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https://github.com/FerdinandZhong/punctuator
A small seq2seq punctuator tool based on DistilBERT
https://github.com/FerdinandZhong/punctuator
bert bert-ner chinese-nlp deep-learning nlp punctuation pytorch seq2seq
Last synced: 28 days ago
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A small seq2seq punctuator tool based on DistilBERT
- Host: GitHub
- URL: https://github.com/FerdinandZhong/punctuator
- Owner: FerdinandZhong
- License: apache-2.0
- Created: 2020-11-19T13:07:34.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-08T08:37:32.000Z (3 months ago)
- Last Synced: 2024-11-09T17:46:32.849Z (about 1 month ago)
- Topics: bert, bert-ner, chinese-nlp, deep-learning, nlp, punctuation, pytorch, seq2seq
- Language: Python
- Homepage:
- Size: 119 MB
- Stars: 50
- Watchers: 3
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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 evaluation. 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](https://huggingface.co/Qishuai/distilbert_punctuator_en)
* `Qishuai/distilbert_punctuator_zh` 📎 [Model details](https://huggingface.co/Qishuai/distilbert_punctuator_zh)
* 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`.
The latest version has the implementation of **`R-Drop`** enhanced training.
[R-Drop github repo](https://github.com/dropreg/R-Drop)
[Paper of R-Drop](https://arxiv.org/abs/2106.14448)
### Example
`examples/english_train_sample.py`
### Training_arguments:
Arguments required for the training pipeline.
- # basic arguments
- `training_corpus(List[List[str]])`: list of sequences for training, longest sequence should be no longer than pretrained LM # noqa: E501
- `validation_corpus(List[List[str]])`: list of sequences for validation, longest sequence should be no longer than pretrained LM # noqa: E501
- `training_tags(List[List[int]])`: tags(int) for training
- `validation_tags(List[List[int]])`: tags(int) for validation
- `model_name_or_path(str)`: name or path of pre-trained model
- `tokenizer_name(str)`: name of pretrained tokenizer
- # training arguments
- `epoch(int)`: number of epoch
- `batch_size(int)`: batch size
- `model_storage_dir(str)`: fine-tuned model storage path
- `label2id(Dict)`: the tags label and id mapping
- `early_stop_count(int)`: after how many epochs to early stop training if valid loss not become smaller. default 3 # noqa: E501
- `gpu_device(int)`: specific gpu card index, default is the CUDA_VISIBLE_DEVICES from environ
- `warm_up_steps(int)`: warm up steps.
- `r_drop(bool)`: whether to train with r-drop
- `r_alpha(int)`: alpha value for kl divengence in the loss, default is 0
- `plot_steps(int)`: record training status to tensorboard among how many steps
- `tensorboard_log_dir(Optional[str])`: the tensorboard logs output directory, default is "runs"
- # model arguments
- `addtional_model_config(Optional[Dict])`: additional configuration for model
You can also train your own NER models with the trainer provided in this repo.
The example can be found in `notebooks/R-drop NER.ipynb`
## Evaluation
Validation of fine-tuned model
### Example
`examples/train_sample.py`
### Validation_arguments:
- `evaluation_corpus(List[List[str]])`: list of sequences for evaluation, longest sequence should be no longer than pretrained LM's max_position_embedding(512)
- `evaluation_tags(List[List[int]])`: tags(int) for evaluation (the GT)
- `model_name_or_path(str)`: name or path of fine-tuned model
- `tokenizer_name(str)`: name of tokenizer
- `batch_size(int)`: batch size
- `label2id(Optional[Dict])`: label2id. Default one is from model config. Pass in this argument if your model doesn't have a label2id inside config
- `gpu_device(int)`: specific gpu card index, default is the CUDA_VISIBLE_DEVICES from environ
## 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
```python
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