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
- Xet hash:
- 390e04aab264935e3a46dab8b1cd786d84969e66871849d1d95db674b70b7fe4
- Size of remote file:
- 243 MB
- SHA256:
- 9a7802ad5b688bed97f6856213302301b8537f29af755a42766886d6a3f88663
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