NERP 1.0.2.2

A pipeline for fine-tuning pre-trained transformers for Named Entity Recognition (NER) tasks

NERP - NER Pipeline

What is it?

NERP (Named Entity Recognition Pipeline) is a python package that offers an easy-to-use pipeline for fine-tuning pre-trained transformers for Named Entity Recognition (NER) tasks.

Main Features

• Different Architectures (BiLSTM, CRF, BiLSTM+CRF)
• Fine-tune a pretrained model
• Save and reload model and train it on a new training data
• Fine-tune a pretrained model with K-Fold Cross-Validation
• Save and reload model and train it on a new training data with K-Fold Cross Validation
• Fine-tune multiple pretrained models
• Prediction on a single text
• Prediction on a CSV file

Config

The user interface consists of only one file config as a YAML. Change it to create the desired configuration.

Sample env.yaml file

torch:
  device: "cuda"
data:
  train_data: 'data/train.csv'
  valid_data: 'data/valid.csv'
  train_valid_split: None
  test_data: 'data/test.csv'
  limit: 10
  tag_scheme: ['B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']

model: 
  archi: "baseline"
  o_tag_cr: True
  max_len: 128 
  dropout: 0.1
  hyperparameters:
    epochs: 1
    warmup_steps: 500
    train_batch_size: 64
    learning_rate: 0.0001
  tokenizer_parameters: 
    do_lower_case: True
  pretrained_models: 
    - roberta-base

train:
  existing_model_path: "roberta-base/model.bin"
  existing_tokenizer_path: "roberta-base/tokenizer"
  output_dir: "output/"

kfold: 
  splits: 2
  seed: 42
  test_on_original: False

inference:
  archi: "bilstm-crf"
  max_len: 128 
  pretrained: "roberta-base"
  model_path: "roberta-base/model.bin"
  tokenizer_path: "roberta-base/tokenizer"
  bulk:
    in_file_path: "data/test.csv"
    out_file_path: "data/output.csv"
  individual:
    text: "Hello from NERP"

Training Parameters

Parameters	Description	Default	Type
device	device: the desired device to use for computation. If not provided by the user, we take a guess.	cuda or cpu	optional
train_data	path to training csv file		required
valid_data	path to validation csv file		optional
train_valid_split	train/valid split ratio if valid data not exists	0.2	optional
test_data	path to testing csv file		required
limit	Limit the number of observations to be returned from a given split. Defaults to None, which implies that the entire data split is returned. (it shoud be a int)	0 (whole data)	optional
tag_scheme	All available NER tags for the given data set EXCLUDING the special outside tag, that is handled separately		required
archi	The desired architecture for the model (baseline, bilstm-crf, bilstm, crf) (str)	baseline	optional
o_tag_cr	To include O tag in the classification report (bool)	True	optional
max_len	the maximum sentence length (number of tokens after applying the transformer tokenizer)	128	optional
dropout	dropout probability (float)	0.1	optional
epochs	number of epochs (int)	5	optional
warmup_steps	number of learning rate warmup steps (int)	500	optional
train_batch_size	batch Size for DataLoader (int)	64	optional
learning_rate	learning rate (float)	0.0001	optional
tokenizer_parameters	list of hyperparameters for tokenizer (dict)	do_lower_case: True	optional
pretrained_models	'huggingface' transformer model (str)	roberta-base	required
existing_model_path	model derived from the transformer (str)		optional
existing_tokenizer_path	tokenizer derived from the transformer (str)		optional
output_dir	path to output directory (str)	models/	optional
kfold	number of splits	0 (no k-fold) (int)	optional
seed	random state value for k-fold (int)	42	optional
test_on_original	True, if you need to test on the original test set for each iteration (bool)	False	optional

Inference Parameters

Parameters	Description	Default	Type
archi	The architecture for the trained model (baseline, bilstm-crf, bilstm, crf) (str)	baseline	optional
max_len	the maximum sentence length (number of tokens after applying the transformer tokenizer)	128	optional
pretrained	'huggingface' transformer model	roberta-base	required
model_path	path to trained model		required
tokenizer_path	path to saved tokenizer folder		optional
tag_scheme	All available NER tags for the given data set EXCLUDING the special outside tag, that is handled separately		required
in_file_path	path to inference file otherwise leave it as empty		optional
out_file_path	path to the output file if the input is a file, otherwise leave it as empty		optional
text	sample inference text for individual prediction	"Hello from NERP"	optional

---

Data Format

Pipeline works with CSV files containing separated tokens and labels on each line. Sentences can be found in the Sentence # column. Labels should already be in the necessary format, e.g. IO, BIO, BILUO, ... The CSV file must contain the last three columns as same as below.

,	Unnamed: 0	Sentence #	Word	Tag
0	0	Sentence: 0	i	o
1	1	Sentence: 0	was	O
2	2	Sentence: 0	at	O
3	3	Sentence: 0	h.w.	B-place
4	4	Sentence: 0	holdings	I-place
5	5	Sentence: 0	pte	I-place

---

Output

After training the model, the pipeline will return the following files in the output directory:

• model.bin - PyTorch NER model
• tokenizer files
• classification-report.csv - logging file
• If k-fold - split datasets, models and tokenizers for each iteration and accuracy file

---

Models

All huggingface transformer-based models are allowed.

---

Usage

Environment Setup

1. Activate a new conda/python environment
2. Install NERP

• via pip

pip install NERP==1.0.2.2

• via repository

git clone --branch v1.0.2.2 https://github.com/Chaarangan/NERP.git
cd NERP && pip install -e .

Initialize NERP

from NERP.models import NERP
model = NERP("env.yaml")

Training a NER model using NERP

1. Train a base model

model.train()

2. Train an already trained model by loading its weights

model.train_after_load_network()

3. Training with K-Fold Cross-Validation

model.train_with_kfold()

4. Train an already trained model with K-Fold Cross Validation after loading its weights

model.train_with_kfold_after_loading_network()

Inference of a NER model using NERP

1. Prediction on a single text through YAML file

output = model.inference_text()
print(output)

1. Prediction on a single text through direct input

output = model.predict("Hello from NERP")
print(output)

3. Prediction on a CSV file

model.inference_bulk()

License

MIT License

Shout-outs

• Thanks to NERDA package to have initiated us to develop this pipeline. We have integrated the NERDA framework with NERP with some modifications from v1.0.0.

Changes from the NERDA(1.0.0) to our NERDA submodule.

1. Method for saving and loading tokenizer
2. Selected pull requests' solutions were added from NERDA PRs
3. Implementation of the classification report
4. Added multiple network architecture support

Cite this work

@inproceedings{nerp,
  title = {NERP},
  author = {Charangan Vasantharajan, Kyaw Zin Tun, Lim Zhi Hao, Chng Eng Siong},
  year = {2022},
  publisher = {{GitHub}},
  url = {https://github.com/Chaarangan/NERP.git}
}

Contributing to NERP

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

Feel free to ask questions and send feedbacks on the mailing list.

If you want to contribute NERP, open a PR.

If you encounter a bug or want to suggest an enhancement, please open an issue.