Triple Branch BERT Siamese Network for fake news classification on LIAR-PLUS dataset in PyTorch
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Updated
Sep 13, 2022 - Python
Triple Branch BERT Siamese Network for fake news classification on LIAR-PLUS dataset in PyTorch
TunBERT is the first release of a pre-trained BERT model for the Tunisian dialect using a Tunisian Common-Crawl-based dataset. TunBERT was applied to three NLP downstream tasks: Sentiment Analysis (SA), Tunisian Dialect Identification (TDI) and Reading Comprehension Question-Answering (RCQA)
Simple State-of-the-Art BERT-Based Sentence Classification with Keras / TensorFlow 2. Built with HuggingFace's Transformers.
BERT which stands for Bidirectional Encoder Representations from Transformations is the SOTA in Transfer Learning in NLP.
This is the code for loading the SenseBERT model, described in our paper from ACL 2020.
The code of Team Rhinobird for Mining the Web of HTML-embedded Product Data Task One at ISWC2020
BERT implementation for radiology full-text reports
A Chinese idiom recommendation system based on BERT pre-training language model.
Part-of-Speech Tagging for simplified and traditional Chinese data with BERT & RoBERTa
Fine-tuning framework for BERT like models on RACE
Code and models for the paper 'Exploring Multi-Modal Representations for Ambiguity Detection & Coreference Resolution in the SIMMC 2.0 Challenge' published at AAAI 2022 DSTC10 Workshop
Quick and easy tutorial to serve HuggingFace sentiment analysis model using torchserve
BERTs based rank relative ratings of toxicity between comments
Arabic Sentiment Analyzer: Integrating State-of-the-Art Pre-trained Sentiment Models ( AraBERT, and AraNet) in a Single Tool.
This repository contains NLP Transfer learning projects with deployment and integration with UI.
Accelerating Growth through the DeepBOT Chatbot
Code and data for the NLLP 2021 paper: `Multi-granular Legal topic Classification on Greek Legislation`
A dark web analysis tool.
This project benchmarks various BERT-based models on the IMDB movie review dataset for sentiment classification, evaluating accuracy, precision, recall, and F1 score.
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