Weak Supervised Fake News Detection with RoBERTa, XLNet, ALBERT, XGBoost and Logistic Regression classifiers.
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Updated
Jun 8, 2021 - Python
Weak Supervised Fake News Detection with RoBERTa, XLNet, ALBERT, XGBoost and Logistic Regression classifiers.
This repository contains the annotated dataset, the unigrams, bigrams and trigrams referenced in the paper "Unmasking People’s Opinions behind Mask Wearing during COVID-19 Pandemic – a Twitter Stance Analysis" submitted to the Symmetry journal.
A named entity recognition system that is flexible and domain-independent by generating labels based on confidence scores. The model, a finetuned Roberta, can identify potential named entities and be customized to fit the needs of various tasks and applications through monitoring and fine-tuning. A Flask app is provided for visualisation.
Training the first Cypriot Large Language Model on the Masked Language Modeling objective for predicting a given masked word token within a given context
Data enrichment with experimental results of the paper 'Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering'
Earth observations, especially satellite data, have produced a wealth of methods and results in meeting global challenges, often presented in unstructured texts such as papers or reports. Accurate extraction of satellite and instrument entities from these unstructured texts can help to link and reuse Earth observation resources.
Mitigating a language model's over-confidence with NLI predictions on Multi-NLI hypotheses with random word order using PAWS (paraphrase) and Winogrande (anaphora).
Large Language Model | BERT (Bidirectional Encoder Representations from Transformers)
Improved stance prediction model
Demo application for predicting Chula faculty from Thai course description
Using Task Specific Knowledge Distillation to obtain DistilRoBERTa model fine-tuned on SST-2 part of the GLUE dataset for sentiment analysis.
Just exploring NLP with 🤗 Transformers
基于tensorflow2.x实现bert及其变体的预训练模型加载架构
Time perception analysis for borderline personality disorder patients
Word embeddings are very useful representations of words that can represent semantic information. This project trains some Word2Vec embeddings, uses RoBERTa (and other embeddings) for semantic text similarity and also does text classifcation
Using BERT-based models for toxic span detection
This repository contains the code for submission made at SemEval 2022 Task 5: MAMI
Code repository for the paper Xu at SemEval2022 Task 4: pre-BERT Neural Network Methods vs post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection.
Extracting Emotion-Cause Pairs from Conversations: A Two-Step Approach Using Emotion Classification and QA Models
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