|
--- |
|
license: apache-2.0 |
|
tags: |
|
- setfit |
|
- sentence-transformers |
|
- text-classification |
|
pipeline_tag: text-classification |
|
language: |
|
- es |
|
--- |
|
|
|
# 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: |
|
|
|
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
|
2. Training a classification head with features from the fine-tuned Sentence Transformer. |
|
|
|
## Usage |
|
|
|
To use this model for inference, first install the SetFit library: |
|
|
|
```bash |
|
python -m pip install setfit |
|
``` |
|
|
|
You can then run inference as follows: |
|
|
|
```python |
|
from setfit import SetFitModel |
|
|
|
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(recipes) |
|
print(preds) |
|
# Map integer predictions to category labels |
|
preds = [labels[pred.item()] for pred in preds] |
|
|
|
print(preds) |
|
``` |
|
|
|
## BibTeX entry and citation info |
|
|
|
```bibtex |
|
@article{https://doi.org/10.48550/arxiv.2209.11055, |
|
doi = {10.48550/ARXIV.2209.11055}, |
|
url = {https://arxiv.org/abs/2209.11055}, |
|
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
|
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
|
title = {Efficient Few-Shot Learning Without Prompts}, |
|
publisher = {arXiv}, |
|
year = {2022}, |
|
copyright = {Creative Commons Attribution 4.0 International} |
|
} |
|
``` |