Instructions to use odunola/guardrail with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use odunola/guardrail with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="odunola/guardrail")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("odunola/guardrail") model = AutoModelForSequenceClassification.from_pretrained("odunola/guardrail") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Trained model to act as a sort of Guardrail system for text given to a Food Recommendation Application. Classifies text unto four different classes. More information here Trained on this dataset
Model Details
Base model is a distill-bert-cased model
Model Description
- Developed by: Odunolaoluwa Jenrola
- Model type: [Bert Encoder-only Transformer Architecture]
- Language(s) (NLP): [Python]
- License: [Apache-2]
- Finetuned from model [optional]: [Distill-Bert-Cased]
How to Get Started with the Model
Use the code below to get started with the model.
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