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
Upload tokenizer_config.json
Browse files- tokenizer_config.json +9 -0
tokenizer_config.json
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{
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"additional_special_tokens": [],
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"do_lower_case": true,
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"eos_token": "</s>",
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"pad_token": "</s>",
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"sp_model_kwargs": {},
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"tokenizer_class": "ReformerTokenizer",
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"unk_token": "<unk>"
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}
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