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# results

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the epi_classify4_gard dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0541
- Precision: 0.875
- Recall: 0.9032
- F1: 0.8889
- Accuracy: 0.986

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["epi_classify4_gard"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "results", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "epi_classify4_gard", "type": "epi_classify4_gard", "args": "default"}, "metrics": [{"type": "precision", "value": 0.875, "name": "Precision"}, {"type": "recall", "value": 0.9032258064516129, "name": "Recall"}, {"type": "f1", "value": 0.8888888888888888, "name": "F1"}, {"type": "accuracy", "value": 0.986, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:epi_classify4_gard model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-epi_classify4_gard #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 




# results

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the epi_classify4_gard dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0541
- Precision: 0.875
- Recall: 0.9032
- F1: 0.8889
- Accuracy: 0.986

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0

### Training results



### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3

# results

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the epi_classify4_gard dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0541
- Precision: 0.875
- Recall: 0.9032
- F1: 0.8889
- Accuracy: 0.986 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0 ### Training results ### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-epi_classify4_gard #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # results

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the epi_classify4_gard dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0541
- Precision: 0.875
- Recall: 0.9032
- F1: 0.8889
- Accuracy: 0.986 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0 ### Training results ### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-epi_classify4_gard #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
# results

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the epi_classify4_gard dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0541
- Precision: 0.875
- Recall: 0.9032
- F1: 0.8889
- Accuracy: 0.986## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

More information needed## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0### Training results### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3