Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use wt3639/EduGBERT_CourseRec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use wt3639/EduGBERT_CourseRec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wt3639/EduGBERT_CourseRec") 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] - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from wt3639/EduGBERT_CourseRec: direct link, hf CLI and curl.
- Browser
- Download file 2.68 GB
-
https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/optimizer.pt
- Command line
-
hf download hf://wt3639/EduGBERT_CourseRec/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/optimizer.pt
2.68 GB
- Xet hash:
- b2f5fed35296e941f6585475bd7a9f968900588f0c20e1c64f4f84691b72f7ef
- Size of remote file:
- 2.68 GB
- SHA256:
- f205c08dd1a2f70396d5405753c76553b9ee1780139f357db42b0e5167c2f3aa
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