Instructions to use xqewec/title_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xqewec/title_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xqewec/title_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xqewec/title_classifier") model = AutoModelForSequenceClassification.from_pretrained("xqewec/title_classifier") - Notebooks
- Google Colab
- Kaggle
Examples:
title: Generating Approximate Solutions to the TTP using a Linear Distance Relaxation cat: cs.AI
title: Chaos Based Mixed Keystream Generation for Voice Data Encryption cat: cs.CR
: A weaving process to define requirements for Cooperative Information System cat: cs.SE
title: A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement cat: cs.CV
title: Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation cat: cs.CL
title: SaaS CloudQual: A Quality Model for Evaluating Software as a Service on the Cloud Computing Environment cat: cs.SE
title: The ASHRAE Great Energy Predictor III competition: Overview and results cat: cs.CY
title: Debugging Neural Machine Translations cat: cs.CL
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