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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-01_55_54
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-01_55_54", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-01_55_54 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_15_41
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: 0.1746
- Precision: 0.3191
- Recall: 0.3382
- F1: 0.3284
- Accuracy: 0.9439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.2908 | 0.1104 | 0.1905 | 0.1398 | 0.8731 |
| No log | 2.0 | 76 | 0.2253 | 0.1682 | 0.3206 | 0.2206 | 0.9114 |
| No log | 3.0 | 114 | 0.2041 | 0.2069 | 0.3444 | 0.2585 | 0.9249 |
| No log | 4.0 | 152 | 0.1974 | 0.2417 | 0.3603 | 0.2894 | 0.9269 |
| No log | 5.0 | 190 | 0.1958 | 0.2707 | 0.3683 | 0.3120 | 0.9299 |
### 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": "finetuned_token_2e-05_16_02_2022-14_15_41", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_15_41 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_18_19
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_18_19", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_18_19 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_20_41
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_20_41", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_20_41 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_23_23
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_23_23", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_23_23 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_25_47
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_25_47", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_25_47 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_28_10
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_28_10", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_28_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_30_32
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_30_32", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_30_32 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_32_56
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_32_56", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_32_56 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_35_19
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_35_19", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_35_19 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_16_02_2022-14_37_42
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: 0.1722
- Precision: 0.3378
- Recall: 0.3615
- F1: 0.3492
- Accuracy: 0.9448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 |
| No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 |
| No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 |
| No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 |
| No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 |
### 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": "finetuned_token_2e-05_16_02_2022-14_37_42", "results": []}]} | ali2066/finetuned_token_2e-05_16_02_2022-14_37_42 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_41_15
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: 0.1742
- Precision: 0.3447
- Recall: 0.3410
- F1: 0.3428
- Accuracy: 0.9455
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3692 | 0.0868 | 0.2030 | 0.1216 | 0.8238 |
| No log | 2.0 | 76 | 0.3198 | 0.1674 | 0.3029 | 0.2157 | 0.8567 |
| No log | 3.0 | 114 | 0.3156 | 0.1520 | 0.3096 | 0.2039 | 0.8510 |
| No log | 4.0 | 152 | 0.3129 | 0.1753 | 0.3266 | 0.2281 | 0.8500 |
| No log | 5.0 | 190 | 0.3038 | 0.1716 | 0.3401 | 0.2281 | 0.8595 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_41_15", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_41_15 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_43_42
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_43_42", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_43_42 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_46_07
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_46_07", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_46_07 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_48_32
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_48_32", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_48_32 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_50_54
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_50_54", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_50_54 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_53_17
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_53_17", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_53_17 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_56_33
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_56_33", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_56_33 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-15_59_50
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-15_59_50", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-15_59_50 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-16_03_05
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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-16_03_05", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-16_03_05 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_2e-05_all_16_02_2022-16_06_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: 0.1750
- Precision: 0.3286
- Recall: 0.3334
- F1: 0.3310
- Accuracy: 0.9447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 |
| No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 |
| No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 |
| No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 |
| No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 |
### 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": "finetuned_token_2e-05_all_16_02_2022-16_06_20", "results": []}]} | ali2066/finetuned_token_2e-05_all_16_02_2022-16_06_20 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_3e-05_all_16_02_2022-16_09_36
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: 0.1630
- Precision: 0.3684
- Recall: 0.3714
- F1: 0.3699
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 |
| No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 |
| No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 |
| No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 |
| No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 |
### 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": "finetuned_token_3e-05_all_16_02_2022-16_09_36", "results": []}]} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_09_36 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_3e-05_all_16_02_2022-16_12_51
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: 0.1630
- Precision: 0.3684
- Recall: 0.3714
- F1: 0.3699
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 |
| No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 |
| No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 |
| No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 |
| No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 |
### 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": "finetuned_token_3e-05_all_16_02_2022-16_12_51", "results": []}]} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_12_51 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_3e-05_all_16_02_2022-16_16_08
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: 0.1630
- Precision: 0.3684
- Recall: 0.3714
- F1: 0.3699
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 |
| No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 |
| No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 |
| No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 |
| No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 |
### 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": "finetuned_token_3e-05_all_16_02_2022-16_16_08", "results": []}]} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_16_08 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_3e-05_all_16_02_2022-16_19_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: 0.1630
- Precision: 0.3684
- Recall: 0.3714
- F1: 0.3699
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 |
| No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 |
| No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 |
| No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 |
| No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 |
### 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": "finetuned_token_3e-05_all_16_02_2022-16_19_24", "results": []}]} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_19_24 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_3e-05_all_16_02_2022-16_22_39
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: 0.1630
- Precision: 0.3684
- Recall: 0.3714
- F1: 0.3699
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 |
| No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 |
| No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 |
| No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 |
| No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 |
### 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": "finetuned_token_3e-05_all_16_02_2022-16_22_39", "results": []}]} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_22_39 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_3e-05_all_16_02_2022-16_25_56
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: 0.1630
- Precision: 0.3684
- Recall: 0.3714
- F1: 0.3699
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 |
| No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 |
| No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 |
| No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 |
| No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 |
### 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": "finetuned_token_3e-05_all_16_02_2022-16_25_56", "results": []}]} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_25_56 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_3e-05_all_16_02_2022-16_29_13
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: 0.1630
- Precision: 0.3684
- Recall: 0.3714
- F1: 0.3699
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 |
| No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 |
| No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 |
| No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 |
| No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 |
### 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": "finetuned_token_3e-05_all_16_02_2022-16_29_13", "results": []}]} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_29_13 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | ali2066/finetuned_token_3e-05_all_16_02_2022-16_32_30 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | ali2066/finetuned_token_itr | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_0.0002_all_16_02_2022-20_14_27
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: 0.1588
- Precision: 0.4510
- Recall: 0.5622
- F1: 0.5005
- Accuracy: 0.9477
## 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: 0.0002
- 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 | 38 | 0.2896 | 0.1483 | 0.1981 | 0.1696 | 0.8745 |
| No log | 2.0 | 76 | 0.2553 | 0.2890 | 0.3604 | 0.3207 | 0.8918 |
| No log | 3.0 | 114 | 0.2507 | 0.246 | 0.4642 | 0.3216 | 0.8925 |
| No log | 4.0 | 152 | 0.2540 | 0.2428 | 0.4792 | 0.3223 | 0.8922 |
| No log | 5.0 | 190 | 0.2601 | 0.2747 | 0.4717 | 0.3472 | 0.8965 |
### 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": "finetuned_token_itr0_0.0002_all_16_02_2022-20_14_27", "results": []}]} | ali2066/finetuned_token_itr0_0.0002_all_16_02_2022-20_14_27 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_0.0002_all_16_02_2022-20_30_01
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: 0.1577
- Precision: 0.4469
- Recall: 0.5280
- F1: 0.4841
- Accuracy: 0.9513
## 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: 0.0002
- 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 | 38 | 0.3553 | 0.1068 | 0.0810 | 0.0922 | 0.8412 |
| No log | 2.0 | 76 | 0.2812 | 0.2790 | 0.4017 | 0.3293 | 0.8684 |
| No log | 3.0 | 114 | 0.2793 | 0.3086 | 0.4586 | 0.3689 | 0.8747 |
| No log | 4.0 | 152 | 0.2766 | 0.3057 | 0.4190 | 0.3535 | 0.8763 |
| No log | 5.0 | 190 | 0.2805 | 0.2699 | 0.4845 | 0.3467 | 0.8793 |
### 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": "finetuned_token_itr0_0.0002_all_16_02_2022-20_30_01", "results": []}]} | ali2066/finetuned_token_itr0_0.0002_all_16_02_2022-20_30_01 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_0.0002_all_16_02_2022-20_45_27
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: 0.1500
- Precision: 0.4739
- Recall: 0.5250
- F1: 0.4981
- Accuracy: 0.9551
## 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: 0.0002
- 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 | 38 | 0.3183 | 0.2024 | 0.2909 | 0.2387 | 0.8499 |
| No log | 2.0 | 76 | 0.3092 | 0.2909 | 0.4181 | 0.3431 | 0.8548 |
| No log | 3.0 | 114 | 0.2928 | 0.2923 | 0.4855 | 0.3650 | 0.8647 |
| No log | 4.0 | 152 | 0.3098 | 0.2832 | 0.4605 | 0.3507 | 0.8641 |
| No log | 5.0 | 190 | 0.3120 | 0.2470 | 0.4374 | 0.3157 | 0.8654 |
### 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": "finetuned_token_itr0_0.0002_all_16_02_2022-20_45_27", "results": []}]} | ali2066/finetuned_token_itr0_0.0002_all_16_02_2022-20_45_27 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_0.0002_all_16_02_2022-21_13_10
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: 0.3057
- Precision: 0.2857
- Recall: 0.4508
- F1: 0.3497
- Accuracy: 0.8741
## 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: 0.0002
- 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 | 0.3018 | 0.2097 | 0.2546 | 0.2300 | 0.8727 |
| No log | 2.0 | 60 | 0.2337 | 0.3444 | 0.3652 | 0.3545 | 0.9024 |
| No log | 3.0 | 90 | 0.2198 | 0.3463 | 0.3869 | 0.3655 | 0.9070 |
| No log | 4.0 | 120 | 0.2112 | 0.3757 | 0.4405 | 0.4056 | 0.9173 |
| No log | 5.0 | 150 | 0.2131 | 0.4163 | 0.5126 | 0.4595 | 0.9212 |
### 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": "finetuned_token_itr0_0.0002_all_16_02_2022-21_13_10", "results": []}]} | ali2066/finetuned_token_itr0_0.0002_all_16_02_2022-21_13_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_0.0002_editorials_16_02_2022-21_07_38
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: 0.1146
- Precision: 0.4662
- Recall: 0.4718
- F1: 0.4690
- Accuracy: 0.9773
## 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: 0.0002
- 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 | 0.0756 | 0.2960 | 0.4505 | 0.3573 | 0.9775 |
| No log | 2.0 | 30 | 0.0626 | 0.3615 | 0.4231 | 0.3899 | 0.9808 |
| No log | 3.0 | 45 | 0.0602 | 0.4898 | 0.5275 | 0.5079 | 0.9833 |
| No log | 4.0 | 60 | 0.0719 | 0.5517 | 0.5275 | 0.5393 | 0.9849 |
| No log | 5.0 | 75 | 0.0754 | 0.5765 | 0.5385 | 0.5568 | 0.9849 |
### 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": "finetuned_token_itr0_0.0002_editorials_16_02_2022-21_07_38", "results": []}]} | ali2066/finetuned_token_itr0_0.0002_editorials_16_02_2022-21_07_38 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_0.0002_essays_16_02_2022-21_04_02
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: 0.2158
- Precision: 0.5814
- Recall: 0.7073
- F1: 0.6382
- Accuracy: 0.9248
## 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: 0.0002
- 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 | 0.3920 | 0.4392 | 0.6069 | 0.5096 | 0.8593 |
| No log | 2.0 | 22 | 0.3304 | 0.4282 | 0.6260 | 0.5085 | 0.8672 |
| No log | 3.0 | 33 | 0.3361 | 0.4840 | 0.6336 | 0.5488 | 0.8685 |
| No log | 4.0 | 44 | 0.3258 | 0.5163 | 0.6641 | 0.5810 | 0.8722 |
| No log | 5.0 | 55 | 0.3472 | 0.5192 | 0.6718 | 0.5857 | 0.8743 |
### 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": "finetuned_token_itr0_0.0002_essays_16_02_2022-21_04_02", "results": []}]} | ali2066/finetuned_token_itr0_0.0002_essays_16_02_2022-21_04_02 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_0.0002_webDiscourse_16_02_2022-21_00_50
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: 0.5530
- Precision: 0.0044
- Recall: 0.0182
- F1: 0.0071
- Accuracy: 0.7268
## 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: 0.0002
- 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 | 0.7051 | 0.0645 | 0.0323 | 0.0430 | 0.4465 |
| No log | 2.0 | 20 | 0.6928 | 0.0476 | 0.0161 | 0.0241 | 0.5546 |
| No log | 3.0 | 30 | 0.6875 | 0.0069 | 0.0484 | 0.0120 | 0.5533 |
| No log | 4.0 | 40 | 0.6966 | 0.0064 | 0.0323 | 0.0107 | 0.5832 |
| No log | 5.0 | 50 | 0.7093 | 0.0061 | 0.0323 | 0.0102 | 0.5742 |
### 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": "finetuned_token_itr0_0.0002_webDiscourse_16_02_2022-21_00_50", "results": []}]} | ali2066/finetuned_token_itr0_0.0002_webDiscourse_16_02_2022-21_00_50 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_2e-05_all_16_02_2022-20_09_36
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: 0.1743
- Precision: 0.3429
- Recall: 0.3430
- F1: 0.3430
- Accuracy: 0.9446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3322 | 0.0703 | 0.1790 | 0.1010 | 0.8318 |
| No log | 2.0 | 76 | 0.2644 | 0.1180 | 0.2343 | 0.1570 | 0.8909 |
| No log | 3.0 | 114 | 0.2457 | 0.1624 | 0.2583 | 0.1994 | 0.8980 |
| No log | 4.0 | 152 | 0.2487 | 0.1486 | 0.2583 | 0.1887 | 0.8931 |
| No log | 5.0 | 190 | 0.2395 | 0.1670 | 0.2694 | 0.2062 | 0.8988 |
### 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": "finetuned_token_itr0_2e-05_all_16_02_2022-20_09_36", "results": []}]} | ali2066/finetuned_token_itr0_2e-05_all_16_02_2022-20_09_36 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_2e-05_all_16_02_2022-20_25_06
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: 0.1778
- Precision: 0.3270
- Recall: 0.3348
- F1: 0.3309
- Accuracy: 0.9439
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.4023 | 0.1050 | 0.2331 | 0.1448 | 0.8121 |
| No log | 2.0 | 76 | 0.3629 | 0.1856 | 0.3414 | 0.2405 | 0.8368 |
| No log | 3.0 | 114 | 0.3329 | 0.1794 | 0.3594 | 0.2394 | 0.8504 |
| No log | 4.0 | 152 | 0.3261 | 0.1786 | 0.3684 | 0.2405 | 0.8503 |
| No log | 5.0 | 190 | 0.3244 | 0.1872 | 0.3684 | 0.2482 | 0.8534 |
### 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": "finetuned_token_itr0_2e-05_all_16_02_2022-20_25_06", "results": []}]} | ali2066/finetuned_token_itr0_2e-05_all_16_02_2022-20_25_06 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_2e-05_all_16_02_2022-20_40_28
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: 0.1736
- Precision: 0.3358
- Recall: 0.3447
- F1: 0.3402
- Accuracy: 0.9452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 38 | 0.3058 | 0.1200 | 0.2102 | 0.1528 | 0.8629 |
| No log | 2.0 | 76 | 0.2488 | 0.1605 | 0.2774 | 0.2034 | 0.9003 |
| No log | 3.0 | 114 | 0.2296 | 0.1947 | 0.2880 | 0.2324 | 0.9057 |
| No log | 4.0 | 152 | 0.2208 | 0.2201 | 0.2986 | 0.2534 | 0.9113 |
| No log | 5.0 | 190 | 0.2235 | 0.2110 | 0.3039 | 0.2491 | 0.9101 |
### 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": "finetuned_token_itr0_2e-05_all_16_02_2022-20_40_28", "results": []}]} | ali2066/finetuned_token_itr0_2e-05_all_16_02_2022-20_40_28 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_2e-05_all_16_02_2022-21_08_55
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: 0.2853
- Precision: 0.1677
- Recall: 0.3106
- F1: 0.2178
- Accuracy: 0.8755
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.3452 | 0.0526 | 0.1055 | 0.0702 | 0.8507 |
| No log | 2.0 | 60 | 0.2598 | 0.1575 | 0.2680 | 0.1984 | 0.8909 |
| No log | 3.0 | 90 | 0.2398 | 0.1866 | 0.2982 | 0.2295 | 0.9007 |
| No log | 4.0 | 120 | 0.2354 | 0.1949 | 0.3049 | 0.2378 | 0.9002 |
| No log | 5.0 | 150 | 0.2314 | 0.2026 | 0.3166 | 0.2471 | 0.9004 |
### 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": "finetuned_token_itr0_2e-05_all_16_02_2022-21_08_55", "results": []}]} | ali2066/finetuned_token_itr0_2e-05_all_16_02_2022-21_08_55 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_2e-05_editorials_16_02_2022-21_05_05
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: 0.1114
- 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 15 | 0.0921 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
| No log | 2.0 | 30 | 0.0816 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
| No log | 3.0 | 45 | 0.0781 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
| No log | 4.0 | 60 | 0.0746 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
| No log | 5.0 | 75 | 0.0737 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
### 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": "finetuned_token_itr0_2e-05_editorials_16_02_2022-21_05_05", "results": []}]} | ali2066/finetuned_token_itr0_2e-05_editorials_16_02_2022-21_05_05 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | ali2066/finetuned_token_itr0_2e-05_essays_16_02_2022-20_16_50 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | ali2066/finetuned_token_itr0_2e-05_essays_16_02_2022-20_32_24 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | ali2066/finetuned_token_itr0_2e-05_essays_16_02_2022-20_47_50 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_2e-05_essays_16_02_2022-21_01_51
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: 0.2525
- Precision: 0.3997
- Recall: 0.5117
- F1: 0.4488
- Accuracy: 0.9115
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 11 | 0.4652 | 0.1528 | 0.3588 | 0.2144 | 0.7851 |
| No log | 2.0 | 22 | 0.3646 | 0.2913 | 0.4847 | 0.3639 | 0.8521 |
| No log | 3.0 | 33 | 0.3453 | 0.3789 | 0.5611 | 0.4523 | 0.8708 |
| No log | 4.0 | 44 | 0.3270 | 0.3673 | 0.5496 | 0.4404 | 0.8729 |
| No log | 5.0 | 55 | 0.3268 | 0.4011 | 0.5725 | 0.4717 | 0.8760 |
### 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": "finetuned_token_itr0_2e-05_essays_16_02_2022-21_01_51", "results": []}]} | ali2066/finetuned_token_itr0_2e-05_essays_16_02_2022-21_01_51 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_2e-05_webDiscourse_16_02_2022-20_58_45
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: 0.6373
- Precision: 0.0024
- Recall: 0.0072
- F1: 0.0036
- Accuracy: 0.6329
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 10 | 0.5913 | 0.0 | 0.0 | 0.0 | 0.7023 |
| No log | 2.0 | 20 | 0.5833 | 0.0 | 0.0 | 0.0 | 0.7062 |
| No log | 3.0 | 30 | 0.5717 | 0.0 | 0.0 | 0.0 | 0.7059 |
| No log | 4.0 | 40 | 0.5696 | 0.0 | 0.0 | 0.0 | 0.7008 |
| No log | 5.0 | 50 | 0.5669 | 0.0 | 0.0 | 0.0 | 0.7010 |
### 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": "finetuned_token_itr0_2e-05_webDiscourse_16_02_2022-20_58_45", "results": []}]} | ali2066/finetuned_token_itr0_2e-05_webDiscourse_16_02_2022-20_58_45 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | ali2066/finetuned_token_itr0_2e-05_webDiscourse_25_02_2022-21_13_46 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04
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: 0.1620
- Precision: 0.3509
- Recall: 0.3793
- F1: 0.3646
- Accuracy: 0.9468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.2997 | 0.1125 | 0.2057 | 0.1454 | 0.8669 |
| No log | 2.0 | 76 | 0.2620 | 0.1928 | 0.2849 | 0.2300 | 0.8899 |
| No log | 3.0 | 114 | 0.2497 | 0.1923 | 0.2906 | 0.2314 | 0.8918 |
| No log | 4.0 | 152 | 0.2474 | 0.1819 | 0.3377 | 0.2365 | 0.8905 |
| No log | 5.0 | 190 | 0.2418 | 0.2128 | 0.3264 | 0.2576 | 0.8997 |
### 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": "finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04", "results": []}]} | ali2066/finetuned_token_itr0_3e-05_all_16_02_2022-20_12_04 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_3e-05_all_16_02_2022-20_27_36
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: 0.1633
- Precision: 0.3632
- Recall: 0.3786
- F1: 0.3707
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3227 | 0.1237 | 0.2397 | 0.1631 | 0.8566 |
| No log | 2.0 | 76 | 0.2874 | 0.2128 | 0.3328 | 0.2596 | 0.8721 |
| No log | 3.0 | 114 | 0.2762 | 0.2170 | 0.3603 | 0.2709 | 0.8844 |
| No log | 4.0 | 152 | 0.2770 | 0.2274 | 0.3690 | 0.2814 | 0.8819 |
| No log | 5.0 | 190 | 0.2771 | 0.2113 | 0.3741 | 0.2701 | 0.8823 |
### 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": "finetuned_token_itr0_3e-05_all_16_02_2022-20_27_36", "results": []}]} | ali2066/finetuned_token_itr0_3e-05_all_16_02_2022-20_27_36 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_3e-05_all_16_02_2022-20_43_00
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: 0.1626
- Precision: 0.3811
- Recall: 0.3865
- F1: 0.3838
- Accuracy: 0.9482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 38 | 0.3697 | 0.0933 | 0.2235 | 0.1317 | 0.8259 |
| No log | 2.0 | 76 | 0.3193 | 0.1266 | 0.2948 | 0.1771 | 0.8494 |
| No log | 3.0 | 114 | 0.3025 | 0.1606 | 0.3160 | 0.2130 | 0.8540 |
| No log | 4.0 | 152 | 0.2978 | 0.1867 | 0.3449 | 0.2422 | 0.8605 |
| No log | 5.0 | 190 | 0.2984 | 0.1706 | 0.3507 | 0.2295 | 0.8551 |
### 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": "finetuned_token_itr0_3e-05_all_16_02_2022-20_43_00", "results": []}]} | ali2066/finetuned_token_itr0_3e-05_all_16_02_2022-20_43_00 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_3e-05_all_16_02_2022-21_11_08
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: 0.2731
- Precision: 0.1928
- Recall: 0.3457
- F1: 0.2475
- Accuracy: 0.8826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 0.3010 | 0.1330 | 0.2345 | 0.1697 | 0.8707 |
| No log | 2.0 | 60 | 0.2446 | 0.1739 | 0.2948 | 0.2188 | 0.8949 |
| No log | 3.0 | 90 | 0.2235 | 0.2446 | 0.3032 | 0.2708 | 0.9080 |
| No log | 4.0 | 120 | 0.2226 | 0.2670 | 0.3350 | 0.2972 | 0.9058 |
| No log | 5.0 | 150 | 0.2166 | 0.2779 | 0.3417 | 0.3065 | 0.9063 |
### 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": "finetuned_token_itr0_3e-05_all_16_02_2022-21_11_08", "results": []}]} | ali2066/finetuned_token_itr0_3e-05_all_16_02_2022-21_11_08 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_3e-05_editorials_16_02_2022-21_06_22
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: 0.1060
- Precision: 0.2003
- Recall: 0.1154
- F1: 0.1464
- Accuracy: 0.9712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 0.0897 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
| No log | 2.0 | 30 | 0.0798 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
| No log | 3.0 | 45 | 0.0743 | 0.08 | 0.0110 | 0.0193 | 0.9801 |
| No log | 4.0 | 60 | 0.0707 | 0.0741 | 0.0110 | 0.0191 | 0.9802 |
| No log | 5.0 | 75 | 0.0696 | 0.2727 | 0.1648 | 0.2055 | 0.9805 |
### 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": "finetuned_token_itr0_3e-05_editorials_16_02_2022-21_06_22", "results": []}]} | ali2066/finetuned_token_itr0_3e-05_editorials_16_02_2022-21_06_22 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_3e-05_essays_16_02_2022-21_02_59
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: 0.2374
- Precision: 0.4766
- Recall: 0.5549
- F1: 0.5127
- Accuracy: 0.9173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 0.4155 | 0.1569 | 0.3168 | 0.2099 | 0.8163 |
| No log | 2.0 | 22 | 0.3584 | 0.3827 | 0.5725 | 0.4587 | 0.8691 |
| No log | 3.0 | 33 | 0.3483 | 0.4353 | 0.5649 | 0.4917 | 0.8737 |
| No log | 4.0 | 44 | 0.3187 | 0.4403 | 0.5916 | 0.5049 | 0.8770 |
| No log | 5.0 | 55 | 0.3188 | 0.4463 | 0.6031 | 0.5130 | 0.8806 |
### 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": "finetuned_token_itr0_3e-05_essays_16_02_2022-21_02_59", "results": []}]} | ali2066/finetuned_token_itr0_3e-05_essays_16_02_2022-21_02_59 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50
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: 0.5450
- Precision: 0.0049
- Recall: 0.0146
- F1: 0.0074
- Accuracy: 0.7431
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | 0.6830 | 0.0109 | 0.0323 | 0.0163 | 0.5685 |
| No log | 2.0 | 20 | 0.7187 | 0.0256 | 0.0323 | 0.0286 | 0.5668 |
| No log | 3.0 | 30 | 0.6839 | 0.0076 | 0.0484 | 0.0131 | 0.5848 |
| No log | 4.0 | 40 | 0.6988 | 0.0092 | 0.0484 | 0.0155 | 0.5918 |
| No log | 5.0 | 50 | 0.7055 | 0.0100 | 0.0484 | 0.0165 | 0.5946 |
### 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": "finetuned_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50", "results": []}]} | ali2066/finetuned_token_itr0_3e-05_webDiscourse_16_02_2022-20_59_50 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | ali2066/sentiment-roberta-large-english_token_itr0_2e-05_all_01_03_2022-04_15_15 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | ali2066/sentiment-roberta-large-english_token_itr0_2e-05_all_01_03_2022-04_16_28 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-roberta-base-sentiment_token_itr0_2e-05_all_01_03_2022-04_19_45
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2858
- Precision: 0.3206
- Recall: 0.4721
- F1: 0.3819
- Accuracy: 0.8762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.3772 | 0.0269 | 0.0326 | 0.0294 | 0.8143 |
| No log | 2.0 | 60 | 0.3052 | 0.2015 | 0.3596 | 0.2583 | 0.8537 |
| No log | 3.0 | 90 | 0.2937 | 0.2737 | 0.4273 | 0.3337 | 0.8722 |
| No log | 4.0 | 120 | 0.2852 | 0.2728 | 0.4348 | 0.3353 | 0.8750 |
| No log | 5.0 | 150 | 0.2676 | 0.2851 | 0.4474 | 0.3483 | 0.8797 |
### 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-base-sentiment_token_itr0_2e-05_all_01_03_2022-04_19_45", "results": []}]} | ali2066/twitter-roberta-base-sentiment_token_itr0_2e-05_all_01_03_2022-04_19_45 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-classification | transformers | {} | ali2066/twitter-roberta-base_sentence_itr0_1e-05_all_01_03_2022-13_38_07 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_base_sentence_itr0_1e-05_all_01_03_2022-13_53_11 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_0.0001_all_01_03_2022-14_26_43 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-14_37_35 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_02_39 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-14_43_21 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_editorials_01_03_2022-15_00_35 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_40_24 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-14_58_58 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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": []}]} | ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_57_21 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | # Testing
This Be A Test | {} | aliaafee/test | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | aliasdasd/asdas | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
fill-mask | transformers | {} | aliasdasd/dummy-model | null | [
"transformers",
"tf",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | alibalali/wav2vec2-base-timit-demo-colab | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | alicejwong1122/gpt2-finetuned-wp2plots | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | alidan/111 | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | alidan/model_name | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | alienspaceman/dreamcatcher-eng | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text-generation | transformers | {} | alienspaceman/rus_dreamgen_fulltext_medium | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | alihilal/arabic | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | 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": []}]} | alina1997/MarianMT | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"en",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | 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": []}]} | alina1997/marian_en_de_test | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"en",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | 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|>"}]} | aliosm/ComVE-distilgpt2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"dataset:ComVE",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | 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|>"}]} | aliosm/ComVE-gpt2-large | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
feature-extraction | 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|>"}]} | aliosm/ComVE-gpt2-medium | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"feature-extraction",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"dataset:ComVE",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | 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|>"}]} | aliosm/ComVE-gpt2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"dataset:ComVE",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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"]} | aliosm/ai-soco-cpp-roberta-small-clas | null | [
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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"]} | aliosm/ai-soco-cpp-roberta-small | null | [
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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"]} | aliosm/ai-soco-cpp-roberta-tiny-96-clas | null | [
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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"]} | aliosm/ai-soco-cpp-roberta-tiny-96 | null | [
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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"]} | aliosm/ai-soco-cpp-roberta-tiny-clas | null | [
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"classification",
"dataset:ai-soco",
"license:mit",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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"]} | aliosm/ai-soco-cpp-roberta-tiny | null | [
"exbert",
"authorship-identification",
"fire2020",
"pan2020",
"ai-soco",
"dataset:ai-soco",
"license:mit",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | alipsezzar/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
null | null | {} | alipsezzar/DialoGPT-medium-rick | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
null | null | {} | alirazmdideh/albert-xlarge-v2-finetuned-squad | null | [
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
|
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-PN-summary | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-multiple-choice | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-qqp | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-sentiment-food | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-sentiment-movie | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
text2text-generation | transformers | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). | {} | alireza7/ARMAN-MSR-persian-base-parsinlu-textual-entailment | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:05+00:00 |
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