Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:8003584
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Shuu12121/CodeSearch-ModernBERT-Crow-v3-large-len1024-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-v3-large-len1024-Plus with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shuu12121/CodeSearch-ModernBERT-Crow-v3-large-len1024-Plus") sentences = [ "WithUUID adds the uuid to the snapshot policy collection get params", "func (o *SnapshotPolicyCollectionGetParams) WithUUID(uuid *string) *SnapshotPolicyCollectionGetParams {\n\to.SetUUID(uuid)\n\treturn o\n}", "func (c *SageMaker) DescribeHumanTaskUi(input *DescribeHumanTaskUiInput) (*DescribeHumanTaskUiOutput, error) {\n\treq, out := c.DescribeHumanTaskUiRequest(input)\n\treturn out, req.Send()\n}", "func (_mock *Client) GetImportRequest(context1 context.Context, getImportRequestArgs git.GetImportRequestArgs) (*git.GitImportRequest, error) {\n\tret := _mock.Called(context1, getImportRequestArgs)\n\n\tif len(ret) == 0 {\n\t\tpanic(\"no return value specified for GetImportRequest\")\n\t}\n\n\tvar r0 *git.GitImportRequest\n\tvar r1 error\n\tif returnFunc, ok := ret.Get(0).(func(context.Context, git.GetImportRequestArgs) (*git.GitImportRequest, error)); ok {\n\t\treturn returnFunc(context1, getImportRequestArgs)\n\t}\n\tif returnFunc, ok := ret.Get(0).(func(context.Context, git.GetImportRequestArgs) *git.GitImportRequest); ok {\n\t\tr0 = returnFunc(context1, getImportRequestArgs)\n\t} else {\n\t\tif ret.Get(0) != nil {\n\t\t\tr0 = ret.Get(0).(*git.GitImportRequest)\n\t\t}\n\t}\n\tif returnFunc, ok := ret.Get(1).(func(context.Context, git.GetImportRequestArgs) error); ok {\n\t\tr1 = returnFunc(context1, getImportRequestArgs)\n\t} else {\n\t\tr1 = ret.Error(1)\n\t}\n\treturn r0, r1\n}" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K