Model Card for aref-j/emotion-classifier-bert-fa-v1
This is a fine-tuned BERT model for classifying emotions in Persian text, specifically detecting 6 emotion categories: ANGRY, FEAR, HAPPY, HATE, SAD, SURPRISE. It was developed using a merged dataset of Persian emotion corpora and is designed for applications like sentiment analysis on Persian tweets.
Model Details
Model Description
This model is a fine-tuned version of ParsBERT (HooshvareLab/bert-base-parsbert-uncased) for emotion classification in Persian text. It uses a BERT base architecture with a sequence classification head to predict one of six emotion labels from input text. The model addresses class imbalance through weighted cross-entropy loss and was trained on a combined dataset of Persian tweets and short texts.
Developed by: Aref Jafary
Model type: Text classification (fine-tuned BERT)
Language(s) (NLP): Persian (fa)
License: MIT
Finetuned from model: HooshvareLab/bert-base-parsbert-uncased
1from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
23model_name ="aref-j/emotion-classifier-bert-fa-v1"45# Load tokenizer and model6tokenizer = AutoTokenizer.from_pretrained(model_name)7model = AutoModelForSequenceClassification.from_pretrained(model_name)89# Create the classification pipeline10classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)1112# Example usage13result = classifier("چه هوای زیبایی امروز است")14print(result)# e.g. [{'label': 'HAPPY', 'score': 0.99}]
Training Details
Training Data
The model was trained on a merged dataset from three Persian emotion corpora:
ArmanEmo: Over 7,000 Persian sentences labeled for 7 emotions., GitHub
EmoPars: 30,000 Persian tweets labeled with 6 basic emotions (Anger, Fear, Happiness, Sadness, Hatred, Wonder).GitHub
ShortPersianEmo: 5,472 short Persian texts labeled for 5 emotions (angry, sad, fear, happy, neutral). GitHub
Datasets were standardized, cleaned (normalization with Parsivar, removal of URLs, mentions, emojis, etc.), deduplicated, and split into 90% train / 10% validation, with ArmanEmo held out for testing.
Training Procedure
Preprocessing
Text was normalized using Parsivar, with character mapping, diacritic removal, and stripping of URLs, mentions, hashtags, emojis, punctuation, digits, and extra spaces. Multi-label instances in EmoPars were converted to single-label via dominant label.
Training Hyperparameters
Training regime: fp32 (assumed, not specified)
Batch size: 32
Epochs: 6
Learning rate: 1e-5
Optimizer: Not specified (default Hugging Face Trainer)
Loss: Weighted cross-entropy to handle class imbalance
Early stopping: After 2 epochs without validation loss improvement
Evaluation
Testing Data, Factors & Metrics
Testing Data
Held-out ArmanEmo test set.
Factors
Evaluation disaggregated by emotion classes (ANGRY, FEAR, HAPPY, HATE, SAD, SURPRISE).
Metrics
Accuracy (overall correct predictions), Macro F1-score (average F1 across classes, treating all equally), Precision, Recall, and Confusion Matrix.
Results
Test Accuracy: 70.88%
Macro F1-Score: 66.35%
Detailed per-class metrics and confusion matrix available in the repository.
BibTeX:
@misc{jafary2023persianemotion,
author = {Aref Jafary},
title = {Persian Emotion Classification with BERT},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ArefJafary/Persian-Emotion-Classification-BERT}}
}
