retail
AReaL-tau2-retail-sft-30Bfunctiongemma-270m-it-retail-actions-v2-temporalretail-banking-llm-chatbot-translation-smallerIndoBERT-Retail-Search-Modelfunctiongemma-270m-it-retail-actions-v2-temporal-Q8_0-GGUFretail_embedding_classifier_v1gemma-4-e2b-it-retail-actions-v2-mergedretail-banking-llm-chatbot-translation-0.0.1
Datasets
All datasets matching “retail”retail-products-philippinesRetailAction
Dataset Card for RetailAction
This is a FiftyOne dataset with 21000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/RetailAction")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/RetailAction.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.retail_gaze
Dataset Card for retail_gaze
This is a FiftyOne dataset with 42 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/retail_gaze")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/retail_gaze.Retail-3I
Dataset Card for Retail-3I
Retail-3I is a retail-domain dataset built on top of tau2-bench for evaluating and training tool-calling LLM agents under three realistic user-intent conditions:
(1) Ambiguous intent: the user request is underspecified.
(2) Changing intent: the user revises or extends the goal after seeing intermediate results.
(3) Infeasible intent: the user request conflicts with tool limits, inventory/policy constraints, or missing capabilities.
There's also a… See the full description on the dataset page: https://huggingface.co/datasets/Ziyiii0-0/Retail-3I.online-retail