readme: ---
  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
  