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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01pupkaZALUPKA /1c-bsl-dataset 1C:Enterprise (BSL) dataset Собрано для построения retrieval-инструмента (MCP-сервер), который даёт AI-агенту знание платформы 1C:Enterprise (язык BSL): понимание чужого legacy-кода и написание нового по образцу реальных примеров + официальным рекомендациям. Два источника знаний: Код — процедуры/функции, извлечённые из открытых репозиториев GitHub. Доки — стандарты разработки its.1c.ru и статьи хаба "1C" на Habr, для концептуальных вопросов "как это правильно делать".… See the full description on the dataset page: https://huggingface.co/datasets/pupkaZALUPKA/1c-bsl-dataset.tabular100K<n<1M3 likes302 downloads2mo agoHugging Face02BSLBSL /month2-agent-runs month2-agent-runs 20 prompts run via POST http://51.79.71.160/agent/run (VPS :8001 via Caddy, redis:7, DatasetTool BSLBSL/month1-spam-sample) with openai/gpt-oss-20b via Groq free (custom key) + heuristic 90-100% p50 27ms. See https://huggingface.co/spaces/BSLBSL/ai-lab-m2-agent and https://github.com/BrunosGits/ai-lab/tree/main/month2 textn<1K0 likes32 downloads10d agoHugging Face03mjaliz /bslm-triplet-2.6M-qp-datagated BSLM Triplet 2.6M Query-Positive-Hard-Negative Dataset Query/product-title triplet dataset used to fine-tune mjaliz/bslm-triplet-2.6M-qp with Sentence Transformers TripletLoss after the pair-training stage. Each row contains a query, a positive product title, and a lexical/confuser hard-negative product title from a different top-level domain/category. Files File Rows Purpose train.jsonl 2,673,575 Triplet training rows dev.jsonl 26,592 Triplet… See the full description on the dataset page: https://huggingface.co/datasets/mjaliz/bslm-triplet-2.6M-qp-data.tabularsentence-similarity1M<n<10M0 likes6 downloads3mo agoHugging Face04mjaliz /bslm-pair-206k-datagated BSLM Pair 206k Query-Title Dataset Query/product-title pair dataset used to fine-tune mjaliz/bslm-pair-206k with Sentence Transformers MultipleNegativesRankingLoss. The dataset was built from ecommerce query-to-anchor candidates by selecting one positive product title per query (top1) to reduce false negatives in in-batch negative training. Files File Rows Purpose train.jsonl 202,149 Pair training rows dev.jsonl 2,063 Pair validation rows test.jsonl… See the full description on the dataset page: https://huggingface.co/datasets/mjaliz/bslm-pair-206k-data.tabularsentence-similarity100K<n<1M0 likes5 downloads3mo agoHugging Face

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