---
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: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9415670650730412
    - name: Recall
      type: recall
      value: 0.9545607539548974
    - name: F1
      type: f1
      value: 0.9480193882667558
    - name: Accuracy
      type: accuracy
      value: 0.9869311826690998
---

<!-- 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: 0.0822
- Precision: 0.9416
- Recall: 0.9546
- F1: 0.9480
- Accuracy: 0.9869

## 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: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0911        | 1.0   | 1756  | 0.0656          | 0.9223    | 0.9372 | 0.9297 | 0.9827   |
| 0.0342        | 2.0   | 3512  | 0.0667          | 0.9259    | 0.9456 | 0.9356 | 0.9851   |
| 0.0203        | 3.0   | 5268  | 0.0705          | 0.9195    | 0.9419 | 0.9306 | 0.9837   |
| 0.0143        | 4.0   | 7024  | 0.0685          | 0.9340    | 0.9500 | 0.9419 | 0.9858   |
| 0.0083        | 5.0   | 8780  | 0.0775          | 0.9362    | 0.9515 | 0.9438 | 0.9864   |
| 0.0027        | 6.0   | 10536 | 0.0822          | 0.9416    | 0.9546 | 0.9480 | 0.9869   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3