GitHub topics: spanbert

Repositories

rajatasusual/information_extractor

information_extractor is a tool that leverages spaCy for coreference resolution and SpanBERT for relation extraction. This project integrates named entity recognition (NER) with relation extraction to identify and analyze relationships between entities in text.

Language: Python - Size: 66.4 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

sudharsan13296/Getting-Started-with-Google-BERT

Build and train state-of-the-art natural language processing models using BERT

Language: Jupyter Notebook - Size: 17 MB - Last synced at: about 1 month ago - Pushed at: almost 4 years ago - Stars: 221 - Forks: 83

mandarjoshi90/coref

BERT for Coreference Resolution

Language: Python - Size: 4.07 MB - Last synced at: 7 months ago - Pushed at: over 2 years ago - Stars: 444 - Forks: 92

shreyas21563/TECPEC Fork of parthivdholaria/TECPEC

Extracting Emotion-Cause Pairs from Conversations: A Two-Step Approach Using Emotion Classification and QA Models

Language: Jupyter Notebook - Size: 235 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

dmitrytsyu/SemEval-2021-ToxicSpansDetection

In the field of Natural Language Processing, the detection of text toxicity has become a significant challenge. To facilitate the research in this sphere, one of the tasks of the International Workshop on Semantic Evaluation 2021 is Toxic Spans Detection. The essential objective of this task is to identify toxic spans within English passages. In this paper, two approaches for toxic spans detection considered: Machine Reading Comprehension and modified Named Entity Recognition approach altogether with Machine Reading Comprehension, but also these approaches are improved by Question Generation method. Furthermore, this research presents the exploratory analysis of provided data, proposes a training process and examines the analysis of the results on F1-score of new state-of-the-art BERT-based Language Models: BERT, ALBERT, RoBERTa, XLM-RoBERTa and SpanBERT. Finally, the best language model obtained an F1-score of 0.688 on the test data.

Language: Jupyter Notebook - Size: 33.6 MB - Last synced at: 11 months ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0