---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme_en
metrics:
- accuracy
- f1
widget:
- text: "My name is Julia, I study at Imperial College, in London"
  example_title: "Example 1"
- text: "My name is Sarah and I live in Paris"
  example_title: "Example 2"
- text: "My name is Clara and I live in Berkeley, California"
  example_title: "Example 3"
model-index:
- name: XLM-RoBERTa-xtreme-en
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xtreme_en
      type: xtreme_en
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9109484079686702
    - name: F1
      type: f1
      value: 0.7544312444026322
---

<!-- 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-xtreme-en

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme_en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2838
- Accuracy: 0.9109
- F1: 0.7544

## 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: 32
- eval_batch_size: 32
- 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 | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6502        | 1.0   | 235  | 0.3328          | 0.8995   | 0.7251 |
| 0.3239        | 2.0   | 470  | 0.2897          | 0.9101   | 0.7473 |
| 0.2644        | 3.0   | 705  | 0.2838          | 0.9109   | 0.7544 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1