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:
- 81404c18bda6148df213b2ad758e18acfb99b5f63466c3a385fe56d26161ea3b
- Size of remote file:
- 243 MB
- SHA256:
- d5a0f885e8c96eda329436e2566ea84bf7664aeef69c07a91c7183044b05fe0e
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