Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
code
xlm-roberta
feature-extraction
mathlib4
lean4
formal-proof
retrieval
code-retrieval
math
text-embeddings-inference
Instructions to use YuxuanGong/lean-RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YuxuanGong/lean-RAG with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YuxuanGong/lean-RAG") sentences = [ "theorem length_take_of_le_length | (s.take n).length = n", "Keywords: less than or equal find if and only natural number lemma proposition | Name: Nat le_find_iff | Kind: lemma | Namespace: Nat", "Keywords: length take le' list theorem proposition | Name: List length_take_le' | Kind: theorem | Namespace: List", "Keywords: take length list theorem proposition | Name: List take_length | Kind: theorem | Namespace: List" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": true, | |
| "mask_token": "<mask>", | |
| "model_max_length": 400, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |