distilbert-punctuator 0.2.1

A small seq2seq punctuator tool based on DistilBERT

Distilbert-punctuator

Introduction

Distilbert-punctuator is a python package provides a bert-based punctuator (fine-tuned model of pretrained huggingface DistilBertForTokenClassification) with following three components:

• data process: funcs for processing user's data to prepare for training. If user perfer to fine-tune the model with his/her own data.

• training: training pipeline and doing validation. User can fine-tune his/her own punctuator with the pipeline

• inference: easy-to-use interface for user to use trained punctuator.

• If user doesn't want to train a punctuator himself/herself, two pre-fined-tuned model from huggingface model hub

  • Qishuai/distilbert_punctuator_en 📎 Model details
  • Qishuai/distilbert_punctuator_zh 📎 Model details

• model examples in huggingface web page.

  • English model

  

  • Simplified Chinese model

  

Installation

• Installing the package from pypi: pip install distilbert-punctuator for directly usage of punctuator.
• Installing the package with option to do data processing pip install distilbert-punctuator[data_process].
• Installing the package with option to train and validate your own model pip install distilbert-punctuator[training]
• For development and contribution
  • clone the repo
  • make install

Data Process

Component for pre-processing the training data. To use this component, please install as pip install distilbert-punctuator[data_process]

The package is providing a simple pipeline for you to generate NER format training data.

Example

examples/data_sample.py

Train

Component for providing a training pipeline for fine-tuning a pretrained DistilBertForTokenClassification model from huggingface.

Example

examples/english_train_sample.py

Training_arguments:

Arguments required for the training pipeline.

• data_file_path(str): path of training data
• model_name_or_path(str): name or path of pre-trained model
• tokenizer_name(str): name of pretrained tokenizer
• split_rate(float): train and validation split rate
• min_sequence_length(int): min sequence length of one sample
• max_sequence_length(int): max sequence length of one sample
• epoch(int): number of epoch
• batch_size(int): batch size
• model_storage_path(str): fine-tuned model storage path
• addtional_model_config(Optional[Dict]): additional configuration for model
• early_stop_count(int): after how many epochs to early stop training if valid loss not become smaller. default 3

Validate

Validation of fine-tuned model

Example

examples/train_sample.py

Validation_arguments:

• data_file_path(str): path of validation data
• model_name_or_path(str): name or path of fine-tuned model
• tokenizer_name(str): name of tokenizer
• min_sequence_length(int): min sequence length of one sample
• max_sequence_length(int): max sequence length of one sample
• batch_size(int): batch size
• tag2id_storage_path(Optional[str]): tag2id storage path. Default one is from model config.

Inference

Component for providing an inference interface for user to use punctuator.

Architecture

 +----------------------+              (child process)
 |   user application   |             +-------------------+
 +                      + <---------->| punctuator server |
 |   +inference object  |             +-------------------+
 +----------------------+

The punctuator will be deployed in a child process which communicates with main process through pipe connection. Therefore user can initialize an inference object and call its punctuation function when needed. The punctuator will never block the main process unless doing punctuation. There is a graceful shutdown methodology for the punctuator, hence user dosen't need to worry about the shutting-down.

Example

examples/inference_sample.py

Inference_arguments

Arguments required for the inference pipeline.

• model_name_or_path(str): name or path of pre-trained model
• tokenizer_name(str): name of pretrained tokenizer
• tag2punctuator(Dict[str, tuple]): tag to punctuator mapping. dbpunctuator.utils provides two default mappings for English and Chinese

  NORMAL_TOKEN_TAG = "O"
  DEFAULT_ENGLISH_TAG_PUNCTUATOR_MAP = {
      NORMAL_TOKEN_TAG: ("", False),
      "COMMA": (",", False),
      "PERIOD": (".", True),
      "QUESTIONMARK": ("?", True),
      "EXLAMATIONMARK": ("!", True),
  }
  
  DEFAULT_CHINESE_TAG_PUNCTUATOR_MAP = {
      NORMAL_TOKEN_TAG: ("", False),
      "C_COMMA": ("，", False),
      "C_PERIOD": ("。", True),
      "C_QUESTIONMARK": ("? ", True),
      "C_EXLAMATIONMARK": ("! ", True),
      "C_DUNHAO": ("、", False),
  }
  

  for own fine-tuned model with different tags, pass in your own mapping
• tag2id_storage_path(Optional[str]): tag2id storage path. Default one is from model config. Pass in this argument if your model doesn't have a tag2id inside config