Python script to train a NER AI Model
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Updated
Jun 24, 2026 - Python
Python script to train a NER AI Model
Custom Named Entity Recognition (NER) with BiLSTM CRF and Spacy
End-to-end NLP experiments for movie-genre prediction from plot summaries in English & Persian—covering document-level baselines, BERT fine-tuning, and NER+LSTM-CRF pipelines, with datasets and evaluation code.
An end-to-end comparative study on Named Entity Recognition (NER) benchmark datasets. This project explores the progression from scratch-built deep learning sequence models (LSTM, BiLSTM, BiLSTM-CRF) to state-of-the-art transformer fine-tuning (DistilBERT). Includes subword-label alignment, strict evaluation using seqeval, and an interactive Gradio
This repo explores token classification for abbreviation and long-form detection using RoBERTa. We evaluate the impact of adding 50% of the PLODv2-filtered dataset, achieving improved F1 and recall. The repo includes methodology, evaluation using seqeval, and confusion matrix analysis.
RoBERTa-based CTI NER training, evaluation, and Gradio demo on the APTNER dataset.
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