---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-en-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9000587199060481
    - name: Recall
      type: recall
      value: 0.909565630192262
    - name: F1
      type: f1
      value: 0.9047872026444719
    - name: Accuracy
      type: accuracy
      value: 0.977246046543747
language:
- en
library_name: transformers
---

<!-- 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-base-uncased-en-ner

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: 0.1434
- Precision: 0.9001
- Recall: 0.9096
- F1: 0.9048
- Accuracy: 0.9772

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

The model was trained on data that follows the [`IOB`](<https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)>) convention. Full tagset with indices:

```python
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0759        | 1.0   | 1756 | 0.1246          | 0.8878    | 0.8973 | 0.8925 | 0.9744   |
| 0.0299        | 2.0   | 3512 | 0.1427          | 0.8911    | 0.9040 | 0.8975 | 0.9749   |
| 0.0152        | 3.0   | 5268 | 0.1434          | 0.9001    | 0.9096 | 0.9048 | 0.9772   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2