Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use onelevelstudio/M-MPNET-BASE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use onelevelstudio/M-MPNET-BASE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("onelevelstudio/M-MPNET-BASE") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use onelevelstudio/M-MPNET-BASE with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("onelevelstudio/M-MPNET-BASE") model = AutoModel.from_pretrained("onelevelstudio/M-MPNET-BASE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 234755b19210259b9b6a48c5331c0b2870077430cbfd10f731c32ebe9dd5417e
- Size of remote file:
- 1.11 GB
- SHA256:
- 769542d94a9f10c3e1151fce093399a75b0f52332ecef8569871e449e4087c44
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