Token Classification task on the Yes We Can dataset
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Updated
Sep 20, 2023 - Jupyter Notebook
Token Classification task on the Yes We Can dataset
Links to my repositories, where I implement a wide variety of Natural Language Processing models using TensorFlow and Hugging Face.
Scrap, token classification and model deployment for a selective process.
Labeled Russian text token-by-token for training models for NER task based samples got from parsing different resources and generated by ChatGPT.
ArabiNizer is a state-of-the-art Arabic named entity recognizer (NER) leveraging the XLMR transformer model with an impressive testing accuracy of 95.00% and a remarkable testing F1-score of 88.00% on the PAN-X.AR subset from XTREME.
End-to-end pipeline for (1) automatic scraping and parsing of NLP research papers, (2) token-level entity annotations in Label Studio, and (3) BERT-based models for span identification and entity recognition
A 16M LLM for POS tagging in African languages
This repo provides scripts for fine-tuning HuggingFace Transformers, setting up pipelines and optimizing token classification models for inference. They are based on my experience developing a custom chatbot, I’m sharing these in the hope they will help others to quickly fine-tune and use models in their projects! 😊
Code for the paper : Black-Box Word-Level Text Boundary Detection in Partially Machine Generated Texts
Applied Deep Learning 深度學習之應用 by Vivian Chen 陳縕儂 at NTU CSIE
Building a multilingual NER app with HuggingFace, Gradio and Comet
Part-Of-Speech tagging in polish with finetuned RoBERTa model
MAPLEv2 - Multi-task Approach for generating blackout Poetry with Linguistic Evaluation
API for Yoda-NER and Yoda-FITS model. NLP models for Google Feed product optimization
A webapp built using Gradio for demonstrating the capabilities of the Spacy NER pipeline.
Token Classification in essay level, paragraph level and sentence level with BERT, DistillBERT and NER
A state-of-the-art Arabic part-of-speech tagger leveraging the XLMR transformer model With an impressive testing accuracy of 97.49% and a remarkable testing F1-score of 96.44% on the Arabic UD Treebank.
Data pipelines for both TensorFlow and PyTorch!
This app searches reddit posts and comments to determine if a product or service has a positive or negative sentiment and predicts top product mentions using Named Entity Recognition
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