---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased_conll2003-sm-all-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9487479131886477
    - name: Recall
      type: recall
      value: 0.9564119824974756
    - name: F1
      type: f1
      value: 0.9525645323499833
    - name: Accuracy
      type: accuracy
      value: 0.9916085822203186
---

<!-- 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_conll2003-sm-all-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0489
- Precision: 0.9487
- Recall: 0.9564
- F1: 0.9526
- Accuracy: 0.9916

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.052         | 1.0   | 3511  | 0.0510          | 0.9374    | 0.9456 | 0.9415 | 0.9898   |
| 0.0213        | 2.0   | 7022  | 0.0497          | 0.9484    | 0.9519 | 0.9501 | 0.9911   |
| 0.0099        | 3.0   | 10533 | 0.0489          | 0.9487    | 0.9564 | 0.9526 | 0.9916   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1