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, AutoModel tokenizer = AutoTokenizer.from_pretrained("onelevelstudio/MPNET-0.3B") model = AutoModel.from_pretrained("onelevelstudio/MPNET-0.3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e738c0f5d2db2400cf44308355d1f306ca04f1188fbecc300314d12400ca4f1e
- Size of remote file:
- 555 MB
- SHA256:
- 1ce03da9c11f922c5daf8a00dbffa92e80fef3a9d53940fb625f15d6adf86b52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.