Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
datadreamer
datadreamer-0.35.0
Synthetic
sentence-similarity
text-embeddings-inference
Instructions to use StyleDistance/mstyledistance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use StyleDistance/mstyledistance with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("StyleDistance/mstyledistance") sentences = [ "彼は技術的な複雑さと格闘し、彼の作品は驚くべき視覚的緊張を生み出した。", "Serviste mariscos frescos en el condado de Middlesex y áreas circundantes.", "Él sirvió mariscos frescos en el condado de Middlesex y áreas circundantes." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Pushed by DataDreamer
Browse filesUpdate training_args.json
- training_args.json +6 -6
training_args.json
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@@ -6,11 +6,11 @@
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"do_predict": false,
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"evaluation_strategy": "steps",
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"prediction_loss_only": false,
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"per_device_train_batch_size":
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"per_device_eval_batch_size":
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"per_gpu_train_batch_size": null,
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"per_gpu_eval_batch_size": null,
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"gradient_accumulation_steps":
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"eval_accumulation_steps": 1,
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"eval_delay": 0,
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"learning_rate": 0.0001,
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"adam_beta2": 0.999,
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"adam_epsilon": 1e-08,
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"max_grad_norm": 1.0,
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"num_train_epochs":
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"max_steps": -1,
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"lr_scheduler_type": "
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"lr_scheduler_kwargs": {},
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"warmup_ratio": 0.0,
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"warmup_steps": 0,
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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/
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"logging_strategy": "steps",
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"logging_first_step": false,
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"logging_steps": 10,
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"do_predict": false,
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"evaluation_strategy": "steps",
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"prediction_loss_only": false,
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"per_device_train_batch_size": 64,
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"per_device_eval_batch_size": 64,
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"per_gpu_train_batch_size": null,
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"per_gpu_eval_batch_size": null,
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"gradient_accumulation_steps": 2,
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"eval_accumulation_steps": 1,
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"eval_delay": 0,
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"learning_rate": 0.0001,
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"adam_beta2": 0.999,
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"adam_epsilon": 1e-08,
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"max_grad_norm": 1.0,
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"num_train_epochs": 2,
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"max_steps": -1,
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"lr_scheduler_type": "constant",
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"lr_scheduler_kwargs": {},
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"warmup_ratio": 0.0,
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"warmup_steps": 0,
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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/Nov29_20-48-36_nlpgpu10.seas.upenn.edu",
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"logging_strategy": "steps",
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"logging_first_step": false,
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"logging_steps": 10,
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