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bert-finetuned-ner

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bert-finetuned-ner
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---

license: apache-2.0
base_model: distilbert/distilbert-base-cased
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: 0.9223001949317738
    - name: Recall
      type: recall
      value: 0.9191824999028636
    - name: F1
      type: f1
      value: 0.9207387082335999
    - name: Accuracy
      type: accuracy
      value: 0.9606758109142285
---


<!-- 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 [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1640
- Precision: 0.9223
- Recall: 0.9192
- F1: 0.9207
- Accuracy: 0.9607

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1926        | 1.0   | 1756 | 0.1809          | 0.9104    | 0.9056 | 0.9080 | 0.9543   |
| 0.1318        | 2.0   | 3512 | 0.1622          | 0.9200    | 0.9156 | 0.9178 | 0.9592   |
| 0.0933        | 3.0   | 5268 | 0.1640          | 0.9223    | 0.9192 | 0.9207 | 0.9607   |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.2.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1