---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: results
  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: 0.9307273626917367
    - name: Recall
      type: recall
      value: 0.9496802423426456
    - name: F1
      type: f1
      value: 0.9401082882132445
    - name: Accuracy
      type: accuracy
      value: 0.9863866486136458
---

<!-- 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. -->

# results

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: 0.0635
- Precision: 0.9307
- Recall: 0.9497
- F1: 0.9401
- Accuracy: 0.9864

## 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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2313        | 0.2847 | 500  | 0.1403          | 0.8444    | 0.8696 | 0.8568 | 0.9626   |
| 0.1088        | 0.5695 | 1000 | 0.0887          | 0.8717    | 0.9098 | 0.8903 | 0.9765   |
| 0.1211        | 0.8542 | 1500 | 0.0846          | 0.9076    | 0.9238 | 0.9156 | 0.9784   |
| 0.0503        | 1.1390 | 2000 | 0.0753          | 0.9101    | 0.9354 | 0.9226 | 0.9814   |
| 0.0493        | 1.4237 | 2500 | 0.0630          | 0.9170    | 0.9421 | 0.9294 | 0.9833   |
| 0.0624        | 1.7084 | 3000 | 0.0705          | 0.9277    | 0.9366 | 0.9321 | 0.9837   |
| 0.0313        | 1.9932 | 3500 | 0.0675          | 0.9270    | 0.9426 | 0.9347 | 0.9843   |
| 0.0335        | 2.2779 | 4000 | 0.0661          | 0.9284    | 0.9492 | 0.9387 | 0.9857   |
| 0.0098        | 2.5626 | 4500 | 0.0693          | 0.9347    | 0.9473 | 0.9410 | 0.9849   |
| 0.0099        | 2.8474 | 5000 | 0.0635          | 0.9307    | 0.9497 | 0.9401 | 0.9864   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1