README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - swiss_judgment_prediction
metrics:
  - accuracy
model-index:
  - name: xlm-roberta-large-xnli-finetuned-mnli-SJP
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: swiss_judgment_prediction
          type: swiss_judgment_prediction
          args: all_languages
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7957142857142857

xlm-roberta-large-xnli-finetuned-mnli-SJP

This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the swiss_judgment_prediction dataset. It achieves the following results on the evaluation set:

• Loss: 1.3456
• Accuracy: 0.7957

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	5	1.8460	0.7956
No log	2.0	10	1.3456	0.7957
No log	3.0	15	1.2799	0.7957
No log	4.0	20	1.2866	0.7957
No log	5.0	25	1.3162	0.7956

Framework versions

• Transformers 4.20.0
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1