Ungreedy subword tokenizer and vocabulary trainer for Python, Go & Javascript

A Python tool for splitting large Markdown files into smaller sections based on a specified token limit. This is particularly useful for processing large Markdown files with GPT models, as it allows the models to handle the data in manageable chunks.

Successfully developed a resume classification model which can accurately classify the resume of any person into its corresponding job with a tremendously high accuracy of more than 99%.

Kingchop ⚔️ is a JavaScript English based library for tokenizing text (chopping text). It uses vast rules for tokenizing, and you can adjust them easily.

The tok function is a JavaScript and Node.js function that processes object instances and tokenizes text arrays. It returns tokenized words number, tokenized words array, and tokenized words concatenated string. It's part of the open-source DropSuit NLP library under the Apache License 2.0.

Successfully developed a news category classification model using fine-tuned BERT which can accurately classify any news text into its respective category i.e. Politics, Business, Technology and Entertainment.

Successfully developed a chatbot model which can provide accurate and concise responses to a wide variety of customer queries regarding the services offered by a particular company as well as general topics.

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

💥Fast State-of-the-Art Tokenizers optimized for Research and Production

Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.

Extract text content from an HTML page, process it, and extract unique words from the processed text. This notebook utilizes various text processing techniques including cleaning, normalization, tokenization, lemmatization or stemming, and stop words removal.