pyabsa 1.14.0

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

Hi, there! Please star this repo if it helps you! Each Star helps PyABSA go further, many thanks.

| Overview | HuggingfaceHub | ABDADatasets | ABSA Models | Colab Tutorials |

Try our demos on Huggingface Space

• Aspect-based sentiment classification (Multilingual) （English, Chinese, etc.）
• Aspect term extraction & sentiment classification (English, Chinese, Arabic, Dutch, French, Russian, Spanish, Turkish, etc.)
• 方面术语提取和情感分类 （中文, etc.）

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

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

Examples

1. Train a model of aspect term extraction

from pyabsa.functional import ATEPCModelList
from pyabsa.functional import Trainer, ATEPCTrainer
from pyabsa.functional import ABSADatasetList
from pyabsa.functional import ATEPCConfigManager

atepc_config = ATEPCConfigManager.get_atepc_config_english()

atepc_config.pretrained_bert = 'microsoft/deberta-v3-base'
atepc_config.model = ATEPCModelList.FAST_LCF_ATEPC
dataset_path = ABSADatasetList.Restaurant14
# or your local dataset: dataset_path = 'your local dataset path'

aspect_extractor = ATEPCTrainer(config=atepc_config,
                                dataset=dataset_path,
                                from_checkpoint='',  # set checkpoint to train on the checkpoint.
                                checkpoint_save_mode=1,
                                auto_device=True
                                ).load_trained_model()

2. Inference Example of aspect term extraction

from pyabsa.functional import ABSADatasetList
from pyabsa.functional import ATEPCCheckpointManager

examples = ['But the staff was so nice to us .',
            'But the staff was so horrible to us .',
            r'Not only was the food outstanding , but the little ` perks \' were great .',
            'It took half an hour to get our check , which was perfect since we could sit , have drinks and talk !',
            'It was pleasantly uncrowded , the service was delightful , the garden adorable , '
            'the food -LRB- from appetizers to entrees -RRB- was delectable .',
            'How pretentious and inappropriate for MJ Grill to claim that it provides power lunch and dinners !'
            ]

inference_source = ABSADatasetList.Restaurant14
aspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='multilingual2')
atepc_result = aspect_extractor.extract_aspect(inference_source=inference_source,
                                               save_result=True,
                                               print_result=True,  # print the result
                                               pred_sentiment=True,  # Predict the sentiment of extracted aspect terms
                                               )

3. 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

# The results of available_checkpoints() depend on the PyABSA version
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.

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

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 is built from LC-ABSA/LCF-ABSA and LCF-ATEPC, and other impressive works such as PyTorch-ABSA and LCFS-BERT.