---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: train
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9427525378598769
    - name: Recall
      type: recall
      value: 0.9533826994278021
    - name: F1
      type: f1
      value: 0.9480378211028366
    - name: Accuracy
      type: accuracy
      value: 0.9866957084829575
---

<!-- 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-finetuned-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: 0.1128
- Precision: 0.9428
- Recall: 0.9534
- F1: 0.9480
- Accuracy: 0.9867

## 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0937        | 1.0   | 1756  | 0.0660          | 0.9179    | 0.9332 | 0.9255 | 0.9825   |
| 0.0378        | 2.0   | 3512  | 0.0766          | 0.9246    | 0.9451 | 0.9348 | 0.9843   |
| 0.0245        | 3.0   | 5268  | 0.0667          | 0.9241    | 0.9409 | 0.9325 | 0.9843   |
| 0.017         | 4.0   | 7024  | 0.0712          | 0.9343    | 0.9505 | 0.9424 | 0.9863   |
| 0.0143        | 5.0   | 8780  | 0.0898          | 0.9366    | 0.9492 | 0.9428 | 0.9855   |
| 0.0049        | 6.0   | 10536 | 0.0964          | 0.9294    | 0.9482 | 0.9387 | 0.9853   |
| 0.0039        | 7.0   | 12292 | 0.1001          | 0.9353    | 0.9512 | 0.9432 | 0.9860   |
| 0.0036        | 8.0   | 14048 | 0.1002          | 0.9388    | 0.9522 | 0.9454 | 0.9862   |
| 0.0018        | 9.0   | 15804 | 0.1049          | 0.9363    | 0.9495 | 0.9428 | 0.9861   |
| 0.0019        | 10.0  | 17560 | 0.1191          | 0.9375    | 0.9497 | 0.9436 | 0.9849   |
| 0.0008        | 11.0  | 19316 | 0.1083          | 0.9396    | 0.9530 | 0.9463 | 0.9864   |
| 0.0003        | 12.0  | 21072 | 0.1064          | 0.9419    | 0.9530 | 0.9475 | 0.9864   |
| 0.0004        | 13.0  | 22828 | 0.1091          | 0.9448    | 0.9527 | 0.9487 | 0.9865   |
| 0.0006        | 14.0  | 24584 | 0.1132          | 0.9464    | 0.9542 | 0.9503 | 0.9867   |
| 0.0004        | 15.0  | 26340 | 0.1128          | 0.9428    | 0.9534 | 0.9480 | 0.9867   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1