datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
filesystem_huggingface_9816_customer_feedback_raw_nucfubxi
Raw Customer Feedback Corpus
Fresh export of anonymized customer feedback records collected from the
company's product channels (mobile app, website, email, in-app). Each record
contains a product reference, a star rating, the customer review text, the
review date, the originating channel, and the current processing status.
This is the source dataset for the CX analytics curation pipeline.
github_fetch_huggingface_terminal_9061_aspcbz_src_customer_feedback
Customer Feedback Corpus
Raw customer feedback messages collected from support channels, surveys, and app reviews. This corpus is the canonical upstream source for the company's published feedback snapshots.
Contents
12,400 feedback messages
Fields: message_id, channel, message_text, created_at, customer_region
Language: English
Usage
Use this dataset as the upstream reference for any published customer-feedback derivative.
tla7941-customer-feedback-sentiment
Customer Feedback Sentiment
Sentiment-labeled customer feedback collected from app store reviews and in-product surveys. This dataset is a derived artifact published on this hub.
Provenance
This dataset was derived from the upstream source dataset:
Roy229/tla7941-customer-support-querylogs
The usage terms of a derived artifact follow the license of its upstream source.
github_fetch_huggingface_terminal_9145_m2k9q4_asset_customer_feedback
Chatbot Response Pairs
A derived dataset used to train a customer-support chatbot.
Description
This dataset contains 6,000 utterance-response pairs derived from support conversations. Each record contains a customer utterance, the agent response, and a category label.
Provenance
This dataset was derived from the following source datasets:
TianfuXinqu/github_fetch_huggingface_terminal_9080_m7k2p9_upstream_forum_crawls… See the full description on the dataset page: https://huggingface.co/datasets/TianfuXinqu/github_fetch_huggingface_terminal_9145_m2k9q4_asset_customer_feedback.fetch_hf_term_notion_gh_7942_target_customer_feedback
Customer Feedback
Overview
Aggregated customer feedback entries with sentiment labels.
Usage
Load the dataset with the datasets library.
License
MIT
Status
Documentation pending update.
Provenance
This dataset is derived from the upstream source zhuq41/fetch_hf_term_notion_gh_7942_source_sentiment_tweets.
au-nz-insurance-customer-feedback-quarterly
au-nz-insurance-customer-feedback-quarterly
Customer feedback for AU and NZ insurance policies for 2026Q2.
Rows: 40
Columns: ['policy_id', 'customer_id', 'feedback_text', 'rating', 'feedback_date', 'region']
github_fetch_huggingface_terminal_9107_8be428b7_derived_customer_feedback
Customer Feedback Dataset
Customer feedback comments prepared for sentiment analysis.
Description
Contains 5,000 annotated customer feedback comments covering product quality,
delivery, and service. Each record has a sentiment label.
Provenance
Derived from the Retail Feedback Corpus published on the Hugging Face Hub.
License
License: cc-by-4.0
customer_feedback_analysis_bert_dataset
Customer Feedback Analysis
Description: Classify customer feedback based on sentiment and topic to identify improvement areas and strengthen customer engagement.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/customer_feedback_analysis_bert"
model = AutoModelForSequenceClassification.from_pretrained(model_name)… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/customer_feedback_analysis_bert_dataset.customerfeedbackMistralcustomerfeedbackscustomer_feedback_analysis_-_company_x_bart_dataset
Customer Feedback Analysis - Company X
Description: Classify customer feedback based on sentiment, topic, and urgency. Prioritize and address customer concerns, improve products and services, and enhance customer satisfaction.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/customer_feedback_analysis_-_company_x_bart"… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/customer_feedback_analysis_-_company_x_bart_dataset.simple-customer-feedback-demo
Simple Customer Feedback Demo
Overview
This dataset is a small synthetic demo created to illustrate how a dataset can be published and documented on Hugging Face.
It contains 10 English customer feedback examples with simple labels and metadata. The purpose of this dataset is to demonstrate the structure of a Dataset Repository, the Dataset Viewer, and the role of a Dataset Card.
This dataset does not contain real customer data.
Dataset Purpose
The… See the full description on the dataset page: https://huggingface.co/datasets/Nawras-99/simple-customer-feedback-demo.customerfeedbacks-llama2-80customer_feedbackCustomerFeedbackClaims
CustomerFeedbackClaims
tags: Classification, Insurance, Claims
Note: This is an AI-generated dataset so its content may be inaccurate or false
Dataset Description: The dataset CustomerFeedbackClaims comprises customer feedback specifically related to insurance claims. The feedback is categorized into various classes that represent the nature of the customer's experience with the claims process. These categories are designed to help an ML practitioner understand the sentiment and key… See the full description on the dataset page: https://huggingface.co/datasets/infinite-dataset-hub/CustomerFeedbackClaims.Customer-Feedback-Analysiscustomer_feedback_emotion_analysis_datasetcustomer_feedback