Instructions to use kdudzic/roberta-base-cookdial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kdudzic/roberta-base-cookdial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kdudzic/roberta-base-cookdial")# Load model directly from transformers import AutoTokenizer, RobertaForMultiLabelSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kdudzic/roberta-base-cookdial") model = RobertaForMultiLabelSequenceClassification.from_pretrained("kdudzic/roberta-base-cookdial") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
library_name: transformers
tags:
- text-classification
widget:
- text: What ingredients do I need?
- Baseline NLU model for the "AMUseBot" cooking taskbot prototype.
roberta-basemodel finetuned with default hyperparameters for 10 epochs on intents from the CookDial (https://github.com/YiweiJiang2015/CookDial) dataset with an extra choose_recipe intent added. Thesimpletransformerslibrary was used for fine-tuning.- Intent mapping: {"0": "affirm", "1": "choose_recipe", "2": "confirm", "3": "goodbye", "4": "greeting", "5": "negate", "6": "other", "7": "req_amount", "8": "req_duration", "9": "req_ingredient", "10": "req_ingredient_list", "11": "req_ingredient_list_ends", "12": "req_ingredient_list_length", "13": "req_instruction", "14": "req_is_recipe_finished", "15": "req_is_recipe_ongoing", "16": "req_parallel_action", "17": "req_repeat", "18": "req_start", "19": "req_substitute", "20": "req_temperature", "21": "req_title", "22": "req_tool", "23": "req_use_all", "24": "thank"}.