Instructions to use asycv/pq-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asycv/pq-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="asycv/pq-Classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("asycv/pq-Classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
language:
- en
tags:
- text-classification
- evaluation
- deberta
- transformers
pipeline_tag: text-classification
base_model:
- microsoft/deberta-v3-small
library_name: transformers
zLoLA-V Quality Classifier
A lightweight classification model built on DeBERTa-v3-small for analyzing input structure, clarity, and intent categories.
Labels
- clear
- vague
- unsafe
- overloaded
- technical
- creative
Intended Uses
- Input dataset filtering
- Evaluation workflows
- Input quality inspection
- Safety preprocessing
Base Model
This model is fine-tuned from:
- microsoft/deberta-v3-small
Example
from transformers import pipeline
clf = pipeline(
"text-classification",
model="asycv/zLoLA-V-p-classifier"
)
clf("Explain transformers in simple terms.")