n\nFinetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective.\nThe model is able to generate a reason why a given natural language statement is against commonsense.## Intended uses & limitations\n\nYou can use the raw model for text generation to generate reasons why natural language statements are against commonsense.#### How to use\n\nYou can use this model directly to generate reasons why the given statement is against commonsense using 'URL' script.\n\n*Note:* make sure that you are using version '2.4.1' of 'transformers' package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.#### Limitations and bias\n\nThe model biased to negate the entered sentence usually instead of producing a factual reason.## Training data\n\nThe model is initialized from the gpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.## Training procedure\n\nEach natural language statement that against commonsense is concatenated with its reference reason with '<|continue|>' as a separator, then the model finetuned using CLM objective.\nThe model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.\n\n<center>\n <img src=\"https://i.URL\n</center>## Eval results\n\nThe model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset."
] |
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] |
null | null | null |
# ai-soco-c++-roberta-small-clas
## Model description
`ai-soco-c++-roberta-small` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from [`ai-soco-c++-roberta-small`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-small) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
## Eval results
The model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-small-clas">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco", "classification"], "datasets": ["ai-soco"], "metrics": ["accuracy"]}
| null |
aliosm/ai-soco-cpp-roberta-small-clas
|
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"c++"
] |
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us
|
# ai-soco-c++-roberta-small-clas
## Model description
'ai-soco-c++-roberta-small' model fine-tuned on AI-SOCO task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from 'ai-soco-c++-roberta-small' model and trained using AI-SOCO dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.
## Eval results
The model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ai-soco-c++-roberta-small-clas",
"## Model description\n\n'ai-soco-c++-roberta-small' model fine-tuned on AI-SOCO task.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-small' model and trained using AI-SOCO dataset to do text classification.",
"## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"## Eval results\n\nThe model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us \n",
"# ai-soco-c++-roberta-small-clas",
"## Model description\n\n'ai-soco-c++-roberta-small' model fine-tuned on AI-SOCO task.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-small' model and trained using AI-SOCO dataset to do text classification.",
"## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"## Eval results\n\nThe model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
43,
17,
30,
28,
19,
40,
70,
32,
34
] |
[
"passage: TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us \n# ai-soco-c++-roberta-small-clas## Model description\n\n'ai-soco-c++-roberta-small' model fine-tuned on AI-SOCO task.#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.#### Limitations and bias\n\nThe model is limited to C++ programming language only.## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-small' model and trained using AI-SOCO dataset to do text classification.## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.## Eval results\n\nThe model achieved 93.19%/92.88% accuracy on AI-SOCO task and ranked in the 4th place.### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
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null | null | null |
# ai-soco-c++-roberta-small
## Model description
From scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-small">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco"], "datasets": ["ai-soco"], "metrics": ["perplexity"]}
| null |
aliosm/ai-soco-cpp-roberta-small
|
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"c++"
] |
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us
|
# ai-soco-c++-roberta-small
## Model description
From scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ai-soco-c++-roberta-small",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.",
"## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.",
"## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us \n",
"# ai-soco-c++-roberta-small",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.",
"## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.",
"## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
40,
14,
45,
37,
28,
19,
30,
90,
34
] |
[
"passage: TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us \n# ai-soco-c++-roberta-small## Model description\n\nFrom scratch pre-trained RoBERTa model with 6 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.#### Limitations and bias\n\nThe model is limited to C++ programming language only.## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
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] |
null | null | null |
# ai-soco-c++-roberta-tiny-96-clas
## Model description
`ai-soco-c++-roberta-tiny-96` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from [`ai-soco-c++-roberta-tiny-96`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny-96) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
## Eval results
The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-96-clas">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco", "classification"], "datasets": ["ai-soco"], "metrics": ["accuracy"]}
| null |
aliosm/ai-soco-cpp-roberta-tiny-96-clas
|
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"c++"
] |
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us
|
# ai-soco-c++-roberta-tiny-96-clas
## Model description
'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.
## Eval results
The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ai-soco-c++-roberta-tiny-96-clas",
"## Model description\n\n'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification.",
"## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"## Eval results\n\nThe model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us \n",
"# ai-soco-c++-roberta-tiny-96-clas",
"## Model description\n\n'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification.",
"## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"## Eval results\n\nThe model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
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[
"passage: TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us \n# ai-soco-c++-roberta-tiny-96-clas## Model description\n\n'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task.#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.#### Limitations and bias\n\nThe model is limited to C++ programming language only.## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification.## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.## Eval results\n\nThe model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
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null | null | null |
# ai-soco-c++-roberta-tiny-96
## Model description
From scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-96">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco"], "datasets": ["ai-soco"], "metrics": ["perplexity"]}
| null |
aliosm/ai-soco-cpp-roberta-tiny-96
|
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"c++"
] |
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us
|
# ai-soco-c++-roberta-tiny-96
## Model description
From scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ai-soco-c++-roberta-tiny-96",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.",
"## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.",
"## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us \n",
"# ai-soco-c++-roberta-tiny-96",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.",
"## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.",
"## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
40,
15,
45,
37,
28,
19,
30,
90,
34
] |
[
"passage: TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us \n# ai-soco-c++-roberta-tiny-96## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 96 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.#### Limitations and bias\n\nThe model is limited to C++ programming language only.## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 16\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
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] |
null | null | null |
# ai-soco-c++-roberta-tiny-clas
## Model description
`ai-soco-c++-roberta-tiny` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from [`ai-soco-c++-roberta-tiny`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
## Eval results
The model achieved 87.66%/87.46% accuracy on AI-SOCO task and ranked in the 9th place.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-clas">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco", "classification"], "datasets": ["ai-soco"], "metrics": ["accuracy"]}
| null |
aliosm/ai-soco-cpp-roberta-tiny-clas
|
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"c++"
] |
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us
|
# ai-soco-c++-roberta-tiny-clas
## Model description
'ai-soco-c++-roberta-tiny' model fine-tuned on AI-SOCO task.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized from 'ai-soco-c++-roberta-tiny' model and trained using AI-SOCO dataset to do text classification.
## Training procedure
The model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.
## Eval results
The model achieved 87.66%/87.46% accuracy on AI-SOCO task and ranked in the 9th place.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ai-soco-c++-roberta-tiny-clas",
"## Model description\n\n'ai-soco-c++-roberta-tiny' model fine-tuned on AI-SOCO task.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-tiny' model and trained using AI-SOCO dataset to do text classification.",
"## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"## Eval results\n\nThe model achieved 87.66%/87.46% accuracy on AI-SOCO task and ranked in the 9th place.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us \n",
"# ai-soco-c++-roberta-tiny-clas",
"## Model description\n\n'ai-soco-c++-roberta-tiny' model fine-tuned on AI-SOCO task.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-tiny' model and trained using AI-SOCO dataset to do text classification.",
"## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"## Eval results\n\nThe model achieved 87.66%/87.46% accuracy on AI-SOCO task and ranked in the 9th place.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
43,
16,
29,
28,
19,
39,
70,
34,
34
] |
[
"passage: TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us \n# ai-soco-c++-roberta-tiny-clas## Model description\n\n'ai-soco-c++-roberta-tiny' model fine-tuned on AI-SOCO task.#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.#### Limitations and bias\n\nThe model is limited to C++ programming language only.## Training data\n\nThe model initialized from 'ai-soco-c++-roberta-tiny' model and trained using AI-SOCO dataset to do text classification.## Training procedure\n\nThe model trained on Google Colab platform using V100 GPU for 10 epochs, 32 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.## Eval results\n\nThe model achieved 87.66%/87.46% accuracy on AI-SOCO task and ranked in the 9th place.### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
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null | null | null |
# ai-soco-c++-roberta-tiny
## Model description
From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.
### BibTeX entry and citation info
```bibtex
@inproceedings{ai-soco-2020-fire,
title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
year = "2020"
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco"], "datasets": ["ai-soco"], "metrics": ["perplexity"]}
| null |
aliosm/ai-soco-cpp-roberta-tiny
|
[
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"c++"
] |
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us
|
# ai-soco-c++-roberta-tiny
## Model description
From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.
## Intended uses & limitations
The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.
#### How to use
You can use the model directly after tokenizing the text using the provided tokenizer with the model files.
#### Limitations and bias
The model is limited to C++ programming language only.
## Training data
The model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.
## Training procedure
The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
</a>
|
[
"# ai-soco-c++-roberta-tiny",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.",
"## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.",
"## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
"TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us \n",
"# ai-soco-c++-roberta-tiny",
"## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.",
"## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.",
"#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.",
"#### Limitations and bias\n\nThe model is limited to C++ programming language only.",
"## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.",
"## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.",
"### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
[
40,
13,
45,
37,
28,
19,
30,
90,
34
] |
[
"passage: TAGS\n#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #dataset-ai-soco #license-mit #region-us \n# ai-soco-c++-roberta-tiny## Model description\n\nFrom scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using AI-SOCO dataset which consists of C++ codes crawled from CodeForces website.## Intended uses & limitations\n\nThe model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language.#### How to use\n\nYou can use the model directly after tokenizing the text using the provided tokenizer with the model files.#### Limitations and bias\n\nThe model is limited to C++ programming language only.## Training data\n\nThe model initialized randomly and trained using AI-SOCO dataset which contains 100K C++ source codes.## Training procedure\n\nThe model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in 'run_language_modelling.py' script. Each continues 4 spaces were converted to a single tab character ('\\t') before tokenization.### BibTeX entry and citation info\n\n\n\n<a href=\"URL\n\t<img width=\"300px\" src=\"URL\n</a>"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
alipsezzar/DialoGPT-medium-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
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-PN-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-parsinlu-multiple-choice
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-parsinlu-qqp
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-parsinlu-sentiment-food
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-parsinlu-sentiment-movie
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-parsinlu-textual-entailment
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-perkey-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-perkey-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-tebyan
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-voa-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base-wiki-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-MSR-persian-base
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-PN-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-parsinlu-multiple-choice
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-parsinlu-qqp
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-parsinlu-sentiment-food
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-parsinlu-sentiment-movie
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-parsinlu-textual-entailment
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-perkey-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-perkey-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-tebyan
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-voa-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SH-persian-base-wiki-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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null | null | null |
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
| null |
alireza7/ARMAN-SH-persian-base
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-PN-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-parsinlu-multiple-choice
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-parsinlu-qqp
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-parsinlu-sentiment-food
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-parsinlu-sentiment-movie
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-parsinlu-textual-entailment
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-perkey-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-perkey-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-tebyan
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-voa-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base-wiki-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-100-persian-base
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-PN-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-parsinlu-multiple-choice
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-parsinlu-qqp
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-parsinlu-sentiment-food
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-parsinlu-sentiment-movie
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-parsinlu-textual-entailment
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-perkey-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-perkey-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-tebyan
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-voa-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base-wiki-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/ARMAN-SS-80-persian-base
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-PN-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-parsinlu-multiple-choice
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-parsinlu-qqp
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-parsinlu-sentiment-food
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-parsinlu-sentiment-movie
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-parsinlu-textual-entailment
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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0.10010140389204025,
0.05595893785357475,
-0.0007451901328749955,
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0.052014973014593124,
0.08704550564289093,
-0.10205619037151337,
-0.0989319309592247
] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-perkey-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-perkey-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-tebyan
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-voa-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base-wiki-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/PEGASUS-persian-base
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/TRANSFORMER-persian-base-PN-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/TRANSFORMER-persian-base-perkey-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/TRANSFORMER-persian-base-perkey-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/TRANSFORMER-persian-base-tebyan
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/TRANSFORMER-persian-base-voa-title
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
More information about models is available [here](https://github.com/alirezasalemi7/ARMAN).
|
{}
|
text2text-generation
|
alireza7/TRANSFORMER-persian-base-wiki-summary
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
More information about models is available here.
|
[] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
40
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
# A conversational model based on the character of Sheldon Cooper from Big Bang Theory.
|
{"tags": ["conversational"]}
|
text-generation
|
alistair7/bbt-diagpt2-model
|
[
"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
|
# A conversational model based on the character of Sheldon Cooper from Big Bang Theory.
|
[
"# A conversational model based on the character of Sheldon Cooper from Big Bang Theory."
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# A conversational model based on the character of Sheldon Cooper from Big Bang Theory."
] |
[
51,
20
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# A conversational model based on the character of Sheldon Cooper from Big Bang Theory."
] |
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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. -->
# bert-base-uncased-pretrain-finetuned-coqa-falt
This model is a fine-tuned version of [alistvt/bert-base-uncased-pretrained-mlm-coqa-stories](https://huggingface.co/alistvt/bert-base-uncased-pretrained-mlm-coqa-stories) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8125
## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.4039 | 0.29 | 2000 | 3.0921 |
| 3.1438 | 0.59 | 4000 | 2.8826 |
| 3.0252 | 0.88 | 6000 | 2.7885 |
| 2.7112 | 1.18 | 8000 | 2.7720 |
| 2.6703 | 1.47 | 10000 | 2.7581 |
| 2.6432 | 1.77 | 12000 | 2.7316 |
| 2.385 | 2.06 | 14000 | 2.7798 |
| 2.3314 | 2.36 | 16000 | 2.7836 |
| 2.3433 | 2.65 | 18000 | 2.7650 |
| 2.3604 | 2.95 | 20000 | 2.7585 |
| 2.2232 | 3.24 | 22000 | 2.8120 |
| 2.2094 | 3.53 | 24000 | 2.7945 |
| 2.2306 | 3.83 | 26000 | 2.8125 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-pretrain-finetuned-coqa-falt", "results": []}]}
|
question-answering
|
alistvt/bert-base-uncased-pretrain-finetuned-coqa-falt
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
bert-base-uncased-pretrain-finetuned-coqa-falt
==============================================
This model is a fine-tuned version of alistvt/bert-base-uncased-pretrained-mlm-coqa-stories on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8125
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: 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 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
40,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #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: 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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\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. -->
# bert-base-uncased-pretrain-finetuned-coqa-falttened
This model is a fine-tuned version of [alistvt/bert-base-uncased-pretrained-mlm-coqa-stories](https://huggingface.co/alistvt/bert-base-uncased-pretrained-mlm-coqa-stories) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8655
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.2886 | 0.29 | 2000 | 3.0142 |
| 3.0801 | 0.59 | 4000 | 2.8347 |
| 2.9744 | 0.88 | 6000 | 2.7643 |
| 2.494 | 1.18 | 8000 | 2.7605 |
| 2.4417 | 1.47 | 10000 | 2.7790 |
| 2.4042 | 1.77 | 12000 | 2.7382 |
| 2.1285 | 2.06 | 14000 | 2.8588 |
| 2.0569 | 2.36 | 16000 | 2.8937 |
| 2.0794 | 2.65 | 18000 | 2.8511 |
| 2.0679 | 2.95 | 20000 | 2.8655 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-pretrain-finetuned-coqa-falttened", "results": []}]}
|
question-answering
|
alistvt/bert-base-uncased-pretrain-finetuned-coqa-falttened
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
bert-base-uncased-pretrain-finetuned-coqa-falttened
===================================================
This model is a fine-tuned version of alistvt/bert-base-uncased-pretrained-mlm-coqa-stories on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8655
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### 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: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### 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 #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### 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"
] |
[
40,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### 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. -->
# bert-base-uncased-pretrained-clm-coqa-stories
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0201 | 1.0 | 2479 | 0.0018 |
| 0.0033 | 2.0 | 4958 | 0.0003 |
| 0.0014 | 3.0 | 7437 | 0.0002 |
### 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"], "model-index": [{"name": "bert-base-uncased-pretrained-clm-coqa-stories", "results": []}]}
|
text-generation
|
alistvt/bert-base-uncased-pretrained-clm-coqa-stories
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-pretrained-clm-coqa-stories
=============================================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0002
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### 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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\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 #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
55,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\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. -->
# bert-base-uncased-pretrained-mlm-coqa-stories
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8310
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0573 | 1.0 | 2479 | 1.8805 |
| 1.9517 | 2.0 | 4958 | 1.8377 |
| 1.9048 | 3.0 | 7437 | 1.8310 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-pretrained-mlm-coqa-stories", "results": []}]}
|
fill-mask
|
alistvt/bert-base-uncased-pretrained-mlm-coqa-stories
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-pretrained-mlm-coqa-stories
=============================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8310
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### 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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\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 #bert #fill-mask #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
47,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\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
|
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://www.aclweb.org/anthology/2021.bsnlp-1.1/).
Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.9.
## Corpus
HerBERT was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
| :------ | ------: | ------: |
| [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M |
| [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M |
| [National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=1)| 1357M | 3.9M |
| [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1056M | 1.1M
| [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.4M |
| [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k |
## Tokenizer
The training dataset was tokenized into subwords using a character level byte-pair encoding (``CharBPETokenizer``) with
a vocabulary size of 50k tokens. The tokenizer itself was trained with a [tokenizers](https://github.com/huggingface/tokenizers) library.
We kindly encourage you to use the ``Fast`` version of the tokenizer, namely ``HerbertTokenizerFast``.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-base-cased")
model = AutoModel.from_pretrained("allegro/herbert-base-cased")
output = model(
**tokenizer.batch_encode_plus(
[
(
"A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.",
"A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy."
)
],
padding='longest',
add_special_tokens=True,
return_tensors='pt'
)
)
```
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{mroczkowski-etal-2021-herbert,
title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish",
author = "Mroczkowski, Robert and
Rybak, Piotr and
Wr{\\'o}blewska, Alina and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1",
pages = "1--10",
}
```
## Authors
The model was trained by [**Machine Learning Research Team at Allegro**](https://ml.allegro.tech/) and [**Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences**](http://zil.ipipan.waw.pl/).
You can contact us at: <a href="mailto:[email protected]">[email protected]</a>
|
{"language": "pl", "license": "cc-by-4.0", "tags": ["herbert"]}
|
feature-extraction
|
allegro/herbert-base-cased
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"herbert",
"pl",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
|
HerBERT
=======
HerBERT is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish.
Model training and experiments were conducted with transformers in version 2.9.
Corpus
------
HerBERT was trained on six different corpora available for Polish language:
Tokenizer
---------
The training dataset was tokenized into subwords using a character level byte-pair encoding (''CharBPETokenizer'') with
a vocabulary size of 50k tokens. The tokenizer itself was trained with a tokenizers library.
We kindly encourage you to use the ''Fast'' version of the tokenizer, namely ''HerbertTokenizerFast''.
Usage
-----
Example code:
License
-------
CC BY 4.0
If you use this model, please cite the following paper:
Authors
-------
The model was trained by Machine Learning Research Team at Allegro and Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences.
You can contact us at: [klejbenchmark@URL](mailto:klejbenchmark@URL)
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[
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n"
] |
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53
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[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# HerBERT tokenizer
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** tokenizer is a character level byte-pair encoding with
vocabulary size of 50k tokens. The tokenizer was trained on [Wolne Lektury](https://wolnelektury.pl/) and a publicly available subset of
[National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=0) with [fastBPE](https://github.com/glample/fastBPE) library.
Tokenizer utilize `XLMTokenizer` implementation from [transformers](https://github.com/huggingface/transformers).
## Tokenizer usage
Herbert tokenizer should be used together with [HerBERT model](https://huggingface.co/allegro/herbert-klej-cased-v1):
```python
from transformers import XLMTokenizer, RobertaModel
tokenizer = XLMTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
outputs = model(encoded_input)
```
## License
CC BY-SA 4.0
## Citation
If you use this tokenizer, please cite the following paper:
```
@inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding",
author = "Rybak, Piotr and
Mroczkowski, Robert and
Tracz, Janusz and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.111",
doi = "10.18653/v1/2020.acl-main.111",
pages = "1191--1201",
}
```
## Authors
Tokenizer was created by **Allegro Machine Learning Research** team.
You can contact us at: <a href="mailto:[email protected]">[email protected]</a>
|
{"language": "pl"}
| null |
allegro/herbert-klej-cased-tokenizer-v1
|
[
"transformers",
"xlm",
"pl",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #xlm #pl #endpoints_compatible #region-us
|
# HerBERT tokenizer
HerBERT tokenizer is a character level byte-pair encoding with
vocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of
National Corpus of Polish with fastBPE library.
Tokenizer utilize 'XLMTokenizer' implementation from transformers.
## Tokenizer usage
Herbert tokenizer should be used together with HerBERT model:
## License
CC BY-SA 4.0
If you use this tokenizer, please cite the following paper:
## Authors
Tokenizer was created by Allegro Machine Learning Research team.
You can contact us at: <a href="mailto:klejbenchmark@URL">klejbenchmark@URL</a>
|
[
"# HerBERT tokenizer\n\nHerBERT tokenizer is a character level byte-pair encoding with\nvocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of\nNational Corpus of Polish with fastBPE library.\nTokenizer utilize 'XLMTokenizer' implementation from transformers.",
"## Tokenizer usage\nHerbert tokenizer should be used together with HerBERT model:",
"## License\nCC BY-SA 4.0\n\nIf you use this tokenizer, please cite the following paper:",
"## Authors\nTokenizer was created by Allegro Machine Learning Research team.\n\nYou can contact us at: <a href=\"mailto:klejbenchmark@URL\">klejbenchmark@URL</a>"
] |
[
"TAGS\n#transformers #xlm #pl #endpoints_compatible #region-us \n",
"# HerBERT tokenizer\n\nHerBERT tokenizer is a character level byte-pair encoding with\nvocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of\nNational Corpus of Polish with fastBPE library.\nTokenizer utilize 'XLMTokenizer' implementation from transformers.",
"## Tokenizer usage\nHerbert tokenizer should be used together with HerBERT model:",
"## License\nCC BY-SA 4.0\n\nIf you use this tokenizer, please cite the following paper:",
"## Authors\nTokenizer was created by Allegro Machine Learning Research team.\n\nYou can contact us at: <a href=\"mailto:klejbenchmark@URL\">klejbenchmark@URL</a>"
] |
[
22,
83,
19,
21,
46
] |
[
"passage: TAGS\n#transformers #xlm #pl #endpoints_compatible #region-us \n# HerBERT tokenizer\n\nHerBERT tokenizer is a character level byte-pair encoding with\nvocabulary size of 50k tokens. The tokenizer was trained on Wolne Lektury and a publicly available subset of\nNational Corpus of Polish with fastBPE library.\nTokenizer utilize 'XLMTokenizer' implementation from transformers.## Tokenizer usage\nHerbert tokenizer should be used together with HerBERT model:## License\nCC BY-SA 4.0\n\nIf you use this tokenizer, please cite the following paper:## Authors\nTokenizer was created by Allegro Machine Learning Research team.\n\nYou can contact us at: <a href=\"mailto:klejbenchmark@URL\">klejbenchmark@URL</a>"
] |
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null | null |
transformers
|
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish Corpora
using only MLM objective with dynamic masking of whole words. For more details, please refer to:
[KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://arxiv.org/abs/2005.00630).
## Dataset
**HerBERT** training dataset is a combination of several publicly available corpora for Polish language:
| Corpus | Tokens | Texts |
| :------ | ------: | ------: |
| [OSCAR](https://traces1.inria.fr/oscar/)| 6710M | 145M |
| [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1084M | 1.1M |
| [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.5M |
| [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k |
| [Allegro Articles](https://allegro.pl/artykuly) | 18M | 33k |
## Tokenizer
The training dataset was tokenized into subwords using [HerBERT Tokenizer](https://huggingface.co/allegro/herbert-klej-cased-tokenizer-v1); a character level byte-pair encoding with
a vocabulary size of 50k tokens. The tokenizer itself was trained on [Wolne Lektury](https://wolnelektury.pl/) and a publicly available subset of
[National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=0) with a [fastBPE](https://github.com/glample/fastBPE) library.
Tokenizer utilizes `XLMTokenizer` implementation for that reason, one should load it as `allegro/herbert-klej-cased-tokenizer-v1`.
## HerBERT models summary
| Model | WWM | Cased | Tokenizer | Vocab Size | Batch Size | Train Steps |
| :------ | ------: | ------: | ------: | ------: | ------: | ------: |
| herbert-klej-cased-v1 | YES | YES | BPE | 50K | 570 | 180k |
## Model evaluation
HerBERT was evaluated on the [KLEJ](https://klejbenchmark.com/) benchmark, publicly available set of nine evaluation tasks for the Polish language understanding.
It had the best average performance and obtained the best results for three of them.
| Model | Average | NKJP-NER | CDSC-E | CDSC-R | CBD | PolEmo2.0-IN\t|PolEmo2.0-OUT | DYK | PSC | AR\t|
| :------ | ------: | ------: | ------: | ------: | ------: | ------: | ------: | ------: | ------: | ------: |
| herbert-klej-cased-v1 | **80.5** | 92.7 | 92.5 | 91.9 | **50.3** | **89.2** |**76.3** |52.1 |95.3 | 84.5 |
Full leaderboard is available [online](https://klejbenchmark.com/leaderboard).
## HerBERT usage
Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.0.
Example code:
```python
from transformers import XLMTokenizer, RobertaModel
tokenizer = XLMTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
outputs = model(encoded_input)
```
HerBERT can also be loaded using `AutoTokenizer` and `AutoModel`:
```python
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
```
## License
CC BY-SA 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding",
author = "Rybak, Piotr and
Mroczkowski, Robert and
Tracz, Janusz and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.111",
doi = "10.18653/v1/2020.acl-main.111",
pages = "1191--1201",
}
```
## Authors
The model was trained by **Allegro Machine Learning Research** team.
You can contact us at: <a href="mailto:[email protected]">[email protected]</a>
|
{"language": "pl"}
| null |
allegro/herbert-klej-cased-v1
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"pl",
"arxiv:2005.00630",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2005.00630"
] |
[
"pl"
] |
TAGS
#transformers #pytorch #jax #roberta #pl #arxiv-2005.00630 #endpoints_compatible #region-us
|
HerBERT
=======
HerBERT is a BERT-based Language Model trained on Polish Corpora
using only MLM objective with dynamic masking of whole words. For more details, please refer to:
KLEJ: Comprehensive Benchmark for Polish Language Understanding.
Dataset
-------
HerBERT training dataset is a combination of several publicly available corpora for Polish language:
Tokenizer
---------
The training dataset was tokenized into subwords using HerBERT Tokenizer; a character level byte-pair encoding with
a vocabulary size of 50k tokens. The tokenizer itself was trained on Wolne Lektury and a publicly available subset of
National Corpus of Polish with a fastBPE library.
Tokenizer utilizes 'XLMTokenizer' implementation for that reason, one should load it as 'allegro/herbert-klej-cased-tokenizer-v1'.
HerBERT models summary
----------------------
Model evaluation
----------------
HerBERT was evaluated on the KLEJ benchmark, publicly available set of nine evaluation tasks for the Polish language understanding.
It had the best average performance and obtained the best results for three of them.
Full leaderboard is available online.
HerBERT usage
-------------
Model training and experiments were conducted with transformers in version 2.0.
Example code:
HerBERT can also be loaded using 'AutoTokenizer' and 'AutoModel':
License
-------
CC BY-SA 4.0
If you use this model, please cite the following paper:
Authors
-------
The model was trained by Allegro Machine Learning Research team.
You can contact us at: [klejbenchmark@URL](mailto:klejbenchmark@URL)
|
[] |
[
"TAGS\n#transformers #pytorch #jax #roberta #pl #arxiv-2005.00630 #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #pl #arxiv-2005.00630 #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# HerBERT
**[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://www.aclweb.org/anthology/2021.bsnlp-1.1/).
Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.9.
## Corpus
HerBERT was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
| :------ | ------: | ------: |
| [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M |
| [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M |
| [National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=1)| 1357M | 3.9M |
| [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1056M | 1.1M
| [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.4M |
| [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k |
## Tokenizer
The training dataset was tokenized into subwords using a character level byte-pair encoding (``CharBPETokenizer``) with
a vocabulary size of 50k tokens. The tokenizer itself was trained with a [tokenizers](https://github.com/huggingface/tokenizers) library.
We kindly encourage you to use the ``Fast`` version of the tokenizer, namely ``HerbertTokenizerFast``.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-large-cased")
model = AutoModel.from_pretrained("allegro/herbert-large-cased")
output = model(
**tokenizer.batch_encode_plus(
[
(
"A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.",
"A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy."
)
],
padding='longest',
add_special_tokens=True,
return_tensors='pt'
)
)
```
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{mroczkowski-etal-2021-herbert,
title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish",
author = "Mroczkowski, Robert and
Rybak, Piotr and
Wr{\'o}blewska, Alina and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1",
pages = "1--10",
}
```
## Authors
The model was trained by [**Machine Learning Research Team at Allegro**](https://ml.allegro.tech/) and [**Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences**](http://zil.ipipan.waw.pl/).
You can contact us at: <a href="mailto:[email protected]">[email protected]</a>
|
{"language": "pl", "license": "cc-by-4.0", "tags": ["herbert"]}
|
feature-extraction
|
allegro/herbert-large-cased
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"herbert",
"pl",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
|
HerBERT
=======
HerBERT is a BERT-based Language Model trained on Polish corpora
using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish.
Model training and experiments were conducted with transformers in version 2.9.
Corpus
------
HerBERT was trained on six different corpora available for Polish language:
Tokenizer
---------
The training dataset was tokenized into subwords using a character level byte-pair encoding (''CharBPETokenizer'') with
a vocabulary size of 50k tokens. The tokenizer itself was trained with a tokenizers library.
We kindly encourage you to use the ''Fast'' version of the tokenizer, namely ''HerbertTokenizerFast''.
Usage
-----
Example code:
License
-------
CC BY 4.0
If you use this model, please cite the following paper:
Authors
-------
The model was trained by Machine Learning Research Team at Allegro and Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences.
You can contact us at: [klejbenchmark@URL](mailto:klejbenchmark@URL)
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n"
] |
[
53
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #herbert #pl #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# plT5 Base
**plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
## Corpus
plT5 was trained on six different corpora available for Polish language:
| Corpus | Tokens | Documents |
| :------ | ------: | ------: |
| [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M |
| [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M |
| [National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=1)| 1357M | 3.9M |
| [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1056M | 1.1M
| [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.4M |
| [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k |
## Tokenizer
The training dataset was tokenized into subwords using a sentencepiece unigram model with
vocabulary size of 50k tokens.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("allegro/plt5-base")
model = AutoModel.from_pretrained("allegro/plt5-base")
```
## License
CC BY 4.0
## Citation
If you use this model, please cite the following paper:
```
@article{chrabrowa2022evaluation,
title={Evaluation of Transfer Learning for Polish with a Text-to-Text Model},
author={Chrabrowa, Aleksandra and Dragan, {\L}ukasz and Grzegorczyk, Karol and Kajtoch, Dariusz and Koszowski, Miko{\l}aj and Mroczkowski, Robert and Rybak, Piotr},
journal={arXiv preprint arXiv:2205.08808},
year={2022}
}
```
## Authors
The model was trained by [**Machine Learning Research Team at Allegro**](https://ml.allegro.tech/) and [**Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences**](http://zil.ipipan.waw.pl/).
You can contact us at: <a href="mailto:[email protected]">[email protected]</a>
|
{"language": "pl", "license": "cc-by-4.0", "tags": ["T5", "translation", "summarization", "question answering", "reading comprehension"], "datasets": ["ccnet", "nkjp", "wikipedia", "open subtitles", "free readings"]}
|
translation
|
allegro/plt5-base
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"T5",
"translation",
"summarization",
"question answering",
"reading comprehension",
"pl",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
plT5 Base
=========
plT5 models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target.
Corpus
------
plT5 was trained on six different corpora available for Polish language:
Tokenizer
---------
The training dataset was tokenized into subwords using a sentencepiece unigram model with
vocabulary size of 50k tokens.
Usage
-----
Example code:
License
-------
CC BY 4.0
If you use this model, please cite the following paper:
Authors
-------
The model was trained by Machine Learning Research Team at Allegro and Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences.
You can contact us at: [klejbenchmark@URL](mailto:klejbenchmark@URL)
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
80
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #T5 #translation #summarization #question answering #reading comprehension #pl #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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