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
Commit ·
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Parent(s): 3355e67
Create README.md
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README.md
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---
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language:
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- en
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tags:
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- sequential-recommendation
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- direct-recommendation
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- explanation-generation
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- text2text-generation
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license: mit
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datasets:
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- Amazon
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- Yelp
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metrics:
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- NDCG
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- HR
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- MAE
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- BLUE
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- ROUGE
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---
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# P5
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Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm
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