llm-semantic-router/feedback-detector
Text Classification • 0.1B • Updated • 7 • 1
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A dataset for training classifiers to detect user satisfaction and dissatisfaction reasons from follow-up messages in conversational AI systems.
This dataset contains user follow-up messages labeled with satisfaction status and dissatisfaction reasons. The key insight is that follow-up messages alone contain sufficient signal for classification—no conversation context is needed.
| Label | Description | Count (Train) |
|---|---|---|
SAT |
User is satisfied | ~3,350 |
NEED_CLARIFICATION |
User needs more explanation | ~3,350 |
WRONG_ANSWER |
System provided incorrect information | ~3,350 |
WANT_DIFFERENT |
User wants alternative options | ~3,350 |
train.jsonl # 13,422 training examples
val.jsonl # 1,494 validation examples
adversarial_test.jsonl # 103 adversarial test examples
Each line is a JSON object:
{"text": "What do you mean by that?", "label_name": "NEED_CLARIFICATION"}
{"text": "Thanks, that's perfect!", "label_name": "SAT"}
{"text": "No, that's incorrect", "label_name": "WRONG_ANSWER"}
{"text": "Show me other options", "label_name": "WANT_DIFFERENT"}
from datasets import load_dataset
dataset = load_dataset("rootfs/user-satisfaction-dataset")
# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]
# Example
print(train[0])
# {'text': 'What do you mean?', 'label_name': 'NEED_CLARIFICATION'}
Derived from public conversational AI datasets:
Pre-trained classifier: rootfs/dissat-4class
@dataset{user_satisfaction_2024,
title={User Satisfaction Classification Dataset},
author={Semantic Router Project},
year={2024},
url={https://huggingface.co/datasets/rootfs/user-satisfaction-dataset}
}
Apache 2.0