Open-source alternatives to VANER

Code, data, and results described in the paper "Mining experimental data from materials science literature with large language models: an evaluation study", https://www.tandfonline.com/doi/full/10.1080/27660400.2024.2356506

If you are just looking to download the LoRA weights directly, use this url: https://figshare.com/ndownloader/files/43044994 and view the data entry on Figshare.

LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition

Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper: On-the-fly Definition Augmentation of LLMs for Biomedical NER

This is the github repository for the paper to be appeared at NAACL 2024 main conference: Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models.

Flair is a transformer-based natural language processing framework used to build and train models for text classification and sequence tagging. It provides a specialized library for generating contextual text embeddings and performing linguistic analysis. The framework includes dedicated tools for named entity recognition, including the identification of specialized biomedical entities across multiple languages. It further supports entity linking to map identified text mentions to unique entries within general or biomedical knowledge bases. The project covers a broad range of language analys

Flair is a natural language processing framework for training and applying models for sequence labeling and text classification. It provides a system for generating word embeddings and identifying semantic entities within text. The framework includes a dedicated system for zero and few-shot learning, enabling text classification and entity extraction using minimal training examples by leveraging pre-trained knowledge. Its capabilities cover named entity recognition, sentiment analysis, and the training of specialized models using custom datasets. It also includes tooling for the visual highl

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

Official code for ACL 2024 paper: VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models.

Code and ressources for evaluating Named Entity Recognition using Few-Shot Prompting with Large Language Models

Code and Data for arXiv paper: PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature.

Please save your dataset in data folder. Note that CoNLL2003 and WNUT2017 are open-source datasets, ACE2004 and ACE2005 are not free. We keep our CoNLL2003 and WNUT2017 train and test JSON files in data folder.

This is the code for our ACL 2024 paper Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction.

This is the github repository for the paper: Retrieval Augmented Instruction Tuning for Open NER with Large Language Models.

Source code and data for ACL 2023 main conference paper DICE: Data-Efficient Clinical Event Extraction with Generative Models.

This is a meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks.

This repository contains code for extracting structured relational data as JSON documents from complex scientific text, with particular application to materials science. For the Llama-2 fine-tuned models and code, see the supplemetary nerre-llama repo.

Project Overview:

Dependencies are managed using Poetry. Use poetry install to install dependencies. You can then use poetry shell to obtain a shell with the created virtual environment activated.

Source code for AAAI 2022 paper: Unified Named Entity Recognition as Word-Word Relation Classification

This repository contains PyTorch implementation and pre-trained models for ASP, described in Autoregressive Structured Prediction with Language Models.

This is the implementation of filter-then-rerank pipeline in Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!. EMNLP'2023 (Findings).