Binary text classification
12 papers with code • 7 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Binary text classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
ECHR
TURINGBENCH
This is not a Dataset
Most implemented papers
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
amritabh/conda-gen-text-detection
•
•
Findings (EMNLP) 2021
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights
Using online EP and the central limit theorem we find an analytical approximation to the Bayes update of this posterior, as well as the resulting Bayes estimates of the weights and outputs.
Learning Representations for Soft Skill Matching
The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs.
MAGE: Machine-generated Text Detection in the Wild
yafuly/mage
•
•
In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources.
Ghostbuster: Detecting Text Ghostwritten by Large Language Models
In conjunction with our model, we release three new datasets of human- and AI-generated text as detection benchmarks in the domains of student essays, creative writing, and news articles.
DACCORD : un jeu de données pour la Détection Automatique d'énonCés COntRaDictoires en français
In this article, we present DACCORD, a new dataset dedicated to the task of automatically detecting contradictions between sentences in French.
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?
With the rapid development and widespread application of Large Language Models (LLMs), the use of Machine-Generated Text (MGT) has become increasingly common, bringing with it potential risks, especially in terms of quality and integrity in fields like news, education, and science.
Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification
Shallow machine learning strategies showed lower overall micro F1 scores, but still higher than deep learning strategies and the baseline.
TweepFake: about Detecting Deepfake Tweets
To prevent this, it is crucial to develop deepfake social media messages detection systems.
Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based Models
sruthisudheer/comment-classification-of-c-code
•
•
The performance of the classical bag of words model and transformer-based models were explored to identify significant features from the given training corpus.
