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A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.
Scripts for an upcoming blog "Extractive vs. Abstractive Summarization" for RaRe Technologies.
Agentic RAG with Llama-3.1-8b model Fine-tuned on medical conversational dataset
End-to-end NLP pipeline: fine-tuned FLAN-T5 summarization with KeyBERT extraction, spaCy NER, ROUGE evaluation, sentence highlighting, and reading level analysis.
A set of serverless functions designed to assist in the monitoring of inputs to language models, including routine and specific inspection of the message queue, as well as event-driven triggering of more complex metric calculation based on (what will eventually be) configurable environment variables; alongside a suite of other tools/functions
Recalculating ROUGE scores for See et al. (2017) test outputs.
First Use of Rouge 1.5.5 / pyrouge in Python
Fine-tuning T5-small on the CNN/DailyMail dataset for abstractive text summarization using Hugging Face Transformers and PyTorch. Includes dataset preprocessing, training, evaluation with ROUGE metrics, and inference scripts for generating concise news summaries.
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