Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
mpnet
fill-mask
feature-extraction
text-embeddings-inference
Instructions to use Portgas37/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Portgas37/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Portgas37/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 Portgas37/MNLP_M2_document_encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Portgas37/MNLP_M2_document_encoder") model = AutoModelForMaskedLM.from_pretrained("Portgas37/MNLP_M2_document_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fceab05ccedfbcf69632e643e9cb718876b4d23967f523bd4119b4ff6c4d55d
- Size of remote file:
- 438 MB
- SHA256:
- d0338d7385e2717ca4eddb7bbec3b9950d3b8dff47dedef0ad556d1c314076bf
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