def initialize(self, embeddings):
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
vocab_sources = [self.dataset['train_file'], self.dataset['valid_file']]
# TODO: make this optional
if 'test_file' in self.dataset:
vocab_sources.append(self.dataset['test_file'])
vocab1, vocab2 = self.reader.build_vocabs(vocab_sources,
min_f=Task._get_min_f(self.config_params),
vocab_file=self.dataset.get('vocab_file'))
# To keep the config file simple, share a list between source and destination (tgt)
features_src = []
features_tgt = None
for feature in self.config_params['features']:
if feature['name'] == 'tgt':
features_tgt = feature
else:
features_src += [feature]
self.src_embeddings, self.feat2src = self._create_embeddings(embeddings_set, vocab1, features_src)
# For now, dont allow multiple vocabs of output
baseline.save_vocabs(self.get_basedir(), self.feat2src)
self.tgt_embeddings, self.feat2tgt = self._create_embeddings(embeddings_set, {'tgt': vocab2}, [features_tgt])
baseline.save_vocabs(self.get_basedir(), self.feat2tgt)
self.tgt_embeddings = self.tgt_embeddings['tgt']
self.feat2tgt = self.feat2tgt['tgt']
def initialize(self, embeddings):
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
vocab1, vocab2 = self.reader.build_vocabs(
[self.dataset['train_file'], self.dataset['valid_file'], self.dataset['test_file']],
min_f=Task._get_min_f(self.config_params),
vocab_file=self.dataset.get('vocab_file')
)
# To keep the config file simple, share a list between source and destination (tgt)
features_src = []
features_tgt = None
for feature in self.config_params['features']:
if feature['name'] == 'tgt':
features_tgt = feature
else:
features_src += [feature]
self.src_embeddings, self.feat2src = self._create_embeddings(embeddings_set, vocab1, features_src)
# For now, dont allow multiple vocabs of output
baseline.save_vocabs(self.get_basedir(), self.feat2src)
self.tgt_embeddings, self.feat2tgt = self._create_embeddings(embeddings_set, {'tgt': vocab2}, [features_tgt])
baseline.save_vocabs(self.get_basedir(), self.feat2tgt)
self.tgt_embeddings = self.tgt_embeddings['tgt']
self.feat2tgt = self.feat2tgt['tgt']
def initialize(self, embeddings):
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
vocabs = self.reader.build_vocab(
[self.dataset['train_file'], self.dataset['valid_file'], self.dataset['test_file']],
min_f=Task._get_min_f(self.config_params),
)
self.embeddings, self.feat2index = self._create_embeddings(embeddings_set, vocabs, self.config_params['features'])
baseline.save_vocabs(self.get_basedir(), self.feat2index)
def initialize(self, embeddings):
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
vocabs = self.reader.build_vocab(
[self.dataset['train_file'], self.dataset['valid_file'], self.dataset['test_file']],
min_f=Task._get_min_f(self.config_params),
)
self.embeddings, self.feat2index = self._create_embeddings(embeddings_set, vocabs, self.config_params['features'])
baseline.save_vocabs(self.get_basedir(), self.feat2index)
def initialize(self, embeddings):
embeddings = read_config_file_or_json(embeddings, 'embeddings')
embeddings_set = index_by_label(embeddings)
self.dataset = DataDownloader(self.dataset, self.data_download_cache).download()
print_dataset_info(self.dataset)
vocab_sources = [self.dataset['train_file'], self.dataset['valid_file']]
# TODO: make this optional
if 'test_file' in self.dataset:
vocab_sources.append(self.dataset['test_file'])
vocabs = self.reader.build_vocab(vocab_sources,
min_f=Task._get_min_f(self.config_params),
vocab_file=self.dataset.get('vocab_file'))
self.embeddings, self.feat2index = self._create_embeddings(embeddings_set, vocabs, self.config_params['features'])
baseline.save_vocabs(self.get_basedir(), self.feat2index)
