Text Classification
Transformers
Safetensors
English
modernbert
dissatisfaction
user-feedback
conversational-ai
semantic-routing
sentiment
text-embeddings-inference
Instructions to use vllm-sr/feedback-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/feedback-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vllm-sr/feedback-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/feedback-detector") model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/feedback-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from vllm-sr/feedback-detector: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
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https://huggingface.co/vllm-sr/feedback-detector/resolve/main/README.md
- Command line
-
hf download hf://vllm-sr/feedback-detector/README.md
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curl -L -o README.md https://huggingface.co/vllm-sr/feedback-detector/resolve/main/README.md
1.11 kB
metadata
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-classification
tags:
- modernbert
- dissatisfaction
- user-feedback
- conversational-ai
- semantic-routing
- sentiment
base_model: answerdotai/ModernBERT-base
datasets:
- rootfs/user-satisfaction-dataset
metrics:
- accuracy
- f1
User Satisfaction Classifier
A ModernBERT-based classifier that detects user satisfaction and dissatisfaction reasons from follow-up messages in conversational AI systems.
Key Insight
Follow-up messages alone contain sufficient signal for classification. No conversation context needed—just pass the user's response directly.
Labels
| Label | Description | Example |
|---|---|---|
SAT |
User is satisfied | "Thanks!", "Perfect", "Great!" |
NEED_CLARIFICATION |
User needs more explanation | "What do you mean?", "Can you explain?" |
WRONG_ANSWER |
System provided incorrect information | "No, that's wrong", "That's not right" |
WANT_DIFFERENT |
User wants alternative options | "Show me others", "What else?" |
License
Apache 2.0