---
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-BERT-UNCASED-BIOBERT
  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: 0.9432138927426685
    - name: Recall
      type: recall
      value: 0.9667549279199764
    - name: F1
      type: f1
      value: 0.9548393345763614
    - name: Accuracy
      type: accuracy
      value: 0.976488513830286
---

<!-- 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-BERT-UNCASED-BIOBERT

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: 0.1163
- Precision: 0.9432
- Recall: 0.9668
- F1: 0.9548
- Accuracy: 0.9765

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4431        | 1.0   | 612  | 0.1173          | 0.9250    | 0.9596 | 0.9420 | 0.9709   |
| 0.139         | 2.0   | 1224 | 0.1097          | 0.9276    | 0.9724 | 0.9495 | 0.9728   |
| 0.0933        | 3.0   | 1836 | 0.0957          | 0.9451    | 0.9686 | 0.9567 | 0.9776   |
| 0.0751        | 4.0   | 2448 | 0.0972          | 0.9392    | 0.9733 | 0.9559 | 0.9771   |
| 0.0536        | 5.0   | 3060 | 0.0978          | 0.9445    | 0.9705 | 0.9573 | 0.9770   |
| 0.0468        | 6.0   | 3672 | 0.1044          | 0.9427    | 0.9661 | 0.9543 | 0.9766   |
| 0.0392        | 7.0   | 4284 | 0.1080          | 0.9396    | 0.9691 | 0.9541 | 0.9765   |
| 0.0376        | 8.0   | 4896 | 0.1151          | 0.9390    | 0.9696 | 0.9540 | 0.9761   |
| 0.0293        | 9.0   | 5508 | 0.1128          | 0.9429    | 0.9674 | 0.9550 | 0.9766   |
| 0.0274        | 10.0  | 6120 | 0.1163          | 0.9432    | 0.9668 | 0.9548 | 0.9765   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0