Nested Named Entity Recognition for Chinese Biomedical Text

Related tags

Text Data & NLPCBioNAMER
Overview

CBio-NAMER

CBioNAMER (Nested nAMed Entity Recognition for Chinese Biomedical Text) is our method used in CBLUE (Chinese Biomedical Language Understanding Evaluation), a benchmark of Nested Named Entity Recognition. We got the 2nd price of the benchmark by 2021/12/07. Single model CBioNAMER also achieves top20 in CBLUE. The score of CBioNAMER has surpassed human(67.0 in F1-score​).

Result

Results of our method:

ensemble

Results of our single model CBioNAMER:

single

Approach

CBioNAMER is a sub-model in our result, which is based on GlobalPointer (a powerful open-source model, thanks for author, we rewrite it with Pytorch) and MacBert.

Usage

First, install PyTorch>=1.7.0. There's no restriction on GPU or CUDA.

Then, install this repo as a Python package:

$ pip install CBioNAMER

Python package transformers==4.6.1 would be automatically installed as well.

API

The CBioNAMER package provides the following methods:

CBioNAMER.load_NER(model_save_path='./checkpoint/macbert-large_dict.pth', maxlen=512, c_size=9, id2c=_id2c, c2c=_c2c)

Returns the pretrained model. It will download the model as necessary. The model would use the first CUDA device if there's any, otherwise using CPU instead.

The model_save_path argument specifies the path of the pretrained model weight.

The maxlen argument specifies the max length of input sentences. The sentences longer than maxlen would be cut off.

The c_size argument specifies the number of entity class. Here is 9 for CBLUE.

The id2c argument specifies the mapping between id and entity class. By default, the id2c argument for CBLUE is:

_id2c = {0: 'dis', 1: 'sym', 2: 'pro', 3: 'equ', 4: 'dru', 5: 'ite', 6: 'bod', 7: 'dep', 8: 'mic'}

The c2c argument specifies the mapping between entity class and its Chinese meaning. By default, the c2c argument for CBLUE is:

_c2c = {'dis': "疾病", 'sym': "临床表现", 'pro': "医疗程序", 'equ': "医疗设备", 'dru': "药物", 'ite': "医学检验项目", 'bod': "身体", 'dep': "科室", 'mic': "微生物类"}

The model returned by CBioNAMER.load_NER() supports the following methods:

model.recognize(text: str, threshold=0)

Given a sentence, returns a list of dictionaries with recognized entity, the format of the dictionary is {'start_idx': entity's starting index, 'end_idx': entity's ending index, 'type': entity class, 'Chinese_type': Chinese meaning of entity class, 'entity': recognized entity}. The threshold argument specifies that the returned list only contains the recognized entity with confidence score higher than threshold.

model.predict_to_file(in_file: str, out_file: str)

Given input and output .json file path, the model would do inference according in_file, and the recognized entity would be saved in out_file. The output file can be submitted to CBLUE. The format of input file is like:

[
  {
    "text": "该技术的应用使某些遗传病的诊治水平得到显著提高。"
  },
    ...
  {
    "text": "There is a sentence."
  }
]

Examples

import CBioNAMER

NER = CBioNAMER.load_NER()
in_file = './CMeEE_test.json'
out_file = './CMeEE_test_answer.json'
NER.predict_to_file(in_file, out_file)
import CBioNAMER

NER = CBioNAMER.load_NER()
text = "该技术的应用使某些遗传病的诊治水平得到显著提高。"
recognized_entity = NER.recognize(text)
print(recognized_entity)
# output:[{'start_idx': 9, 'end_idx': 11, 'type': 'dis', 'Chinese_type': '疾病', 'entity': '遗传病'}]
You might also like...
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.

anaGo anaGo is a Python library for sequence labeling(NER, PoS Tagging,...), implemented in Keras. anaGo can solve sequence labeling tasks such as nam

Hiroki Nakayama 1.4k Feb 17, 2021
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.

NeuroNER NeuroNER is a program that performs named-entity recognition (NER). Website: neuroner.com. This page gives step-by-step instructions to insta

Franck Dernoncourt 1.5k Feb 17, 2021
Pytorch-Named-Entity-Recognition-with-BERT
Pytorch-Named-Entity-Recognition-with-BERT

BERT NER Use google BERT to do CoNLL-2003 NER ! Train model using Python and Inference using C++ ALBERT-TF2.0 BERT-NER-TENSORFLOW-2.0 BERT-SQuAD Requi

Kamal Raj 1.1k Dec 25, 2022
Tool to add main subject to items on Wikidata using a WMFs CirrusSearch for named entity recognition or a manually supplied list of QIDs
Tool to add main subject to items on Wikidata using a WMFs CirrusSearch for named entity recognition or a manually supplied list of QIDs

ItemSubjector Tool made to add main subject statements to items based on the title using a home-brewed CirrusSearch-based Named Entity Recognition alg

Dennis Priskorn 9 Nov 17, 2022
Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition
Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition

Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition

null 0 Feb 13, 2022
Use Google's BERT for named entity recognition （CoNLL-2003 as the dataset）.

For better performance, you can try NLPGNN, see NLPGNN for more details. BERT-NER Version 2 Use Google's BERT for named entity recognition （CoNLL-2003

Kaiyinzhou 1.2k Dec 26, 2022
Named Entity Recognition API used by TEI Publisher

TEI Publisher Named Entity Recognition API This repository contains the API used by TEI Publisher's web-annotation editor to detect entities in the in

e-editiones.org 14 Nov 15, 2022
RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2

RoNER RoNER is a Named Entity Recognition model based on a pre-trained BERT transformer model trained on RONECv2. It is meant to be an easy to use, hi

Stefan Dumitrescu 9 Nov 7, 2022
Spacy-ginza-ner-webapi - Named Entity Recognition API with spaCy and GiNZA
Spacy-ginza-ner-webapi - Named Entity Recognition API with spaCy and GiNZA

Named Entity Recognition API with spaCy and GiNZA I wrote a blog post about this

Yuki Okuda 3 Feb 27, 2022
Releases(v0.0.1)
Owner
GitHub Repository
Natural Language Processing library built with AllenNLP 🌲🌱

Custom Natural Language Processing with big and small models 🌲🌱

Recognai 65 Sep 13, 2022
Treemap visualisation of Maya scene files

Ever wondered which nodes are responsible for that 600 mb+ Maya scene file? Features Fast, resizable UI Parsing at 50 mb/sec Dependency-free, single-f

Marcus Ottosson 76 Nov 12, 2022
A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You can find two approaches for achieving this in this repo.

multitask-learning-transformers A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You

Shahrukh Khan 48 Jan 02, 2023
A Multi-modal Model Chinese Spell Checker Released on ACL2021.

ReaLiSe ReaLiSe is a multi-modal Chinese spell checking model. This the office code for the paper Read, Listen, and See: Leveraging Multimodal Informa

DaDa 106 Dec 29, 2022
Text-to-Speech for Belarusian language

title emoji colorFrom colorTo sdk app_file pinned Belarusian TTS 🐸 green green gradio app.py false Belarusian TTS 📢 🤖 Belarusian TTS (text-to-speec

Yurii Paniv 1 Nov 27, 2021
A framework for implementing federated learning

This is partly the reproduction of the paper of [Privacy-Preserving Federated Learning in Fog Computing](DOI: 10.1109/JIOT.2020.2987958. 2020)

DavidChen 46 Sep 23, 2022
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Microsoft 105 Jan 08, 2022
Finally, some decent sample sentences

tts-dataset-prompts This repository aims to be a decent set of sentences for people looking to clone their own voices (e.g. using Tacotron 2). Each se

hecko 19 Dec 13, 2022
Fast, general, and tested differentiable structured prediction in PyTorch

Torch-Struct: Structured Prediction Library A library of tested, GPU implementations of core structured prediction algorithms for deep learning applic

HNLP 1.1k Dec 16, 2022
Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5

NLP-Summarizer Natural language processing summarizer using 3 state of the art Transformer models: BERT, GPT2, and T5 This project aimed to provide in

Samuel Sharkey 1 Feb 07, 2022
source code for paper: WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach.

WhiteningBERT Source code and data for paper WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach. Preparation git clone https://github.com

49 Dec 17, 2022
Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.

Flexible interface for high performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra. What is Lightning Tran

Pytorch Lightning 581 Dec 21, 2022
Built for cleaning purposes in military institutions

Ferramenta do AL Construído para fins de limpeza em instituições militares. Instalação Requer python = 3.2 pip install -r requirements.txt Usagem Exe

0 Aug 13, 2022
Implementation of paper Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa.

RoBERTaABSA This repo contains the code for NAACL 2021 paper titled Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoB

106 Nov 28, 2022
Indonesia spellchecker with python

indonesia-spellchecker Ganti kata yang terdapat pada file teks.txt untuk diperiksa kebenaran kata. Run on local machine python3 main.py

Rahmat Agung Julians 1 Sep 14, 2022
Concept Modeling: Topic Modeling on Images and Text

Concept is a technique that leverages CLIP and BERTopic-based techniques to perform Concept Modeling on images.

Maarten Grootendorst 120 Dec 27, 2022
Script and models for clustering LAION-400m CLIP embeddings.

clustering-laion400m Script and models for clustering LAION-400m CLIP embeddings. Models were fit on the first million or so image embeddings. A subje

Peter Baylies 22 Oct 04, 2022
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
Stuff related to Ben Eater's 8bit breadboard computer

8bit breadboard computer simulator This is an assembler + simulator/emulator of Ben Eater's 8bit breadboard computer. For a version with its RAM upgra

Marijn van Vliet 29 Dec 29, 2022
This repository contains the code, models and datasets discussed in our paper "Few-Shot Question Answering by Pretraining Span Selection"

Splinter This repository contains the code, models and datasets discussed in our paper "Few-Shot Question Answering by Pretraining Span Selection", to

Ori Ram 88 Dec 31, 2022