def _tf_extractive_qa_reader(model_module_constructor,
resources_or_conf: Union[dict, SharedResources]):
from jack.readers.extractive_qa.shared import XQAInputModule, XQAOutputModule
shared_resources = create_shared_resources(resources_or_conf)
input_module = XQAInputModule(shared_resources)
model_module = model_module_constructor(shared_resources)
output_module = XQAOutputModule()
return TFReader(shared_resources, input_module, model_module,
output_module)
def fastqa_reader(resources_or_conf: Union[dict, SharedResources] = None):
"""Creates a FastQA reader instance (extractive qa model)."""
from jack.readers.extractive_qa.fastqa import FastQAModule
from jack.readers.extractive_qa.shared import XQAInputModule, XQAOutputModule
shared_resources = create_shared_resources(resources_or_conf)
input_module = XQAInputModule(shared_resources)
model_module = FastQAModule(shared_resources)
output_module = XQAOutputModule(shared_resources)
return TFReader(shared_resources, input_module, model_module,
output_module)
def bidaf_reader(resources_or_conf: Union[dict, SharedResources] = None):
"""Creates a FastQA model as described in https://arxiv.org/abs/1703.04816 (extractive qa model)."""
from jack.readers.extractive_qa.shared import XQAInputModule, XQAOutputModule
from jack.readers.extractive_qa.bidaf import BiDAF
shared_resources = create_shared_resources(resources_or_conf)
input_module = XQAInputModule(shared_resources)
model_module = BiDAF(shared_resources)
output_module = XQAOutputModule(shared_resources)
return TFReader(shared_resources, input_module, model_module,
output_module)
def test_fastqa():
tf.reset_default_graph()
data = load_jack('tests/test_data/squad/snippet_jtr.json')
# fast qa must be initialized with existing embeddings, so we create some
embeddings = load_embeddings('./tests/test_data/glove.840B.300d_top256.txt', 'glove')
# we need a vocabulary (with embeddings for our fastqa_reader, but this is not always necessary)
vocab = Vocab(emb=embeddings, init_from_embeddings=True)
# ... and a config
config = {
"batch_size": 1,
"repr_dim": 10,
"repr_dim_input": embeddings.lookup.shape[1],
"with_char_embeddings": True
}
# create/setup reader
shared_resources = SharedResources(vocab, config)
input_module = XQAInputModule(shared_resources)
model_module = FastQAModule(shared_resources)
output_module = XQAOutputModule()
reader = TFReader(shared_resources, input_module, model_module, output_module)
reader.setup_from_data(data, is_training=True)
loss = reader.model_module.tensors[Ports.loss]
optimizer = tf.train.AdagradOptimizer(learning_rate=0.01)
min_op = optimizer.minimize(loss)
session = model_module.tf_session
session.run(tf.global_variables_initializer())
for epoch in range(0, 10):
for batch in reader.input_module.batch_generator(data, 1, False):
feed_dict = reader.model_module.convert_to_feed_dict(batch)
loss_value, _ = session.run((loss, min_op), feed_dict=feed_dict)
print(loss_value)