---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-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: 0.5496503496503496
    - name: Recall
      type: recall
      value: 0.36422613531047265
    - name: F1
      type: f1
      value: 0.4381270903010034
    - name: Accuracy
      type: accuracy
      value: 0.9468667179618706
---

<!-- 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. -->

# distilbert-base-multilingual-cased-WNUT-ner

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3516
- Precision: 0.5497
- Recall: 0.3642
- F1: 0.4381
- Accuracy: 0.9469

## 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  | 0.2727          | 0.6626    | 0.2530 | 0.3662 | 0.9402   |
| No log        | 2.0   | 426  | 0.2636          | 0.5895    | 0.2715 | 0.3718 | 0.9429   |
| 0.1729        | 3.0   | 639  | 0.2933          | 0.5931    | 0.3040 | 0.4020 | 0.9447   |
| 0.1729        | 4.0   | 852  | 0.2861          | 0.5437    | 0.3457 | 0.4227 | 0.9453   |
| 0.0503        | 5.0   | 1065 | 0.3270          | 0.5627    | 0.3494 | 0.4311 | 0.9455   |
| 0.0503        | 6.0   | 1278 | 0.3277          | 0.5451    | 0.3531 | 0.4286 | 0.9463   |
| 0.0503        | 7.0   | 1491 | 0.3471          | 0.5828    | 0.3457 | 0.4340 | 0.9467   |
| 0.0231        | 8.0   | 1704 | 0.3594          | 0.5801    | 0.3457 | 0.4332 | 0.9464   |
| 0.0231        | 9.0   | 1917 | 0.3550          | 0.5567    | 0.3503 | 0.4300 | 0.9467   |
| 0.0121        | 10.0  | 2130 | 0.3516          | 0.5497    | 0.3642 | 0.4381 | 0.9469   |


### Framework versions

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