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 scheduler.pt from wt3639/EduGBERT_CourseRec: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/scheduler.pt
- Command line
-
hf download hf://wt3639/EduGBERT_CourseRec/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/scheduler.pt
1.06 kB
- Xet hash:
- 54818d34c2b6ed0da478f8effc584db2bca2f1640c41d89814dd99de397b8abb
- Size of remote file:
- 1.06 kB
- SHA256:
- de8135331009ad2e046c06d44515654f339cf301c974ca002c4daf95ab69e2cc
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