---
license: mit
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-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: 0.6251511487303507
    - name: Recall
      type: recall
      value: 0.47914735866543096
    - name: F1
      type: f1
      value: 0.5424973767051418
    - name: Accuracy
      type: accuracy
      value: 0.952295460374455
---

<!-- 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. -->

# xlm-roberta-base-WNUT-ner

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3376
- Precision: 0.6252
- Recall: 0.4791
- F1: 0.5425
- Accuracy: 0.9523

## 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  | 0.2787          | 0.5650    | 0.3383 | 0.4232 | 0.9418   |
| No log        | 2.0   | 426  | 0.2535          | 0.6225    | 0.4004 | 0.4873 | 0.9485   |
| 0.177         | 3.0   | 639  | 0.2773          | 0.6594    | 0.3911 | 0.4910 | 0.9497   |
| 0.177         | 4.0   | 852  | 0.2651          | 0.6098    | 0.4708 | 0.5314 | 0.9526   |
| 0.0551        | 5.0   | 1065 | 0.3076          | 0.6026    | 0.4652 | 0.5251 | 0.9514   |
| 0.0551        | 6.0   | 1278 | 0.3031          | 0.6343    | 0.4662 | 0.5374 | 0.9531   |
| 0.0551        | 7.0   | 1491 | 0.3319          | 0.6336    | 0.4680 | 0.5384 | 0.9523   |
| 0.0276        | 8.0   | 1704 | 0.3430          | 0.6508    | 0.4560 | 0.5362 | 0.9526   |
| 0.0276        | 9.0   | 1917 | 0.3342          | 0.6138    | 0.4773 | 0.5370 | 0.9521   |
| 0.0157        | 10.0  | 2130 | 0.3376          | 0.6252    | 0.4791 | 0.5425 | 0.9523   |


### Framework versions

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