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:
# pip install -U transformers accelerate # 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
Download pytorch_model.bin from jwieting/paraphrastic_test: direct link, hf CLI and curl.
- Browser
- Download file 205 MB
-
https://huggingface.co/jwieting/paraphrastic_test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jwieting/paraphrastic_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jwieting/paraphrastic_test/resolve/main/pytorch_model.bin
205 MB
- Xet hash:
- f588db0c333d49094fd07477b2abfdeca6256f8bb39dc9849ab570a53c465343
- Size of remote file:
- 205 MB
- SHA256:
- 382cf0ad302d7a635d92c8cf9b77d2c1ba0fcafc7f79690d4f493c878aa347f8
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