Sentence Similarity
sentence-transformers
Safetensors
English
gpt2
feature-extraction
code-retrieval
embeddings
Instructions to use aysinghal/ide-code-retrieval-gpt2-large-llm2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aysinghal/ide-code-retrieval-gpt2-large-llm2vec with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aysinghal/ide-code-retrieval-gpt2-large-llm2vec") 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
- Xet hash:
- 061c9d08540a209e07ae9ce6d226d821164e2b004eecff38cbeeb65030530937
- Size of remote file:
- 3.1 GB
- SHA256:
- a7a930788e60678f749bfb7649c65ca947c61d1d900ee28e63f39058d75423c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.