datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RetailBanking-Conversations
Dataset Description
RetailBanking-Conversations is a synthetic dataset designed to train and evaluate language models in the retail banking domain, it has been created using the open source library wizardSdata that eable the creation of synthetic datasets in any field.
The dataset contains 320 realistic conversations, across 160 unique financial profiles and 10 key retail banking topics, between financial advisors and clients, covering 10 main categories of banking products and… See the full description on the dataset page: https://huggingface.co/datasets/danystar/RetailBanking-Conversations.retail-bank-servicing-alignment-sft
Retail Bank Servicing Alignment SFT
The training corpus for the Granite retail-bank servicing agent. It is the
released tool-use SFT corpus merged with a servicing-alignment continuation
curriculum that teaches multi-turn behaviours the base corpus does not: what to
do when the customer says "that one", when a policy question interrupts a
transfer, when the agent's own previous turn was wrong, and when the honest
answer is that the agent cannot see what it was asked about.
Every… See the full description on the dataset page: https://huggingface.co/datasets/spkc83/retail-bank-servicing-alignment-sft.retail-voice-concise
retail-voice-concise
Made with the whileai SDK · Collection: Register
The same speaking register as
airline-voice-concise,
trained on a different agent. A retail support agent that leads with the
answer and stops.
This exists to test the limitation stated on the airline card: that nothing
there showed the register transfers off airline content. It does. Same
constitution, same recipe, different world, different tools, different
records.
Trained on this set, Qwen3-4B goes from… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/retail-voice-concise.qwen3.5-9B-tau2bench-retail-traces
Qwen3.5-9B τ²-bench retail traces (judged)
Curated retail-domain traces collected on tau2-bench by running Qwen3.5-9B
(with and without memory-rule injection) on the canonical 114-task retail
pool. Each trace is judged by two independent signals:
Canonical tau2-bench evaluator (canonical_reward ∈ {{0, 1}})
— the official task-success metric, combining a DB-state check and
gpt-4.1-2025-04-14 NL-assertion verifier.
Blind process-quality judge (judge_retail.*)
— a retail-shaped… See the full description on the dataset page: https://huggingface.co/datasets/KermitCO/qwen3.5-9B-tau2bench-retail-traces.aprm-sft-thoughts-tau2-retail-policy_best-adamw30-lp0
Act-PRM SFT thoughts — tau2-bench retail
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT / RL… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-tau2-retail-policy_best-adamw30-lp0.RetailOpt-190
RetailOpt-190: A Retail Supply Chain Benchmark for Text-to-Optimization
RetailOpt-190 is a solver-validated benchmark for evaluating semantic reliability in text-to-optimization. It tests whether LLM-based agents can reconstruct the intended optimization structure—not just produce runnable code.
Dataset Summary
RetailOpt-190 contains 190 retail supply chain optimization instances designed to test compositional consistency in LLM-generated optimization code. Each… See the full description on the dataset page: https://huggingface.co/datasets/Jacoblian/RetailOpt-190.retailopt-10k-verified
RetailOpt-10K Verified
Solver-verified expansion of Jacoblian/RetailOpt-190.
Overview
Property
Value
Base scenarios
190
Variations per base
53
Target total
10,070
Solver
Pyomo + HiGHS
Verification
Optimal objective per scenario
Variation Axes
Each base scenario is perturbed along:
demand_scale — scale demand curves (0.7–1.4×)
capacity_scale — scale production and cold storage capacity (0.6–1.3×)
cost_multiplier — scale… See the full description on the dataset page: https://huggingface.co/datasets/alirezaaminzadeh/retailopt-10k-verified.aprm-sft-thoughts-tau2-retail-base_best-adamw30-lp0
Act-PRM SFT thoughts — tau2-bench retail
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z)
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what downstream SFT / RL… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-tau2-retail-base_best-adamw30-lp0.tau-dev-task-retail-v1
tau-dev-task-retail-v1
Multi-turn tool-calling SFT dataset (915 records, 765 / 50 / 100 train / validation / test) derived from sierra-research/tau-bench retail-domain trajectories.
Meant to be used as a benchmark dataset for developing and validating data processing, training, and eval workflows involving tool use. Note: tau-bench is a widely-used public benchmark and many recently-trained models may have encountered variants of these trajectories during training, so be mindful of… See the full description on the dataset page: https://huggingface.co/datasets/lefft/tau-dev-task-retail-v1.RetailBanking-Conversations
Dataset Description
RetailBanking-Conversations is a synthetic dataset designed to train and evaluate language models in the retail banking domain, it has been created using the open source library wizardSdata that eable the creation of synthetic datasets in any field.
The dataset contains 320 realistic conversations, across 160 unique financial profiles and 10 key retail banking topics, between financial advisors and clients, covering 10 main categories of banking products and… See the full description on the dataset page: https://huggingface.co/datasets/oopere/RetailBanking-Conversations.aprm-sft-thoughts-tau2-retail
Act-PRM SFT thoughts — tau2-bench retail
Act-PRM (Action Process Reward Models) infers the latent thoughts behind
logged, action-only agent demonstrations via an offline EM. For each
logged action x in state s we sample G=4 candidate thoughts z,
score each by the length-penalized action likelihood
reward(z) = p(x | s, z) - 0.15 * len_frac
(len_frac grows with the thought's token length), and mark the best thought
(argmax reward). The (thought + action) span is then what… See the full description on the dataset page: https://huggingface.co/datasets/mzio/aprm-sft-thoughts-tau2-retail.retail-bank-agent-sft
Retail Bank Agent Tool-Use SFT
This dataset contains 9,000 deterministic, fictional retail-banking
conversations for supervised fine-tuning of a conversational tool-using model.
Dataset: https://huggingface.co/datasets/spkc83/retail-bank-agent-sft
Training revision:
183e7e1ed1aba9c3d7155e7b83b64dc854935055
Source: https://github.com/spkc83/retail-bank-servicing
Model: https://huggingface.co/spkc83/retail-bank-agent-9b
Public POC:… See the full description on the dataset page: https://huggingface.co/datasets/spkc83/retail-bank-agent-sft.retail-shop-enquiries
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
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Language(s) (NLP): [More Information Needed]
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Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/pythontech9/retail-shop-enquiries.