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run_ner.py
851 lines (779 loc) · 61.5 KB
/
run_ner.py
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from __future__ import absolute_import, division, print_function
import math
import argparse
import glob
import json
import logging
import os
import random
import numpy as np
import torch
import torch.nn.functional as F
from pytorch_transformers import (WEIGHTS_NAME, AdamW, BertConfig,
BertForTokenClassification, BertTokenizer,
WarmupLinearSchedule)
from torch import nn
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from printOutput import token_predition_write
from multi_label_ner_performance import classification_report
from Data_process.plain2conll import process_plain_text
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
class Ner(BertForTokenClassification):
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,valid_ids=None,attention_mask_label=None):
sequence_output = self.bert(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask,head_mask=None)[0]
batch_size,max_len,feat_dim = sequence_output.shape
valid_output = torch.zeros(batch_size,max_len,feat_dim,dtype=torch.float32,device='cuda')
for i in range(batch_size):
jj = -1
for j in range(max_len):
if valid_ids[i][j].item() == 1:
jj += 1
valid_output[i][jj] = sequence_output[i][j]
sequence_output = self.dropout(valid_output)
logits = self.classifier(sequence_output)
if labels is not None:
labels = labels.float()
loss_fct = nn.BCEWithLogitsLoss()
# Only keep active parts of the loss
attention_mask_label = None
if attention_mask_label is not None:
active_loss = attention_mask_label.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)[active_loss]
active_labels = labels.view(-1)[active_loss]
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1, self.num_labels))
return loss
else:
return logits
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids, label_id, valid_ids=None, label_mask=None):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.valid_ids = valid_ids
self.label_mask = label_mask
def readfile(filename):
'''
read file
'''
f = open(filename)
data = []
sentence = []
label= []
for line in f:
if len(line)==0 or line.startswith('-DOCSTART') or line[0]=="\n":
if len(sentence) > 0:
data.append((sentence,label))
sentence = []
label = []
continue
splits = line.split(' ')
sentence.append(splits[0])
label.append([i.strip() for i in splits[5:]])
if len(sentence) >0:
data.append((sentence,label))
sentence = []
label = []
return data
def readfile_arc(filename):
'''
read file
'''
f = open(filename)
data = []
sentence = []
label= []
for line in f:
if len(line)==0 or line.startswith('-DOCSTART') or line[0]=="\n":
if len(sentence) > 0:
data.append((sentence,label))
sentence = []
label = []
continue
splits = line.split(' ')
sentence.append(splits[0])
label.append(['O'])
if len(sentence) >0:
data.append((sentence,label))
sentence = []
label = []
return data
def readfile_plain_text(data_dir, plain_text, max_seq_length):
'''
read file
'''
process_plain_text(data_dir, plain_text)
base_filename = os.path.basename(plain_text)
f = open(os.path.join(data_dir, 'conll_'+base_filename))
data = []
sentence = []
label= []
for line in f:
if len(line)==0 or line.startswith('-DOCSTART') or line[0]=="\n":
if len(sentence) > 0:
try:
assert len(sentence) <= max_seq_length
except AssertionError:
print('Your sentence length is greater than the current maximum sentence length, please set the --max_seq_length=128(default 64) or much longer')
raise
else:
data.append((sentence,label))
sentence = []
label = []
continue
splits = line.split(' ')
sentence.append(splits[0])
label.append(['O'])
if len(sentence) >0:
try:
assert len(sentence) <= max_seq_length
except AssertionError:
print(
'Your sentence length is greater than the current maximum sentence length, please set the --max_seq_length=128(default 64) or much longer')
raise
else:
data.append((sentence,label))
sentence = []
label = []
return data
def readfile_json_text(data_dir, json_text, max_seq_length):
'''
read file
'''
with open(json_text, 'r') as file:
sentences = json.load(file)
data = []
for sentence in sentences:
try:
assert len(sentence) <= max_seq_length
except AssertionError:
print('Your sentence length is greater than the current maximum sentence length, please set the --max_seq_length=128(default 64) or much longer')
raise
else:
label = [['O'] for i in range(len(sentence))]
data.append((sentence, label))
return data
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_ARC_test_examples(self, data_dir):
return NotImplementedError()
def get_plain_text_examples(self, data_dir, plain_text_data, max_seq_length):
return NotImplementedError()
def get_json_text_examples(self, data_dir, plain_text_data, max_seq_length):
return NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
return readfile(input_file)
@classmethod
def _read_arc_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
return readfile_arc(input_file)
@classmethod
def _read_plain_text(cls, data_dir, plain_text, max_seq_length, quotechar=None):
"""Reads a tab separated value file."""
return readfile_plain_text(data_dir, plain_text, max_seq_length)
@classmethod
def _read_json_text(cls, data_dir, json_text, max_seq_length, quotechar=None):
"""Reads a tab separated value file."""
return readfile_json_text(data_dir, json_text, max_seq_length)
class NerProcessor(DataProcessor):
"""Processor for the CoNLL-2003 data set."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train_spacy.txt")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "valid_spacy.txt")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "test_spacy.txt")), "test")
def get_ARC_test_examples(self, data_dir):
return self._create_arc_examples(self._read_arc_tsv(os.path.join(data_dir, "ARC_test_spacy.txt")), "test")
def get_plain_text_examples(self, data_dir, plain_text_data, max_seq_length):
return self._create_plain_text_examples(self._read_plain_text(data_dir, plain_text_data, max_seq_length), "test")
def get_json_text_examples(self, data_dir, json_text_data, max_seq_length):
return self._create_json_text_examples(self._read_json_text(data_dir, json_text_data, max_seq_length), "test")
def get_labels(self):
return ['B-TypesOfEvent', 'I-OtherAnimalProperties', 'I-NUMBER', 'B-FossilTypesIndexFossil', 'I-Fungi', 'I-Verify', 'I-AbsorbEnergy', 'I-Conductivity', 'I-States', 'B-LivingThing', 'B-Patents', 'B-Circuits', 'B-MeasurementsForHeatChange', 'B-RepresentingElementsAndMolecules', 'B-IllnessPreventionCuring', 'B-Release', 'B-Directions', 'B-Comet', 'B-PartsOfChemicalReactions', 'B-Asteroid', 'B-TimeMeasuringTools', 'B-TidesHighTideLowTide', 'I-MetalSolids', 'B-Represent', 'I-PlantCellPart', 'I-NaturalSelection', 'I-EcosystemsEnvironment', 'B-SystemProcessStages', 'B-Energy', 'B-Occupation', 'I-SeasonsFallAutumnWinterSpringSummer', 'B-Evolution', 'I-TraceFossil', 'B-ScientificMeetings', 'I-PartsOfTheExcretorySystem', 'B-TectonicPlates', 'B-ExamplesOfSounds', 'B-Comparisons', 'I-AvoidReject', 'B-ManmadeObjects', 'B-ClassesOfElements', 'I-CelestialMeasurements', 'I-VerbsForLocate', 'B-Inertia', 'I-Bacteria', 'I-Mutation', 'B-Classify', 'B-ProduceEnergy', 'B-Metabolism', 'I-Numbers', 'B-NaturalPhenomena', 'I-BusinessNames', 'I-Negations', 'I-PopularMedia', 'B-Complexity', 'I-Sickness', 'B-PlantCellPart', 'I-NutritiveSubstancesForAnimalsOrPlants', 'B-TIME', 'I-ColorChangingActions', 'B-PushingActions', 'I-ProduceEnergy', 'I-WeatherPhenomena', 'B-CelestialEvents', 'B-GalaxyParts', 'B-SystemOfCommunication', 'B-Goal', 'B-ExcretoryActions', 'I-Substances', 'I-GroupsOfOrganisms', 'B-Collect', 'B-SpaceAgencies', 'B-SyntheticMaterial', 'I-FossilFuel', 'B-AmphibianAnimalPart', 'B-PhysicalProperty', 'I-AnimalAdditionalCategories', 'I-Source', 'I-GeometricMeasurements', 'I-Relevant', 'I-Permit', 'I-GeographicFormationParts', 'I-ElectricityAndCircuits', 'B-GenericTerms', 'B-PhaseTransitionPoint', 'B-ArcheologicalProcessTechnique', 'B-Property', 'I-Result', 'B-Health', 'I-Mass', 'B-FormChangingActions', 'B-CarbonCycle', 'B-Protist', 'I-Release', 'B-Vehicle', 'I-EndocrineSystem', 'I-RelativeNumber', 'I-CapillaryAction', 'B-MammalAnimalPart', 'I-ImmuneSystem', 'I-ActionsForAnimals', 'B-Examine', 'B-LunarPhases', 'I-PartsOfABuilding', 'B-Experimentation', 'B-PartsOfTheNervousSystem', 'I-CirculatorySystem', 'I-ORGANIZATION', 'B-Soil', 'B-Angiosperm', 'I-PullingActions', 'B-TrueFormFossil', 'B-ActionsForNutrition', 'B-ExamplesOfHabitats', 'B-ClothesTextiles', 'I-NaturalResources', 'B-ReproductiveSystem', 'B-PERCENT', 'B-WordsForData', 'I-Extinction', 'B-DigestiveSubstances', 'I-Agriculture', 'B-ActionsForAnimals', 'I-Amphibian', 'I-Property', 'B-Sky', 'I-Minerals', 'I-NaturalPhenomena', 'B-GeographicFormationParts', 'B-Start', 'I-CastFossilMoldFossil', 'B-Meals', 'B-MoneyTerms', 'I-PartsOfEarthLayers', 'B-Depth', 'B-MeasuringSpeed', 'B-PH', 'B-PlantNutrients', 'B-AtomComponents', 'I-OuterPlanets', 'B-Choose', 'B-Relevant', 'B-ChemicalProduct', 'I-DistanceComparison', 'I-VariablesControls', 'B-SeasonsFallAutumnWinterSpringSummer', 'I-RespiratorySystem', 'B-PrenatalOrganismStates', 'B-Viewpoint', 'I-PartsOfABusiness', 'I-ConstructiveDestructiveForces', 'B-EarthPartsGrossGroundAtmosphere', 'I-DigestionActions', 'B-Shape', 'I-GeneticProperty', 'B-Reactions', 'I-ImportanceComparison', 'I-TimeUnit', 'I-TechnologicalComponent', 'B-MeteorologicalModels', 'B-Medicine', 'I-Injuries', 'I-ScientificMethod', 'I-StructuralAdaptation', 'B-Meteor', 'B-PullingForces', 'I-Continents', 'I-SpaceAgencies', 'B-OrganicProcesses', 'B-Growth', 'I-Habitat', 'I-Sedimentary', 'I-Width', 'B-SpeedUnit', 'B-HumanPart', 'I-Medicine', 'I-Separation', 'B-CoolingToolsFood', 'I-Galaxy', 'I-Require', 'B-Development', 'B-VisualComparison', 'I-Language', 'B-Groups', 'B-Method', 'B-MetalSolids', 'I-AtmosphericLayers', 'B-TemporalProperty', 'B-AnimalPart', 'B-Mixtures', 'I-WaterVehiclePart', 'B-Pattern', 'B-PartsOfTheCirculatorySystem', 'I-AmountChangingActions', 'B-ElectricalEnergySource', 'B-OtherAnimalProperties', 'B-CelestialObject', 'I-Gene', 'B-ORGANIZATION', 'I-TemporalProperty', 'I-Transportation', 'O', 'I-Asteroid', 'I-PH', 'B-BirdAnimalPart', 'B-Value', 'B-OrganicCompounds', 'B-FiltrationTool', 'I-Event', 'B-BusinessNames', 'I-LearnedBehavior', 'I-Vehicle', 'I-BeliefKnowledge', 'B-PressureMeasuringTool', 'I-OtherHumanProperties', 'B-Agriculture', 'I-PartsOfTheCirculatorySystem', 'B-Continents', 'B-ThermalEnergy', 'B-EnvironmentalPhenomena', 'B-GroupsOfOrganisms', 'B-Spectra', 'B-SpaceMissionsEGApolloGeminiMercury', 'B-Insect', 'B-Buy', 'B-ImmuneSystem', 'B-AtmosphericLayers', 'I-OrganismRelationships', 'I-ReleaseEnergy', 'I-Occupation', 'I-Meteorology', 'B-RelativeNumber', 'B-Differentiate', 'B-Planet', 'B-PartsOfTheFoodChain', 'B-MolecularProperties', 'B-NUMBER', 'B-PopularMedia', 'I-EnergyWaves', 'I-PERSON', 'B-NaturalResources', 'I-Particles', 'B-Day', 'B-VisualProperty', 'B-Genetics', 'I-MassMeasuringTool', 'B-FoodChain', 'I-ClothesTextiles', 'B-Group', 'B-EnvironmentalDamageDestruction', 'I-ElementalComponents', 'B-MarkersOfTime', 'B-TypesOfChemicalReactions', 'B-PartsOfTheIntegumentarySystem', 'B-ImportanceComparison', 'B-PartsOfABusiness', 'B-ObjectPart', 'I-CellsAndGenetics', 'B-Employment', 'B-PlantProcesses', 'I-MagneticDevice', 'I-PowerUnit', 'B-ObjectQuantification', 'I-LiquidHoldingContainersRecepticles', 'B-Composition', 'B-TraceFossil', 'I-Viewpoint', 'I-SystemAndFunctions', 'I-ConstructionTools', 'B-ElectromagneticSpectrum', 'I-ManmadeObjects', 'I-Spectra', 'I-Cycles', 'B-ElementalComponents', 'B-Result', 'I-VisualProperty', 'B-Human', 'I-Plant', 'I-ComputingDevice', 'B-NonlivingPartsOfTheEnvironment', 'B-GeneticProperty', 'B-PropertiesOfFood', 'B-FossilFuel', 'I-ArcheologicalProcessTechnique', 'B-Fossils', 'B-AmountChangingActions', 'I-Protist', 'I-OtherGeographicWords', 'B-Arachnid', 'B-CelestialMovement', 'B-BlackHole', 'I-ElectricityGeneration', 'I-SeparatingMixtures', 'I-VolumeUnit', 'I-FossilForming', 'B-SkeletalSystem', 'B-LiquidMovement', 'I-EclipseEvents', 'I-Aquatic', 'B-ParticleMovement', 'I-Star', 'I-PropertyOfProduction', 'I-PropertyOfMotion', 'B-PartsOfDNA', 'B-Separate', 'B-Substances', 'I-TectonicPlates', 'I-Inheritance', 'I-Energy', 'B-PhaseChangingActions', 'B-BusinessIndustry', 'B-Metamorphic', 'I-PhasesOfWater', 'B-ChangeInLocation', 'I-PartsOfTheRespiratorySystem', 'I-ChangeInto', 'B-MeasuresOfAmountOfLight', 'B-AnimalCellPart', 'B-Compound', 'B-PERSON', 'I-ElectricalEnergySource', 'B-ReleaseEnergy', 'I-GeologicTheories', 'B-WrittenMedia', 'I-Position', 'I-ScientificMeetings', 'I-Angiosperm', 'I-EnvironmentalPhenomena', 'B-OtherDescriptionsForPlantsBiennialLeafyEtc', 'B-AbsorbEnergy', 'B-MagneticDevice', 'B-YearNumerals', 'B-Observe', 'I-PartsOfTheSkeletalSystem', 'I-Planet', 'B-AmountComparison', 'I-Quality', 'B-PartsOfARepresentation', 'B-ChemicalProperty', 'B-Language', 'I-PartsOfTheMuscularSystem', 'B-Cell', 'I-Senses', 'B-Material', 'B-Rarity', 'I-Help', 'B-IntegumentarySystem', 'I-TypeOfConsumer', 'B-Cities', 'I-PhysicalActivity', 'B-AvoidReject', 'I-Forests', 'B-WeatherPhenomena', 'B-Device', 'I-TechnologicalInstrument', 'I-ManMadeGeographicFormations', 'B-DwarfPlanets', 'I-Use', 'B-Vacuum', 'B-ElectricalProperty', 'B-AcademicMedia', 'B-Birth', 'B-OuterPlanets', 'I-PullingForces', 'B-GuidelinesAndRules', 'I-CelestialLightOnEarth', 'B-TemperatureMeasuringTools', 'I-Validity', 'I-Rock', 'I-ObservationInstrumentsTelescopeBinoculars', 'I-LightMovement', 'I-Comparisons', 'B-PlantPart', 'B-PerformingResearch', 'I-EnvironmentalDamageDestruction', 'I-HeatingAppliance', 'I-OrganicCompounds', 'I-ObservationPlacesEGObservatory', 'B-GeologicTheories', 'B-Difficulty', 'B-Communicate', 'B-Extinction', 'I-CleanUp', 'I-Observe', 'B-PartsOfAGroup', 'I-SpaceProbes', 'B-WeightMeasuringTool', 'I-UnderwaterEcosystem', 'B-ChangeInComposition', 'I-ChemicalProcesses', 'I-PartsOfTheNervousSystem', 'B-CardinalDirectionsNorthEastSouthWest', 'I-NaturalMaterial', 'B-Measurements', 'I-FoodChain', 'I-VisualComparison', 'B-Sedimentary', 'B-Quality', 'B-StateOfMatter', 'B-TransferEnergy', 'B-SoundProducingObject', 'I-CarbonCycle', 'I-FiltrationTool', 'B-Uptake', 'B-Wetness', 'B-FossilLocationImplicationsOfLocationEXMarineAnimalFossilsInTheGrandCanyon', 'I-DigestiveSubstances', 'I-MagneticForce', 'I-PushingForces', 'B-Response', 'I-SoundProducingObject', 'I-LivingThing', 'B-Succeed', 'I-Move', 'I-Shape', 'I-Rarity', 'B-MuscularSystemActions', 'I-Identify', 'I-VolumeMeasuringTool', 'B-ElectricalUnit', 'I-Nebula', 'B-Safety', 'B-Animal', 'B-Verify', 'B-GeographicFormations', 'B-Audiences', 'I-Harm', 'B-Foods', 'I-IncreaseDecrease', 'I-ScientificTools', 'B-Occur', 'B-AstronomicalDistanceUnitsLightYearAstronomicalUnitAu', 'I-MarkersOfTime', 'I-Locations', 'I-SkeletalSystem', 'I-Color', 'B-OtherEnergyResources', 'B-Meteorology', 'B-Flammability', 'B-ElectricalEnergy', 'B-PartsOfTheDigestiveSystem', 'I-Composition', 'B-Classification', 'I-Insect', 'I-GeneticProcesses', 'I-Height', 'I-SimpleMachines', 'I-PropertiesOfSickness', 'I-AcademicMedia', 'B-Identify', 'B-VehicularSystemsParts', 'B-Indicate', 'I-MineralProperties', 'I-Size', 'I-MuscularSystem', 'B-Use', 'B-PropertiesOfSickness', 'B-AbilityAvailability', 'I-MineralFormations', 'B-Separation', 'B-Calculations', 'B-PropertiesOfWaves', 'B-DATE', 'I-TIME', 'I-TypesOfIllness', 'I-Fossils', 'B-Undiscovered', 'I-Taxonomy', 'B-PartsOfObservationInstruments', 'B-Nutrition', 'B-Cycles', 'B-ResistanceStrength', 'I-PhaseChanges', 'I-GeographicFormations', 'B-AnimalSystemsProcesses', 'B-CapillaryAction', 'B-PropertyOfMotion', 'B-FossilRecordTimeline', 'B-Taxonomy', 'B-Require', 'I-PlantNutrients', 'B-Break', 'I-PartsOfBodiesOfWater', 'B-Gymnosperm', 'B-SpecificNamedBodiesOfWater', 'B-WaitStay', 'I-ChemicalProperty', 'I-Biology', 'B-GeopoliticalLocations', 'I-RelativeTime', 'B-PartsOfTheReproductiveSystem', 'I-Distance', 'B-AnimalAdditionalCategories', 'B-ManMadeGeographicFormations', 'B-LOCATION', 'I-WrittenMedia', 'I-WaterVehicle', 'B-Unknown', 'I-Sky', 'B-Frequency', 'B-ComputingDevice', 'I-Cell', 'B-Homeostasis', 'I-AtomicProperties', 'B-PullingActions', 'B-BacteriaPart', 'B-EclipseEvents', 'B-Brightness', 'I-Classify', 'B-Plant', 'B-WaterVehiclePart', 'I-Age', 'I-ActionsForNutrition', 'I-ObjectPart', 'B-Touch', 'I-Alter', 'I-Response', 'I-EndocrineActions', 'B-Size', 'I-Goal', 'B-LightProducingObject', 'B-Rock', 'I-PartsOfDNA', 'B-PerformAnActivity', 'B-DistanceUnit', 'I-Reproduction', 'I-MedicalTerms', 'B-WeatherDescriptions', 'B-PoorHealth', 'B-SolidMatter', 'B-Stability', 'I-LiquidMatter', 'I-Igneous', 'B-OrganismRelationships', 'I-Frequency', 'B-DURATION', 'I-GeographicFormationProcess', 'B-AquaticAnimalPart', 'I-MuscularSystemActions', 'B-ManmadeLocations', 'B-ColorChangingActions', 'B-Appliance', 'I-Animal', 'B-Permit', 'B-ResultsOfDecomposition', 'I-Traffic', 'B-PartsOfTheMuscularSystem', 'I-VehicularSystemsParts', 'B-Advertising', 'B-PartsOfAChromosome', 'B-Cause', 'I-Mammal', 'B-ChemicalProcesses', 'B-TypesOfWaterInBodiesOfWater', 'I-Represent', 'I-Device', 'I-Homeostasis', 'B-Minerals', 'B-Hardness', 'B-Texture', 'B-BodiesOfWater', 'B-ReplicatingResearch', 'B-LevelOfInclusion', 'I-OtherEnergyResources', 'I-EnergyUnit', 'B-AtomicProperties', 'B-Matter', 'B-SpacecraftHumanRated', 'B-SimpleMachines', 'I-ClassesOfElements', 'B-Currents', 'B-TheUniverseUniverseAndItsParts', 'B-Representation', 'B-EndocrineActions', 'I-ExamplesOfSounds', 'B-CleanUp', 'B-SeparatingMixtures', 'I-TransferEnergy', 'B-RelativeDirection', 'B-Precipitation', 'I-ConservationLaws', 'B-Particles', 'B-PrepositionalDirections', 'I-Communicate', 'B-Transportation', 'B-TemperatureUnit', 'I-LayersOfTheEarth', 'I-Gymnosperm', 'I-Separate', 'I-BlackHole', 'I-TheUniverseUniverseAndItsParts', 'I-Stability', 'B-Gender', 'I-ThermalEnergy', 'B-Help', 'I-PartsOfTheImmuneSystem', 'I-Create', 'B-BeliefKnowledge', 'B-Eukaryote', 'I-PartsOfTheDigestiveSystem', 'I-SoundEnergy', 'B-Age', 'B-Believe', 'I-Collect', 'B-TheoryOfPhysics', 'B-QualityComparison', 'B-SafetyEquipment', 'B-SystemParts', 'B-FossilForming', 'I-DigestiveSystem', 'I-Calculations', 'I-Relations', 'B-Consumption', 'I-FormChangingActions', 'B-EnergyWaves', 'I-CosmologicalTheoriesBigBangBigCrunch', 'I-ChemicalProduct', 'B-RelativeTime', 'B-InsectAnimalPart', 'B-OutbreakClassification', 'B-ScientificTheoryExperimentationAndHistory', 'B-PerformingExperimentsWell', 'I-OrganicProcesses', 'B-PercentUnit', 'I-Speed', 'B-ContainBeComposedOf', 'B-BehavioralAdaptation', 'B-LearnedBehavior', 'I-StarLayers', 'I-SpecificNamedBodiesOfWater', 'B-MineralFormations', 'I-AnimalClassificationMethod', 'B-ElectricityAndCircuits', 'B-AstronomyAeronautics', 'B-MagneticDirectionMeasuringTool', 'B-CirculatorySystem', 'B-CookingToolsFood', 'B-GeneticRelations', 'I-PartsOfTheReproductiveSystem', 'B-PartsOfWaterCycle', 'B-SystemAndFunctions', 'B-MagneticEnergy', 'I-Divide', 'I-ExcretoryActions', 'I-Cause', 'B-ActionsForTides', 'I-ViewingTools', 'B-StructuralAdaptation', 'B-MassMeasuringTool', 'I-PlantPart', 'I-AnimalPart', 'B-LiquidHoldingContainersRecepticles', 'B-LandVehicle', 'B-Preserve', 'I-ConcludingResearch', 'I-Wetness', 'I-AirVehicle', 'B-Injuries', 'B-InnerPlanets', 'B-DistanceComparison', 'I-SensoryTerms', 'B-ActionsForAgriculture', 'B-PartsOfEarthLayers', 'I-RespirationActions', 'I-AnimalSystemsProcesses', 'B-PartsOfWaves', 'B-TimeUnit', 'I-Appliance', 'I-SpaceMissionsEGApolloGeminiMercury', 'B-InheritedBehavior', 'I-TemperatureMeasuringTools', 'B-AnalyzingResearch', 'B-EndocrineSystem', 'I-StopRemove', 'B-TypesOfTerrestrialEcosystems', 'B-Source', 'B-ActUponSomething', 'I-GalaxyParts', 'B-ElectricityMeasuringTool', 'I-Bird', 'I-MechanicalMovement', 'I-Pressure', 'B-NationalityOrigin', 'B-Mutation', 'B-SpacecraftSubsystem', 'B-CelestialLightOnEarth', 'B-GeometricUnit', 'I-WaitStay', 'I-ActionsForTides', 'B-Problem', 'B-Blood', 'B-AnimalClassificationMethod', 'I-Soil', 'I-LandVehicle', 'B-ApparentCelestialMovement', 'B-Compete', 'I-ProbabilityAndCertainty', 'I-Choose', 'B-GeographicFormationProcess', 'I-Reactions', 'I-SystemOfCommunication', 'I-FeedbackMechanism', 'B-RelativeLocations', 'I-Temperature', 'I-TidesHighTideLowTide', 'I-WeightMeasuringTool', 'B-MuscularSystem', 'B-Rigidity', 'B-Satellite', 'I-Currents', 'I-ChemicalChange', 'I-Start', 'B-PartsOfRNA', 'B-VolumeMeasuringTool', 'I-ElectricalEnergy', 'B-PhasesOfWater', 'I-RepresentingElementsAndMolecules', 'B-MassUnit', 'I-Material', 'I-LunarPhases', 'B-TheoryOfMatter', 'B-Width', 'I-Experimentation', 'I-Measurements', 'B-States', 'I-IntegumentarySystem', 'B-Mammal', 'I-MeteorologicalModels', 'I-Associate', 'B-CombineAdd', 'I-Products', 'I-GeneticRelations', 'B-GranularSolids', 'B-ElectricityGeneration', 'I-HardnessUnit', 'B-CellsAndGenetics', 'B-Locations', 'I-Adaptation', 'B-Forests', 'I-FossilLocationImplicationsOfLocationEXMarineAnimalFossilsInTheGrandCanyon', 'B-Negations', 'I-LocationChangingActions', 'I-MassUnit', 'B-Conductivity', 'I-SolarSystem', 'I-CookingToolsFood', 'I-GaseousMatter', 'I-TypesOfTerrestrialEcosystems', 'I-TimeMeasuringTools', 'B-LightMovement', 'B-LivingDying', 'B-ConservationLaws', 'I-Nutrition', 'B-SubstancesProducedByPlantProcesses', 'B-GroupsOfScientists', 'I-WavePerception', 'B-ResponseType', 'I-NonlivingPartsOfTheEnvironment', 'B-PlanetParts', 'B-LiquidMatter', 'B-PropertyOfProduction', 'B-IncreaseDecrease', 'B-TerrestrialLocations', 'B-Relations', 'B-Height', 'I-BacteriaPart', 'I-Element', 'B-Element', 'I-PhysicalProperty', 'I-Precipitation', 'B-Monera', 'B-SeedlessVascular', 'I-Discovery', 'I-EarthPartsGrossGroundAtmosphere', 'B-VerbsForLocate', 'B-Toxins', 'B-Permeability', 'B-Star', 'I-PhaseChangingActions', 'I-Human', 'B-Year', 'B-WordsForOffspring', 'I-Compound', 'I-GroupsOfScientists', 'I-ElectricAppliance', 'B-Igneous', 'B-ChemicalChange', 'I-PartsOfAVirus', 'B-EmergencyServices', 'B-Move', 'B-HardnessUnit', 'B-EcosystemsEnvironment', 'B-CoolingAppliance', 'I-TrueFormFossil', 'B-Mass', 'I-NationalityOrigin', 'B-Nebula', 'B-StopRemove', 'I-Behaviors', 'B-Hypothesizing', 'B-Traffic', 'B-QuestionActivityType', 'B-Adaptation', 'I-ReproductiveSystem', 'I-InsectAnimalPart', 'I-FossilTypesIndexFossil', 'B-PostnatalOrganismStages', 'B-WaterVehicle', 'B-ReptileAnimalPart', 'I-SafetyEquipment', 'B-Sickness', 'B-PhysicalChange', 'I-Metabolism', 'I-LightProducingObject', 'I-Unknown', 'B-PartsOfEndocrineSystem', 'B-Biology', 'B-PressureUnit', 'I-ElectricalUnit', 'B-ConcludingResearch', 'B-TechnologicalComponent', 'B-GeometricSpatialObjects', 'I-OtherOrganismProperties', 'I-PERCENT', 'I-ChangeInLocation', 'B-ConstructionTools', 'I-AstronomicalDistanceUnitsLightYearAstronomicalUnitAu', 'I-Genetics', 'B-PhysicalActivity', 'B-ORDINAL', 'B-NaturalSelection', 'I-PhaseTransitionPoint', 'B-ForceUnit', 'B-LayersOfTheEarth', 'I-ScientificAssociationsAdministrations', 'I-Groups', 'I-NorthernHemisphereLocations', 'I-CirculationActions', 'B-Changes', 'I-TheoryOfPhysics', 'B-RespiratorySystem', 'B-LightExaminingTool', 'I-NervousSystem', 'I-ActUponSomething', 'I-Constellation', 'I-SouthernHemisphereLocations', 'B-ArithmeticMeasure', 'I-EmergencyServices', 'B-OtherOrganismProperties', 'B-PartsOfBodiesOfWater', 'I-Cost', 'B-Exemplar', 'I-SpaceVehicle', 'I-CellProcesses', 'B-SpaceVehicle', 'B-PartsOfTheExcretorySystem', 'B-DigestiveSystem', 'I-LivingDying', 'B-SoundEnergy', 'B-Inheritance', 'I-WordsForData', 'B-SouthernHemisphereLocations', 'I-Believe', 'B-Validity', 'I-HumanPart', 'B-GeneticProcesses', 'B-CosmologicalTheoriesBigBangBigCrunch', 'B-WordsRelatingToCosmologicalTheoriesExpandContract', 'I-AquaticAnimalPart', 'I-ExamplesOfHabitats', 'I-BodiesOfWater', 'I-Blood', 'B-ViewingTools', 'B-NaturalMaterial', 'B-GaseousMovement', 'B-Death', 'B-Surpass', 'B-PowerUnit', 'B-ObservationInstrumentsTelescopeBinoculars', 'I-ResultsOfDecomposition', 'I-ApparentCelestialMovement', 'B-ElectricAppliance', 'B-Geography', 'B-Bird', 'I-DensityUnit', 'B-GapsAndCracks', 'B-OtherProperties', 'B-Harm', 'I-Development', 'I-SolidMatter', 'B-PartsOfAVirus', 'I-ElectromagneticSpectrum', 'B-Actions', 'B-Discovery', 'B-OpportunitiesAndTheirExtent', 'I-Permeability', 'I-AreaUnit', 'I-ContainBeComposedOf', 'B-TypesOfIllness', 'I-Safety', 'B-SensoryTerms', 'I-DistanceMeasuringTools', 'B-AngleMeasuringTools', 'I-LifeCycle', 'B-PartsOfTheSkeletalSystem', 'I-WordsRelatingToCosmologicalTheoriesExpandContract', 'B-Position', 'I-GeologicalEonsErasPeriodsEpochsAges', 'B-FrequencyUnit', 'B-StateOfBeing', 'B-Temperature', 'B-DigestionActions', 'B-Habitat', 'B-Gene', 'I-LevelOfInclusion', 'I-Force', 'B-ObservationPlacesEGObservatory', 'I-CoolingAppliance', 'B-Ability', 'I-Countries', 'B-Alter', 'B-Reptile', 'B-LifeCycle', 'B-Cost', 'I-PerformAnActivity', 'B-SolarSystem', 'I-ResistanceStrength', 'I-PartsOfChemicalReactions', 'I-CelestialMovement', 'I-TypesOfChemicalReactions', 'B-Create', 'I-Circuits', 'I-GranularSolids', 'I-PropertiesOfSoil', 'I-ManmadeLocations', 'B-Fungi', 'B-ChangeInto', 'I-ExcretorySystem', 'B-Aquatic', 'I-AbilityAvailability', 'B-AirVehicle', 'B-Color', 'B-Scientists', 'B-Speciation', 'I-Eukaryote', 'I-Comet', 'B-MagneticForce', 'B-RespirationActions', 'I-LiquidMovement', 'I-InheritedBehavior', 'I-Monera', 'B-Associate', 'I-PropertiesOfFood', 'I-Occur', 'B-DensityUnit', 'B-UnderwaterEcosystem', 'B-Numbers', 'B-GeometricMeasurements', 'I-PerformingResearch', 'I-Rigidity', 'B-StarLayers', 'B-Magnetic', 'I-MolecularProperties', 'I-DURATION', 'B-Force', 'I-AmountComparison', 'I-RelativeDirection', 'I-ScientificTheoryExperimentationAndHistory', 'B-PartsOfABuilding', 'I-Uptake', 'B-AreaUnit', 'I-SoundMeasuringTools', 'I-Evolution', 'I-Gravity', 'B-Moon', 'I-ObjectQuantification', 'B-ScientificAssociationsAdministrations', 'I-TheoryOfMatter', 'I-PrenatalOrganismStates', 'B-CastFossilMoldFossil', 'I-TemperatureUnit', 'B-PushingForces', 'I-Consumption', 'B-ObservationTechniques', 'I-MoneyTerms', 'I-Representation', 'I-AnalyzingResearch', 'B-Behaviors', 'B-PartsOfASolution', 'B-ScientificMethod', 'I-PlanetParts', 'B-Constellation', 'B-SoundMeasuringTools', 'B-Bryophyte', 'I-CelestialObject', 'B-ExcretorySystem', 'B-SpaceProbes', 'I-BehavioralAdaptation', 'I-TerrestrialLocations', 'I-DATE', 'I-FossilRecordTimeline', 'B-Light', 'I-DistanceUnit', 'I-Reptile', 'B-MedicalTerms', 'I-TypesOfWaterInBodiesOfWater', 'B-ProbabilityAndCertainty', 'I-MeasurementsForHeatChange', 'I-Hypothesizing', 'B-Reproduction', 'I-CombineAdd', 'I-ElectricalProperty', 'B-Divide', 'B-ChangesToResources', 'B-TimesOfDayDayNight', 'I-ObservationTechniques', 'B-PartsOfTheImmuneSystem', 'B-NervousSystem', 'I-Light', 'I-StateOfMatter', 'B-Bacteria', 'I-MeasuringSpeed', 'I-PerformingExperimentsWell', 'I-Geography', 'I-PlantProcesses', 'B-PhaseChanges', 'B-Length', 'B-NutritiveSubstancesForAnimalsOrPlants', 'I-BusinessIndustry', 'I-PartsOfEndocrineSystem', 'I-ChangeInComposition', 'I-Length', 'B-DistanceMeasuringTools', 'I-Cities', 'B-Speed', 'I-AtomComponents', 'I-StateOfBeing', 'B-Amphibian', 'B-ScientificTools', 'B-Products', 'I-StarTypes', 'I-Examine', 'B-CellProcesses', 'I-LOCATION', 'I-Preserve', 'B-CirculationActions', 'I-CardinalNumber', 'I-PrepositionalDirections', 'B-Distance', 'B-Galaxy', 'I-BirdAnimalPart', 'B-LocationChangingActions', 'I-RelativeLocations', 'B-Event', 'B-PropertiesOfSoil', 'B-WavePerception', 'B-EnergyUnit', 'I-Foods', 'B-Countries', 'I-SpacecraftHumanRated', 'B-OtherHumanProperties', 'B-VolumeUnit', 'I-IllnessPreventionCuring', 'B-PartsOfTheEye', 'B-CelestialMeasurements', 'I-ParticleMovement', 'I-TypesOfEvent', 'I-MagneticEnergy', 'I-Toxins', 'B-Archaea', 'B-GeologicalEonsErasPeriodsEpochsAges', 'B-CardinalNumber', 'B-Gravity', 'B-MineralProperties', 'I-Unit', 'I-Problem', 'B-PartsOfTheRespiratorySystem', 'B-Pressure', 'I-AnimalCellPart', 'B-AcidityUnit', 'B-NorthernHemisphereLocations', 'I-PhysicalChange', 'B-Senses', 'B-MechanicalMovement', 'I-Exemplar', 'I-TimesOfDayDayNight', 'B-VariablesControls', 'B-TechnologicalInstrument', 'I-Magnetic', 'I-Scientists', 'B-StarTypes', 'I-MagneticDirectionMeasuringTool', 'I-Touch', 'I-SpeedUnit', 'B-Months', 'I-SystemParts', 'I-PartsOfObservationInstruments', 'I-ArithmeticMeasure', 'I-Mixtures', 'B-FeedbackMechanism', 'B-GaseousMatter', 'B-TypeOfConsumer', 'B-Unit', 'B-OtherGeographicWords', 'B-HeatingAppliance', 'I-PartsOfARepresentation', 'B-ConstructiveDestructiveForces',
'I-GeopoliticalLocations', "[CLS]", "[SEP]"]
def _create_examples(self,lines,set_type):
examples = []
for i,(sentence,label) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = ' '.join(sentence)
text_b = None
label = label
examples.append(InputExample(guid=guid,text_a=text_a,text_b=text_b,label=label))
return examples
def _create_arc_examples(self,lines,set_type):
examples = []
for i, (sentence,label) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = ' '.join(sentence)
text_b = None
label = label
examples.append(InputExample(guid=guid,text_a=text_a,text_b=text_b,label=label))
return examples
def _create_plain_text_examples(self,lines,set_type):
examples = []
for i, (sentence,label) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = ' '.join(sentence)
text_b = None
label = label
examples.append(InputExample(guid=guid,text_a=text_a,text_b=text_b,label=label))
return examples
def _create_json_text_examples(self,lines,set_type):
examples = []
for i, (sentence,label) in enumerate(lines):
guid = "%s-%s" % (set_type, i)
text_a = ' '.join(sentence)
text_b = None
label = label
examples.append(InputExample(guid=guid,text_a=text_a,text_b=text_b,label=label))
return examples
def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
label_map = {label : i for i, label in enumerate(label_list,0)}
features = []
for (ex_index,example) in enumerate(examples):
textlist = example.text_a.split(' ')
labellist = example.label
tokens = []
labels = []
valid = []
label_mask = []
for i, word in enumerate(textlist):
token = tokenizer.tokenize(word)
tokens.extend(token)
label_1 = labellist[i]
for m in range(len(token)):
if m == 0:
labels.append(label_1)
valid.append(1)
label_mask.append(1)
else:
valid.append(0)
if len(tokens) >= max_seq_length - 1:
tokens = tokens[0:(max_seq_length - 2)]
labels = labels[0:(max_seq_length - 2)]
valid = valid[0:(max_seq_length - 2)]
label_mask = label_mask[0:(max_seq_length - 2)]
ntokens = []
segment_ids = []
label_ids = []
ntokens.append("[CLS]")
segment_ids.append(0)
valid.insert(0,1)
label_mask.insert(0,1)
label_ids.append(np.array([int(i==label_map["[CLS]"]) for i in range(len(label_list))]).tolist())
for i, token in enumerate(tokens):
ntokens.append(token)
segment_ids.append(0)
if len(labels) > i:
temp_one_hop = np.zeros(len(label_list))
for item in labels[i]:
temp_one_hop[label_map[item]]=1
label_ids.append(temp_one_hop.tolist())
ntokens.append("[SEP]")
segment_ids.append(0)
valid.append(1)
label_mask.append(1)
label_ids.append(np.array([int(i==label_map["[SEP]"]) for i in range(len(label_list))]).tolist())
input_ids = tokenizer.convert_tokens_to_ids(ntokens)
input_mask = [1] * len(input_ids)
label_mask = [1] * len(label_ids)
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
label_ids.append(np.zeros(len(label_list)).tolist())
valid.append(1)
label_mask.append(0)
while len(label_ids) < max_seq_length:
label_ids.append(np.zeros(len(label_list)).tolist())
label_mask.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(label_ids) == max_seq_length
assert len(valid) == max_seq_length
assert len(label_mask) == max_seq_length
if ex_index < 5:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("tokens: %s" % " ".join(
[str(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
# logger.info("label: %s (id = %d)" % (example.label, label_ids))
features.append(
InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_ids,
valid_ids=valid,
label_mask=label_mask))
return features
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--plain_text",
default=None,
type=str,
help="The plain_text_data directory")
parser.add_argument("--json_text",
default=None,
type=str,
help="The json_text_data directory")
## Other parameters
parser.add_argument("--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_eval_test",
action='store_true',
help="Whether to run eval on the test set.")
parser.add_argument("--do_eval_ARCtest",
action='store_true',
help="Whether to run eval on the ARC test set.")
parser.add_argument("--do_eval_plain_text",
action='store_true',
help="Whether to run eval on the plain text data.")
parser.add_argument("--do_eval_json_text",
action='store_true',
help="Whether to run eval on the json text data.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
parser.add_argument("--train_batch_size",
default=64,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--group_length",
default=1000,
type=int,
help="In order to avoid the out of cpu memory, if your number of sentences is greater than this, divide it into groups")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument('--save_epochs', type=int, default=20,
help="Save checkpoint every X updates steps.")
parser.add_argument("--weight_decay", default=0.01, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
args = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
processors = {"ner":NerProcessor}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = 0
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
# Prepare model
config = BertConfig.from_pretrained(args.bert_model, num_labels=num_labels, finetuning_task=args.task_name)
model = Ner.from_pretrained(args.bert_model,
from_tf = False,
config = config)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias','LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
warmup_steps = int(args.warmup_proportion * num_train_optimization_steps)
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps, t_total=num_train_optimization_steps)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
label_map = {i : label for i, label in enumerate(label_list,0)}
if args.do_train:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
#print('all_label_ids shape: ', all_label_ids.shape)
all_valid_ids = torch.tensor([f.valid_ids for f in train_features], dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids,all_valid_ids,all_lmask_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
model.train()
for epoch_flag in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, valid_ids,l_mask = batch
loss = model(input_ids, segment_ids, input_mask, label_ids,valid_ids,l_mask)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
if args.local_rank in [-1, 0] and args.save_epochs > 0 and (epoch_flag+1) % args.save_epochs == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(epoch_flag+1))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model,'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
label_map = {i: label for i, label in enumerate(label_list, 0)}
model_config = {"bert_model": args.bert_model, "do_lower": args.do_lower_case,
"max_seq_length": args.max_seq_length, "num_labels": len(label_list),
"label_map": label_map}
json.dump(model_config, open(os.path.join(output_dir, "model_config.json"), "w"))
# Save a trained model and the associated configuration
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
label_map = {i : label for i, label in enumerate(label_list,0)}
model_config = {"bert_model":args.bert_model,"do_lower":args.do_lower_case,"max_seq_length":args.max_seq_length,"num_labels":len(label_list),"label_map":label_map}
json.dump(model_config,open(os.path.join(args.output_dir,"model_config.json"),"w"))
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
print('checkpoint： ', checkpoint)
checkpoint_epoch = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
model = Ner.from_pretrained(checkpoint)
model.to(device)
model.eval()
if args.do_eval_ARCtest:
eval_examples = processor.get_ARC_test_examples(args.data_dir)
eval_examples_length = math.ceil(len(eval_examples)/args.group_length)
all_eval_examples = []
for idx in range(eval_examples_length):
if (idx+1) == eval_examples_length:
all_eval_examples.append(eval_examples[int(idx*args.group_length):])
else:
all_eval_examples.append(eval_examples[int(idx*args.group_length):int((idx+1)*args.group_length)])
elif args.do_eval_plain_text:
eval_examples = processor.get_plain_text_examples(args.data_dir, args.plain_text, args.max_seq_length)
eval_examples_length = math.ceil(len(eval_examples) / args.group_length)
all_eval_examples = []
for idx in range(eval_examples_length):
if (idx + 1) == eval_examples_length:
all_eval_examples.append(eval_examples[int(idx * args.group_length):])
else:
all_eval_examples.append(eval_examples[int(idx * args.group_length):int((idx + 1) * args.group_length)])
elif args.do_eval_json_text:
eval_examples = processor.get_json_text_examples(args.data_dir, args.json_text, args.max_seq_length)
eval_examples_length = math.ceil(len(eval_examples) / args.group_length)
all_eval_examples = []
for idx in range(eval_examples_length):
if (idx + 1) == eval_examples_length:
all_eval_examples.append(eval_examples[int(idx * args.group_length):])
else:
all_eval_examples.append(eval_examples[int(idx * args.group_length):int((idx + 1) * args.group_length)])
elif args.do_eval_test:
eval_examples = processor.get_test_examples(args.data_dir)
all_eval_examples = [eval_examples]
else:
eval_examples = processor.get_dev_examples(args.data_dir)
all_eval_examples = [eval_examples]
all_y_true = []
all_y_pred = []
for single_eval_examples in all_eval_examples:
eval_features = convert_examples_to_features(single_eval_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(single_eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
all_valid_ids = torch.tensor([f.valid_ids for f in eval_features], dtype=torch.long)
all_lmask_ids = torch.tensor([f.label_mask for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_valid_ids,
all_lmask_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
y_true = []
y_pred = []
label_map = {i: label for i, label in enumerate(label_list, 0)}
for input_ids, input_mask, segment_ids, label_ids, valid_ids, l_mask in tqdm(eval_dataloader,
desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
valid_ids = valid_ids.to(device)
label_ids = label_ids.to(device)
l_mask = l_mask.to(device)
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask, valid_ids=valid_ids, attention_mask_label=l_mask)
logits = F.sigmoid(logits)
logits = logits.detach().cpu().numpy()
final_logits = (logits >= 0.4).astype(int)
label_ids = label_ids.to('cpu').numpy()
input_mask = input_mask.to('cpu').numpy()
for i, label in enumerate(label_ids):
temp_1 = []
temp_2 = []
flag = 0
for j, m in enumerate(label):
if j == 0:
continue
elif flag == len(label_map) - 1:
y_true.append(temp_1)
y_pred.append(temp_2)
break
else:
temp_label_ids_list = []
temp_logits_list = []
for label_temp_idx, label_temp in enumerate(label_ids[i][j]):
if int(label_temp) == 1:
flag = label_temp_idx
if flag == len(label_map) - 1:
break
temp_label_ids_list.append(label_map[label_temp_idx])
for logit_temp_idx, logits_temp in enumerate(final_logits[i][j]):
if flag == len(label_map) - 1:
break
if int(logits_temp) == 1:
temp_logits_list.append(label_map[logit_temp_idx])
if flag != len(label_map) - 1:
temp_1.append(temp_label_ids_list)
temp_2.append(temp_logits_list)
all_y_true.extend(y_true)
all_y_pred.extend(y_pred)
if args.do_eval_ARCtest:
token_predition_write(os.path.join(args.data_dir, "ARC_test_spacy.txt"), all_y_pred, checkpoint, 'ARC_test')
elif args.do_eval_plain_text:
base_filename = os.path.basename(args.plain_text)
token_predition_write(os.path.join(args.data_dir, 'conll_'+base_filename), all_y_pred, checkpoint,
base_filename)
elif args.do_eval_json_text:
base_filename = os.path.basename(args.json_text)
token_predition_write(args.json_text, all_y_pred, checkpoint,
'json_text')
elif args.do_eval_test:
report = classification_report(all_y_true, all_y_pred, digits=4)
logger.info("\n%s", report)
output_eval_file = os.path.join(checkpoint, "test_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** test results *****")
logger.info("\n%s", report)
writer.write(report)
else:
report = classification_report(all_y_true, all_y_pred, digits=4)
logger.info("\n%s", report)
output_eval_file = os.path.join(checkpoint, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
logger.info("\n%s", report)
writer.write(report)
if __name__ == "__main__":
main()