README.md

---
library_name: transformers
tags: []
---

#### 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](https://www.kaggle.com/datasets/nelgiriyewithana/emotions), 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
```python
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