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HandBook for BiLSTM+CRF repo

1. wiki for learning system

1.1 machine learning systems work following two step orders:

A. train the model based on labeled dataset.
B. make inference based on the above trained model.

Thus, whatever we do with the model, we first should train the model. Afterwards, we can make prediction on testset, or any text you want to predict with.

1.2 why BiLSTM+CRF?

For this question, please refer to the following materials.

2. system.config

Don't change the item key name at will.

Use # to comment out the configure item.

A. Status

mode=api_service
# string: train/test/interactive_predict/api_service

B. Datasets(Input/Output)

datasets_fold=data/example_datasets3
train_file=train.csv
dev_file=dev.csv
test_file=test.csv

delimiter=t
# string: (t: "\t";"table")|(b: "backspace";" ")|(other, e.g., '|||', ...)

use_pretrained_embedding=False
token_emb_dir=data/example_datasets3/word.emb

Be aware of the following three path.

vocabs_dir=data/example_datasets3/vocabs

log_dir=data/example_datasets3/logs

checkpoints_dir=checkpoints/BILSTM-CRFs-datasets3

C. Labeling Scheme

Be very careful of the following settings.

label_scheme=BIO
# string: BIO/BIESO

The system support at max 2 level of the label scheme. You need to make some modification on the source to adapt to more complicated labeling schemes.

label_level=2
# int, 1:BIO/BIESO; 2:BIO/BIESO + suffix
# max to 2

hyphen=_
# string: -|_, for connecting the prefix and suffix: `B_PER', `I_LOC'

The suffix for the second level labels.

suffix=[NR,NS,NT]
# unnecessary if label_level=1

labeling_level:

  • for English: （word: hello），（char: h）
  • for Chinese: （word: 你好），（char: 你）
labeling_level=word
# string: word/char

To measure the performance of the model, you have to specify the metrics. Following are the most used indicators.

Note that the f1 is compulsory. You can define any other metrics, in the codes.

measuring_metrics=[precision,recall,f1,accuracy]
# string: accuracy|precision|recall|f1

D. Model Configuration

use_crf=True

cell_type=LSTM
# LSTM, GRU
biderectional=True
encoder_layers=1

embedding_dim must be consistent with the one in token_emb_dir file.

embedding_dim=100

hidden_dim=100

cautions! set a LARGE number as possible as u can.

The max_sequence_length will be fix after training, and during inferring, those texts having length larger than this will be truncated.

max_sequence_length=300

We implement the self attention (Transformer style).

use_self_attention=False
attention_dim=500

To use the GPU, set tf.CUDA_VISIBLE_DEVICES=0,1,...

CUDA_VISIBLE_DEVICES=0

for reproduction.

seed=42

E. Training Settings

epoch=300
batch_size=100

dropout=0.5
learning_rate=0.005

optimizer=Adam
#string: GD/Adagrad/AdaDelta/RMSprop/Adam

checkpoints_max_to_keep=3
print_per_batch=20

early_stop: if the model did'nt progress within patient times of iterations of training, the training processing will be terminated.

is_early_stop=True
patient=5
# unnecessary if is_early_stop=False

checkpoint_name=model-CRFs

F. Testing Settings

output_test_file=test.out

is_output_sentence_entity=True
output_sentence_entity_file=test.entity.out
# unnecessary if is_output_sentence_entity=False

G. Api service Settings

unnecessary to change the default setting if you operate at the local host.

if you display the web page within the Intranet, you may change the ip=0.0.0.0.

ip=127.0.0.1
port=8000

3. more tips:

  • Once the settings of all parameters are decided during training, it's not allowed to change them during inference e.g., test, interactive_predict, api_service.
  • Then training time and iteration of those models with attention module is much longer than that without attention module.
  • attention_dim should be an even number.
  • in tools fold:
    • statis.py can calculate the statistics for your dataset.
    • calcu_measure_testout.py can compute the metrics based on test.out and test.csv
  • ...