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
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "old_models/paraphrase-multilingual-mpnet-base-v2/0_Transformer"} |