RoBERTa-base-finetuned-emotion

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RoBERTa-base-finetuned-emotion

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

• Loss: 0.1629
• Accuracy: 0.933
• Precision: 0.8945
• Recall: 0.9002
• F1: 0.8968

Model description

This is a RoBERTa model fine-tuned on the emotion to determine whether a text is within any of the six categories: 'sadness', 'joy', 'love', 'anger', 'fear', 'surprise'. The Trainer API was used to train the model.

Intended uses & limitations

Training and evaluation data

🤗 load_dataset package was used to load the data from the hub.

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: 5

Training results

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3