A text processing pipeline for turning unstructured text data into hierarchical datasets
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Updated
May 19, 2020 - Python
A text processing pipeline for turning unstructured text data into hierarchical datasets
Sentiment analysis - Pytorch
Sentiment Analysis of Kaggle Yelp Reviews using FastText.
spam classifier with a dataset of 5000 mail
Humor Detection App with Streamlit and fastText
In this notebook, using tweets collected randomly from some contries(Egypt , France , and Turkey ), and Facebook's model fastText for language identification, we interpret and analyze the data in various ways.
Data velds encapsulating fasttext wordembeddings models trained on the Austria Media Corpus.
Minimal FastText-based language identifier with support for Balochi, Urdu, Persian and Arabic.
Various types of datascience projects
Successfully developed a Named Entity Recognition (NER) model for German text using a Bidirectional LSTM with Attention on the Multilingual NER dataset, effectively identifying entities across multilingual corpora with contextual understanding.
This repository is reimplementation of fast-text classification model which was introduced in 2017 ACL "Bag of Tricks for Efficient Text Classification" paper
NLP-related data science projects
Successfully developed a Named Entity Recognition (NER) model using a Bidirectional GRU with Attention on the MIT Movies dataset to identify and classify movie-related entities like titles, actors, and genres.
Successfully developed a Named Entity Recognition (NER) model on the CoNLL-2003 dataset using a Bidirectional LSTM with Attention mechanism to accurately identify entities such as persons, locations, organizations, and miscellaneous categories in English text.
Successfully developed a Named Entity Recognition (NER) model on the BC5CDR dataset using Stacked Bidirectional GRUs with Attention mechanism, designed to accurately identify chemical and disease entities from biomedical texts.
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