Feature Extraction
Transformers
Safetensors
bert
code
embeddings
file-embedding
r2
repositories
repository-library
research-library
t4_repo
text-embeddings-inference
Instructions to use PeytonT/file-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/file-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PeytonT/file-embedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PeytonT/file-embedding") model = AutoModel.from_pretrained("PeytonT/file-embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec694c634910fee61b0406326fb559327f2685eed7eb81c9c38204bafb960139
- Size of remote file:
- 5.84 kB
- SHA256:
- 0486e2ced4478591587cd4f8eeb7b64fad1a0079b70a0b04b78fffefb5775e0b
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