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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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01MorpheusIndustries /Logical_Reasoning_Chainsdocumentn<1K0 likes212 downloads6mo agoHugging Face02jsdfghdhtrseriu /synthetic-indian-logical-reasoning-CoTyes text1K<n<10K1 likes129 downloads2mo agoHugging Face03Indigo-Patricia /logical-form-d87cef logical-form-d87cef Synthetic sensors test data: 50 rows in data.csv. All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations. Fields sample_id: random identifier for this generated sample. row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/Indigo-Patricia/logical-form-d87cef.tabularn<1K0 likes63 downloads14d agoHugging Face04Bluel0la /Creative_Stories_Logical_Reasoningtextn<1K4 likes43 downloads2y agoHugging Face05Liebert28 /Nanbeige-3B-Logical-FailuresTechnical Challenge: Blind Spots of Nanbeige4.1-3B Model Tested: Nanbeige/Nanbeige4.1-3B Loading Protocol: The model was loaded via transformers on a standard Google Colab T4 GPU. To prevent CUDA Out-Of-Memory (OOM) errors, the weights were downcast using torch_dtype=torch.float16 and mapped to VRAM using device_map="auto". Analysis of Blind Spots As a base model lacking Supervised Fine-Tuning (SFT) or RLHF, Nanbeige4.1-3B exhibits severe zero-shot degradation. The 10 failures in this dataset… See the full description on the dataset page: https://huggingface.co/datasets/Liebert28/Nanbeige-3B-Logical-Failures.texttext-generationn<1K0 likes7 downloads7mo agoHugging Face06lowry02 /logical-textstext10K<n<100K0 likes2 downloads5mo agoHugging Face

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