Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use Matisse6410/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matisse6410/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matisse6410/MNLP_M2_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] - Transformers
How to use Matisse6410/MNLP_M2_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Matisse6410/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("Matisse6410/MNLP_M2_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
pipeline_tag: sentence-similarity
Matisse6410/MNLP_M2_document_encoder
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space.