platzi-distilroberta-base-mrpc-glue-joselier

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platzi-distilroberta-base-mrpc-glue-joselier

This model is a fine-tuned version of distilroberta-base on the glue and the mrpc datasets. It achieves the following results on the evaluation set:

• Loss: 0.5993
• Accuracy: 0.8211
• F1: 0.8809

Model description

This model uses transfer learning to classify 2 sentences (a string of 2 sentences separated by a comma) in "Equivalent" or "Not Equivalent". The model platzi-distilroberta-base-mrpc-glue-joselier was programmed as part of a class from Platzi's course "Curso de Transfer Learning con Hugging Face"

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3