Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use bchan007/fnctech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bchan007/fnctech with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bchan007/fnctech") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use bchan007/fnctech with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bchan007/fnctech") model = AutoModel.from_pretrained("bchan007/fnctech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bchan007/fnctech: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/bchan007/fnctech/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://bchan007/fnctech@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bchan007/fnctech/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- 97fd5f3644fd64d9393b84e4f68058b7f5c3d28879a06efb2c493571c836c649
- Size of remote file:
- 438 MB
- SHA256:
- c129ef640df0765cd7736cee6863c55ffc2871fb6e3c669b167046030e932ade
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