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 pytorch_model.bin from wt3639/EduGBERT_CourseRec: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wt3639/EduGBERT_CourseRec/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wt3639/EduGBERT_CourseRec/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 33ac4b7c117be32bd86a2f93a580d4ab2ea0db56d236b4ae724c9090da1eaf42
- Size of remote file:
- 1.34 GB
- SHA256:
- 5ee95ea14730bc3adbca5f22b51fcbb0001da2aa48cd7ffb8638607118cbb631
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