Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use p0x0q-dev/bge-m3-sparse-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use p0x0q-dev/bge-m3-sparse-experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("p0x0q-dev/bge-m3-sparse-experimental") 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
Commit ·
a971cda
1
Parent(s): 473c7e6
Refactor handler.py to include max_length parameter in model.encode()
Browse files- __pycache__/handler.cpython-311.pyc +0 -0
- handler.py +1 -1
__pycache__/handler.cpython-311.pyc
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Binary files a/__pycache__/handler.cpython-311.pyc and b/__pycache__/handler.cpython-311.pyc differ
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handler.py
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@@ -27,7 +27,7 @@ class EndpointHandler():
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# encodeメソッドの実行前に時間を記録
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start_time = time.time()
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result = self.model.encode(inputs, return_dense=False, return_sparse=True)
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# encodeメソッドの実行後に時間を記録
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end_time = time.time()
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# encodeメソッドの実行前に時間を記録
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start_time = time.time()
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result = self.model.encode(inputs, return_dense=False, return_sparse=True, max_length=1024)
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# encodeメソッドの実行後に時間を記録
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end_time = time.time()
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