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
Create README.md
Browse files
README.md
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---
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license: openrail
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datasets:
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- arxiv_dataset
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metrics:
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- code_eval
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---
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title: Generating Approximate Solutions to the TTP using a Linear Distance Relaxation
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cat: cs.AI
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title: Chaos Based Mixed Keystream Generation for Voice Data Encryption
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cat: cs.CR
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: A weaving process to define requirements for Cooperative Information System
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cat: cs.SE
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title: A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement
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cat: cs.CV
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title: Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
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cat: cs.CL
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title: SaaS CloudQual: A Quality Model for Evaluating Software as a Service on the Cloud Computing Environment
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cat: cs.SE
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title: The ASHRAE Great Energy Predictor III competition: Overview and results
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cat: cs.CY
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title: Debugging Neural Machine Translations
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cat: cs.CL
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