---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
datasets:
- wikiann
model-index:
- name: xlm-roberta-base-finetuned-panx-all
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: wikiann
      type: wikiann
      config: en
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.843189280620875
      verified: true
    - name: Precision
      type: precision
      value: 0.8410061269097046
      verified: true
    - name: Recall
      type: recall
      value: 0.8568527450211155
      verified: true
    - name: F1
      type: f1
      value: 0.8488554853827908
      verified: true
    - name: loss
      type: loss
      value: 0.6632214784622192
      verified: true
---

<!-- 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-finetuned-panx-all

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/04_multilingual-ner.ipynb).

It achieves the following results on the evaluation set:
- Loss: 0.1739
- F1: 0.8581

## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2912        | 1.0   | 835  | 0.1883          | 0.8238 |
| 0.1548        | 2.0   | 1670 | 0.1738          | 0.8480 |
| 0.101         | 3.0   | 2505 | 0.1739          | 0.8581 |


### Framework versions

- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3