Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
albert
feature-extraction
Instructions to use sentence-transformers/paraphrase-albert-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/paraphrase-albert-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/paraphrase-albert-base-v2") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use sentence-transformers/paraphrase-albert-base-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/paraphrase-albert-base-v2") model = AutoModel.from_pretrained("sentence-transformers/paraphrase-albert-base-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 448df6c02e320fbdd5e0d96eee9e42ff312379794fedf0483c02cf0ef4cfa4a6
- Size of remote file:
- 46.7 MB
- SHA256:
- 1960b5c07391dce901125dfc63d893f8918bfacd8aa87514fe191fe3f90d34b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.