---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-WNUT-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wnut_17
      type: wnut_17
      config: wnut_17
      split: test
      args: wnut_17
    metrics:
    - name: Precision
      type: precision
      value: 0.5913669064748202
    - name: Recall
      type: recall
      value: 0.3809082483781279
    - name: F1
      type: f1
      value: 0.463359639233371
    - name: Accuracy
      type: accuracy
      value: 0.9500726682055228
---

<!-- 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-base-multilingual-cased-WNUT-ner

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3832
- Precision: 0.5914
- Recall: 0.3809
- F1: 0.4634
- Accuracy: 0.9501

## 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: 16
- eval_batch_size: 16
- 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   | 213  | 0.2791          | 0.6008    | 0.2817 | 0.3836 | 0.9427   |
| No log        | 2.0   | 426  | 0.2697          | 0.6520    | 0.3299 | 0.4382 | 0.9479   |
| 0.148         | 3.0   | 639  | 0.2846          | 0.5783    | 0.3661 | 0.4484 | 0.9492   |
| 0.148         | 4.0   | 852  | 0.3032          | 0.6248    | 0.3642 | 0.4602 | 0.9500   |
| 0.0413        | 5.0   | 1065 | 0.3355          | 0.5729    | 0.3568 | 0.4397 | 0.9495   |
| 0.0413        | 6.0   | 1278 | 0.3343          | 0.5714    | 0.3892 | 0.4631 | 0.9501   |
| 0.0413        | 7.0   | 1491 | 0.3522          | 0.5877    | 0.3818 | 0.4629 | 0.9500   |
| 0.0182        | 8.0   | 1704 | 0.3844          | 0.6120    | 0.3698 | 0.4610 | 0.9499   |
| 0.0182        | 9.0   | 1917 | 0.3847          | 0.5986    | 0.3828 | 0.4669 | 0.9504   |
| 0.008         | 10.0  | 2130 | 0.3832          | 0.5914    | 0.3809 | 0.4634 | 0.9501   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2