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Merge pull request #165 from kermitt2/max-epoch-param
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add parameter --max-epoch
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@kermitt2
kermitt2 authored Nov 29, 2023
2 parents 3671cd4 + 194767d commit 236c2e3
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Showing 6 changed files with 220 additions and 97 deletions.
76 changes: 53 additions & 23 deletions delft/applications/datasetTagger.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -8,17 +8,19 @@

from delft.sequenceLabelling import Sequence
from delft.sequenceLabelling.reader import load_data_and_labels_json_offsets
from delft.utilities.Utilities import t_or_f
from delft.utilities.misc import parse_number_ranges

def configure(architecture, output_path=None, max_sequence_length=-1, batch_size=-1, embeddings_name=None,
max_epoch=-1, use_ELMo=False, patience=-1):
def configure(architecture, output_path=None, max_sequence_length=-1,
batch_size=-1, embeddings_name=None,
max_epoch=-1, use_ELMo=False, patience=-1, early_stop=None):
"""
Set up the default parameters based on the model type.
"""
model_name = 'datasets'

multiprocessing = True
early_stop = True
o_early_stop = True

if "BERT" in architecture:
# architectures with some transformer layer/embeddings inside
Expand Down Expand Up @@ -57,18 +59,26 @@ def configure(architecture, output_path=None, max_sequence_length=-1, batch_size
if max_epoch == -1:
max_epoch = 60

if early_stop is not None:
o_early_stop = early_stop

if patience == -1:
patience = 5

return batch_size, max_sequence_length, model_name, embeddings_name, max_epoch, multiprocessing, early_stop, patience
return batch_size, max_sequence_length, model_name, embeddings_name, max_epoch, multiprocessing, o_early_stop, patience


# train a model with all available data
def train(embeddings_name=None, architecture='BidLSTM_CRF', transformer=None,
input_path=None, output_path=None, fold_count=1,
features_indices=None, max_sequence_length=-1,
batch_size=-1, max_epoch=-1, use_ELMo=False, patience=-1,
learning_rate=None, multi_gpu=False):
input_path=None, output_path=None, fold_count=1,
features_indices=None, max_sequence_length=-1,
batch_size=-1, use_ELMo=False,
max_epoch=-1,
patience=-1,
learning_rate=None,
early_stop=None,
multi_gpu=False):

print('Loading data...')
if input_path is None:
x_all1 = y_all1 = x_all2 = y_all2 = x_all3 = y_all3 = []
Expand Down Expand Up @@ -98,7 +108,8 @@ def train(embeddings_name=None, architecture='BidLSTM_CRF', transformer=None,
embeddings_name,
max_epoch,
use_ELMo,
patience)
patience,
early_stop)
model = Sequence(model_name,
recurrent_dropout=0.50,
embeddings_name=embeddings_name,
Expand Down Expand Up @@ -130,9 +141,14 @@ def train(embeddings_name=None, architecture='BidLSTM_CRF', transformer=None,

# split data, train a model and evaluate it
def train_eval(embeddings_name=None, architecture='BidLSTM_CRF', transformer=None,
input_path=None, output_path=None, fold_count=1,
features_indices=None, max_sequence_length=-1, batch_size=-1, max_epoch=-1, use_ELMo=False,
patience=-1, learning_rate=None, multi_gpu=False):
input_path=None, output_path=None, fold_count=1,
features_indices=None, max_sequence_length=-1, batch_size=-1, use_ELMo=False,
max_epoch=-1,
patience=-1,
learning_rate=None,
early_stop=None,
multi_gpu=False):

print('Loading data...')
if input_path is None:
x_all1 = y_all1 = x_all2 = y_all2 = x_all3 = y_all3 = []
Expand Down Expand Up @@ -164,7 +180,8 @@ def train_eval(embeddings_name=None, architecture='BidLSTM_CRF', transformer=Non
embeddings_name,
max_epoch,
use_ELMo,
patience=patience)
patience=patience,
early_stop=early_stop)
model = Sequence(model_name,
recurrent_dropout=0.50,
embeddings_name=embeddings_name,
Expand Down Expand Up @@ -285,10 +302,17 @@ def annotate_text(texts, output_format, architecture='BidLSTM_CRF', features=Non
parser.add_argument("--patience", type=int, default=-1, help="patience, number of extra epochs to perform after "
"the best epoch before stopping a training.")
parser.add_argument("--learning-rate", type=float, default=None, help="Initial learning rate")

parser.add_argument("--max-epoch", type=int, default=-1,
help="Maximum number of epochs.")
parser.add_argument("--early-stop", type=t_or_f, default=None,
help="Force early training termination when metrics scores are not improving " +
"after a number of epochs equals to the patience parameter.")
parser.add_argument("--multi-gpu", default=False,
help="Enable the support for distributed computing (the batch size needs to be set accordingly using --batch-size)",
action="store_true")


args = parser.parse_args()

action = args.action
Expand All @@ -302,6 +326,8 @@ def annotate_text(texts, output_format, architecture='BidLSTM_CRF', features=Non
use_ELMo = args.use_ELMo
patience = args.patience
learning_rate = args.learning_rate
max_epoch = args.max_epoch
early_stop = args.early_stop
multi_gpu = args.multi_gpu

if transformer is None and embeddings_name is None:
Expand All @@ -310,16 +336,18 @@ def annotate_text(texts, output_format, architecture='BidLSTM_CRF', features=Non

if action == "train":
train(embeddings_name=embeddings_name,
architecture=architecture,
transformer=transformer,
input_path=input_path,
output_path=output,
max_sequence_length=max_sequence_length,
batch_size=batch_size,
use_ELMo=use_ELMo,
patience=patience,
learning_rate=learning_rate,
multi_gpu=multi_gpu)
architecture=architecture,
transformer=transformer,
input_path=input_path,
output_path=output,
max_sequence_length=max_sequence_length,
batch_size=batch_size,
use_ELMo=use_ELMo,
patience=patience,
learning_rate=learning_rate,
max_epoch=max_epoch,
early_stop=early_stop,
multi_gpu=multi_gpu)

if action == "eval":
if args.fold_count is not None and args.fold_count > 1:
Expand All @@ -343,6 +371,8 @@ def annotate_text(texts, output_format, architecture='BidLSTM_CRF', features=Non
use_ELMo=use_ELMo,
patience=patience,
learning_rate=learning_rate,
max_epoch=max_epoch,
early_stop=early_stop,
multi_gpu=multi_gpu)

if action == "tag":
Expand Down
69 changes: 45 additions & 24 deletions delft/applications/grobidTagger.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -8,13 +8,13 @@

from delft.sequenceLabelling import Sequence
from delft.sequenceLabelling.reader import load_data_and_labels_crf_file
from delft.utilities.Utilities import longest_row
from delft.utilities.Utilities import longest_row, t_or_f

MODEL_LIST = ['affiliation-address', 'citation', 'date', 'header', 'name-citation', 'name-header', 'software', 'figure', 'table', 'reference-segmenter', 'segmentation', 'funding-acknowledgement']


def configure(model, architecture, output_path=None, max_sequence_length=-1, batch_size=-1,
embeddings_name=None, max_epoch=-1, use_ELMo=False, patience=-1):
embeddings_name=None, max_epoch=-1, use_ELMo=False, patience=-1, early_stop=None):
"""
Set up the default parameters based on the model type.
"""
Expand All @@ -24,7 +24,7 @@ def configure(model, architecture, output_path=None, max_sequence_length=-1, bat
model_name = 'grobid-' + model

multiprocessing = True
early_stop = True
o_early_stop = True

if architecture and "BERT" in architecture:
# architectures with some transformer layer/embeddings inside
Expand Down Expand Up @@ -56,7 +56,7 @@ def configure(model, architecture, output_path=None, max_sequence_length=-1, bat
batch_size = 8
if max_sequence_length == -1:
max_sequence_length = 512
early_stop = False
o_early_stop = False
if max_epoch == -1:
max_epoch = 30
elif model.startswith("funding"):
Expand Down Expand Up @@ -144,13 +144,17 @@ def configure(model, architecture, output_path=None, max_sequence_length=-1, bat
if patience == -1:
patience = 5

return batch_size, max_sequence_length, model_name, embeddings_name, max_epoch, multiprocessing, early_stop, patience
if early_stop is not None:
o_early_stop = early_stop

return batch_size, max_sequence_length, model_name, embeddings_name, max_epoch, multiprocessing, o_early_stop, patience


# train a GROBID model with all available data
def train(model, embeddings_name=None, architecture=None, transformer=None, input_path=None,
output_path=None, features_indices=None, max_sequence_length=-1, batch_size=-1, max_epoch=-1,
use_ELMo=False, incremental=False, input_model_path=None, patience=-1, learning_rate=None, multi_gpu=False):

def train(model, embeddings_name=None, architecture=None, transformer=None, input_path=None,
output_path=None, features_indices=None, max_sequence_length=-1, batch_size=-1, max_epoch=-1,
use_ELMo=False, incremental=False, input_model_path=None, patience=-1, learning_rate=None, early_stop=None, multi_gpu=False):

print('Loading data...')
if input_path == None:
Expand All @@ -176,7 +180,8 @@ def train(model, embeddings_name=None, architecture=None, transformer=None, inpu
embeddings_name,
max_epoch,
use_ELMo,
patience)
patience, early_stop)

model = Sequence(model_name,
recurrent_dropout=0.50,
embeddings_name=embeddings_name,
Expand Down Expand Up @@ -217,7 +222,9 @@ def train(model, embeddings_name=None, architecture=None, transformer=None, inpu
def train_eval(model, embeddings_name=None, architecture='BidLSTM_CRF', transformer=None,
input_path=None, output_path=None, fold_count=1,
features_indices=None, max_sequence_length=-1, batch_size=-1, max_epoch=-1,
use_ELMo=False, incremental=False, input_model_path=None, patience=-1, learning_rate=None, multi_gpu=False):
use_ELMo=False, incremental=False, input_model_path=None, patience=-1,
learning_rate=None, early_stop=None, multi_gpu=False):

print('Loading data...')
if input_path is None:
x_all, y_all, f_all = load_data_and_labels_crf_file('data/sequenceLabelling/grobid/'+model+'/'+model+'-060518.train')
Expand All @@ -243,7 +250,8 @@ def train_eval(model, embeddings_name=None, architecture='BidLSTM_CRF', transfor
embeddings_name,
max_epoch,
use_ELMo,
patience)
patience,
early_stop)
model = Sequence(model_name,
recurrent_dropout=0.50,
embeddings_name=embeddings_name,
Expand Down Expand Up @@ -410,6 +418,13 @@ class Tasks:
parser.add_argument("--patience", type=int, default=-1, help="patience, number of extra epochs to perform after "
"the best epoch before stopping a training.")
parser.add_argument("--learning-rate", type=float, default=None, help="Initial learning rate")

parser.add_argument("--max-epoch", type=int, default=-1,
help="Maximum number of epochs for training.")
parser.add_argument("--early-stop", type=t_or_f, default=None,
help="Force early training termination when metrics scores are not improving " +
"after a number of epochs equals to the patience parameter.")

parser.add_argument("--multi-gpu", default=False,
help="Enable the support for distributed computing (the batch size needs to be set accordingly using --batch-size)",
action="store_true")
Expand All @@ -430,6 +445,8 @@ class Tasks:
incremental = args.incremental
patience = args.patience
learning_rate = args.learning_rate
max_epoch = args.max_epoch
early_stop = args.early_stop
multi_gpu = args.multi_gpu

if architecture is None:
Expand All @@ -441,19 +458,21 @@ class Tasks:

if action == Tasks.TRAIN:
train(model,
embeddings_name=embeddings_name,
architecture=architecture,
transformer=transformer,
input_path=input_path,
output_path=output,
max_sequence_length=max_sequence_length,
batch_size=batch_size,
use_ELMo=use_ELMo,
incremental=incremental,
input_model_path=input_model_path,
patience=patience,
learning_rate=learning_rate,
multi_gpu=multi_gpu)
embeddings_name=embeddings_name,
architecture=architecture,
transformer=transformer,
input_path=input_path,
output_path=output,
max_sequence_length=max_sequence_length,
batch_size=batch_size,
use_ELMo=use_ELMo,
incremental=incremental,
input_model_path=input_model_path,
patience=patience,
learning_rate=learning_rate,
max_epoch=max_epoch,
early_stop=early_stop,
multi_gpu=multi_gpu)

if action == Tasks.EVAL:
if args.fold_count is not None and args.fold_count > 1:
Expand All @@ -479,6 +498,8 @@ class Tasks:
incremental=incremental,
input_model_path=input_model_path,
learning_rate=learning_rate,
max_epoch=max_epoch,
early_stop=early_stop,
multi_gpu=multi_gpu)

if action == Tasks.TAG:
Expand Down
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