---
license: cc-by-nc-sa-4.0
base_model: Babelscape/wikineural-multilingual-ner
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-Colab
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: validation
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.6477093206951027
    - name: Recall
      type: recall
      value: 0.4904306220095694
    - name: F1
      type: f1
      value: 0.5582028590878148
    - name: Accuracy
      type: accuracy
      value: 0.9344202521095948
---

<!-- 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-Colab

This model is a fine-tuned version of [Babelscape/wikineural-multilingual-ner](https://huggingface.co/Babelscape/wikineural-multilingual-ner) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4102
- Precision: 0.6477
- Recall: 0.4904
- F1: 0.5582
- Accuracy: 0.9344

## 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   | 425  | 0.3037          | 0.5963    | 0.5072 | 0.5482 | 0.9321   |
| 0.0672        | 2.0   | 850  | 0.3751          | 0.6604    | 0.4653 | 0.5460 | 0.9316   |
| 0.0451        | 3.0   | 1275 | 0.4102          | 0.6477    | 0.4904 | 0.5582 | 0.9344   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3