---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-large-uncased
model-index:
- name: bert-large-uncased-en-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - type: precision
      value: 0.9094335178424214
      name: Precision
    - type: recall
      value: 0.9164490861618799
      name: Recall
    - type: f1
      value: 0.9129278240822842
      name: F1
    - type: accuracy
      value: 0.979076030660866
      name: Accuracy
---

<!-- 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-large-uncased-en-ner

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1402
- Precision: 0.9094
- Recall: 0.9164
- F1: 0.9129
- Accuracy: 0.9791

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0787        | 1.0   | 1756 | 0.1122          | 0.8932    | 0.9046 | 0.8989 | 0.9761   |
| 0.0406        | 2.0   | 3512 | 0.1312          | 0.9042    | 0.9124 | 0.9083 | 0.9777   |
| 0.0177        | 3.0   | 5268 | 0.1402          | 0.9094    | 0.9164 | 0.9129 | 0.9791   |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2