Sentence Similarity
sentence-transformers
PyTorch
roberta
feature-extraction
text-embeddings-inference
Instructions to use ncoop57/codeformer-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ncoop57/codeformer-java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ncoop57/codeformer-java") 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 1_Pooling/config.json from ncoop57/codeformer-java: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/ncoop57/codeformer-java/resolve/cc029d5f135f3bfe655a753ecf7dffc00858e7a3/1_Pooling/config.json
- Command line
-
hf download hf://ncoop57/codeformer-java@cc029d5f135f3bfe655a753ecf7dffc00858e7a3/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/ncoop57/codeformer-java/resolve/cc029d5f135f3bfe655a753ecf7dffc00858e7a3/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |