def main():
parser = argparse.ArgumentParser("Train our ELMo model on SQuAD")
parser.add_argument("loss_mode", choices=['default', 'confidence'])
parser.add_argument("output_dir")
parser.add_argument("--dim", type=int, default=90)
parser.add_argument("--l2", type=float, default=0)
parser.add_argument("--mode",
choices=["input", "output", "both", "none"],
default="both")
parser.add_argument("--top_layer_only", action="store_true")
parser.add_argument("--no-tfidf",
action='store_true',
help="Don't add TF-IDF negative examples")
args = parser.parse_args()
out = args.output_dir + "-" + datetime.now().strftime("%m%d-%H%M%S")
dim = args.dim
recurrent_layer = CudnnGru(dim, w_init=TruncatedNormal(stddev=0.05))
if args.loss_mode == 'default':
n_epochs = 24
answer_encoder = SingleSpanAnswerEncoder()
predictor = BoundsPredictor(
ChainBiMapper(first_layer=recurrent_layer,
second_layer=recurrent_layer))
batcher = ClusteredBatcher(45, ContextLenKey(), False, False)
data = DocumentQaTrainingData(SquadCorpus(), None, batcher, batcher)
elif args.loss_mode == 'confidence':
if args.no_tfidf:
prepro = SquadDefault()
n_epochs = 15
else:
prepro = SquadTfIdfRanker(NltkPlusStopWords(True), 4, True)
n_epochs = 50
answer_encoder = DenseMultiSpanAnswerEncoder()
predictor = ConfidencePredictor(ChainBiMapper(
first_layer=recurrent_layer,
second_layer=recurrent_layer,
),
AttentionEncoder(),
FullyConnected(80, activation="tanh"),
aggregate="sum")
eval_dataset = RandomParagraphSetDatasetBuilder(
100, 'flatten', True, 0)
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True,
False)
data = PreprocessedData(SquadCorpus(),
prepro,
StratifyParagraphsBuilder(train_batching, 1),
eval_dataset,
eval_on_verified=False)
data.preprocess(1)
params = trainer.TrainParams(trainer.SerializableOptimizer(
"Adadelta", dict(learning_rate=1.0)),
ema=0.999,
max_checkpoints_to_keep=2,
async_encoding=10,
num_epochs=n_epochs,
log_period=30,
eval_period=1200,
save_period=1200,
best_weights=("dev", "b17/text-f1"),
eval_samples=dict(dev=None, train=8000))
lm_reduce = MapperSeq(
ElmoLayer(args.l2,
layer_norm=False,
top_layer_only=args.top_layer_only),
DropoutLayer(0.5),
)
model = AttentionWithElmo(
encoder=DocumentAndQuestionEncoder(answer_encoder),
lm_model=SquadContextConcatSkip(),
append_before_atten=(args.mode == "both" or args.mode == "output"),
append_embed=(args.mode == "both" or args.mode == "input"),
max_batch_size=128,
word_embed=FixedWordEmbedder(vec_name="glove.840B.300d",
word_vec_init_scale=0,
learn_unk=False,
cpu=True),
char_embed=CharWordEmbedder(LearnedCharEmbedder(word_size_th=14,
char_th=49,
char_dim=20,
init_scale=0.05,
force_cpu=True),
MaxPool(Conv1d(100, 5, 0.8)),
shared_parameters=True),
embed_mapper=SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
),
lm_reduce=None,
lm_reduce_shared=lm_reduce,
per_sentence=False,
memory_builder=NullBiMapper(),
attention=BiAttention(TriLinear(bias=True), True),
match_encoder=SequenceMapperSeq(
FullyConnected(dim * 2, activation="relu"),
ResidualLayer(
SequenceMapperSeq(
VariationalDropoutLayer(0.8),
recurrent_layer,
VariationalDropoutLayer(0.8),
StaticAttentionSelf(TriLinear(bias=True),
ConcatWithProduct()),
FullyConnected(dim * 2, activation="relu"),
)), VariationalDropoutLayer(0.8)),
predictor=predictor)
with open(__file__, "r") as f:
notes = f.read()
notes = str(sorted(args.__dict__.items(),
key=lambda x: x[0])) + "\n" + notes
trainer.start_training(
data, model, params,
[LossEvaluator(),
SpanEvaluator(bound=[17], text_eval="squad")], ModelDir(out), notes)
def main():
parser = argparse.ArgumentParser(
description='Train a model on document-level SQuAD')
parser.add_argument(
'mode',
choices=["paragraph", "confidence", "shared-norm", "merge", "sigmoid"])
parser.add_argument("name", help="Output directory")
args = parser.parse_args()
mode = args.mode
out = args.name + "-" + datetime.now().strftime("%m%d-%H%M%S")
corpus = SquadCorpus()
if mode == "merge":
# Adds paragraph start tokens, since we will be concatenating paragraphs together
pre = WithIndicators(True, para_tokens=False, doc_start_token=False)
else:
pre = None
model = get_model(50, 100, args.mode, pre)
if mode == "paragraph":
# Run in the "standard" known-paragraph setting
if model.preprocessor is not None:
raise NotImplementedError()
n_epochs = 26
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True,
False)
eval_batching = ClusteredBatcher(45, ContextLenKey(), False, False)
data = DocumentQaTrainingData(corpus, None, train_batching,
eval_batching)
eval = [LossEvaluator(), SpanEvaluator(bound=[17], text_eval="squad")]
else:
eval_set_mode = {
"confidence": "flatten",
"sigmoid": "flatten",
"shared-norm": "group",
"merge": "merge"
}[mode]
eval_dataset = RandomParagraphSetDatasetBuilder(
100, eval_set_mode, True, 0)
if mode == "confidence" or mode == "sigmoid":
if mode == "sigmoid":
# needs to be trained for a really long time for reasons unknown, even this might be too small
n_epochs = 100
else:
n_epochs = 50 # more epochs since we only "see" the label very other epoch-osh
train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3),
True, False)
data = PreprocessedData(
SquadCorpus(),
SquadTfIdfRanker(NltkPlusStopWords(True), 4, True,
model.preprocessor),
StratifyParagraphsBuilder(train_batching, 1),
eval_dataset,
eval_on_verified=False,
)
else:
n_epochs = 26
data = PreprocessedData(
SquadCorpus(),
SquadTfIdfRanker(NltkPlusStopWords(True), 4, True,
model.preprocessor),
StratifyParagraphSetsBuilder(25, args.mode == "merge", True,
1),
eval_dataset,
eval_on_verified=False,
)
eval = [LossEvaluator(), MultiParagraphSpanEvaluator(17, "squad")]
data.preprocess(1)
with open(__file__, "r") as f:
notes = f.read()
notes = args.mode + "\n" + notes
trainer.start_training(data, model, train_params(n_epochs), eval,
model_dir.ModelDir(out), notes)