Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
roberta
feature-extraction
text-embeddings-inference
Instructions to use sdadas/st-polish-paraphrase-from-mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/st-polish-paraphrase-from-mpnet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/st-polish-paraphrase-from-mpnet") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use sdadas/st-polish-paraphrase-from-mpnet with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/st-polish-paraphrase-from-mpnet") model = AutoModel.from_pretrained("sdadas/st-polish-paraphrase-from-mpnet", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"special_tokens_map_file": null,
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"tokenizer_class": "
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"trim_offsets": true,
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"unk_token": "<unk>"
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}
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"special_tokens_map_file": null,
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"tokenizer_class": "PreTrainedTokenizerFast",
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"trim_offsets": true,
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"unk_token": "<unk>"
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}
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