Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
Generated from Trainer
dataset_size:1879136
loss:CachedGISTEmbedLoss
text-embeddings-inference
Instructions to use nlpai-lab/KURE-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nlpai-lab/KURE-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nlpai-lab/KURE-v1") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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### Training Procedure
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loss: CachedGISTEmbedLoss
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- **loss:** Used **[CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss)** by sentence-transformers
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- **batch size:** 4096
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- **learning rate:** 2e-05
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- 2,000,000 examples
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### Training Procedure
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- **loss:** Used **[CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss)** by sentence-transformers
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- **batch size:** 4096
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- **learning rate:** 2e-05
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