Overview
A DistilBERT trained model for sentence sentiment classification. Classifies sentences into 6 emotions - sadness, joy, love, anger, fear, surprise.
Dataset used for model
Model trained on Emotions Dataset, which is based on English Twitter messages and has 6 labels.
How the model was created
The model was trained using DistilBertForSequenceClassification.from_pretrained with problem_type="single_label_classification" for 10 epochs with a learning rate of 5e-5 and weight decay of 0.01.
Inference
from transformers import pipeline
classifier = pipeline("text-classification", model="entfane/distilbert-emotion-recognition")
text_to_predict = ["I hate going there, it is so boring", "That's so wonderful!"]
result = classifier(text_to_predict)
print(result) # contains a list of dictionaries (one for each output)
Summary
Evaluation of output using dataset test split gives:
- Accuracy: 0.942
- Precision: 0.950
- Recall: 0.942
- F1: 0.942
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