Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Matjac5/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Matjac5/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matjac5/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] - Transformers
How to use Matjac5/MNLP_M3_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Matjac5/MNLP_M3_document_encoder") model = AutoModel.from_pretrained("Matjac5/MNLP_M3_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f655014ecd1fd5465406151e9a4f50d8f403f3389dfdf6edc90ebe1e1c86a455
- Size of remote file:
- 434 MB
- SHA256:
- 324dad14713c70552c22c6cebaec6573a2c1fd1ded25be2f1fd4f6eaffc0d74b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.