distilbert-base-uncased-finetuned-emotions-dataset

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distilbert-base-uncased-finetuned-emotions-dataset

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

• Loss: 0.2428
• Accuracy: 0.9395
• F1: 0.9396

Model description

The model has been trained to classify text inputs into distinct emotional categories based on the fine-tuned understanding of the emotions dataset. The fine-tuned model has demonstrated high accuracy and F1 scores on the evaluation set.

Intended uses & limitations

Intended Uses

• Sentiment analysis
• Emotional classification in text
• Emotion-based recommendation systems

Limitations

• May show biases based on the training dataset
• Optimized for emotional classification and may not cover nuanced emotional subtleties

Training and evaluation data

Emotions dataset with labeled emotional categories here.

The emotional categories are as follows:

• LABEL_0: sadness
• LABEL_1: joy
• LABEL_2: love
• LABEL_3: anger
• LABEL_4: fear
• LABEL_5: surprise

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: 10

Training results

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0