Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
deberta-v2
feature-extraction
text-embeddings-inference
Instructions to use xushijie/polyBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use xushijie/polyBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("xushijie/polyBERT") sentences = [ "[*]CC[*]", "[*]COC[*]", "[*]CC(C)C[*]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use xushijie/polyBERT with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("xushijie/polyBERT") model = AutoModel.from_pretrained("xushijie/polyBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 536fd43a2719d8e4a5b8fe85ce41ab3d6a797c1c8eb8ad26b02e1e929d550016
- Size of remote file:
- 101 MB
- SHA256:
- 469d1928fe140f3d8d93f69e69a11aebf540e7a2043b38a9a6758cf0bb640a9c
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