---
language: 
  - hi
  - en
datasets: 
  - utkarsharora100/google_go_emotions_hindi_translated
metrics: 
  - accuracy: 0.8085
  - precision: 0.7996
  - recall: 0.8085
  - f1: 0.7983
  - confusion_matrix:
      anger: [2, 0, 0, 1, 2, 0]
      disgust: [0, 1, 0, 0, 0, 0]
      joy: [0, 0, 6, 0, 0, 0]
      surprise: [0, 0, 0, 3, 1, 0]
      neutral: [1, 0, 1, 0, 23, 1]
      sadness: [0, 0, 0, 0, 2, 3]
  - classification_report:
      anger:
        precision: 0.67
        recall: 0.40
        f1-score: 0.50
        support: 5
      disgust:
        precision: 1.00
        recall: 1.00
        f1-score: 1.00
        support: 1
      joy:
        precision: 0.86
        recall: 1.00
        f1-score: 0.92
        support: 6
      surprise:
        precision: 0.82
        recall: 0.88
        f1-score: 0.85
        support: 26
      neutral:
        precision: 0.75
        recall: 0.60
        f1-score: 0.67
        support: 5
      sadness:
        precision: 0.75
        recall: 0.75
        f1-score: 0.75
        support: 4
      weighted_avg:
        precision: 0.80
        recall: 0.81
        f1-score: 0.80
        support: 47

model_name: "vashuag/HindiEmotion"
base_model: "ai4bharat/indic-bert"
pipeline_tag: "text-classification"
tags:
  - emotion-detection
  - hindi
  - huggingface
  - text-classification

training:
  - epochs: 10
  - batch_size: 16
  - learning_rate: 1e-5

resources:
  - colab_demo: "https://colab.research.google.com/drive/1OaXK2L-2A7adFv-lcEDHcHwKiR22O3Je?usp=sharing"
  - kaggle_notebook: "https://www.kaggle.com/code/vashuagarwal/emotion-indicbert"

summary: |
  The model achieved its best performance on Epoch 5, with an accuracy of 0.6997, F1 score of 0.6750, precision of 0.6761, recall of 0.6997, and ROC AUC of 0.8207. 
  The model shows stable performance across later epochs, with slight fluctuations in metrics but generally consistent results.

usage: |
  ```python
  from transformers import pipeline

  # Load the model pipeline
  emotion_model = pipeline("text-classification", model="vashuag/HindiEmotion", return_all_scores=True)

  # Example prediction
  text = "आप बहुत अच्छे हैं"  # Translation: "You are very good."
  predictions = emotion_model(text)
  print(predictions)