---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: ace
      split: validation
      args: ace
    metrics:
    - name: Precision
      type: precision
      value: 0.20394736842105263
    - name: Recall
      type: recall
      value: 0.2897196261682243
    - name: F1
      type: f1
      value: 0.23938223938223938
    - name: Accuracy
      type: accuracy
      value: 0.817741935483871
---

<!-- 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 wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6372
- Precision: 0.2039
- Recall: 0.2897
- F1: 0.2394
- Accuracy: 0.8177

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 13   | 0.7383          | 0.1463    | 0.1121 | 0.1270 | 0.7737   |
| No log        | 2.0   | 26   | 0.6586          | 0.1618    | 0.2056 | 0.1811 | 0.8075   |
| No log        | 3.0   | 39   | 0.6372          | 0.2039    | 0.2897 | 0.2394 | 0.8177   |


### Framework versions

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