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 training_args.bin from wt3639/EduGBERT_CourseRec: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/training_args.bin
- Command line
-
hf download hf://wt3639/EduGBERT_CourseRec/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/training_args.bin
4.79 kB
- Xet hash:
- a398ee56f9eac4fa47d90ecee3545ca4e5ebea572c6167779460cc2747de8e75
- Size of remote file:
- 4.79 kB
- SHA256:
- 57a09e5f6331a10592ac6b6d12ddd1fda5539f30eedca8a311d494d942bed63b
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