def __init__(self, exp_name: str):
self.exp_name = exp_name
self.model_dir = 'saved_models'
self.hyper = Hyper(os.path.join('experiments',
self.exp_name + '.json'))
self.gpu = self.hyper.gpu
if self.hyper.is_bert == 'ERNIE':
self.preprocessor = Chinese_selection_preprocessing(self.hyper)
elif self.hyper.is_bert == "bert_bilstem_crf":
self.preprocessor = NYT_selection_preprocessing(self.hyper)
elif self.hyper.is_bert == "nyt_bert_tokenizer":
self.preprocessor = NYT_bert_selection_preprocessing(self.hyper)
elif self.hyper.is_bert == "nyt11_bert_tokenizer":
self.preprocessor = NYT11_bert_selection_preprocessing(self.hyper)
elif self.hyper.is_bert == "nyt10_bert_tokenizer":
self.preprocessor = NYT10_bert_selection_preprocessing(self.hyper)
self.metrics = F1_triplet()
def __init__(self, exp_name: str):
self.exp_name = exp_name
self.model_dir = 'saved_models'
self.hyper = Hyper(os.path.join('experiments',
self.exp_name + '.json'))
self.gpu = self.hyper.gpu
self.preprocessor = None
self.selection_metrics = F1_selection()
self.optimizer = None
self.model = None
def __init__(self, exp_name: str):
self.exp_name = exp_name
self.model_dir = 'save_models'
self.hyper = Hyper(os.path.join('experiments',
self.exp_name + '.json'))
self.gpu = self.hyper.gpu
self.criterion = None
self.preprocessor = None
self.optimizer = None
self.model = None
self.metrics = [F1_resnet(i) for i in range(4)]
def __init__(self, exp_name: str):
self.exp_name = exp_name
self.model_dir = 'saved_models'
self.device = torch.device('cpu')
self.hyper = Hyper(os.path.join('experiments',
self.exp_name + '.json'))
self.gpu = self.hyper.gpu
self.preprocessor = None
self.triplet_metrics = F1_triplet()
self.ner_metrics = F1_ner()
self.optimizer = None
self.model = None
def __init__(self, exp_name: str, model_name):
self.exp_name = exp_name
self.model_dir = 'saved_models'
self.model_name = model_name
self.hyper = Hyper(os.path.join('experiments',
self.exp_name + '.json'))
self.hyper.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.gpu = self.hyper.gpu
self.preprocessor = None
self.triplet_metrics = F1_triplet()
self.ner_metrics = F1_ner()
self.save_err = SaveError()
self.save_rc = SaveRecord(self.exp_name)
self.p_metrics = F1_P()
self.optimizer = None
self.model = None
self.model_p = None
def __init__(self, exp_name: str):
self.exp_name = exp_name
self.model_dir = "saved_models"
self.hyper = Hyper(os.path.join("experiments",
self.exp_name + ".json"))
self.gpu = self.hyper.gpu
self.preprocessor = self._preprocessor(self.hyper.model)
# self.metrics = F1_triplet()
self.optimizer = None
self.model = None
self.Dataset, self.Loader = self._init_loader(self.hyper.model)
logging.basicConfig(
filename=os.path.join("experiments", self.exp_name + ".log"),
filemode="w",
format="%(asctime)s - %(message)s",
level=logging.INFO,
)