Phobert Named Entity Reconigtion

Using Phobert model by VinAI Research for NER task on various datasets

Tested on VLSP2016 test set using PhoBERT-base yield 94.7 F1 score

Check the notebook for more detail

Implemented in undertheseanlp NER task

Phobert with `transformers`

Installation

Python 3.6+, Pytorch 1.1.0+ (or TensorFlow 2.0+)
Install transformers:
- git clone https://github.com/huggingface/transformers.git
- cd transformers
- pip3 install --upgrade

Pre-trained models

Model	#params	Arch.	Pre-training data
`vinai/phobert-base`	135M	base	20GB of texts
`vinai/phobert-large`	370M	large	20GB of texts

You can fine-tune with either large model or base model , with base model train faster but large model give better result.
For general use, just fine-tune on base model as large model only give a slightly better result

Tokenization

Data must be tokenized before fine-tune Using VnCoreNLP's word segmenter to pre-process input raw texts

A word segmenter must be applied to produce word-segmented texts before feeding to PhoBERT.
As PhoBERT employed the RDRSegmenter from VnCoreNLP to pre-process the pre-training data

VncoreNLP installation

# Install the vncorenlp python wrapper
pip3 install vncorenlp

# Download VnCoreNLP-1.1.1.jar & its word segmentation component (i.e. RDRSegmenter) 
mkdir -p vncorenlp/models/wordsegmenter
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/VnCoreNLP-1.1.1.jar
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/vi-vocab
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/wordsegmenter.rdr
mv VnCoreNLP-1.1.1.jar vncorenlp/ 
mv vi-vocab vncorenlp/models/wordsegmenter/
mv wordsegmenter.rdr vncorenlp/models/wordsegmenter/

Tokenization xample usage

# See more details at: https://github.com/vncorenlp/VnCoreNLP

# Load rdrsegmenter from VnCoreNLP
from vncorenlp import VnCoreNLP
rdrsegmenter = VnCoreNLP("/Absolute-path-to/vncorenlp/VnCoreNLP-1.1.1.jar", annotators="wseg", max_heap_size='-Xmx500m') 

# Input 
text = "Ông Nguyễn Khắc Chúc  đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."

# To perform word (and sentence) segmentation
sentences = rdrsegmenter.tokenize(text) 
for sentence in sentences:
	print(" ".join(sentence))

Run fine-tuning for NER task

The config.json file contain the arguments for run_ner, you can change the parameters in the config file or just run from command line

json config

{
    "data_dir": "bert-ner/data",
    "model_name_or_path": "vinai/phobert-base",
    "labels": "label.txt path",
    "output_dir": "phobert-ner",
    "max_seq_length": 128,
    "num_train_epochs": 3,
    "per_device_train_batch_size": 32,
    "save_steps": 750,
    "seed": 1,
    "do_train": true,
    "do_eval": true,
    "do_predict": true
}
python3 run_ner.py config.json

command line

python run_ner.py \
--data_dir /bert-ner/data \
--model_name_or_path vinai/phobert-base \
--labels label_path \
--output_dir phobert-ner \
--max_seq_length 128 \
--num_train_epochs 3 \
--per_device_train_batch_size 32 \
--save_steps 750 \
--seed 1 \
--do_train true \
--do_eval true \
--do_predict true \

VLSP2016 NER data

Data have been preprocessing with word segmentation and POS tagging. The data consist of five columns, in which two columns are separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence.

The first column is the word
The second column is its POS tag
The third column is its chunking tag
The fourth column is its NE label
The fifth column is its nested NE label

word	POS	chunking	NE	nested NE
Anh	N	B-NP	O	O
Thanh	Np	I-NP	B-PER	O
là	V	B-VP	O	O
cán_bộ	N	B-NP	O	O
Uỷ ban	N	B-NP	B-ORG	O
nhân_dân	N	I-NP	I-ORG	O
Thành_phố	N	I-NP	I-ORG	B-LOC
Hà_Nội	Np	I-NP	I-ORG	I-LOC
.	.	O	O	O

This script only use word and NE column so the training data look like this

word	NE
Anh	O
Thanh	B-PER
là	O
cán_bộ	O
Uỷ ban	B-ORG
nhân_dân	I-ORG
Thành_phố	I-ORG
Hà_Nội	I-ORG
.	O

preprocess the VLSP2016 dataset with preprocessing.py script to get the training data format

Custom NER dataset

if you want to use with custom dataset, the data should be formatted as

word(space)tag

e.g.:

tắc đường ở Hà_Nội

tắc 0
đường 0
ở 0
Hà_Nội B_LOC

Get the unique labels from data

cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt

the tagging follow conll2003 dataset as

"O"        # Outside of a named entity
"B-MISC"   # Beginning of a miscellaneous entity right after another miscellaneous entity
"I-MISC"   # Miscellaneous entity
"B-PER"    # Beginning of a person's name right after another person's name
"I-PER"    # Person's name
"B-ORG"    # Beginning of an organisation right after another organisation
"I-ORG"    # Organisation
"B-LOC"    # Beginning of a location right after another location
"I-LOC"    # Location

clean up multiple blank lines

awk '!NF {if (++n <= 1) print; next}; {n=0;print}' train.txt > train_clean.txt
awk '!NF {if (++n <= 1) print; next}; {n=0;print}' dev.txt > dev_clean.txt
awk '!NF {if (++n <= 1) print; next}; {n=0;print}' test.txt > test_clean.txt

Acknowledgements

Pretrained model Phobert by VinAI Research NER dataset from VLSP2016 dataset

If you want to get access to VLSP2016 dataset, sign the user agreement and mail to VLSP association

If this project help you, please give me a star 🌟 🌟 🌟

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Avi197/Phobert-Named-Entity-Reconigtion

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