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README.md
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Before deploying these classifiers on the full data, we performed quick validations. We held out a portion of the LLM-labeled data (or did cross-validation) to ensure the fastText model was accurately capturing the LLM’s judgments. In most cases, we observed high accuracy (many domains saw >80–90% precision on the held-out set, since the categories are fairly distinct).
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## Citation
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@article{bajaj2026rexbert,
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title = {RexBERT: Context Specialized Bidirectional Encoders for E-commerce},
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author = {Bajaj, Rahul and Garg, Anuj},
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year = {2026},
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doi = {10.48550/arXiv.2602.04605},
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url = {https://arxiv.org/abs/2602.04605}
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}
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Before deploying these classifiers on the full data, we performed quick validations. We held out a portion of the LLM-labeled data (or did cross-validation) to ensure the fastText model was accurately capturing the LLM’s judgments. In most cases, we observed high accuracy (many domains saw >80–90% precision on the held-out set, since the categories are fairly distinct).
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## Citation
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```bibtex
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@article{bajaj2026rexbert,
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title = {RexBERT: Context Specialized Bidirectional Encoders for E-commerce},
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author = {Bajaj, Rahul and Garg, Anuj},
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year = {2026},
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doi = {10.48550/arXiv.2602.04605},
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url = {https://arxiv.org/abs/2602.04605}
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}
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```
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