lancopku/Avg-Avg
[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
This project helps machine learning researchers evaluate and improve how well their natural language processing (NLP) models detect unusual or unexpected text. It provides tools to train models and extract sentence features, which are then used as input to various out-of-distribution (OOD) detection algorithms. The output helps researchers understand which methods are most effective at identifying text that falls outside a model's typical training data.
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Use this if you are an NLP researcher or machine learning engineer focused on improving the robustness and reliability of text-based AI systems against unexpected inputs.
Not ideal if you are looking for a plug-and-play solution for general text classification or sentiment analysis, or if you are not familiar with deep learning model training and evaluation.
Stars
18
Forks
5
Language
Python
License
MIT
Category
Last pushed
Jun 14, 2023
Commits (30d)
0
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