import json import numpy as np import torch import torch.nn as nn from collections import namedtuple from copy import deepcopy from torch.nn.utils import clip_grad_norm_ from torch.optim import Adam, SGD from utils.io_ import seeds, Writer, get_logger, Index2Instance, prepare_data, write_extra_labels from utils.models.sequence_tagger import Sequence_Tagger from utils import load_word_embeddings from utils.tasks.seqeval import accuracy_score, f1_score, precision_score, recall_score uid = uuid.uuid4().hex[:6] logger = get_logger('SequenceTagger') def read_arguments(): args_ = argparse.ArgumentParser(description='Sovling SequenceTagger') args_.add_argument('--dataset', choices=['ontonotes', 'ud'], help='Dataset', required=True) args_.add_argument('--domain', help='domain', required=True) args_.add_argument('--rnn_mode', choices=['RNN', 'LSTM', 'GRU'], help='architecture of rnn', required=True) args_.add_argument('--task', default='distance_from_the_root',
import numpy as np import torch import torch.nn as nn from collections import namedtuple from utils.io_ import seeds, Writer, get_logger, prepare_data, rearrange_splits from utils.models.parsing_gating import BiAffine_Parser_Gated from utils import load_word_embeddings from utils.tasks import parse import time from torch.nn.utils import clip_grad_norm_ from torch.optim import Adam, SGD import uuid uid = uuid.uuid4().hex[:6] logger = get_logger('GraphParser') def read_arguments(): args_ = argparse.ArgumentParser(description='Sovling GraphParser') args_.add_argument('--dataset', choices=['ontonotes', 'ud'], help='Dataset', required=True) args_.add_argument('--domain', help='domain/language', required=True) args_.add_argument('--rnn_mode', choices=['RNN', 'LSTM', 'GRU'], help='architecture of rnn', required=True) args_.add_argument('--gating', action='store_true',
import numpy as np import torch import torch.nn as nn from collections import namedtuple from utils.io_ import seeds, Writer, get_logger, prepare_data, rearrange_splits from utils.models.parsing_gating import BiAffine_Parser_Gated from utils import load_word_embeddings from utils.tasks import parse import os from subprocess import Popen, PIPE, run from torch.nn.utils import clip_grad_norm_ from torch.optim import Adam, SGD uid = uuid.uuid4().hex[:6] logger = get_logger('GraphParser_for_DA') def read_arguments(): args_ = argparse.ArgumentParser(description='Sovling GraphParser_for_DA') args_.add_argument('--dataset', choices=['ud', 'ontonotes'], help='Dataset', required=True) args_.add_argument('--src_domain', help='source domain/language', required=True) args_.add_argument('--tgt_domain', help='target domain/language', required=True) args_.add_argument('--rnn_mode',