Sentence Similarity
sentence-transformers
Safetensors
English
roberta
datadreamer
datadreamer-0.35.0
Synthetic
feature-extraction
text-embeddings-inference
Instructions to use StyleDistance/styledistance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use StyleDistance/styledistance with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("StyleDistance/styledistance") sentences = [ "Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from future competitions.", "We're raising funds 2 improve our school's storage facilities and add new playground equipment!", "Did you hear about the Wales wing? He'll hate to withdraw due to injuries from future competitions." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Inference
- Notebooks
- Google Colab
- Kaggle
Pushed by DataDreamer
Browse filesUpdate training_args.json
- training_args.json +1 -1
training_args.json
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"log_level": "passive",
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"log_level_replica": "warning",
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"log_on_each_node": true,
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"logging_dir": "./output/train-styledistance-model/_checkpoints/runs/Jul17_08-
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"logging_strategy": "steps",
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"logging_first_step": false,
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"logging_steps": 1,
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"log_level": "passive",
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"log_level_replica": "warning",
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"log_on_each_node": true,
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"logging_dir": "./output/train-styledistance-model/_checkpoints/runs/Jul17_08-46-22_nlpgpu04.seas.upenn.edu",
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"logging_strategy": "steps",
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"logging_first_step": false,
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"logging_steps": 1,
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