pyabsa 1.8.12

This tool provides the state-of-the-art models for aspect term extraction (ATE), aspect polarity classification (APC), and text classification.

PyABSA - Open Framework for Aspect-based Sentiment Analysis

PyABSA is a personal project which received many contributions from all the contributors. Please feel free to help make it developing, with regards for all the people who contribute to PyABSA. I am glad if PyABSA helps you, please star this repo as Each Star helps PyABSA go further, many thanks.

Annotate Your Own Dataset

The repo ABSADatasets provides an open-source dataset annotating tool, you can easily annotate your dataset before using PyABSA.

Fit on Your Existing Dataset

• First, refer to ABSADatasets to prepare your dataset into acceptable format.
• You can PR to contribute your dataset and use it like ABDADatasets.your_dataset (All the datasets are for research only, shall not danger your data copyright)

Training based on Existing Checkpoints

Have no enough data to train your model, here are what you can do:

• Combine multiple datasets with your dataset to train your model
• Resume training from shared checkpoints, see train_based_on_checkpoint.py, train_atepc_based_on_checkpoint.py

Learn to Use FindFile

PyABSA uses FindFile to locate the target file(s) so you can specify a dataset/checkpoint path by keywords instead of using absolute path. e.g.,

dataset = './laptop' # relative path
dataset = 'ABSOLUTE_PATH/laptop/' # absolute path
dataset = 'laptop' # dataset name, char-case un-sensitive
dataset = 'lapto' # search any path containing the 'lapto' or 'aptop' string

checkpoint = 'lcfs' # checkpoint path assignment is similar to above methods

Learn to Use AutoCuda

Auto select the free cuda for training & inference PyABSA use the AutoCUDA to support automatic cuda assignment, but you can still set a preferred device.

auto_device = True  # to auto assign a cuda device for training / inference
auto_device = False  # to use cpu
auto_device = 'cuda:1'  # to specify a preferred device
auto_device = 'cpu'  # to specify a preferred device
auto_device = 'allcuda'  # use all cuda to train

Use Human-readable Labels in Your Dataset

PyABSA encourages you to use string labels instead of numbers. e.g., sentiment labels = {negative, positive, Neutral, unknown}

• What labels you use in the dataset, what labels will be output in inference
• You can train a model using multiple datasets with same sentiment labels, and you can even contribute and define a combination of datasets here!
• The version information of PyABSA is also available in the output while loading checkpoints training args.

For Syntax-Parsing Models

The default SpaCy english model is en_core_web_sm, if you didn't install it, PyABSA will download/install it automatically.

If you would like to change english model (or other pre-defined options), you can get/set as following:

from pyabsa.functional.config.apc_config_manager import APCConfigManager
from pyabsa.functional.config.atepc_config_manager import ATEPCConfigManager
from pyabsa.functional.config.classification_config_manager import ClassificationConfigManager

# Set
APCConfigManager.set_apc_config_english({'spacy_model': 'en_core_web_lg'})
ATEPCConfigManager.set_atepc_config_english({'spacy_model': 'en_core_web_lg'})
ClassificationConfigManager.set_classification_config_english({'spacy_model': 'en_core_web_lg'})

# Get
APCConfigManager.get_apc_config_english()
ATEPCConfigManager.get_atepc_config_english()
ClassificationConfigManager.get_classification_config_english()

# Manually Set spaCy nlp Language object
from pyabsa.core.apc.dataset_utils.apc_utils import configure_spacy_model

nlp = configure_spacy_model(APCConfigManager.get_apc_config_english())

Package Overview

pyabsa	package root (including all interfaces)
pyabsa.functional	recommend interface entry
pyabsa.functional.checkpoint	checkpoint manager entry, inference model entry
pyabsa.functional.dataset	datasets entry
pyabsa.functional.config	predefined config manager
pyabsa.functional.trainer	training module, every trainer return a inference model

Installation

Please do not install the version without corresponding release note to avoid installing a test version.

install via pip

To use PyABSA, install the latest version from pip or source code:

pip install -U pyabsa

install via source

git clone https://github.com/yangheng95/PyABSA --depth=1
cd PyABSA 
python setup.py install

Quick Start

• Create a new python environment (Recommended) and install latest pyabsa
• Find a suitable demo script (ATEPC , APC , Text Classification) to prepare your training script. (Welcome to share your demo script)
• Format or Annotate your dataset referring to ABSADatasets or use public dataset in ABSADatasets
• Init your config to specify Model, Dataset, hyper-parameters
• Training your model and get checkpoints
• Share your checkpoint and dataset

Learning to Use Checkpoint

Get available checkpoints from Google Drive

PyABSA will check the latest available checkpoints before and load the latest checkpoint from Google Drive. To view available checkpoints, you can use the following code and load the checkpoint by name:

from pyabsa import available_checkpoints

checkpoint_map = available_checkpoints()  # show available checkpoints of PyABSA of current version 

If you can not access to Google Drive, you can download our checkpoints and load the unzipped checkpoint manually. 如果您无法访问谷歌Drive，您可以从此处 (提取码：ABSA) 下载我们预训练的模型，并加载模型（本仓库为个人业余项目，没有精力再维护百度云，如果您可以帮助管理国内checkpoint的保存和下载请联系我）。

How to use our pretrained checkpoints on your dataset

• Aspect terms extraction & polarity classification
• Aspect polarity classification

How to share checkpoints (e.g., checkpoints trained on your custom dataset) with community

Datasets

More datasets are available at ABSADatasets.

1. Twitter
2. Laptop14
3. Restaurant14
4. Restaurant15
5. Restaurant16
6. Phone
7. Car
8. Camera
9. Notebook
10. MAMS
11. TShirt
12. Television
13. MOOC
14. Shampoo
15. Multilingual (The sum of all datasets.)

You don't have to download the datasets, as the datasets will be downloaded automatically.

Model Support

Except for the following models, we provide a template model involving LCF vec, you can develop your model based on the LCF-APC model template or LCF-ATEPC model template.

ATEPC

1. LCF-ATEPC
2. LCF-ATEPC-LARGE (Dual BERT)
3. FAST-LCF-ATEPC
4. LCFS-ATEPC
5. LCFS-ATEPC-LARGE (Dual BERT)
6. FAST-LCFS-ATEPC
7. BERT-BASE

APC

Bert-based APC models

1. SLIDE-LCF-BERT (Faster & Performs Better than LCF/LCFS-BERT)
2. SLIDE-LCFS-BERT (Faster & Performs Better than LCF/LCFS-BERT)
3. LCF-BERT (Reimplemented & Enhanced)
4. LCFS-BERT (Reimplemented & Enhanced)
5. FAST-LCF-BERT (Faster with slightly performance loss)
6. FAST_LCFS-BERT (Faster with slightly performance loss)
7. LCF-DUAL-BERT (Dual BERT)
8. LCFS-DUAL-BERT (Dual BERT)
9. BERT-BASE
10. BERT-SPC
11. LCA-Net
12. DLCF-DCA-BERT *

Bert-based APC baseline models

1. AOA_BERT
2. ASGCN_BERT
3. ATAE_LSTM_BERT
4. Cabasc_BERT
5. IAN_BERT
6. LSTM_BERT
7. MemNet_BERT
8. MGAN_BERT
9. RAM_BERT
10. TD_LSTM_BERT
11. TC_LSTM_BERT
12. TNet_LF_BERT

GloVe-based APC baseline models

1. AOA
2. ASGCN
3. ATAE-LSTM
4. Cabasc
5. IAN
6. LSTM
7. MemNet
8. MGAN
9. RAM
10. TD-LSTM
11. TD-LSTM
12. TNet_LF

Contribution

We expect that you can help us improve this project, and your contributions are welcome. You can make a contribution in many ways, including:

• Share your custom dataset in PyABSA and ABSADatasets
• Integrates your models in PyABSA. (You can share your models whether it is or not based on PyABSA. if you are interested, we will help you)
• Raise a bug report while you use PyABSA or review the code (PyABSA is a individual project driven by enthusiasm so your help is needed)
• Give us some advice about feature design/refactor (You can advise to improve some feature)
• Correct/Rewrite some error-messages or code comment (The comments are not written by native english speaker, you can help us improve documents)
• Create an example script in a particular situation (Such as specify a SpaCy model, pretrained-bert type, some hyperparameters)
• Star this repository to keep it active

Notice

The LCF is a simple and adoptive mechanism proposed for ABSA. Many models based on LCF has been proposed and achieved SOTA performance. Developing your models based on LCF will significantly improve your ABSA models. If you are looking for the original proposal of local context focus, please redirect to the introduction of LCF. If you are looking for the original codes of the LCF-related papers, please redirect to LC-ABSA / LCF-ABSA or LCF-ATEPC.

Acknowledgement

This work build from LC-ABSA/LCF-ABSA and LCF-ATEPC, and other impressive works such as PyTorch-ABSA and LCFS-BERT.