---
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: 0.656957928802589
    - name: Recall
      type: recall
      value: 0.48564593301435405
    - name: F1
      type: f1
      value: 0.5584594222833563
    - name: Accuracy
      type: accuracy
      value: 0.933951453276383
---

<!-- 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: 0.3577
- Precision: 0.6570
- Recall: 0.4856
- F1: 0.5585
- Accuracy: 0.9340

## 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  | 0.3191          | 0.6465    | 0.4091 | 0.5011 | 0.9273   |
| 0.1595        | 2.0   | 850  | 0.3320          | 0.6719    | 0.4629 | 0.5482 | 0.9316   |
| 0.0681        | 3.0   | 1275 | 0.3577          | 0.6570    | 0.4856 | 0.5585 | 0.9340   |


### Framework versions

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