Feature Extraction
Transformers
PyTorch
English
t5
sequential-recommendation
direct-recommendation
explanation-generation
text2text-generation
custom_code
Instructions to use makitanikaze/P5_toys_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use makitanikaze/P5_toys_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="makitanikaze/P5_toys_base", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("makitanikaze/P5_toys_base", trust_remote_code=True) model = AutoModel.from_pretrained("makitanikaze/P5_toys_base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c3d46a1543d47ae52ba77698b65f726e0c58914632c826703e30a8789175991
- Size of remote file:
- 893 MB
- SHA256:
- bffd5d7be539f774313ca3af7703b629b2a6de34b922e57744c8657e2f811b8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.