---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-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: 0.9437070282658518
    - name: Recall
      type: recall
      value: 0.9691575953711876
    - name: F1
      type: f1
      value: 0.9562630025642267
    - name: Accuracy
      type: accuracy
      value: 0.977555086732302
---

<!-- 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-uncased-finetuned-ner

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1107
- Precision: 0.9437
- Recall: 0.9692
- F1: 0.9563
- Accuracy: 0.9776

## 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
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4381        | 1.0   | 612  | 0.1172          | 0.9235    | 0.9536 | 0.9383 | 0.9689   |
| 0.1389        | 2.0   | 1224 | 0.1117          | 0.9247    | 0.9731 | 0.9483 | 0.9717   |
| 0.0935        | 3.0   | 1836 | 0.0962          | 0.9433    | 0.9662 | 0.9546 | 0.9769   |
| 0.0758        | 4.0   | 2448 | 0.0926          | 0.9408    | 0.9736 | 0.9569 | 0.9771   |
| 0.0536        | 5.0   | 3060 | 0.0958          | 0.9404    | 0.9722 | 0.9561 | 0.9769   |
| 0.0476        | 6.0   | 3672 | 0.1029          | 0.9418    | 0.9681 | 0.9548 | 0.9761   |
| 0.0395        | 7.0   | 4284 | 0.1023          | 0.9425    | 0.9720 | 0.9570 | 0.9769   |
| 0.0375        | 8.0   | 4896 | 0.1091          | 0.9426    | 0.9695 | 0.9559 | 0.9771   |
| 0.0299        | 9.0   | 5508 | 0.1080          | 0.9451    | 0.9693 | 0.9570 | 0.9778   |
| 0.0266        | 10.0  | 6120 | 0.1107          | 0.9437    | 0.9692 | 0.9563 | 0.9776   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3