Natural Language Processing (NLP)
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Updated
May 29, 2024 - Jupyter Notebook
Natural Language Processing (NLP)
This repository contains projects that classify texts using a variety of machine learning and deep learning models. The projects show use-cases of classifying text data through Natural Language Processing methods.
OCR, extract and classify documents. In addition, annotate documents and build your own NLP and Computer Vision models using Python by downloading the data. Find examples in our Colab Notebooks, e. g. how to fine-tune Flair.
KANs for text classification on GLUE tasks
NucliaDB, The AI Search database for RAG
MTEB: Massive Text Embedding Benchmark
Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
This repository contains various tools intended for handling Natural Language Processing (NLP) tasks
Business Establishment Automated Classification of NAICS
Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more .
BEE (Bug rEport analyzEr), a tool for structuring and analyzing bug reports
💫 Industrial-strength Natural Language Processing (NLP) in Python
In this project we have tried to do multi-label hate-speech classification in Bengali and Hindi language using fill-mask transformer models.
Repo containing a Framework to preprocess, vectorize, and train ML models on textual data.
Graph Convolutional Networks for Text Classification
In this project we have tried to do multi-label hate-speech classification in Bengali and Hindi language using fill-mask transformer models.
The Python Code Tutorials
Predict emotions (happiness, anger, sadness) from WhatsApp chat data using machine learning and deep learning models. Includes text normalization, vectorization (TF-IDF, BoW, Word2Vec, GloVe), and model evaluation.
Optimized Craigslist's classification system by creating an algorithm combining LSTM and Random Forest for Text and Image Classification respectively
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