---
library_name: transformers
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: 0.9310117181052979
    - name: Recall
      type: recall
      value: 0.9493436553349041
    - name: F1
      type: f1
      value: 0.9400883259728355
    - name: Accuracy
      type: accuracy
      value: 0.9857685288750221
---

<!-- 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: 0.0575
- Precision: 0.9310
- Recall: 0.9493
- F1: 0.9401
- Accuracy: 0.9858

## 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: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2212        | 0.5695 | 500  | 0.0748          | 0.8824    | 0.9167 | 0.8992 | 0.9791   |
| 0.0698        | 1.1390 | 1000 | 0.0596          | 0.9141    | 0.9387 | 0.9263 | 0.9836   |
| 0.0465        | 1.7084 | 1500 | 0.0627          | 0.9235    | 0.9411 | 0.9322 | 0.9846   |
| 0.0313        | 2.2779 | 2000 | 0.0593          | 0.9315    | 0.9497 | 0.9405 | 0.9858   |
| 0.0244        | 2.8474 | 2500 | 0.0575          | 0.9310    | 0.9493 | 0.9401 | 0.9858   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0