---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_classify
  results: []
datasets:
- eriktks/conll2003
pipeline_tag: token-classification
library_name: flair
---

<!-- 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. -->

# token_classify

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0632
- Precision: 0.9295
- Recall: 0.9497
- F1: 0.9395
- Accuracy: 0.9857

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.077         | 1.0   | 1756 | 0.0641          | 0.9060    | 0.9340 | 0.9198 | 0.9816   |
| 0.0346        | 2.0   | 3512 | 0.0695          | 0.9234    | 0.9419 | 0.9326 | 0.9840   |
| 0.0211        | 3.0   | 5268 | 0.0632          | 0.9295    | 0.9497 | 0.9395 | 0.9857   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1