README.md

---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9342824035755669
    - name: Recall
      type: recall
      value: 0.9498485358465163
    - name: F1
      type: f1
      value: 0.9420011683217892
    - name: Accuracy
      type: accuracy
      value: 0.9861217401542356
language:
- en
library_name: transformers
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0636
- Precision: 0.9343
- Recall: 0.9498
- F1: 0.9420
- Accuracy: 0.9861

## Model description

This is a model for Named entity recognition NER

## Intended uses & limitations

Open source

## Training and evaluation data

The conll2003 dataset

## 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: 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0757        | 1.0   | 1756 | 0.0638          | 0.9215    | 0.9362 | 0.9288 | 0.9833   |
| 0.0352        | 2.0   | 3512 | 0.0667          | 0.9360    | 0.9482 | 0.9421 | 0.9858   |
| 0.0215        | 3.0   | 5268 | 0.0636          | 0.9343    | 0.9498 | 0.9420 | 0.9861   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1