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bert-finetuned-ner-wikiann

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bert-finetuned-ner-wikiann
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-wikiann
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: en
      split: validation
      args: en
    metrics:
    - name: Precision
      type: precision
      value: 0.817410347659458
    - name: Recall
      type: recall
      value: 0.8443376219425986
    - name: F1
      type: f1
      value: 0.830655817511649
    - name: Accuracy
      type: accuracy
      value: 0.9269314725039668
---

<!-- 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-wikiann

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3147
- Precision: 0.8174
- Recall: 0.8443
- F1: 0.8307
- Accuracy: 0.9269

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2826        | 1.0   | 2500 | 0.2833          | 0.7952    | 0.8265 | 0.8105 | 0.9205   |
| 0.2052        | 2.0   | 5000 | 0.2620          | 0.8013    | 0.8371 | 0.8188 | 0.9255   |
| 0.1356        | 3.0   | 7500 | 0.3147          | 0.8174    | 0.8443 | 0.8307 | 0.9269   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3