Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use smikulas/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use smikulas/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("smikulas/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 smikulas/MNLP_M2_document_encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("smikulas/MNLP_M2_document_encoder") model = AutoModel.from_pretrained("smikulas/MNLP_M2_document_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from smikulas/MNLP_M2_document_encoder: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/smikulas/MNLP_M2_document_encoder/resolve/main/model.safetensors
- Command line
-
hf download hf://smikulas/MNLP_M2_document_encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/smikulas/MNLP_M2_document_encoder/resolve/main/model.safetensors
90.9 MB
- Xet hash:
- 68a5ecd4176d4a04ef43b2d41ed64e9d1e7f859247d351f456558b7f1382e508
- Size of remote file:
- 90.9 MB
- SHA256:
- 1377e9af0ca0b016a9f2aa584d6fc71ab3ea6804fae21ef9fb1416e2944057ac
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