Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:2392064
loss:CachedMultipleNegativesSymmetricRankingLoss
text-embeddings-inference
Instructions to use Shuu12121/CodeSearch-ModernBERT-Crow-v1.1-Plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shuu12121/CodeSearch-ModernBERT-Crow-v1.1-Plus with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shuu12121/CodeSearch-ModernBERT-Crow-v1.1-Plus") sentences = [ "Encapsulates the work with test rules.\n@param {array} aRules The test rules\n@constructor\n@private", "createImageResizer = (width, height) => (source) => {\n const resized = new PNG({ width, height, fill: true });\n PNG.bitblt(source, resized, 0, 0, source.width, source.height, 0, 0);\n return resized;\n}", "TestRules = function (aRules) {\n\t\t\tthis._aRules = aRules;\n\t\t}", "function addEventTypeNameToConfig(_ref, isInteractive) {\n var topEvent = _ref[0],\n event = _ref[1];\n\n var capitalizedEvent = event[0].toUpperCase() + event.slice(1);\n var onEvent = 'on' + capitalizedEvent;\n\n var type = {\n phasedRegistrationNames: {\n bubbled: onEvent,\n captured: onEvent + 'Capture'\n },\n dependencies: [topEvent],\n isInteractive: isInteractive\n };\n eventTypes$4[event] = type;\n topLevelEventsToDispatchConfig[topEvent] = type;\n}" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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