metadata

library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: distilbert-base-uncased-finetuned-emotion
    results:
      - task:
          type: text-classification
        dataset:
          name: dar-ai/emotion
          type: dair-ai/emotion
        metrics:
          - name: f1
            type: f1
            value: 0.9237
datasets:
  - dair-ai/emotion
language:
  - en
pipeline_tag: text-classification

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