---
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased
  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: 0.935222001325381
    - name: Recall
      type: recall
      value: 0.9500168293503871
    - name: F1
      type: f1
      value: 0.9425613624979129
    - name: Accuracy
      type: accuracy
      value: 0.985915700241361
---

<!-- 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-cased

This model was trained from scratch on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0646
- Precision: 0.9352
- Recall: 0.9500
- F1: 0.9426
- Accuracy: 0.9859

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0771        | 1.0   | 1756 | 0.0778          | 0.9094    | 0.9323 | 0.9207 | 0.9792   |
| 0.0406        | 2.0   | 3512 | 0.0575          | 0.9314    | 0.9502 | 0.9407 | 0.9860   |
| 0.0226        | 3.0   | 5268 | 0.0646          | 0.9352    | 0.9500 | 0.9426 | 0.9859   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1