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
Update 1_Pooling/config.json
Browse files- 1_Pooling/config.json +1 -1
1_Pooling/config.json
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens":
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": true,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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