Sentence Similarity
sentence-transformers
Safetensors
greenleaf_embed
embeddings
legal
retrieval
feature-extraction
mteb
mleb
legal-tech
case-law
contracts
judicialmind
custom_code
Instructions to use judicialmind/greenleaf-law-embed-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use judicialmind/greenleaf-law-embed-tiny with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("judicialmind/greenleaf-law-embed-tiny", trust_remote_code=True) 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
File size: 627 Bytes
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"backend": "tokenizers",
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"eos_token": "<|endoftext|>",
"errors": "replace",
"is_local": true,
"local_files_only": false,
"mask_token": "â½Ĺ",
"max_length": 256,
"model_max_length": 1024,
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"pad_token": "<|endoftext|>",
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"padding_side": "right",
"sep_token": "<|endoftext|>",
"split_special_tokens": false,
"stride": 0,
"tokenizer_class": "Qwen2Tokenizer",
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"truncation_strategy": "longest_first",
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}
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