---
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: 0.34523809523809523
    - name: Recall
      type: recall
      value: 0.5420560747663551
    - name: F1
      type: f1
      value: 0.4218181818181818
    - name: Accuracy
      type: accuracy
      value: 0.8688172043010752
---

<!-- 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: 0.5677
- Precision: 0.3452
- Recall: 0.5421
- F1: 0.4218
- Accuracy: 0.8688

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 13   | 0.5728          | 0.2077    | 0.3551 | 0.2621 | 0.8199   |
| No log        | 2.0   | 26   | 0.5687          | 0.2889    | 0.3645 | 0.3223 | 0.8312   |
| No log        | 3.0   | 39   | 0.5447          | 0.2857    | 0.4486 | 0.3491 | 0.8425   |
| No log        | 4.0   | 52   | 0.5509          | 0.2881    | 0.4766 | 0.3592 | 0.8489   |
| No log        | 5.0   | 65   | 0.5751          | 0.3121    | 0.4579 | 0.3712 | 0.8511   |
| No log        | 6.0   | 78   | 0.5358          | 0.3851    | 0.5794 | 0.4627 | 0.8667   |
| No log        | 7.0   | 91   | 0.5484          | 0.3491    | 0.5514 | 0.4275 | 0.8645   |
| No log        | 8.0   | 104  | 0.5671          | 0.3580    | 0.5421 | 0.4312 | 0.8672   |
| No log        | 9.0   | 117  | 0.5666          | 0.3494    | 0.5421 | 0.4249 | 0.8688   |
| No log        | 10.0  | 130  | 0.5677          | 0.3452    | 0.5421 | 0.4218 | 0.8688   |


### Framework versions

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