Update README.md

README.md

@@ -37,6 +37,8 @@ widget:
 
 ---
 
+# Model Card for sgarbi/bert-fda-nutrition-ner
+ 
 This is a BERT sequence labeling model, is designed for Named Entity Recognition (NER) in the context of FDA nutrition labeling. It aims to identify and classify various nutritional elements from text dataproviding a structured interpretation of the content typically found on nutrition labels.
 
 
@@ -72,6 +74,68 @@ The training data for the `sgarbi/bert-fda-nutrition-ner` model was thoughtfully
 - The model should not be used to make recommendations about nutrition or health - it only extracts nutritional entities from text. Any nutrition advice should come from qualified experts.
 - The model may have biases related to the language and phrasing on certain types of food product labels. It should be re-evaluated periodically on new test sets.
 
+## Label Map
+
+The following is the label map used in the model, defining the various entity types that the model can recognize:
+
+```python
+label_map = {
+    'O': 0,
+    'B-MACRONUTRIENTS': 1,
+    'I-MACRONUTRIENTS': 2,
+    'B-PROXIMATES': 3,
+    'I-PROXIMATES': 4,
+    'B-PROTEINS': 5,
+    'I-PROTEINS': 6,
+    'B-FIBER': 7,
+    'I-FIBER': 8,
+    'B-CARBOHYDRATES': 9,
+    'I-CARBOHYDRATES': 10,
+    'B-SUGARS': 11,
+    'I-SUGARS': 12,
+    'B-ALCOHOLS': 13,
+    'I-ALCOHOLS': 14,
+    'B-PHYSICALPROPERTIES': 15,
+    'I-PHYSICALPROPERTIES': 16,
+    'B-ORGANICCOMPOUNDS': 17,
+    'I-ORGANICCOMPOUNDS': 18,
+    'B-WATER': 19,
+    'I-WATER': 20,
+    'B-STIMULANTS': 21,
+    'I-STIMULANTS': 22,
+    'B-PRESERVATIVES': 23,
+    'I-PRESERVATIVES': 24,
+    'B-MINERALS': 25,
+    'I-MINERALS': 26,
+    'B-VITAMINS': 27,
+    'I-VITAMINS': 28,
+    'B-CAROTENOIDS': 29,
+    'I-CAROTENOIDS': 30,
+    'B-OTHER': 31,
+    'I-OTHER': 32,
+    'B-AMINOACIDS': 33,
+    'I-AMINOACIDS': 34,
+    'B-LIPIDS': 35,
+    'I-LIPIDS': 36,
+    'B-ANTIOXIDANTS': 37,
+    'I-ANTIOXIDANTS': 38,
+    'B-PHYTOCHEMICALS': 39,
+    'I-PHYTOCHEMICALS': 40,
+    'B-DIETARYFIBER': 41,
+    'I-DIETARYFIBER': 42,
+    'B-INGREDIENTS': 43,
+    'I-INGREDIENTS': 44,
+    'B-QUANTITY': 45,
+    'I-QUANTITY': 46,
+    'B-COUNTRY_ORIGIN': 47,
+    'I-COUNTRY_ORIGIN': 48,
+    'B-SERVING_SIZE': 49,
+    'I-SERVING_SIZE': 50,
+    'B-PACKAGE_WEIGHT': 51,
+    'I-PACKAGE_WEIGHT': 52,
+}
+
+
 ## Caveats and Recommendations
 
 - The model may struggle with typos, uncommon ingredients, or unusual phrasing not seen during training.  
@@ -80,6 +144,4 @@ The training data for the `sgarbi/bert-fda-nutrition-ner` model was thoughtfully
 
 Here are some example model outputs on the provided text:
 
-{example_outputs}
-
-Let me know if you would like me to expand or modify the model card draft in any way. I'm happy to incorporate any additional details you think are important to include.
+{example_outputs}