Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 pipeline_tag: text-classification
 ---
 
-# /var/folders/8k/9n5qrgr57h5g5f36sz40b6w80000gq/T/tmp16ua2v1n/mhammadkhan/negation-categories-classifier
+#mhammadkhan/negation-categories-classifier
 
 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -27,10 +27,39 @@ You can then run inference as follows:
 ```python
 from setfit import SetFitModel
 
-# Download from Hub and run inference
-model = SetFitModel.from_pretrained("/var/folders/8k/9n5qrgr57h5g5f36sz40b6w80000gq/T/tmp16ua2v1n/mhammadkhan/negation-categories-classifier")
+model = SetFitModel.from_pretrained("mhammadkhan/negation-categories-classifier")
+
+# Define category labels
+labels = {0: "dairy-free", 1: "gluten-free", 2: "nut-free", 3: "soy-free", 4: "vegan"}
+
+# Define input recipes
+recipes = [
+    {"text": "Tacos de Coliflor Vegana", "ingredients":["cauliflower", "taco seasoning", "corn tortillas", "avocado", "salsa", "cilantro", "lime wedges"]},
+    {"text": "Pulpo a la Gallega Sin Gluten", "ingredients":["octopus", "potatoes", "paprika", "olive oil", "salt"]},
+    {"text": "Creamy Tomato Soup", "ingredients":["tomatoes", "vegetable broth", "onion", "garlic", "coconut milk"]},
+    {"text": "Chicken Alfredo Pasta", "ingredients":["chicken breast", "pasta", "broccoli", "mushrooms", "cashew cream"]},
+    {"text": "Cheesy Broccoli Casserole", "ingredients":["broccoli", "almond milk", "nutritional yeast", "gluten-free breadcrumbs"]},
+    {"text": "Gluten-Free Pizza", "ingredients":["gluten-free pizza crust", "tomato sauce", "mozzarella cheese", "mushrooms", "bell peppers"]},
+    {"text": "Quinoa Salad with Roasted Vegetables", "ingredients":["quinoa", "roasted sweet potato", "roasted Brussels sprouts", "dried cranberries", "almonds"]},
+    {"text": "Gluten-Free Chocolate Chip Cookies", "ingredients":["gluten-free flour", "brown sugar", "baking soda", "chocolate chips", "coconut oil"]},
+    {"text": "Chicken Satay Skewers", "ingredients":["chicken breast", "coconut milk", "peanut butter", "soy sauce", "lime juice"]},
+    {"text": "Pesto Pasta Salad", "ingredients":["pasta", "basil", "parmesan cheese", "pine nuts", "olive oil"]},
+    {"text": "Maple-Glazed Salmon", "ingredients":["salmon", "maple syrup", "pecans", "butter", "garlic"]},
+    {"text": "Beef and Broccoli Stir-Fry", "ingredients":["beef sirloin", "broccoli", "carrots", "garlic", "ginger", "cornstarch"]},
+    {"text": "Creamy Mushroom Soup", "ingredients":["mushrooms", "vegetable broth", "onion", "garlic", "cashew cream"]},
+    {"text": "Lemon-Garlic Roasted Chicken", "ingredients":["chicken thighs", "lemon juice", "garlic", "olive oil", "rosemary"]},
+    {"text": "Vegan Lasagna", "ingredients":["lasagna noodles", "tofu ricotta", "marinara sauce", "spinach", "mushrooms"]},
+    {"text": "Chickpea Curry", "ingredients":["chickpeas", "coconut milk", "tomatoes", "spinach", "curry powder"]},
+    {"text": "Vegan Banana Bread", "ingredients":["flour", "bananas", "sugar", "baking powder", "almond milk"]},
+]
+
 # Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+preds = model(recipes)
+print(preds)
+# Map integer predictions to category labels
+preds = [labels[pred.item()] for pred in preds]
+
+print(preds)
 ```
 
 ## BibTeX entry and citation info