Feature Extraction
Transformers
PyTorch
English
t5
sequential-recommendation
direct-recommendation
explanation-generation
text2text-generation
custom_code
Instructions to use makitanikaze/P5_beauty_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use makitanikaze/P5_beauty_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="makitanikaze/P5_beauty_small", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("makitanikaze/P5_beauty_small", trust_remote_code=True) model = AutoModel.from_pretrained("makitanikaze/P5_beauty_small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
tags:
- sequential-recommendation
- direct-recommendation
- explanation-generation
- text2text-generation
license: mit
datasets:
- Amazon
- Yelp
metrics:
- NDCG
- HR
- MAE
- BLUE
- ROUGE
P5
Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm
