---
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: NER-finetuning-BBU-CM-V1
  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: 0.9299403078856425
    - name: Recall
      type: recall
      value: 0.9512587038028923
    - name: F1
      type: f1
      value: 0.9404787121372591
    - name: Accuracy
      type: accuracy
      value: 0.9771331458040319
---

<!-- 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. -->

# NER-finetuning-BBU-CM-V1

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: 0.1111
- Precision: 0.9299
- Recall: 0.9513
- F1: 0.9405
- Accuracy: 0.9771

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4331        | 1.0   | 612  | 0.1091          | 0.8914    | 0.9413 | 0.9156 | 0.9713   |
| 0.1313        | 2.0   | 1224 | 0.1077          | 0.8941    | 0.9494 | 0.9209 | 0.9718   |
| 0.0869        | 3.0   | 1836 | 0.0888          | 0.9308    | 0.9555 | 0.9430 | 0.9786   |
| 0.0726        | 4.0   | 2448 | 0.0957          | 0.9253    | 0.9578 | 0.9413 | 0.9767   |
| 0.0507        | 5.0   | 3060 | 0.0936          | 0.9287    | 0.9554 | 0.9419 | 0.9770   |
| 0.0451        | 6.0   | 3672 | 0.1051          | 0.9276    | 0.9538 | 0.9405 | 0.9762   |
| 0.0383        | 7.0   | 4284 | 0.1038          | 0.9218    | 0.9576 | 0.9394 | 0.9760   |
| 0.036         | 8.0   | 4896 | 0.1094          | 0.9245    | 0.9533 | 0.9387 | 0.9765   |
| 0.0284        | 9.0   | 5508 | 0.1082          | 0.9296    | 0.9516 | 0.9404 | 0.9768   |
| 0.0256        | 10.0  | 6120 | 0.1111          | 0.9299    | 0.9513 | 0.9405 | 0.9771   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3