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# BioNLP13CG_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: 0.1817
- Seqeval classification report:                                  precision    recall  f1-score   support

                     Amino_acid       0.54      0.43      0.48        89
              Anatomical_system       0.00      0.00      0.00        41
                         Cancer       0.84      0.84      0.84      3620
                           Cell       0.00      0.00      0.00        11
             Cellular_component       0.00      0.00      0.00         7
Developing_anatomical_structure       0.00      0.00      0.00        37
           Gene_or_gene_product       0.90      0.92      0.91       540
   Immaterial_anatomical_entity       0.63      0.65      0.64        82
         Multi-tissue_structure       0.63      0.71      0.67       144
                          Organ       0.00      0.00      0.00        56
                       Organism       0.86      0.17      0.28        36
           Organism_subdivision       0.83      0.86      0.84      1086
             Organism_substance       0.87      0.81      0.84       484
         Pathological_formation       0.92      0.92      0.92      1430
                Simple_chemical       0.58      0.72      0.64       304
                         Tissue       0.79      0.82      0.80       341

                      micro avg       0.84      0.82      0.83      8308
                      macro avg       0.52      0.49      0.49      8308
                   weighted avg       0.82      0.82      0.82      8308


## 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log        | 0.99  | 95   | 0.2278          |                                  precision    recall  f1-score   support

                     Amino_acid       0.48      0.15      0.22        89
              Anatomical_system       0.00      0.00      0.00        41
                         Cancer       0.81      0.80      0.80      3620
                           Cell       0.00      0.00      0.00        11
             Cellular_component       0.00      0.00      0.00         7
Developing_anatomical_structure       0.00      0.00      0.00        37
           Gene_or_gene_product       0.80      0.90      0.84       540
   Immaterial_anatomical_entity       0.48      0.59      0.52        82
         Multi-tissue_structure       0.62      0.45      0.52       144
                          Organ       0.00      0.00      0.00        56
                       Organism       0.00      0.00      0.00        36
           Organism_subdivision       0.75      0.84      0.79      1086
             Organism_substance       0.83      0.77      0.80       484
         Pathological_formation       0.90      0.86      0.88      1430
                Simple_chemical       0.53      0.69      0.60       304
                         Tissue       0.74      0.73      0.73       341

                      micro avg       0.79      0.78      0.78      8308
                      macro avg       0.43      0.42      0.42      8308
                   weighted avg       0.77      0.78      0.77      8308
 |
| No log        | 2.0   | 191  | 0.1850          |                                  precision    recall  f1-score   support

                     Amino_acid       0.52      0.40      0.46        89
              Anatomical_system       0.00      0.00      0.00        41
                         Cancer       0.83      0.84      0.84      3620
                           Cell       0.00      0.00      0.00        11
             Cellular_component       0.00      0.00      0.00         7
Developing_anatomical_structure       0.00      0.00      0.00        37
           Gene_or_gene_product       0.89      0.92      0.90       540
   Immaterial_anatomical_entity       0.56      0.65      0.60        82
         Multi-tissue_structure       0.60      0.69      0.64       144
                          Organ       0.00      0.00      0.00        56
                       Organism       1.00      0.17      0.29        36
           Organism_subdivision       0.80      0.87      0.83      1086
             Organism_substance       0.87      0.79      0.83       484
         Pathological_formation       0.91      0.93      0.92      1430
                Simple_chemical       0.57      0.72      0.64       304
                         Tissue       0.77      0.79      0.78       341

                      micro avg       0.82      0.83      0.82      8308
                      macro avg       0.52      0.49      0.48      8308
                   weighted avg       0.81      0.83      0.82      8308
 |
| No log        | 2.98  | 285  | 0.1817          |                                  precision    recall  f1-score   support

                     Amino_acid       0.54      0.43      0.48        89
              Anatomical_system       0.00      0.00      0.00        41
                         Cancer       0.84      0.84      0.84      3620
                           Cell       0.00      0.00      0.00        11
             Cellular_component       0.00      0.00      0.00         7
Developing_anatomical_structure       0.00      0.00      0.00        37
           Gene_or_gene_product       0.90      0.92      0.91       540
   Immaterial_anatomical_entity       0.63      0.65      0.64        82
         Multi-tissue_structure       0.63      0.71      0.67       144
                          Organ       0.00      0.00      0.00        56
                       Organism       0.86      0.17      0.28        36
           Organism_subdivision       0.83      0.86      0.84      1086
             Organism_substance       0.87      0.81      0.84       484
         Pathological_formation       0.92      0.92      0.92      1430
                Simple_chemical       0.58      0.72      0.64       304
                         Tissue       0.79      0.82      0.80       341

                      micro avg       0.84      0.82      0.83      8308
                      macro avg       0.52      0.49      0.49      8308
                   weighted avg       0.82      0.82      0.82      8308
 |


### 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": "BioNLP13CG_SciBERT_NER", "results": []}]}
transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:allenai/scibert_scivocab_uncased autotrain_compatible endpoints_compatible region:us