datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
swebench-verified-deepseek-v4-flash-failure-analysis
SWE-bench Verified runs & failure analysis — DeepSeek-V4-flash (local) × mini-swe-agent
Per-instance analysis of SWE-bench Verified runs of a locally-served DeepSeek-V4-flash model
driven by mini-swe-agent, graded with the official
SWE-bench harness. Each instance carries the full agent trajectory, a readable transcript, the
submitted patch, the harness test output, deterministic metrics, and a hand-verified qualitative
root-cause diagnosis.
Current numbers (resolve rates… See the full description on the dataset page: https://huggingface.co/datasets/daaain/swebench-verified-deepseek-v4-flash-failure-analysis.VAB-vulnerability-analysis-benchmark
FBE and VAB
Two small benchmarks for security code analysis. Both grade without an LLM judge, so runs are cheap
and repeatable.
FBE (find-the-bug)
14 code snippets, each with one planted vulnerability. Ask the model to analyze the code, then check
whether it actually found the flaw.
Grading uses concept groups: the answer has to contain at least one synonym from every required group.
Four numbers come out:
found, did it identify the real vulnerability (this is… See the full description on the dataset page: https://huggingface.co/datasets/MK4-Research/VAB-vulnerability-analysis-benchmark.chainscope-analysis
ChainScope Qwen3-8B Faithfulness Analysis Dataset
This dataset contains Chain-of-Thought (CoT) faithfulness evaluation data for Qwen3-8B, including hidden state activations, labeled sentences, and evaluation results.
Dataset Description
We evaluated CoT faithfulness using the ChainScope methodology:
Generate CoT responses for comparison questions (e.g., "Is A > B?")
Generate "reversed" CoT responses for the opposite question ("Is B > A?")
Compare whether the model's… See the full description on the dataset page: https://huggingface.co/datasets/massines3a/chainscope-analysis.logistics-cx-transcript-analysis-chatml
OmniCX Logistics CX Dataset (Research Preview)
Dataset Summary
This dataset is designed for structured extraction of logistics and customer-experience (CX) signals from multi-turn support conversations.
Each record uses ChatML-style messages with:
a fixed system instruction
a user transcript
an assistant JSON payload matching LogisticsCXMetrics
This release is a research preview and should not be treated as a production-certified benchmark.
Project repository:… See the full description on the dataset page: https://huggingface.co/datasets/mangesh-ux/logistics-cx-transcript-analysis-chatml.cve-analysis
CVE & Vulnerability Analysis Dataset
A comprehensive vulnerability analysis and CVE research dataset. Each row is a detailed security analysis covering root cause, exploitation methodology, detection rules (Sigma/Splunk/Suricata), CVSS v3.1 scoring, MITRE ATT&CK mapping, and remediation guidance — verified by the same model in an independent review pass.
Overview
This dataset contains 9,999 structured vulnerability analyses across 20 security domains. Unlike simple… See the full description on the dataset page: https://huggingface.co/datasets/sh111111111111111/cve-analysis.financial-analysis-sft-100k
Financial Analysis SFT (100K)
100,000 ShareGPT conversations demonstrating expert-level financial analysis across DCF modeling, unit economics, LBO analysis, credit analysis, earnings interpretation, comparable company analysis, and financial ratio analysis.
Motivation
Financial AI is a critical enterprise capability — investment analysts, CFOs, startup founders, and finance teams need models that can reason through complex financial questions with the precision… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/financial-analysis-sft-100k.Circuit-Analysis-Reasoning-Sample
⚡ EngineeringWays Data Lab: Circuit Analysis Reasoning Dataset (Free Sample)
This is a free 50-item sample of the EngineeringWays Circuit Analysis Reasoning Dataset. It is designed specifically for fine-tuning Large Language Models (LLMs) in advanced STEM problem-solving, featuring strict Chain-of-Thought (CoT) reasoning.
Want the complete, deduplicated 592-item master dataset? 👉 Get the LoRA-Ready Master File on Payhip
🚀 Dataset Overview
Most math and physics… See the full description on the dataset page: https://huggingface.co/datasets/EngineeringWays/Circuit-Analysis-Reasoning-Sample.adaption-market-analysis-sec
Market Analysis & News Instruction Dataset (SEC XBRL-grounded)
Instruction-tuning data for financial analysis — fundamentals, growth and ratio arithmetic, trend and risk reading, filing navigation and comparability caveats — built from real XBRL facts, with every stated figure independently re-derived.
Built for the Adaption Labs AutoScientist Challenge Part 2, Market Analysis & News track.
What is in it
Rows
5,068 (4,501 train / 567 eval)
Task… See the full description on the dataset page: https://huggingface.co/datasets/miscusi/adaption-market-analysis-sec.autoscientist-market-analysis-lenitnes-dataset
autoscientist-market-analysis-lenitnes-dataset
The adapted dataset used to fine-tune
Papajams/autoscientist-market-analysis-lenitnes
for the Adaption Labs AutoScientist Challenge Part 2 (Market-Analysis &
News category).
Composition
Total rows
27,965
Real seed rows (production DB)
1002
Unique source signals
272
Augmented rows (~19K domain + ~8K diversity)
AutoScientist-augmented
Seed provenance (real data): the lenitnes production platform… See the full description on the dataset page: https://huggingface.co/datasets/Papajams/autoscientist-market-analysis-lenitnes-dataset.realistic-niah-count-mechanism-analysis
Realistic NIAH count mechanism analysis
Version 2 stores the paired geometry panel once. The default
geometry_shared configuration contains 300 unique V4.4 stimulus rows: 200
discovery rows (seeds 1234-1253) and 100 held-out confirmation rows (seeds
1254-1263), with counts 1-10 balanced within every seed. Each pair_id is now
one row rather than two duplicated mode rows.
The common row contains the passage, gold records, slots, active needle spans,
hard negatives, design metadata… See the full description on the dataset page: https://huggingface.co/datasets/twistshan/realistic-niah-count-mechanism-analysis.turkish-doc-summary-review-analysis-30k-jsonl
⚠️ Superseded by v2
Bu v1 dataset'te exact duplicate yoktu; ancak belge ve cevap şablonları fazla tekrar ediyordu.
Güncel v2 sürümünü kullanın:
https://huggingface.co/datasets/kilicai/turkish-doc-summary-review-analysis-30k-jsonl-v2
Generated by ML Intern
This dataset repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
Try ML Intern: https://smolagents-ml-intern.hf.space
Source code:… See the full description on the dataset page: https://huggingface.co/datasets/kilicai/turkish-doc-summary-review-analysis-30k-jsonl.turkish-doc-summary-review-analysis-30k-jsonl-v2
Turkish Document Summary Review Analysis 30K JSONL v2
Tek dosya: train.jsonl.
Bu v2 sürümü, v1'de görülen tekrar sorununu çözmek için yeniden üretildi:
Daha fazla belge türü: proje önerisi, tutanak, denetim notu, şikâyet dosyası, politika taslağı, saha raporu, bütçe değerlendirmesi, risk kayıt formu, karar destek belgesi, olay inceleme raporu vb.
Daha fazla alt görev: 30 farklı task_type.
Exact duplicate + semantic template duplicate kontrolü.
Cevap şablonları belgeye özel risk… See the full description on the dataset page: https://huggingface.co/datasets/kilicai/turkish-doc-summary-review-analysis-30k-jsonl-v2.Emotional_Sentiment_AnalysisEmotional Sentiment Analysis Dataset for LLaMA-2 Fine-tuning
(The formatted version can be directly used for fine tuning which contain only the formatted text, while the dataset.csv contain all the text, emotion, response and the formatted text)
This dataset contains conversational data for training and fine-tuning language models for emotional sentiment analysis and response generation. The dataset includes user inputs, their corresponding emotional states, and tailored chatbot responses… See the full description on the dataset page: https://huggingface.co/datasets/VaisakhKrishna/Emotional_Sentiment_Analysis.blind-spot-analysis-gptneo
Blind-Spots of GPT-Neo 1.3B
Overview
This dataset evaluates the EleutherAI GPT-Neo 1.3B base model by testing 10 diverse prompts in reasoning, translation, arithmetic, factual knowledge, and scientific explanation. Each prompt is evaluated against the expected correct output and blind-spot category.
Model Used
GPT-Neo 1.3B (Base Pre-trained Model)
Source: https://huggingface.co/EleutherAI/gpt-neo-1.3B
Methodology
Prepare prompts targeting known… See the full description on the dataset page: https://huggingface.co/datasets/Mihiret/blind-spot-analysis-gptneo.e-cars-analysis