README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - clinc_oos
metrics:
  - accuracy
model-index:
  - name: roberta-large-finetuned-clinc-123
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: clinc_oos
          type: clinc_oos
          args: plus
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.925483870967742

roberta-large-finetuned-clinc-123

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

• Loss: 0.7226
• Accuracy: 0.9255

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: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: sagemaker_data_parallel
• num_devices: 8
• total_train_batch_size: 128
• total_eval_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.0576	1.0	120	5.0269	0.0068
4.5101	2.0	240	2.9324	0.7158
1.9757	3.0	360	0.7226	0.9255

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.2+cu113
• Datasets 1.18.4
• Tokenizers 0.11.6