---
license: mit
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-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: 0.7013977128335451
    - name: Recall
      type: recall
      value: 0.5115848007414272
    - name: F1
      type: f1
      value: 0.5916398713826366
    - name: Accuracy
      type: accuracy
      value: 0.9570402667350603
---

<!-- 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-large-WNUT-ner

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3570
- Precision: 0.7014
- Recall: 0.5116
- F1: 0.5916
- Accuracy: 0.9570

## 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  | 0.2223          | 0.5588    | 0.4495 | 0.4982 | 0.9504   |
| No log        | 2.0   | 426  | 0.2326          | 0.6602    | 0.4430 | 0.5302 | 0.9514   |
| 0.1516        | 3.0   | 639  | 0.2792          | 0.6846    | 0.4124 | 0.5147 | 0.9520   |
| 0.1516        | 4.0   | 852  | 0.2417          | 0.6510    | 0.5134 | 0.5741 | 0.9574   |
| 0.0427        | 5.0   | 1065 | 0.2954          | 0.6850    | 0.4856 | 0.5683 | 0.9544   |
| 0.0427        | 6.0   | 1278 | 0.3033          | 0.6761    | 0.4893 | 0.5677 | 0.9557   |
| 0.0427        | 7.0   | 1491 | 0.3502          | 0.7007    | 0.4838 | 0.5724 | 0.9563   |
| 0.0178        | 8.0   | 1704 | 0.3712          | 0.6995    | 0.4875 | 0.5745 | 0.9563   |
| 0.0178        | 9.0   | 1917 | 0.3541          | 0.6951    | 0.4986 | 0.5807 | 0.9569   |
| 0.0068        | 10.0  | 2130 | 0.3570          | 0.7014    | 0.5116 | 0.5916 | 0.9570   |


### Framework versions

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