Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use EmaRimoldi/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EmaRimoldi/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EmaRimoldi/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 EmaRimoldi/MNLP_M2_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("EmaRimoldi/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("EmaRimoldi/MNLP_M2_document_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cabc513b0eef66bbe429c10b305f1051f52e942633f9ab08a3e9999a25e9e9ba
- Size of remote file:
- 133 MB
- SHA256:
- 32ed5a30285dd435b59979b997f7d1c337486ad0b53d3ac0bfc78d779368452e
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