Update README.md

README.md

@@ -1,26 +1,88 @@
 ---
-datasets:
-- utkarsharora100/google_go_emotions_hindi_translated
-language:
-- hi
-- en
-metrics:
-- accuracy 0.6997
-- f1 0.6750
-- precision 0.6761
-- recall 0.6997
-- roc_auc 0.8207
-base_model:
-- ai4bharat/indic-bert
-pipeline_tag: text-classification
----
-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.
-Training Details
-Model: ai4bharat/indic-bert
-Dataset: Google Go Emotions Hindi Translated
-Labels: Anger, Disgust, Fear, Joy, Neutral, Sadness, Surprise
-Epochs: 10
-Batch Size: 16
-Learning Rate: 1e-5
+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)