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
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	250	0.3163	0.905	0.9042
No log	2.0	500	0.2197	0.9235	0.9237

Framework versions

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