README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - xtreme
metrics:
  - f1
model-index:
  - name: xml-roberta-base-finetuned-panx-fr
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xtreme
          type: xtreme
          args: PAN-X.fr
        metrics:
          - name: F1
            type: f1
            value: 0.8393729984830608
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: xtreme
          type: xtreme
          config: PAN-X.fr
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.850120764491739
            verified: true
          - name: Precision
            type: precision
            value: 0.8471802714586121
            verified: true
          - name: Recall
            type: recall
            value: 0.8521433699107784
            verified: true
          - name: F1
            type: f1
            value: 0.8496545729934237
            verified: true
          - name: loss
            type: loss
            value: 0.5212014317512512
            verified: true

xml-roberta-base-finetuned-panx-fr

This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset. It achieves the following results on the evaluation set:

• Loss: 0.2691
• F1: 0.8394

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
No log	1.0	191	0.3150	0.7993
No log	2.0	382	0.2799	0.8213
No log	3.0	573	0.2691	0.8394

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0
• Datasets 2.1.0
• Tokenizers 0.12.1