Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

Related tags

Text Data & NLPnlpcrfpytorchchinesespanneralbertbertsoftmaxfocal-lossadversarial-traininglabelsmoothing
Overview

Chinese NER using Bert

BERT for Chinese NER.

dataset list

  1. cner: datasets/cner
  2. CLUENER: https://github.com/CLUEbenchmark/CLUENER

model list

  1. BERT+Softmax
  2. BERT+CRF
  3. BERT+Span

requirement

  1. 1.1.0 =< PyTorch < 1.5.0
  2. cuda=9.0
  3. python3.6+

input format

Input format (prefer BIOS tag scheme), with each character its label for one line. Sentences are splited with a null line.

美	B-LOC
国	I-LOC
的	O
华	B-PER
莱	I-PER
士	I-PER

我	O
跟	O
他	O

run the code

  1. Modify the configuration information in run_ner_xxx.py or run_ner_xxx.sh .
  2. sh scripts/run_ner_xxx.sh

note: file structure of the model

├── prev_trained_model
|  └── bert_base
|  |  └── pytorch_model.bin
|  |  └── config.json
|  |  └── vocab.txt
|  |  └── ......

CLUENER result

The overall performance of BERT on dev:

Accuracy (entity) Recall (entity) F1 score (entity)
BERT+Softmax 0.7897 0.8031 0.7963
BERT+CRF 0.7977 0.8177 0.8076
BERT+Span 0.8132 0.8092 0.8112
BERT+Span+adv 0.8267 0.8073 0.8169
BERT-small(6 layers)+Span+kd 0.8241 0.7839 0.8051
BERT+Span+focal_loss 0.8121 0.8008 0.8064
BERT+Span+label_smoothing 0.8235 0.7946 0.8088

ALBERT for CLUENER

The overall performance of ALBERT on dev:

model version Accuracy(entity) Recall(entity) F1(entity) Train time/epoch
albert base_google 0.8014 0.6908 0.7420 0.75x
albert large_google 0.8024 0.7520 0.7763 2.1x
albert xlarge_google 0.8286 0.7773 0.8021 6.7x
bert google 0.8118 0.8031 0.8074 -----
albert base_bright 0.8068 0.7529 0.7789 0.75x
albert large_bright 0.8152 0.7480 0.7802 2.2x
albert xlarge_bright 0.8222 0.7692 0.7948 7.3x

Cner result

The overall performance of BERT on dev(test):

Accuracy (entity) Recall (entity) F1 score (entity)
BERT+Softmax 0.9586(0.9566) 0.9644(0.9613) 0.9615(0.9590)
BERT+CRF 0.9562(0.9539) 0.9671(0.9644) 0.9616(0.9591)
BERT+Span 0.9604(0.9620) 0.9617(0.9632) 0.9611(0.9626)
BERT+Span+focal_loss 0.9516(0.9569) 0.9644(0.9681) 0.9580(0.9625)
BERT+Span+label_smoothing 0.9566(0.9568) 0.9624(0.9656) 0.9595(0.9612)
Owner
Weitang Liu
weibo: https://weibo.com/277974397
Weitang Liu
GitHub Repository
Journey is a NLP-Powered Developer assistant

Journey Journey is a NLP-Powered Developer assistant Using on the powerful Natural Language Processing library Mindmeld, this projects aims to assist

Christian Eilers 21 Dec 11, 2022
A high-level Python library for Quantum Natural Language Processing

lambeq About lambeq is a toolkit for quantum natural language processing (QNLP). Documentation: https://cqcl.github.io/lambeq/ Getting started Prerequ

Cambridge Quantum 315 Jan 01, 2023
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Dec 30, 2022
An easy-to-use framework for BERT models, with trainers, various NLP tasks and detailed annonations

FantasyBert English | 中文 Introduction An easy-to-use framework for BERT models, with trainers, various NLP tasks and detailed annonations. You can imp

Fan 137 Oct 26, 2022
Some embedding layer implementation using ivy library

ivy-manual-embeddings Some embedding layer implementation using ivy library. Just for fun. It is based on NYCTaxiFare dataset from kaggle (cut down to

Ishtiaq Hussain 2 Feb 10, 2022
LegalNLP - Natural Language Processing Methods for the Brazilian Legal Language

LegalNLP - Natural Language Processing Methods for the Brazilian Legal Language ⚖️ The library of Natural Language Processing for Brazilian legal lang

Felipe Maia Polo 125 Dec 20, 2022
Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Depenencies

PyStanfordDependencies Python interface for converting Penn Treebank trees to Universal Dependencies and Stanford Dependencies. Example usage Start by

David McClosky 64 May 08, 2022
Bnagla hand written document digiiztion

Bnagla hand written document digiiztion This repo addresses the problem of digiizing hand written documents in Bangla. Documents have definite fields

Mushfiqur Rahman 1 Dec 10, 2021
Just Another Telegram Ai Chat Bot Written In Python With Pyrogram.

OkaeriChatBot Just another Telegram AI chat bot written in Python using Pyrogram. Requirements Python 3.7 or higher.

Wahyusaputra 2 Dec 23, 2021
TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech

TFPNER TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech Named entity recognition (NER), which aims at identifyin

1 Feb 07, 2022
Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence models

PEGASUS library Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence models, or PEGASUS, uses self-supervised

Google Research 1.4k Dec 22, 2022
Predicting the usefulness of reviews given the review text and metadata surrounding the reviews.

Predicting Yelp Review Quality Table of Contents Introduction Motivation Goal and Central Questions The Data Data Storage and ETL EDA Data Pipeline Da

Jeff Johannsen 3 Nov 27, 2022
SimpleChinese2 集成了许多基本的中文NLP功能，使基于 Python 的中文文字处理和信息提取变得简单方便。

SimpleChinese2 SimpleChinese2 集成了许多基本的中文NLP功能，使基于 Python 的中文文字处理和信息提取变得简单方便。 声明 本项目是为方便个人工作所创建的，仅有部分代码原创。

Ming 30 Dec 02, 2022
Beyond Accuracy: Behavioral Testing of NLP models with CheckList

CheckList This repository contains code for testing NLP Models as described in the following paper: Beyond Accuracy: Behavioral Testing of NLP models

Marco Tulio Correia Ribeiro 1.8k Dec 28, 2022
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 1.5k Dec 05, 2022
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

Poincaré Embeddings for Learning Hierarchical Representations PyTorch implementation of Poincaré Embeddings for Learning Hierarchical Representations

Facebook Research 1.6k Dec 29, 2022
End-2-end speech synthesis with recurrent neural networks

Introduction New: Interactive demo using Google Colaboratory can be found here TTS-Cube is an end-2-end speech synthesis system that provides a full p

Tiberiu Boros 214 Dec 07, 2022
DLO8012: Natural Language Processing & CSL804: Computational Lab - II

NATURAL-LANGUAGE-PROCESSING-AND-COMPUTATIONAL-LAB-II DLO8012: NLP & CSL804: CL-II [SEMESTER VIII] Syllabus NLP - Reference Books THE WALL MEGA SATISH

AMEY THAKUR 7 Apr 28, 2022
CLIPfa: Connecting Farsi Text and Images

CLIPfa: Connecting Farsi Text and Images OpenAI released the paper Learning Transferable Visual Models From Natural Language Supervision in which they

Sajjad Ayoubi 66 Dec 14, 2022
PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis

PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis

YangHeng 567 Jan 07, 2023