avanishd/distilbert-base-uncased-finetuned-emotion

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distilbert-base-uncased-finetuned-emotion

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: 0.2197
• —Accuracy: 0.9235
• —F1: 0.9237

Model description

This model can classify English text into one of six emotion categories: sadness, joy, love, anger, fear, and surprise.

Intended uses & limitations

More information needed

How to Use

from transformers import pipeline

model_name = "avanishd/distilbert-base-uncased-finetuned-emotion"

classifier = pipeline("text-classification", model=model_name)

text = "I am happy"

prediction = classifier(text)

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-05
• —trainbatchsize: 64
• —evalbatchsize: 64
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 2

Training results

Framework versions

• —Transformers 4.50.3
• —Pytorch 2.6.0+cu124
• —Datasets 3.5.0
• —Tokenizers 0.21.1