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# token-classification-bert-base-uncased

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0480
- Precision: 0.9466
- Recall: 0.9544
- F1: 0.9505
- Accuracy: 0.9899

## 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
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "token-classification-bert-base-uncased", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9465865464863963, "name": "Precision"}, {"type": "recall", "value": 0.9543924604510265, "name": "Recall"}, {"type": "f1", "value": 0.9504734769127628, "name": "F1"}, {"type": "accuracy", "value": 0.9898757836532845, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:conll2003 base_model:bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us