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# JNLPBA_SciBERT_NER

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1472
- Seqeval classification report:               precision    recall  f1-score   support

         DNA       0.83      0.89      0.86      2106
         RNA       0.88      0.89      0.88      3516
   cell_line       0.74      0.80      0.77       526
   cell_type       0.78      0.83      0.80      1475
     protein       0.98      0.97      0.98     37428

   micro avg       0.96      0.96      0.96     45051
   macro avg       0.84      0.87      0.86     45051
weighted avg       0.96      0.96      0.96     45051


## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.233         | 1.0   | 582  | 0.1513          |               precision    recall  f1-score   support

         DNA       0.82      0.89      0.85      2106
         RNA       0.87      0.89      0.88      3516
   cell_line       0.72      0.79      0.76       526
   cell_type       0.79      0.78      0.79      1475
     protein       0.98      0.97      0.98     37428

   micro avg       0.95      0.95      0.95     45051
   macro avg       0.84      0.87      0.85     45051
weighted avg       0.95      0.95      0.95     45051
 |
| 0.138         | 2.0   | 1164 | 0.1486          |               precision    recall  f1-score   support

         DNA       0.85      0.85      0.85      2106
         RNA       0.89      0.87      0.88      3516
   cell_line       0.71      0.80      0.75       526
   cell_type       0.77      0.82      0.79      1475
     protein       0.98      0.97      0.98     37428

   micro avg       0.96      0.95      0.95     45051
   macro avg       0.84      0.86      0.85     45051
weighted avg       0.96      0.95      0.96     45051
 |
| 0.1191        | 3.0   | 1746 | 0.1472          |               precision    recall  f1-score   support

         DNA       0.83      0.89      0.86      2106
         RNA       0.88      0.89      0.88      3516
   cell_line       0.74      0.80      0.77       526
   cell_type       0.78      0.83      0.80      1475
     protein       0.98      0.97      0.98     37428

   micro avg       0.96      0.96      0.96     45051
   macro avg       0.84      0.87      0.86     45051
weighted avg       0.96      0.96      0.96     45051
 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "JNLPBA_SciBERT_NER", "results": []}]}
transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:allenai/scibert_scivocab_uncased autotrain_compatible endpoints_compatible region:us