Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
custom_code
text-embeddings-inference
Instructions to use jwieting/paraphrastic_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jwieting/paraphrastic_test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jwieting/paraphrastic_test", trust_remote_code=True) 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 jwieting/paraphrastic_test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jwieting/paraphrastic_test", trust_remote_code=True) model = AutoModel.from_pretrained("jwieting/paraphrastic_test", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79a10cce5e9f8a641f474ecf9db9f9f1231728d32785a949555e24061da7c86c
- Size of remote file:
- 1.15 MB
- SHA256:
- 5a1b43bf2d72f4e976933092297e3f666c3a22a7e55f99156543f2e867f27fa1
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