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zeroshot.py
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import os,random, pickle, json, argparse, time, torch, logging, warnings
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from data import CorpusQA, CorpusSC, CorpusTC, CorpusPO, CorpusPA
from utils import evaluateQA, evaluateNLI, evaluateNER, evaluatePOS, evaluatePA
from model import BertMetaLearning
from itertools import cycle
from tqdm import tqdm
from datapath import loc
from transformers import (
WEIGHTS_NAME,
AdamW,
get_linear_schedule_with_warmup,
squad_convert_examples_to_features,
)
from transformers.data.metrics.squad_metrics import (
compute_predictions_log_probs,
compute_predictions_logits,
squad_evaluate,
)
logging.getLogger("transformers.tokenization_utils").setLevel(logging.ERROR)
warnings.filterwarnings("ignore")
parser = argparse.ArgumentParser()
parser.add_argument('--lr', type=float, default=3e-5, help='learning rate')
parser.add_argument('--dropout', type=float, default=0.1, help='')
parser.add_argument('--hidden_dims', type=int, default=768, help='')
parser.add_argument('--sc_labels', type=int, default=3, help='')
parser.add_argument('--qa_labels', type=int, default=2, help='')
parser.add_argument('--tc_labels', type=int, default=10, help='')
parser.add_argument('--po_labels', type=int, default=18, help='')
parser.add_argument('--pa_labels', type=int, default=2, help='')
parser.add_argument('--qa_batch_size', type=int, default=8, help='batch size')
parser.add_argument('--sc_batch_size', type=int, default=32, help='batch size')
parser.add_argument('--tc_batch_size', type=int, default=32, help='batch size')
parser.add_argument('--po_batch_size', type=int, default=32, help='batch_size')
parser.add_argument('--pa_batch_size', type=int, default=8, help='batch size')
parser.add_argument('--seed', type=int, default=0, help='seed for numpy and pytorch')
parser.add_argument('--log_interval', type=int, default=100, help='Print after every log_interval batches')
parser.add_argument('--cuda', action='store_true',help='use CUDA')
parser.add_argument('--save', type=str, default='saved/', help='')
parser.add_argument('--load', type=str, default='', help='')
parser.add_argument('--model_name', type=str, default='model.pt', help='')
parser.add_argument('--grad_clip', type=float, default=1.0)
parser.add_argument('--task',type=str,default='qa_hi')
parser.add_argument("--n_best_size", default=20, type=int)
parser.add_argument("--max_answer_length", default=30, type=int)
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--warmup", default=0, type=int)
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
args = parser.parse_args()
print (args)
logger = {'args':vars(args)}
logger['train_loss'] = []
logger['val_loss'] = []
logger['val_metric'] = []
logger['train_metric'] = []
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if not os.path.exists(args.save):
os.makedirs(args.save)
if torch.cuda.is_available():
if not args.cuda:
# print("WARNING: You have a CUDA device, so you should probably run with --cuda")
args.cuda = True
torch.cuda.manual_seed_all(args.seed)
DEVICE = torch.device("cuda" if args.cuda else "cpu")
def load_data(task_lang):
[task, lang] = task_lang.split('_')
if task == 'qa':
test_corpus = CorpusQA(loc['test'][task_lang][0], loc['test'][task_lang][1])
batch_size = args.qa_batch_size
elif task == 'sc':
test_corpus = CorpusSC(loc['test'][task_lang][0], loc['test'][task_lang][1])
batch_size = args.sc_batch_size
elif task == 'tc':
test_corpus = CorpusTC(loc['test'][task_lang][0])
batch_size = args.tc_batch_size
elif task == 'po':
test_corpus = CorpusPO(loc['test'][task_lang][0])
batch_size = args.po_batch_size
elif task == 'pa':
test_corpus = CorpusPA(loc['test'][task_lang][0])
batch_size = args.pa_batch_size
return test_corpus, batch_size
test_corpus, batch_size = load_data(args.task)
test_dataloader = DataLoader(test_corpus, batch_size = batch_size, pin_memory = True, drop_last = True)
model = BertMetaLearning(args).to(DEVICE)
if args.load != '':
model = torch.load(args.load)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optim = AdamW(optimizer_grouped_parameters, lr = args.lr, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optim, num_warmup_steps=args.warmup, num_training_steps=10000)
def test():
model.eval()
if 'qa' in args.task:
result = evaluateQA(model, test_corpus, 'test_'+args.task, args.save)
print ('test_f1 {:10.8f}'.format(result['f1']))
with open(os.path.join(args.save,'test.json'), 'w') as outfile:
json.dump(result, outfile)
test_loss = -result['f1']
elif 'sc' in args.task:
test_loss, test_acc = evaluateNLI(model, test_dataloader, DEVICE)
print ('test_loss {:10.8f} test_acc {:6.4f}'.format(test_loss, test_acc))
elif 'tc' in args.task:
test_loss, test_acc = evaluateNER(model, test_dataloader, DEVICE)
print ('test_loss {:10.8f} test_acc {:6.4f}'.format(test_loss, test_acc))
elif 'po' in args.task:
test_loss, test_acc = evaluatePOS(model, test_dataloader, DEVICE)
print ('test_loss {:10.8f} test_acc {:6.4f}'.format(test_loss, test_acc))
elif 'pa' in args.task:
test_loss, test_acc = evaluatePA(model, test_dataloader, DEVICE)
print ('test_loss {:10.8f} test_acc {:6.4f}'.format(test_loss, test_acc))
return test_loss
if __name__ == '__main__':
test()