Update README.md

README.md

@@ -60,7 +60,7 @@ import torch
 classifier = pipeline(
     "text-classification", 
     model="cirimus/modernbert-large-go-emotions",
-    return_all_scores=True
+    top_k=5
 )
 
 text = "I am so happy and excited about this opportunity!"
@@ -72,15 +72,15 @@ top_5 = sorted_preds[:5]
 
 print("\nTop 5 emotions detected:")
 for pred in top_5:
-    print(f"{pred['label']}: {pred['score']:.3f}")
+    print(f"\t{pred['label']:10s} : {pred['score']:.3f}")
 
 # Example output:
 # Top 5 emotions detected:
-# joy: 0.784
-# excitement: 0.735
-# admiration: 0.013
-# gratitude: 0.003
-# amusement: 0.003
+#        joy        : 0.784
+#        excitement : 0.735
+#        admiration : 0.013
+#        gratitude  : 0.003
+#        amusement  : 0.003
 ```
 
 ### How the Model Was Created
@@ -110,17 +110,9 @@ The model was evaluated on the test split of the GoEmotions dataset, using a thr
 
 Using the default threshold of 0.5.
 
-*Macro Averages (test)*
-  - Accuracy: `0.971`
-  - Precision: `0.611`
-  - Recall: `0.410`
-  - F1: `0.472`
-  - MCC: `0.475`
-
-*Per-Label Results (test)*
-
 | Label          | Accuracy | Precision | Recall | F1    | MCC   | Support | Threshold |
 |----------------|----------|-----------|--------|-------|-------|---------|-----------|
+| **macro avg**  | 0.971    | 0.611     | 0.410  | 0.472 | 0.475 | 5427    | 0.5       |
 | admiration     | 0.946    | 0.739     | 0.653  | 0.693 | 0.666 | 504     | 0.5       |
 | amusement      | 0.982    | 0.817     | 0.814  | 0.816 | 0.807 | 264     | 0.5       |
 | anger          | 0.968    | 0.671     | 0.237  | 0.351 | 0.387 | 198     | 0.5       |
@@ -152,19 +144,12 @@ Using the default threshold of 0.5.
 
 **Optimal Results**:
 
-Using the best threshold for each label based on the training set (tuned on F1).
-
-*Macro Averages (test)*
-- Accuracy: `0.968`
-- Precision: `0.591`
-- Recall: `0.528`
-- F1: `0.550`
-- MCC: `0.536`
+Using the best threshold for each label based on the training set (tuned on F1), tested on the test set:
 
-*Per-Label Results (test)*
 
 | Label          | Accuracy | Precision | Recall | F1    | MCC   | Support | Threshold |
 |----------------|----------|-----------|--------|-------|-------|---------|-----------|
+| **macro avg**  | 0.968    | 0.591     | 0.528  | 0.550 | 0.536 | 5427    | various   |
 | admiration     | 0.947    | 0.722     | 0.702  | 0.712 | 0.683 | 504     | 0.40      |
 | amusement      | 0.983    | 0.812     | 0.848  | 0.830 | 0.821 | 264     | 0.45      |
 | anger          | 0.966    | 0.548     | 0.460  | 0.500 | 0.485 | 198     | 0.25      |