Feature Extraction
Transformers
PyTorch
English
t5
sequential-recommendation
direct-recommendation
explanation-generation
text2text-generation
custom_code
Instructions to use makitanikaze/P5_sports_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use makitanikaze/P5_sports_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="makitanikaze/P5_sports_small", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("makitanikaze/P5_sports_small", trust_remote_code=True) model = AutoModel.from_pretrained("makitanikaze/P5_sports_small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5fa968bd43c7a39c711a4909df86222a9505587abc4093baeec5f91c7f7ca9ed
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size 243033400
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