Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use onelevelstudio/MPNET-0.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use onelevelstudio/MPNET-0.3B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("onelevelstudio/MPNET-0.3B") 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/MPNET-0.3B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("onelevelstudio/MPNET-0.3B") model = AutoModelForMultimodalLM.from_pretrained("onelevelstudio/MPNET-0.3B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 464d9707ba4c0a24f2203fa8b9c0291895739f6ba5cf81f96841bcb453472196
- Size of remote file:
- 1.11 GB
- SHA256:
- 04faa200d4c80196ce85dc5d7979310370bbd20c2e07b6251bb3d55576d1288c
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