---
base_model:
- FacebookAI/xlm-roberta-large
datasets:
- google/xtreme
language:
- en
library_name: transformers
license: mit
metrics:
- f1
- accuracy
- precision
- recall
pipeline_tag: token-classification
model-index:
- name: xlm-roberta-large-panx-en
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: google/xtreme
      type: google/xtreme
      config: PAN-X.en
      split: validation
    metrics:
    - type: precision
      value: 0.834659287443791
      name: Precision
    - type: recall
      value: 0.852891276685989
      name: Recall
    - type: f1
      value: 0.8436767945176742
      name: F1
    - type: accuracy
      value: 0.9357306049468561
      name: Accuracy
---

# XLM-RoBERTa-Large-PANX-WikiAnn-en

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the [google/xtreme](https://huggingface.co/datasets/google/xtreme) dataset (English split of the PAN-X).
It achieves the following results on the evaluation set:
- Loss: 0.2569
- Precision: 0.8347
- Recall: 0.8529
- F1: 0.8437
- Accuracy: 0.9357

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1