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

Last update: Jan 03, 2023

Overview

Chinese NER using Bert

BERT for Chinese NER.

dataset list

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

model list

BERT+Softmax
BERT+CRF
BERT+Span

requirement

1.1.0 =< PyTorch < 1.5.0
cuda=9.0
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

Modify the configuration information in run_ner_xxx.py or run_ner_xxx.sh .
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)

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

Related tags

Overview

Chinese NER using Bert

dataset list

model list

requirement

input format

run the code

CLUENER result

ALBERT for CLUENER

Cner result

Owner

Weitang Liu

A sentence aligner for comparable corpora

Takes a string and puts it through different languages in Google Translate a requested amount of times, returning nonsense.

NLP Text Classification

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

Diaformer: Automatic Diagnosis via Symptoms Sequence Generation

Pipeline for training LSA models using Scikit-Learn.

CredData is a set of files including credentials in open source projects

A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP

Japanese NLP Library

A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format

NeuralQA: A Usable Library for Question Answering on Large Datasets with BERT

Constituency Tree Labeling Tool

A Streamlit web app that generates Rick and Morty stories using GPT2.

This repository implements a brute-force spellchecker utilizing the Damerau-Levenshtein edit distance.

Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.

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

🌸 fastText + Bloom embeddings for compact, full-coverage vectors with spaCy

Code to reprudece NeurIPS paper: Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks

Snowball compiler and stemming algorithms

An open-source NLP library: fast text cleaning and preprocessing.