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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: openrail | |
| datasets: | |
| - arxiv_dataset | |
| metrics: | |
| - code_eval | |
| 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 | |