bleu-score

Evaluation code for various unsupervised automated metrics for Natural Language Generation.

A neural network to generate captions for an image using CNN and RNN with BEAM Search.

A modular library built on top of Keras and TensorFlow to generate a caption in natural language for any input image.

Python code for various NLP metrics

The LSTM model generates captions for the input images after extracting features from pre-trained VGG-16 model. (Computer Vision, NLP, Deep Learning, Python)

A basic AI chat using the OpenAI API and its GPT-3 models

Useful python NLP tools (evaluation, GUI interface, tokenization)

An end-to-end image captioning project using a CNN encoder (ResNet-50) and LSTM decoder in PyTorch. Includes vocabulary building, preprocessing, training with BLEU evaluation, and inference. Generates natural language captions for images with saved metrics, model checkpoints, and visualization outputs.

A visual and interactive scoring environment for machine translation systems.

PyTorch implementation of "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin

To evaluate machine translation, they use several methods, some of which we fully implemented

BLEU Score in Rust

Scripts for an upcoming blog "Extractive vs. Abstractive Summarization" for RaRe Technologies.

Implementation for paper BLEU: a Method for Automatic Evaluation of Machine Translation

Abstractive text summarization done with the help of LSTMs using encoder-decoder model which was able to achieve accuracy of 77.27% on training set and cumulative BLEU-4 score of 0.8800 on test set.

Semantic Message Extraction for Text Based Data With Deep Neural Nets

Generate caption on images using CNN Encoder- LSTM Decoder structure

⚡ Seq2Seq model combines Attention mechanism

Creating RAG from Scratch . Creating RAG using the langchain. Creating RAG using llama indexing and Qdrant db

Machine learning tools for NLP programming.