---
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: bert-finetuned-ner-tokenclass
  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: 0.845360824742268
    - name: Recall
      type: recall
      value: 0.8986301369863013
    - name: F1
      type: f1
      value: 0.8711819389110226
    - name: Accuracy
      type: accuracy
      value: 0.9742014742014742
---

<!-- 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. -->

# bert-finetuned-ner-tokenclass

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: 0.0920
- Precision: 0.8454
- Recall: 0.8986
- F1: 0.8712
- Accuracy: 0.9742

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 125  | 0.1217          | 0.7805    | 0.8575 | 0.8172 | 0.9678   |
| No log        | 2.0   | 250  | 0.0968          | 0.8394    | 0.8877 | 0.8628 | 0.9713   |
| No log        | 3.0   | 375  | 0.0920          | 0.8454    | 0.8986 | 0.8712 | 0.9742   |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.22.2