def main():
###############################################################################
# Load data
###############################################################################
dictionary = data.Dictionary()
train_corpus = data.Corpus(dictionary)
dev_corpus = data.Corpus(dictionary)
test_corpus = data.Corpus(dictionary)
task_names = ['snli', 'multinli'] if args.task == 'allnli' else [args.task]
for task in task_names:
skip_first_line = True if task == 'sick' else False
train_corpus.parse(task,
args.data,
'train.txt',
args.tokenize,
num_examples=args.max_example,
skip_first_line=skip_first_line)
if task == 'multinli':
dev_corpus.parse(task, args.data, 'dev_matched.txt', args.tokenize)
dev_corpus.parse(task, args.data, 'dev_mismatched.txt',
args.tokenize)
test_corpus.parse(task,
args.data,
'test_matched.txt',
args.tokenize,
is_test_corpus=False)
test_corpus.parse(task,
args.data,
'test_mismatched.txt',
args.tokenize,
is_test_corpus=False)
else:
dev_corpus.parse(task,
args.data,
'dev.txt',
args.tokenize,
skip_first_line=skip_first_line)
test_corpus.parse(task,
args.data,
'test.txt',
args.tokenize,
is_test_corpus=False,
skip_first_line=skip_first_line)
print('train set size = ', len(train_corpus.data))
print('development set size = ', len(dev_corpus.data))
print('test set size = ', len(test_corpus.data))
print('vocabulary size = ', len(dictionary))
# save the dictionary object to use during testing
helper.save_object(dictionary,
args.save_path + args.task + '_dictionary.pkl')
embeddings_index = helper.load_word_embeddings(args.word_vectors_directory,
args.word_vectors_file,
dictionary.word2idx)
print('number of OOV words = ', len(dictionary) - len(embeddings_index))
# ###############################################################################
# # Build the model
# ###############################################################################
model = SentenceClassifier(dictionary, embeddings_index, args)
optim_fn, optim_params = helper.get_optimizer(args.optimizer)
optimizer = optim_fn(filter(lambda p: p.requires_grad, model.parameters()),
**optim_params)
best_acc = 0
if args.cuda:
model = model.cuda()
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# ###############################################################################
# # Train the model
# ###############################################################################
train = Train(model, optimizer, dictionary, embeddings_index, args,
best_acc)
bestmodel = train.train_epochs(train_corpus, dev_corpus, args.start_epoch,
args.epochs)
test_batches = helper.batchify(test_corpus.data, args.batch_size)
if 'multinli' in task_names:
print(
'Skipping evaluating best model. Evaluate using the test script.')
else:
test_accuracy, test_f1 = evaluate(bestmodel, test_batches, dictionary)
print('accuracy: %.2f%%' % test_accuracy)
print('f1: %.2f%%' % test_f1)
args.max_doc_length)
train_corpus.parse(args.data + 'train.txt', max_example=args.max_example)
print('train set size = ', len(train_corpus))
dev_corpus = data.Corpus(args.tokenize, args.max_query_length,
args.max_doc_length)
dev_corpus.parse(args.data + 'dev.txt')
print('development set size = ', len(dev_corpus))
dictionary = data.Dictionary()
dictionary.build_dict(train_corpus, args.max_words)
# save the dictionary object to use during testing
helper.save_object(dictionary, args.save_path + 'dictionary.p')
print('vocabulary size = ', len(dictionary))
embeddings_index = helper.load_word_embeddings(args.word_vectors_directory,
args.word_vectors_file,
dictionary.word2idx)
print('number of OOV words = ', len(dictionary) - len(embeddings_index))
# ###############################################################################
# # Build the model
# ###############################################################################
model = NSRF(dictionary, embeddings_index, args)
print(model)
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
args.lr)
best_loss = sys.maxsize
param_dict = helper.count_parameters(model)
print('number of trainable parameters = ',
ndcg_10 += NDCG(score, click_labels, 5)
map = map / num_batches
ndcg_1 = ndcg_1 / num_batches
ndcg_3 = ndcg_3 / num_batches
ndcg_10 = ndcg_10 / num_batches
print('MAP - ', map)
print('NDCG@1 - ', ndcg_1)
print('NDCG@3 - ', ndcg_3)
print('NDCG@10 - ', ndcg_10)
if __name__ == "__main__":
dictionary = helper.load_object(args.save_path + 'dictionary.p')
embeddings_index = helper.load_word_embeddings(
args.word_vectors_directory, 'glove.840B.300d.query.clicks.txt')
model = CNN_ARC_II(dictionary, embeddings_index, args)
if 'CUDA_VISIBLE_DEVICES' in os.environ:
cuda_visible_devices = [
int(x) for x in os.environ['CUDA_VISIBLE_DEVICES'].split(',')
]
if len(cuda_visible_devices) > 1:
model = torch.nn.DataParallel(model,
device_ids=cuda_visible_devices)
if args.cuda:
model = model.cuda()
helper.load_model_states_from_checkpoint(
model, os.path.join(args.save_path, 'model_best.pth.tar'),
'state_dict')
dictionary,
args.max_query_length,
args.max_doc_length,
is_test_corpus=True)
print('train set size = ', len(train_corpus.data))
print('dev set size = ', len(dev_corpus.data))
print('vocabulary size = ', len(dictionary))
# save the dictionary object to use during testing
helper.save_object(dictionary, args.save_path + 'dictionary.p')
# embeddings_index = helper.load_word_embeddings(args.word_vectors_directory, args.word_vectors_file)
# helper.save_word_embeddings(args.word_vectors_directory, 'glove.840B.300d.match.tensor.txt', embeddings_index,
# dictionary.idx2word)
embeddings_index = helper.load_word_embeddings(
args.word_vectors_directory, 'glove.840B.300d.match.tensor.txt')
print('Number of OOV words = ', len(dictionary) - len(embeddings_index))
# ###############################################################################
# # Build the model
# ###############################################################################
model = CNN_ARC_II(dictionary, embeddings_index, args)
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
args.lr)
best_loss = -1
param_dict = helper.count_parameters(model)
print('Number of trainable parameters = ',
numpy.sum(list(param_dict.values())))
args = util.get_args()
###############################################################################
# Load data
###############################################################################
dictionary = data.Dictionary()
test_corpus = data.Corpus(args.data, 'session_test.txt', dictionary)
print('test set size = ', len(test_corpus.data))
print('vocabulary size = ', len(dictionary))
# embeddings_index = helper.load_word_embeddings(args.word_vectors_directory, args.word_vectors_file)
# helper.save_word_embeddings(args.word_vectors_directory, 'glove.840B.300d.desm.txt', embeddings_index,
# dictionary.idx2word)
embeddings_index = helper.load_word_embeddings(args.word_vectors_directory,
'glove.840B.300d.desm.txt')
print('Number of OOV words = ', len(dictionary) - len(embeddings_index))
# ###############################################################################
# # Test
# ###############################################################################
def compute_document_embedding(document_body):
emb = numpy.zeros(300)
total_found = 0
for term in document_body:
if term in embeddings_index:
emb = numpy.add(numpy.array(embeddings_index[term]), emb)
total_found += 1
'session_train.txt', dictionary,
args.max_query_length, args.max_doc_length)
dev_corpus = data.Corpus(args.data + 'session_with_clicks/',
'session_dev.txt',
dictionary,
args.max_query_length,
args.max_doc_length,
is_test_corpus=True)
print('train set size = ', len(train_corpus))
print('dev set size = ', len(dev_corpus))
print('vocabulary size = ', len(dictionary))
# save the dictionary object to use during testing
helper.save_object(dictionary, args.save_path + 'dictionary.p')
embeddings_index = helper.load_word_embeddings(args.word_vectors_directory,
args.word_vectors_file)
helper.save_word_embeddings(args.word_vectors_directory,
'glove.6B.200d.query.clicks.txt', embeddings_index,
dictionary.idx2word)
# embeddings_index = helper.load_word_embeddings(args.word_vectors_directory, 'glove.6B.200d.query.clicks.txt')
print('Number of OOV words = ', len(dictionary) - len(embeddings_index))
# ###############################################################################
# # Build the model
# ###############################################################################
model = NSRM(dictionary, embeddings_index, args)
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
args.lr)
best_loss = -1
def main():
# if output directory doesn't exist, create it
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
# set the random seed manually for reproducibility.
numpy.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
else:
torch.cuda.manual_seed(args.seed)
print('\ncommand-line params : {0}\n'.format(sys.argv[1:]))
print('{0}\n'.format(args))
###############################################################################
# Load data
###############################################################################
dictionary = data.Dictionary()
tasks = []
train_dict, dev_dict = {}, {}
if 'quora' in args.task:
print('**Task name : Quora**')
# load quora dataset
quora_train = data.Corpus(args.data, dictionary)
quora_train.parse('quora/train.txt', 'quora', args.tokenize,
args.max_example)
print('Found {} pairs of train sentences.'.format(len(
quora_train.data)))
quora_dev = data.Corpus(args.data, dictionary)
quora_dev.parse('quora/dev.txt', 'quora', args.tokenize)
print('Found {} pairs of dev sentences.'.format(len(quora_dev.data)))
quora_test = data.Corpus(args.data, dictionary)
quora_test.parse('quora/test.txt', 'quora', args.tokenize)
print('Found {} pairs of test sentences.'.format(len(quora_test.data)))
tasks.append(('quora', 2))
train_dict['quora'] = quora_train
dev_dict['quora'] = quora_dev
if 'snli' in args.task:
print('**Task name : SNLI**')
# load snli dataset
snli_train = data.Corpus(args.data, dictionary)
snli_train.parse('snli/train.txt', 'snli', args.tokenize,
args.max_example)
print('Found {} pairs of train sentences.'.format(len(
snli_train.data)))
snli_dev = data.Corpus(args.data, dictionary)
snli_dev.parse('snli/dev.txt', 'snli', args.tokenize)
print('Found {} pairs of dev sentences.'.format(len(snli_dev.data)))
snli_test = data.Corpus(args.data, dictionary)
snli_test.parse('snli/test.txt', 'snli', args.tokenize)
print('Found {} pairs of test sentences.'.format(len(snli_test.data)))
tasks.append(('snli', 3))
train_dict['snli'] = snli_train
dev_dict['snli'] = snli_dev
if 'multinli' in args.task:
print('**Task name : Multi-NLI**')
# load multinli dataset
multinli_train = data.Corpus(args.data, dictionary)
multinli_train.parse('multinli/train.txt', 'multinli', args.tokenize,
args.max_example)
print('Found {} pairs of train sentences.'.format(
len(multinli_train.data)))
multinli_dev = data.Corpus(args.data, dictionary)
multinli_dev.parse('multinli/dev_matched.txt', 'multinli',
args.tokenize)
multinli_dev.parse('multinli/dev_mismatched.txt', 'multinli',
args.tokenize)
print('Found {} pairs of dev sentences.'.format(len(
multinli_dev.data)))
multinli_test = data.Corpus(args.data, dictionary)
multinli_test.parse('multinli/test_matched.txt', 'multinli',
args.tokenize)
multinli_test.parse('multinli/test_mismatched.txt', 'multinli',
args.tokenize)
print('Found {} pairs of test sentences.'.format(
len(multinli_test.data)))
tasks.append(('multinli', 3))
train_dict['multinli'] = multinli_train
dev_dict['multinli'] = multinli_dev
if 'allnli' in args.task:
print('**Task name : AllNLI**')
# load allnli dataset
allnli_train = data.Corpus(args.data, dictionary)
allnli_train.parse('snli/train.txt', 'snli', args.tokenize,
args.max_example)
allnli_train.parse('multinli/train.txt', 'multinli', args.tokenize,
args.max_example)
print('Found {} pairs of train sentences.'.format(
len(allnli_train.data)))
allnli_dev = data.Corpus(args.data, dictionary)
allnli_dev.parse('snli/dev.txt', 'snli', args.tokenize)
allnli_dev.parse('multinli/dev_matched.txt', 'multinli', args.tokenize)
allnli_dev.parse('multinli/dev_mismatched.txt', 'multinli',
args.tokenize)
print('Found {} pairs of dev sentences.'.format(len(allnli_dev.data)))
allnli_test = data.Corpus(args.data, dictionary)
allnli_test.parse('snli/test.txt', 'snli', args.tokenize)
allnli_test.parse('multinli/test_matched.txt', 'multinli',
args.tokenize)
allnli_test.parse('multinli/test_mismatched.txt', 'multinli',
args.tokenize)
print('Found {} pairs of test sentences.'.format(len(
allnli_test.data)))
tasks.append(('allnli', 3))
train_dict['allnli'] = allnli_train
dev_dict['allnli'] = allnli_dev
print('\nvocabulary size = ', len(dictionary))
# save the dictionary object to use during testing
helper.save_object(dictionary, args.save_path + 'dictionary.p')
embeddings_index = helper.load_word_embeddings(args.word_vectors_directory,
args.word_vectors_file,
dictionary.word2idx)
print('number of OOV words = ', len(dictionary) - len(embeddings_index))
# ###############################################################################
# # Build the model
# ###############################################################################
if not tasks:
return
model = MultitaskDomainAdapter(dictionary, embeddings_index, args, tasks)
print(model)
optim_fn, optim_params = helper.get_optimizer(args.optimizer)
optimizer = optim_fn(filter(lambda p: p.requires_grad, model.parameters()),
**optim_params)
best_accuracy = 0
# for training on multiple GPUs. use CUDA_VISIBLE_DEVICES=0,1 to specify which GPUs to use
if 'CUDA_VISIBLE_DEVICES' in os.environ:
cuda_visible_devices = [
int(x) for x in os.environ['CUDA_VISIBLE_DEVICES'].split(',')
]
if len(cuda_visible_devices) > 1:
model = torch.nn.DataParallel(model,
device_ids=cuda_visible_devices)
if args.cuda:
model = model.cuda()
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_accuracy = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict']['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# ###############################################################################
# # Train the model
# ###############################################################################
train = Train(model, optimizer, dictionary, embeddings_index, args,
best_accuracy)
train.set_train_dev_corpus(train_dict, dev_dict)
train.train_epochs(args.start_epoch, args.epochs)
