Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:193623
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use benjamintli/modernbert-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use benjamintli/modernbert-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("benjamintli/modernbert-code") sentences = [ "@Override\n public void encode(final OtpOutputStream buf) {\n final int arity = elems.length;\n\n buf.write_tuple_head(arity);\n\n for (int i = 0; i < arity; i++) {\n buf.write_any(elems[i]);\n }\n }", "fetch function with the same interface than in cozy-client-js", "Convert this tuple to the equivalent Erlang external representation.\n\n@param buf\nan output stream to which the encoded tuple should be written.", "Delete a customer by it's id.\n\n@param int $id The id\n\n@return bool\n@throws \\Throwable in case something went wrong when deleting." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
3498658 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"is_local": false,
"mask_token": "[MASK]",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "TokenizersBackend",
"unk_token": "[UNK]"
}
|