# -*- coding: utf-8 -*-
# file: aste_configuration.py
# time: 02/11/2022 19:55
# author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# github: https://github.com/yangheng95
# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research
# Copyright (C) 2022. All Rights Reserved.
import copy
from pyabsa.framework.configuration_class.configuration_template import ConfigManager
from pyabsa.tasks.AspectSentimentTripletExtraction.models.model import EMCGCN
# if you find the optimal param set of some situation, e.g., some model on some datasets
# please share the main use template main
[docs]
_aste_config_template = {
"model": EMCGCN,
"task": "triplet",
"optimizer": "",
"learning_rate": 1e-3,
"cache_dataset": True,
"warmup_step": -1,
"deep_ensemble": False,
"use_bert_spc": True,
"max_seq_len": 120,
"patience": 99999,
"sigma": 0.3,
"dropout": 0,
"l2reg": 0.000001,
"num_epoch": 10,
"batch_size": 16,
"seed": 52,
"output_dim": 3,
"log_step": 10,
"dynamic_truncate": True,
"srd_alignment": True, # for srd_alignment
"evaluate_begin": 0,
"similarity_threshold": 1, # disable same text check for different examples
"cross_validate_fold": -1,
"use_amp": False,
"overwrite_cache": False,
"epochs": 100,
"adam_epsilon": 1e-8,
"weight_decay": 0.0,
"emb_dropout": 0.5,
"num_layers": 1,
"pooling": "avg",
"gcn_dim": 300,
"relation_constraint": True,
"symmetry_decoding": False,
}
[docs]
_aste_config_base = {
"model": EMCGCN,
"optimizer": "adamw",
"learning_rate": 0.00002,
"pretrained_bert": "yangheng/deberta-v3-base-absa-v1.1",
"cache_dataset": True,
"warmup_step": -1,
"deep_ensemble": False,
"patience": 5,
"use_bert_spc": True,
"max_seq_len": 80,
"SRD": 3,
"dlcf_a": 2, # the a in dlcf_dca_bert
"dca_p": 1, # the p in dlcf_dca_bert
"dca_layer": 3, # the layer in dlcf_dca_bert
"use_syntax_based_SRD": False,
"sigma": 0.3,
"lcf": "cdw",
"lsa": False,
"window": "lr",
"eta": 1,
"eta_lr": 0.1,
"dropout": 0,
"l2reg": 0.000001,
"num_epoch": 10,
"batch_size": 16,
"initializer": "xavier_uniform_",
"seed": 52,
"output_dim": 3,
"log_step": 10,
"dynamic_truncate": True,
"srd_alignment": True, # for srd_alignment
"evaluate_begin": 0,
"similarity_threshold": 1, # disable same text check for different examples
"cross_validate_fold": -1, # split train and test datasets into 5 folds and repeat 3 trainer
"overwrite_cache": False,
}
[docs]
_aste_config_english = {
"model": EMCGCN,
"optimizer": "adamw",
"learning_rate": 0.00002,
"pretrained_bert": "yangheng/deberta-v3-base-absa-v1.1",
"cache_dataset": True,
"warmup_step": -1,
"deep_ensemble": False,
"patience": 99999,
"use_bert_spc": True,
"max_seq_len": 80,
"SRD": 3,
"dlcf_a": 2, # the a in dlcf_dca_bert
"dca_p": 1, # the p in dlcf_dca_bert
"dca_layer": 3, # the layer in dlcf_dca_bert
"use_syntax_based_SRD": False,
"sigma": 0.3,
"lcf": "cdw",
"lsa": False,
"window": "lr",
"eta": 1,
"eta_lr": 0.1,
"dropout": 0.5,
"l2reg": 0.000001,
"num_epoch": 10,
"batch_size": 16,
"initializer": "xavier_uniform_",
"seed": 52,
"output_dim": 3,
"log_step": 5,
"dynamic_truncate": True,
"srd_alignment": True, # for srd_alignment
"evaluate_begin": 0,
"similarity_threshold": 1, # disable same text check for different examples
"cross_validate_fold": -1, # split train and test datasets into 5 folds and repeat 3 trainer
}
[docs]
_aste_config_multilingual = {
"model": EMCGCN,
"optimizer": "adamw",
"learning_rate": 0.00002,
"pretrained_bert": "microsoft/mdeberta-v3-base",
"use_bert_spc": True,
"cache_dataset": True,
"warmup_step": -1,
"deep_ensemble": False,
"patience": 99999,
"max_seq_len": 80,
"SRD": 3,
"dlcf_a": 2, # the a in dlcf_dca_bert
"dca_p": 1, # the p in dlcf_dca_bert
"dca_layer": 3, # the layer in dlcf_dca_bert
"use_syntax_based_SRD": False,
"sigma": 0.3,
"lcf": "cdw",
"lsa": False,
"window": "lr",
"eta": 1,
"eta_lr": 0.1,
"dropout": 0.5,
"l2reg": 0.000001,
"num_epoch": 10,
"batch_size": 16,
"initializer": "xavier_uniform_",
"seed": 52,
"output_dim": 3,
"log_step": 5,
"dynamic_truncate": True,
"srd_alignment": True, # for srd_alignment
"evaluate_begin": 0,
"similarity_threshold": 1, # disable same text check for different examples
"cross_validate_fold": -1
# split train and test datasets into 5 folds and repeat 3 trainer
}
[docs]
_aste_config_chinese = {
"model": EMCGCN,
"optimizer": "adamw",
"learning_rate": 0.00002,
"pretrained_bert": "bert-base-chinese",
"use_bert_spc": True,
"cache_dataset": True,
"warmup_step": -1,
"deep_ensemble": False,
"patience": 99999,
"max_seq_len": 80,
"SRD": 3,
"dlcf_a": 2, # the a in dlcf_dca_bert
"dca_p": 1, # the p in dlcf_dca_bert
"dca_layer": 3, # the layer in dlcf_dca_bert
"use_syntax_based_SRD": False,
"sigma": 0.3,
"lcf": "cdw",
"lsa": False,
"window": "lr",
"eta": 1,
"eta_lr": 0.1,
"dropout": 0.5,
"l2reg": 0.00001,
"num_epoch": 10,
"batch_size": 16,
"initializer": "xavier_uniform_",
"seed": 52,
"output_dim": 3,
"log_step": 5,
"dynamic_truncate": True,
"srd_alignment": True, # for srd_alignment
"evaluate_begin": 0,
"similarity_threshold": 1, # disable same text check for different examples
"cross_validate_fold": -1, # split train and test datasets into 5 folds and repeat 3 trainer
}
[docs]
class ASTEConfigManager(ConfigManager):
def __init__(self, args, **kwargs):
"""
Available Params: {'model': None,
'optimizer': "",
'learning_rate': 0.00002,
'pretrained_bert': "yangheng/deberta-v3-base-absa-v1.1",
'cache_dataset': True,
'warmup_step': -1,
'deep_ensemble': False,
'patience': 99999,
'use_bert_spc': True,
'max_seq_len': 80,
'SRD': 3,
'lsa': False,
'dlcf_a': 2, # the a in dlcf_dca_bert
'dca_p': 1, # the p in dlcf_dca_bert
'dca_layer': 3, # the layer in dlcf_dca_bert
'use_syntax_based_SRD': False,
'sigma': 0.3,
'lcf': "cdw",
'window': "lr",
'eta': 1,
'eta_lr': 0.1,
'dropout': 0,
'l2reg': 0.000001,
'num_epoch': 10,
'batch_size': 16,
'initializer': 'xavier_uniform_',
'seed': {52, 214}
'output_dim': 3,
'log_step': 10,
'dynamic_truncate': True,
'srd_alignment': True, # for srd_alignment
'evaluate_begin': 0,
'similarity_threshold': 1, # disable same text check for different examples
'cross_validate_fold': -1 # split train and test datasets into 5 folds and repeat 3 trainer
}
:param args:
:param kwargs:
"""
super().__init__(args, **kwargs)
@staticmethod
[docs]
def set_aste_config(configType: str, newitem: dict):
if isinstance(newitem, dict):
if configType == "template":
_aste_config_template.update(newitem)
elif configType == "base":
_aste_config_base.update(newitem)
elif configType == "english":
_aste_config_english.update(newitem)
elif configType == "chinese":
_aste_config_chinese.update(newitem)
elif configType == "multilingual":
_aste_config_multilingual.update(newitem)
else:
raise ValueError(
"Wrong value of configuration_class type supplied, please use one from following type: template, base, english, chinese, multilingual, glove, bert_baseline"
)
else:
raise TypeError(
"Wrong type of new configuration_class item supplied, please use dict e.g.{'NewConfig': NewValue}"
)
@staticmethod
[docs]
def set_aste_config_template(newitem):
ASTEConfigManager.set_aste_config("template", newitem)
@staticmethod
[docs]
def set_aste_config_base(newitem):
ASTEConfigManager.set_aste_config("base", newitem)
@staticmethod
[docs]
def set_aste_config_english(newitem):
ASTEConfigManager.set_aste_config("english", newitem)
@staticmethod
[docs]
def set_aste_config_chinese(newitem):
ASTEConfigManager.set_aste_config("chinese", newitem)
@staticmethod
[docs]
def set_aste_config_multilingual(newitem):
ASTEConfigManager.set_aste_config("multilingual", newitem)
@staticmethod
[docs]
def get_aste_config_template():
_aste_config_template.update(_aste_config_template)
return ASTEConfigManager(copy.deepcopy(_aste_config_template))
@staticmethod
[docs]
def get_aste_config_base():
_aste_config_template.update(_aste_config_base)
return ASTEConfigManager(copy.deepcopy(_aste_config_template))
@staticmethod
[docs]
def get_aste_config_english():
_aste_config_template.update(_aste_config_english)
return ASTEConfigManager(copy.deepcopy(_aste_config_template))
@staticmethod
[docs]
def get_aste_config_chinese():
_aste_config_template.update(_aste_config_chinese)
return ASTEConfigManager(copy.deepcopy(_aste_config_template))
@staticmethod
[docs]
def get_aste_config_multilingual():
_aste_config_template.update(_aste_config_multilingual)
return ASTEConfigManager(copy.deepcopy(_aste_config_template))