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
File size: 128 Bytes
c7e735c | 1 2 3 4 5 6 7 | {
"__version__": {
"sentence_transformers": "2.0.0",
"transformers": "4.7.0",
"pytorch": "1.9.0+cu102"
}
} |