Feature Extraction
sentence-transformers
Safetensors
Vietnamese
English
bert
sentence-similarity
qdrant
vietnamese
tourist-notebook
text-embeddings-inference
Instructions to use lmtri0312/tramy-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lmtri0312/tramy-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lmtri0312/tramy-encoder") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Fix module import paths to sentence_transformers.models
Browse files- modules.json +3 -3
modules.json
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@@ -3,18 +3,18 @@
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.
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}
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]
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"idx": 0,
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"name": "0",
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"type": "sentence_transformers.models.Transformer"
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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