Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use MusYW/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MusYW/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MusYW/MNLP_M3_document_encoder") 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
Download tokenizer_config.json from MusYW/MNLP_M3_document_encoder: direct link, hf CLI and curl.
- Browser
- Download file 342 Bytes
-
https://huggingface.co/MusYW/MNLP_M3_document_encoder/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://MusYW/MNLP_M3_document_encoder/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/MusYW/MNLP_M3_document_encoder/resolve/main/tokenizer_config.json
342 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |