---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: biobert_json
      type: biobert_json
      config: Biobert_json
      split: validation
      args: Biobert_json
    metrics:
    - name: Precision
      type: precision
      value: 0.941812865497076
    - name: Recall
      type: recall
      value: 0.966852487135506
    - name: F1
      type: f1
      value: 0.9541684299619129
    - name: Accuracy
      type: accuracy
      value: 0.9754933560689555
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-cased-finetuned-ner

This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1119
- Precision: 0.9418
- Recall: 0.9669
- F1: 0.9542
- Accuracy: 0.9755

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1824        | 1.0   | 1224 | 0.1170          | 0.9227    | 0.9563 | 0.9392 | 0.9686   |
| 0.1162        | 2.0   | 2448 | 0.1138          | 0.9277    | 0.9654 | 0.9462 | 0.9717   |
| 0.0756        | 3.0   | 3672 | 0.1025          | 0.9398    | 0.9685 | 0.9540 | 0.9751   |
| 0.051         | 4.0   | 4896 | 0.1076          | 0.9425    | 0.9691 | 0.9556 | 0.9759   |
| 0.0423        | 5.0   | 6120 | 0.1119          | 0.9418    | 0.9669 | 0.9542 | 0.9755   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3