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Repository: fhamborg/NewsMTSC
Branch: main
Commit: b9d9b79704ed
Files: 126
Total size: 30.6 MB
Directory structure:
gitextract_ate4vaz2/
├── .gitignore
├── .idea/
│ ├── .gitignore
│ ├── NewsMTSC.iml
│ ├── misc.xml
│ ├── modules.xml
│ └── vcs.xml
├── CODE_OF_CONDUCT.md
├── LICENSE
├── MANIFEST.in
├── NewsSentiment/
│ ├── .gitignore
│ ├── DatasetPreparer.py
│ ├── SentimentClasses.py
│ ├── __init__.py
│ ├── __main__.py
│ ├── combinations_default.py
│ ├── consts.py
│ ├── controller.py
│ ├── controller_data/
│ │ └── datasets/
│ │ ├── NewsMTSC-dataset/
│ │ │ ├── devtest_mt.jsonl
│ │ │ ├── devtest_rw.jsonl
│ │ │ ├── readme.md
│ │ │ └── train.jsonl
│ │ ├── acl14twitter/
│ │ │ ├── test.raw.jsonl
│ │ │ └── train.raw.jsonl
│ │ ├── newsmtsc-train-and-test-mt/
│ │ │ ├── devtest_mtsc_only.jsonl
│ │ │ └── train.jsonl
│ │ ├── newsmtsc-train-and-test-rw/
│ │ │ ├── devtest_mtsc_and_single_primaries.jsonl
│ │ │ └── train.jsonl
│ │ ├── semeval14laptops/
│ │ │ ├── Laptops_Test_Gold.xml.seg.jsonl
│ │ │ └── Laptops_Train.xml.seg.jsonl
│ │ └── semeval14restaurants/
│ │ ├── Restaurants_Test_Gold.xml.seg.jsonl
│ │ └── Restaurants_Train.xml.seg.jsonl
│ ├── converter_huggingface.py
│ ├── createoverview.py
│ ├── customexceptions.py
│ ├── dataset.py
│ ├── diskdict.py
│ ├── download.py
│ ├── earlystopping.py
│ ├── evaluator.py
│ ├── experiments/
│ │ └── default/
│ │ └── datasets/
│ │ ├── newsmtsc-mt/
│ │ │ ├── dev.jsonl
│ │ │ ├── test.jsonl
│ │ │ └── train.jsonl
│ │ ├── newsmtsc-mt-hf/
│ │ │ ├── dev.jsonl
│ │ │ ├── test.jsonl
│ │ │ └── train.jsonl
│ │ ├── newsmtsc-rw/
│ │ │ ├── dev.jsonl
│ │ │ ├── test.jsonl
│ │ │ └── train.jsonl
│ │ └── newsmtsc-rw-hf/
│ │ ├── dev.jsonl
│ │ ├── test.jsonl
│ │ └── train.jsonl
│ ├── fxlogger.py
│ ├── infer.py
│ ├── inferrest.py
│ ├── knowledge/
│ │ ├── __init__.py
│ │ ├── bingliuopinion/
│ │ │ ├── bingliuopinion.py
│ │ │ ├── converter.py
│ │ │ ├── license.txt
│ │ │ ├── negative-words.txt
│ │ │ ├── opinion_polarity.ddict
│ │ │ └── positive-words.txt
│ │ ├── knowledgeutils.py
│ │ ├── liwc/
│ │ │ ├── data/
│ │ │ │ ├── .gitignore
│ │ │ │ └── readme.txt
│ │ │ ├── dic.py
│ │ │ ├── liwc.py
│ │ │ ├── liwchelper.py
│ │ │ └── trie.py
│ │ ├── mpqasubjectivity/
│ │ │ ├── converter.py
│ │ │ ├── mpqasubjectivity.py
│ │ │ ├── subjclueslen1-HLTEMNLP05.tff
│ │ │ └── subjclueslen1-HLTEMNLP05.tff.ddict
│ │ ├── nrcemolex/
│ │ │ ├── NRC-Emotion-Lexicon-Wordlevel-v0.92.txt
│ │ │ ├── NRC-Emotion-Lexicon-Wordlevel-v0.92.txt.ddict
│ │ │ ├── converter.py
│ │ │ └── nrcemolex.py
│ │ └── zeros/
│ │ └── zerosknowledge.py
│ ├── layers/
│ │ ├── AggregatorForBert.py
│ │ ├── __init__.py
│ │ ├── attention.py
│ │ ├── dynamic_rnn.py
│ │ ├── point_wise_feed_forward.py
│ │ ├── pytorchnlpattention.py
│ │ └── squeeze_embedding.py
│ ├── losses/
│ │ ├── __init__.py
│ │ ├── crossentropycrossweight.py
│ │ ├── crossentropylosslsr.py
│ │ ├── crossentropylosswithconfidence.py
│ │ └── seq2seqloss.py
│ ├── models/
│ │ ├── FXBaseModel.py
│ │ ├── FXEnsemble.py
│ │ ├── __init__.py
│ │ ├── ensemble.py
│ │ ├── ensembleb.py
│ │ ├── multitargets/
│ │ │ ├── __init__.py
│ │ │ ├── contrasting.py
│ │ │ ├── random_multi.py
│ │ │ ├── seq2seq.py
│ │ │ ├── seq2seq_without_targetmask.py
│ │ │ ├── tdbertlikemultitarget.py
│ │ │ └── tdbertlikemultitarget_dense.py
│ │ └── singletarget/
│ │ ├── __init__.py
│ │ ├── aen.py
│ │ ├── grutscsingle.py
│ │ ├── lcf.py
│ │ ├── lcf2.py
│ │ ├── lcfs.py
│ │ ├── lcfst.py
│ │ ├── lcft.py
│ │ ├── notargetcls.py
│ │ ├── random_single.py
│ │ ├── spc.py
│ │ ├── td_bert.py
│ │ ├── td_bert_qa.py
│ │ └── tdbertlikesingle.py
│ ├── plotter_utils.py
│ ├── pretrained_models/
│ │ ├── .gitignore
│ │ └── state_dicts/
│ │ └── .gitignore
│ ├── results/
│ │ └── .gitignore
│ └── train.py
├── README.md
├── READMEpypi.md
├── hubconf.py
├── pyproject.toml
├── pythoninfo.md
└── setup.cfg
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
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# User-specific stuff
.idea/**/workspace.xml
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.idea/**/usage.statistics.xml
.idea/**/dictionaries
.idea/**/shelf
# Generated files
.idea/**/contentModel.xml
# Sensitive or high-churn files
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# Gradle
.idea/**/gradle.xml
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# Gradle and Maven with auto-import
# When using Gradle or Maven with auto-import, you should exclude module files,
# since they will be recreated, and may cause churn. Uncomment if using
# auto-import.
# .idea/modules.xml
# .idea/*.iml
# .idea/modules
# *.iml
# *.ipr
# CMake
cmake-build-*/
# Mongo Explorer plugin
.idea/**/mongoSettings.xml
# File-based project format
*.iws
# IntelliJ
out/
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.idea_modules/
# JIRA plugin
atlassian-ide-plugin.xml
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.idea/replstate.xml
# Crashlytics plugin (for Android Studio and IntelliJ)
com_crashlytics_export_strings.xml
crashlytics.properties
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# Editor-based Rest Client
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================================================
FILE: .idea/.gitignore
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# Default ignored files
/shelf/
/workspace.xml
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
# Editor-based HTTP Client requests
/httpRequests/
================================================
FILE: .idea/NewsMTSC.iml
================================================
<?xml version="1.0" encoding="UTF-8"?>
<module type="JAVA_MODULE" version="4">
<component name="NewModuleRootManager" inherit-compiler-output="true">
<exclude-output />
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/NewsSentiment.egg-info" />
<excludeFolder url="file://$MODULE_DIR$/dist" />
</content>
<orderEntry type="jdk" jdkName="Python 3.7 (newsmtsc)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>
================================================
FILE: .idea/misc.xml
================================================
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="PhpWorkspaceProjectConfiguration" backward_compatibility_performed="true" />
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<output url="file://$PROJECT_DIR$/out" />
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FILE: .idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<module fileurl="file://$PROJECT_DIR$/.idea/NewsMTSC.iml" filepath="$PROJECT_DIR$/.idea/NewsMTSC.iml" />
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FILE: .idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<mapping directory="" vcs="Git" />
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================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at felix.hamborg@uni-konstanz.de. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
[homepage]: http://contributor-covenant.org
[version]: http://contributor-covenant.org/version/1/4/
================================================
FILE: LICENSE
================================================
This project uses code from various projects, who may have their own license or copyright.
- ABSA-PyTorch by Yury Soong et al., available at https://github.com/songyouwei/ABSA-PyTorch
- early-stopping-pytorch by Bjarten, available at https://github.com/Bjarten/early-stopping-pytorch
For newly developed files, the license information is:
Copyright 2020 Felix Hamborg
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
================================================
FILE: MANIFEST.in
================================================
include READMEpypi.md
include pythoninfo.md
================================================
FILE: NewsSentiment/.gitignore
================================================
================================================
FILE: NewsSentiment/DatasetPreparer.py
================================================
"""
Used by controller.py to prepare datasets for an experiment, thereby most importantly considering whether the experiment
uses cross validation or not.
Currently, the class is partially hard coded for the poltsanews dataset.
"""
import math
import os
import random
from collections import Counter
from shutil import copyfile
import jsonlines
from tabulate import tabulate
from NewsSentiment.SentimentClasses import SentimentClasses
from NewsSentiment.fxlogger import get_logger
class DatasetPreparer:
def __init__(self, name, basepath_datasets, task_format, human, non_human):
# only used for newsmtsc format
SentimentClasses.Sentiment3ForNewsMtsc()
self.basepath_datasets = basepath_datasets
self.name = name
self.human_created_filenames = human
self.non_human_created_filenames = non_human
self.human_created_filepaths = [
self.get_filepath_by_name(x) for x in self.human_created_filenames
]
self.non_human_created_filepaths = [
self.get_filepath_by_name(x) for x in self.non_human_created_filenames
]
self.data_types = ["human", "nonhum"]
self.sets_info = None
self.random_seed = 1337
random.seed(self.random_seed)
self.logger = get_logger()
self.examples_human = self.files_to_dictlst(self.human_created_filepaths)
self.examples_nonhum = self.files_to_dictlst(self.non_human_created_filepaths)
self.task_format = task_format
self.logger.info(
"shuffling example lists with seed {}".format(self.random_seed)
)
random.shuffle(self.examples_human)
random.shuffle(self.examples_nonhum)
self.logger.info(
"{} examples read created by humans (from: {})".format(
len(self.examples_human), self.human_created_filepaths
)
)
self.logger.info(
"{} examples read not created by humans (from: {})".format(
len(self.examples_nonhum), self.non_human_created_filepaths
)
)
def get_filepath_by_name(self, filename):
return os.path.join(self.basepath_datasets, self.name, filename)
def file_to_dictlst(self, filepath):
dict_lst = []
with jsonlines.open(filepath, "r") as reader:
for line in reader:
dict_lst.append(line)
self.logger.debug("{} examples read from {}".format(len(dict_lst), filepath))
return dict_lst
def files_to_dictlst(self, list_filepaths):
dict_lst = []
for filepath in list_filepaths:
dict_lst.extend(self.file_to_dictlst(filepath))
return dict_lst
def create_slices(self, data_type):
"""
:param sets_info:
:param data_type: 'human', 'nonhum'
:return:
"""
set_names = list(self.sets_info.keys())
assert data_type in self.data_types
# get some vars
_id_relative_weight = "{}-rel-weight".format(data_type)
_id_examples = "{}-examples".format(data_type)
prev_split_pos = 0
for set_index, set_name in enumerate(set_names):
cur_setinfo = self.sets_info[set_name]
if _id_relative_weight in cur_setinfo:
cur_relative_weight = cur_setinfo[_id_relative_weight]
if cur_relative_weight:
split_pos = prev_split_pos + math.floor(
cur_relative_weight * len(self.examples_human)
)
if set_index == len(set_names) - 1:
# just to be sure to not miss a single example because of rounding
split_pos = len(self.examples_human)
example_slice = self.examples_human[prev_split_pos:split_pos]
cur_setinfo[_id_examples] = example_slice
self.logger.info(
"{}: added {} {} examples ({:.2f})".format(
set_name, len(example_slice), data_type, cur_relative_weight
)
)
prev_split_pos = split_pos
else:
cur_setinfo[_id_examples] = []
def merge_slices(self):
total_examples_count = 0
for set_name, cur_set in self.sets_info.items():
cur_merged_data = []
for data_type in self.data_types:
_id_examples = "{}-examples".format(data_type)
if _id_examples in cur_set:
cur_merged_data.extend(cur_set[_id_examples])
cur_set["examples"] = cur_merged_data
total_examples_count += len(cur_merged_data)
for set_name, cur_set in self.sets_info.items():
cur_set["examples-rel"] = len(cur_set["examples"]) / total_examples_count
def _get_label_counts(self, tasks):
label_counts = Counter()
if self.task_format == "newsmtsc":
for task in tasks:
assert len(task["targets"]) >= 1, f"no target in task: {task}"
for target in task["targets"]:
label = SentimentClasses.polarity2label(target["polarity"])
label_counts[label] += 1
elif self.task_format == "newstsc":
for task in tasks:
label_counts[task["label"]] += 1
else:
raise NotImplementedError(f"unknown data format. {self.task_format}")
return dict(label_counts)
def print_set_info(self):
header = [
"set_name",
"human rel",
"human abs",
"non-hum rel",
"non-hum abs",
"rel",
"abs",
"pos",
"neu",
"neg",
]
rows = []
for set_name, cur_set in self.sets_info.items():
if "file" in cur_set:
num_lines = sum(
1 for line in open(self.get_filepath_by_name(cur_set["file"]), encoding="utf8")
)
row = [set_name, -1, -1, -1, -1, cur_set["file"], num_lines, -1, -1, -1]
else:
label_counts = self._get_label_counts(
[*(cur_set["human-examples"]), *(cur_set["nonhum-examples"])]
)
row = [
set_name,
cur_set["human-rel-weight"],
len(cur_set["human-examples"]),
cur_set["nonhum-rel-weight"],
len(cur_set["nonhum-examples"]),
cur_set["examples-rel"],
len(cur_set["examples"]),
label_counts["positive"],
label_counts["neutral"],
label_counts["negative"],
]
rows.append(row)
self.logger.info("\n" + tabulate(rows, header))
def init_set(self, sets_info):
self.sets_info = sets_info
set_names = list(self.sets_info.keys())
weights_human = Counter()
weights_nonhum = Counter()
nonnull_set_names = []
self.filecopy_sets = {}
# sum weights and thereby filter datasets that are 0 in size
for set_name in set_names:
setinfo = self.sets_info[set_name]
if "file" in setinfo:
self.filecopy_sets[set_name] = setinfo["file"]
else:
weights_human[set_name] = setinfo["human-weight"]
weights_nonhum[set_name] = setinfo["nonhum-weight"]
if setinfo["human-weight"] + setinfo["nonhum-weight"] > 0:
nonnull_set_names.append(set_name)
else:
self.logger.info(
"discard {}, because would be empty".format(set_name)
)
set_names = nonnull_set_names
human_weight_sum = sum(weights_human.values())
nonhuman_weight_sum = sum(weights_nonhum.values())
# add relative weights
for set_name in set_names:
cur_setinfo = self.sets_info[set_name]
if cur_setinfo["human-weight"]:
cur_setinfo["human-rel-weight"] = (
cur_setinfo["human-weight"] / human_weight_sum
)
else:
cur_setinfo["human-rel-weight"] = 0
if cur_setinfo["nonhum-weight"]:
cur_setinfo["nonhum-rel-weight"] = (
cur_setinfo["nonhum-weight"] / nonhuman_weight_sum
)
else:
cur_setinfo["nonhum-rel-weight"] = 0
# at this point, setsinfo contains only positive (non-0) relative weights (or no such key, if the abs. pad_value was 0, too)
# create slices
self.create_slices("human")
self.create_slices("nonhum")
# merge human and non human sets in each set
self.merge_slices()
self.print_set_info()
def export(self, savepath):
for set_name, cur_set in self.sets_info.items():
if "file" not in cur_set:
set_savefolder = os.path.join(savepath, self.name)
os.makedirs(set_savefolder, exist_ok=True)
set_savepath = os.path.join(set_savefolder, set_name + ".jsonl")
with jsonlines.open(set_savepath, "w") as writer:
writer.write_all(cur_set["examples"])
self.logger.debug(
"created set (abs={}) at {}".format(
len(cur_set["examples"]), set_savepath
)
)
for set_name, filename in self.filecopy_sets.items():
set_savefolder = os.path.join(savepath, self.name)
os.makedirs(set_savefolder, exist_ok=True)
set_savepath = os.path.join(set_savefolder, set_name + ".jsonl")
set_sourcepath = self.get_filepath_by_name(filename)
copyfile(set_sourcepath, set_savepath)
self.logger.debug("copied set to {}".format(set_savepath))
@classmethod
def poltsanews_rel801010_allhuman(cls, basepath):
name = "poltsanews"
task_format = "newstsc_old"
human_created_filenames = ["human.jsonl"]
non_human_created_filenames = ["train_20191021_233454.jsonl"]
dprep = cls(
name,
basepath,
task_format,
human_created_filenames,
non_human_created_filenames,
)
sets_info = {
"train": {"human-weight": 80, "nonhum-weight": 0},
"dev": {"human-weight": 10, "nonhum-weight": 0},
"test": {"human-weight": 10, "nonhum-weight": 0},
}
dprep.init_set(sets_info)
return dprep, name, task_format
@classmethod
def poltsanews_crossval8010_allhuman(cls, basepath):
name = "poltsanews"
task_format = "newstsc_old"
human_created_filenames = ["human.jsonl"]
non_human_created_filenames = ["train_20191021_233454.jsonl"]
dprep = cls(
name,
basepath,
task_format,
human_created_filenames,
non_human_created_filenames,
)
sets_info = {
"crossval": {"human-weight": 80, "nonhum-weight": 0},
"test": {"human-weight": 10, "nonhum-weight": 0},
}
dprep.init_set(sets_info)
return dprep, name, task_format
@classmethod
def acl14twitter(cls, basepath):
name = "acl14twitter"
task_format = "newsmtsc"
human_created_filenames = ["train.raw.jsonl"]
non_human_created_filenames = []
dprep = cls(
name,
basepath,
task_format,
human_created_filenames,
non_human_created_filenames,
)
sets_info = {
"train": {"human-weight": 80, "nonhum-weight": 0},
"dev": {"human-weight": 10, "nonhum-weight": 0},
"test": {"file": "test.raw.jsonl"},
}
dprep.init_set(sets_info)
return dprep, name, task_format
@classmethod
def semeval14laptops(cls, basepath):
name = "semeval14laptops"
task_format = "newsmtsc"
human_created_filenames = ["Laptops_Train.xml.seg.jsonl"]
non_human_created_filenames = []
dprep = cls(
name,
basepath,
task_format,
human_created_filenames,
non_human_created_filenames,
)
sets_info = {
"train": {"human-weight": 80, "nonhum-weight": 0},
"dev": {"human-weight": 10, "nonhum-weight": 0},
"test": {"file": "Laptops_Test_Gold.xml.seg.jsonl"},
}
dprep.init_set(sets_info)
return dprep, name, task_format
@classmethod
def semeval14restaurants(cls, basepath):
name = "semeval14restaurants"
task_format = "newsmtsc"
human_created_filenames = ["Restaurants_Train.xml.seg.jsonl"]
non_human_created_filenames = []
dprep = cls(
name,
basepath,
task_format,
human_created_filenames,
non_human_created_filenames,
)
sets_info = {
"train": {"human-weight": 80, "nonhum-weight": 0},
"dev": {"human-weight": 10, "nonhum-weight": 0},
"test": {"file": "Restaurants_Test_Gold.xml.seg.jsonl"},
}
dprep.init_set(sets_info)
return dprep, name, task_format
@classmethod
def newsmtsc_devtest_mt(cls, basepath):
name = "newsmtsc-train-and-test-mt"
task_format = "newsmtsc"
human_created_filenames = ["devtest_mtsc_only.jsonl"]
non_human_created_filenames = []
dprep = cls(
name,
basepath,
task_format,
human_created_filenames,
non_human_created_filenames,
)
sets_info = {
"train": {"file": "train.jsonl"},
"dev": {"human-weight": 30, "nonhum-weight": 0},
"test": {"human-weight": 70, "nonhum-weight": 0},
}
dprep.init_set(sets_info)
return dprep, name, task_format
@classmethod
def newsmtsc_devtest_rw(cls, basepath):
name = "newsmtsc-train-and-test-rw"
task_format = "newsmtsc"
human_created_filenames = ["devtest_mtsc_and_single_primaries.jsonl"]
non_human_created_filenames = []
dprep = cls(
name,
basepath,
task_format,
human_created_filenames,
non_human_created_filenames,
)
sets_info = {
"train": {"file": "train.jsonl"},
"dev": {"human-weight": 30, "nonhum-weight": 0},
"test": {"human-weight": 70, "nonhum-weight": 0},
}
dprep.init_set(sets_info)
return dprep, name, task_format
if __name__ == "__main__":
DatasetPreparer.sentinews("controller_data/datasets")
================================================
FILE: NewsSentiment/SentimentClasses.py
================================================
from typing import Iterable
class SentimentClasses:
FILLUP_POLARITY_VALUE = -100
FILLUP_POLARITY_LABEL = "fillup"
SENTIMENT_CLASSES = None
@staticmethod
def initialize(sentiment_classes: dict):
SentimentClasses.SENTIMENT_CLASSES = sentiment_classes
@staticmethod
def get_num_classes():
return len(SentimentClasses.SENTIMENT_CLASSES)
@staticmethod
def __evaluate_boundary(given_value: float, boundary: tuple):
operator = boundary[0]
value = boundary[1]
if operator == "<=":
return given_value <= value
elif operator == "<":
return given_value < value
elif operator == ">=":
return given_value >= value
elif operator == ">":
return given_value > value
elif operator == "==":
return given_value == value
else:
raise ValueError
@staticmethod
def __evaluate_boundaries_of_class(
given_value: float, sentiment_boundaries: Iterable[tuple]
):
assert len(sentiment_boundaries) >= 1
for boundary in sentiment_boundaries:
is_valid = SentimentClasses.__evaluate_boundary(given_value, boundary)
if not is_valid:
return False
return True
@staticmethod
def __get_legacy_information():
# self.polarity_associations = {"positive": 2, "neutral": 1, "negative": 0}
# self.polarity_associations_inv = {2: "positive", 1: "neutral", 0: "negative"}
# self.sorted_expected_label_values = [0, 1, 2]
# self.sorted_expected_label_names = ["negative", "neutral", "positive"]
sentiment_labels = list(SentimentClasses.SENTIMENT_CLASSES.keys())
sentiment_normalized_values = []
for label in sentiment_labels:
sentiment_normalized_values.append(
SentimentClasses.SENTIMENT_CLASSES[label]["normalized_polarity"]
)
polarity_associations = {}
polarity_associations_inv = {}
for label, value in zip(sentiment_labels, sentiment_normalized_values):
polarity_associations[label] = value
polarity_associations_inv[value] = label
return {
"polarity_associations": polarity_associations,
"polarity_associations_inv": polarity_associations_inv,
"sorted_expected_label_values": sentiment_normalized_values,
"sorted_expected_label_names": sentiment_labels,
}
@staticmethod
def get_sorted_expected_label_names():
return SentimentClasses.__get_legacy_information()[
"sorted_expected_label_names"
]
@staticmethod
def get_sorted_expected_label_values():
return SentimentClasses.__get_legacy_information()[
"sorted_expected_label_values"
]
@staticmethod
def get_polarity_associations():
return SentimentClasses.__get_legacy_information()["polarity_associations"]
@staticmethod
def get_polarity_associations_inverse():
return SentimentClasses.__get_legacy_information()["polarity_associations_inv"]
@staticmethod
def __find_sentiment_class(polarity: float):
resulting_class = None
for sentiment_label, info in SentimentClasses.SENTIMENT_CLASSES.items():
sentiment_boundaries = info["boundaries"]
sentiment_normalized_polarity = info["normalized_polarity"]
is_in_class_boundaries = SentimentClasses.__evaluate_boundaries_of_class(
polarity, sentiment_boundaries
)
if is_in_class_boundaries:
# check polarity is not in another class, too
assert (
resulting_class is None
), f"overlapping sentiment classes; previous class: {resulting_class}"
resulting_class = (sentiment_label, sentiment_normalized_polarity)
# check that a class was found
assert resulting_class, f"result is not defined for polarity: {polarity}"
return resulting_class
@staticmethod
def polarity2label(polarity: float) -> str:
if polarity == SentimentClasses.FILLUP_POLARITY_VALUE:
return SentimentClasses.FILLUP_POLARITY_LABEL
sentiment_class = SentimentClasses.__find_sentiment_class(polarity)
label = sentiment_class[0]
return label
@staticmethod
def polarity2normalized_polarity(polarity: float) -> int:
if polarity == SentimentClasses.FILLUP_POLARITY_VALUE:
return int(SentimentClasses.FILLUP_POLARITY_VALUE)
sentiment_class = SentimentClasses.__find_sentiment_class(polarity)
normalized_polarity = sentiment_class[1]
return normalized_polarity
@staticmethod
def Sentiment3ForNewsMtsc():
sentiment_classes = {
"positive": {
"boundaries": [(">=", 5), ("<=", 7)],
"normalized_polarity": 2,
},
"neutral": {"boundaries": [(">", 3), ("<", 5)], "normalized_polarity": 1},
"negative": {
"boundaries": [(">=", 1), ("<=", 3)],
"normalized_polarity": 0,
},
}
SentimentClasses.initialize(sentiment_classes)
@staticmethod
def SentimentStrong3ForNewsMtsc():
sentiment_classes = {
"positive": {
"boundaries": [(">=", 6), ("<=", 7)],
"normalized_polarity": 2,
},
"neutral": {"boundaries": [(">", 2), ("<", 6)], "normalized_polarity": 1},
"negative": {
"boundaries": [(">=", 1), ("<=", 2)],
"normalized_polarity": 0,
},
}
SentimentClasses.initialize(sentiment_classes)
@staticmethod
def SentimentWeak3ForNewsMtsc():
sentiment_classes = {
"positive": {
"boundaries": [(">=", 4.5), ("<=", 7)],
"normalized_polarity": 2,
},
"neutral": {
"boundaries": [(">", 3.5), ("<", 4.5)],
"normalized_polarity": 1,
},
"negative": {
"boundaries": [(">=", 1), ("<=", 3.5)],
"normalized_polarity": 0,
},
}
SentimentClasses.initialize(sentiment_classes)
================================================
FILE: NewsSentiment/__init__.py
================================================
from NewsSentiment.infer import TargetSentimentClassifier
================================================
FILE: NewsSentiment/__main__.py
================================================
import argparse
from NewsSentiment.download import Download
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='NewsSentiment')
subparsers = parser.add_subparsers(dest='action')
subparser_download = subparsers.add_parser('download', help=Download.add_subparser.__doc__)
Download.add_subparser(subparser_download)
args = parser.parse_args()
action = args.action
del args.action
if action == 'download':
Download.run_from_parser(args)
================================================
FILE: NewsSentiment/combinations_default.py
================================================
from NewsSentiment.consts import BERT_BASE_UNCASED
combinations_default_0 = {
"own_model_name": [
# baselines: single
"notargetclsbert",
"lcf_bert",
"lcf_bert2",
"lcfs_bert",
"lcft_bert",
"aen_bert",
"spc_bert",
"tdbert",
"tdbert-qa-mul",
"tdbert-qa-con",
# own models: single
"tdbertlikesingle",
"lcfst_bert",
"grutsc",
# own models: multi
"tdbertlikemulti",
# "tdbertlikemulti_dense",
"seq2seq",
"seq2seq_withouttargetmask",
"contrasting",
# baselines
# "random_single",
# "random_multi",
],
"optimizer": ["adam"],
"initializer": ["xavier_uniform_"],
"learning_rate": ["2e-5", "3e-5", "5e-5"],
"batch_size": [
"16",
# "32",
], # ['16', '32'],
"balancing": ["None"], # ['None', 'lossweighting', 'oversampling'],
"devmode": ["False"],
"num_epoch": ["2", "3", "4"],
"loss": [
"crossentropy",
"crossentropy_lsr",
"sequence",
"crossentropy_crossweight",
],
# "spc_lm_representation_distilbert": ["mean_last"],
# ['sum_last', 'sum_last_four', 'sum_last_two', 'sum_all', 'mean_last', 'mean_last_four', 'mean_last_two', 'mean_all'],
# "spc_lm_representation": ["pooler_output"],
# ['pooler_output', 'sum_last', 'sum_last_four', 'sum_last_two', 'sum_all', 'mean_last', 'mean_last_four', 'mean_last_two', 'mean_all'],
# "spc_input_order": ["text_target"], # 'target_text',
# "aen_lm_representation": ["last"],
# ['last', 'sum_last_four', 'sum_last_two', 'sum_all', 'mean_last_four'], # 'mean_last_two', 'mean_all'],
"eval_only_after_last_epoch": ["True"],
"local_context_focus": ["cdm", "cdw"],
"SRD": ["3", "4", "5"],
"pretrained_model_name": ["default"],
# ['default', 'bert_news_ccnc_10mio_3ep', 'laptops_and_restaurants_2mio_ep15', 'laptops_1mio_ep30', 'restaurants_10mio_ep3'],
"state_dict": ["None"],
# ['None', 'lcf_bert_acl14twitter_val_recall_avg_0.7349_epoch3', 'lcf_bert_semeval14laptops_val_recall_avg_0.7853_epoch3', 'lcf_bert_semeval14restaurants_val_recall_avg_0.7672_epoch2', 'lcf_bert_newstsc_val_recall_avg_0.5954_epoch3'],
"single_targets": [
"True"
], # using conditions in controller.py, we have single_targets only for single target models
"multi_targets": [
"True"
], # using conditions in controller.py, we have multi_targets only for multi target models
"targetclasses": [
"newsmtsc3",
#"newsmtsc3strong",
#"newsmtsc3weak",
],
"knowledgesources": [
"nrc_emotions", "mpqa_subjectivity", "bingliu_opinion", "liwc",
"nrc_emotions mpqa_subjectivity", "nrc_emotions liwc",
"nrc_emotions bingliu_opinion", "mpqa_subjectivity bingliu_opinion",
"mpqa_subjectivity liwc", "bingliu_opinion liwc",
"nrc_emotions mpqa_subjectivity bingliu_opinion",
"nrc_emotions mpqa_subjectivity liwc",
"nrc_emotions liwc bingliu_opinion",
"liwc mpqa_subjectivity bingliu_opinion",
"nrc_emotions mpqa_subjectivity bingliu_opinion liwc",
"zeros",
],
"is_use_natural_target_phrase_for_spc": [
"True",
"False"
],
"default_lm": [
BERT_BASE_UNCASED,
],
"coref_mode_in_training": [
"ignore",
"in_targetmask",
"additional_examples"
],
}
================================================
FILE: NewsSentiment/consts.py
================================================
BERT_BASE_UNCASED = "bert-base-uncased"
ROBERTA_BASE = "roberta-base"
XLNET_BASE_CASED = "xlnet-base-cased"
ALBERT_BASE = "albert-base-v2"
ALBERT_LARGE = "albert-large-v2"
ALBERT_XLARGE = "albert-xlarge-v2"
ALBERT_XXLARGE = "albert-xxlarge-v2"
__DEFAULT_LM = None
def set_default_lm(new_name: str):
global __DEFAULT_LM
__DEFAULT_LM = new_name
def get_default_lm():
return __DEFAULT_LM
FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS = "text_ids_with_special_tokens"
FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS_TARGET_MASK = (
FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS + "_target_mask"
)
FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS_SELECTED_KNOWLEDGE_SOURCES = (
FIELD_TEXT_IDS_WITH_SPECIAL_TOKENS + "_selectedknowledgesources"
)
FIELD_IS_OVERFLOW = "is_overflow"
FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS = (
"text_then_target_ids_with_special_tokens"
)
# we used to have text-then-target target mask here, but won't use it,
# since it would be identical to the text target mask (since we only
# want to mark the target within the text, but not in the 2nd target
# component)
# FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS_TARGET_MASK = (
# FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS + "_target_mask"
# )
# same for knowledge sources
# FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS_KNOWLEDGE_SOURCES = (
# FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS + "_selectedknowledgesources"
# )
FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS_SEGMENT_IDS = (
FIELD_TEXT_THEN_TARGET_IDS_WITH_SPECIAL_TOKENS + "_segment_ids"
)
FIELD_TARGET_IDS_WITH_SPECIAL_TOKENS = "target_ids_with_special_tokens"
FIELD_SYNTAX_HOP_DISTANCE_TO_TARGET = "syntax_hop_distance_to_target"
FIELD_SYNTAX_DEPENDENCY_MATRIX = "syntax_dependency_matrix"
================================================
FILE: NewsSentiment/controller.py
================================================
"""
Definitions:
setup: represents a fixed, unique combination of for instance:
* model, e.g., BERT, DISTILBERT
* hyper-parameters, such as learning rate loss function, weights of classes for loss function
* others, such as softmax,
* input style, e.g., input style: QA style, AEN, ...
Procedure:
Given a single annotated dataset named alldat (also called both.jsonl, since it consists of both splits of the devtest
set):
* splits alldat into testdat and remainderdat
Given a set of experiment setup descriptions
1) Iterate setup descriptions, for each:
a) create 10-fold CV to well evaluate the performance of that particular setup
b) return model from best epoch (or from best fold???!?)
2) retrievel all best performing models and test them on testdat
3) return model that performs best on testdat
"""
from threading import Lock
import argparse
import multiprocessing
import os
import subprocess
import time
from collections import Counter, defaultdict
from datetime import datetime
from itertools import product
from jsonlines import jsonlines
from tabulate import tabulate
from tqdm import tqdm
from NewsSentiment.combinations_ensemble import combinations_ensemble
from NewsSentiment.combinations_final_lcf2 import combinations_final_lcf2
from NewsSentiment.combinations_final_nostance import combinations_final_nostance
from NewsSentiment.combinations_final_spc import combinations_final_spc
from NewsSentiment.combinations_final_stance0 import combinations_final_stance0
from NewsSentiment.combinations_final_stance1 import combinations_final_stance1
from NewsSentiment.combinations_final_tdbert import combinations_final_tdbert
from NewsSentiment.combinations_top import combinations_top
from NewsSentiment.DatasetPreparer import DatasetPreparer
from NewsSentiment.combinations_default import combinations_default_0
from NewsSentiment.combinations_top4otherdomains import combinations_top4otherdomains
from NewsSentiment.combinations_top_coref16 import combinations_top_coref16
from NewsSentiment.combinations_top_coref8 import combinations_top_coref8
from NewsSentiment.combinations_top_coref8_stanceno import combinations_top_coref8_stanceno
from NewsSentiment.combinations_top_finetuned import combinations_top_finetuned
from NewsSentiment.diskdict import DiskDict
from NewsSentiment.fxlogger import get_logger
from NewsSentiment.train import str2bool
completed_tasks = None # will be DiskDict later
completed_tasks_in_this_run_count = 0
lock = Lock()
def start_worker(
experiment_id,
experiment_named_id,
named_combination,
cmd,
human_cmd,
experiment_path,
running_processes,
):
if "--device" in cmd:
device_index_in_list = cmd.index("--device")
device = cmd[device_index_in_list + 1]
else:
device = -1
running_processes[experiment_id] = (True, device)
logger = get_logger()
logger.debug("starting single setup: {}".format(human_cmd))
with open(os.path.join(experiment_path, "stdlog.out"), "w") as file_stdout, open(
os.path.join(experiment_path, "stdlog.err"), "w"
) as file_stderr:
completed_process = subprocess.run(cmd, stdout=file_stdout, stderr=file_stderr)
experiment_details = get_experiment_result_detailed(experiment_path)
running_processes[experiment_id] = (False, device)
return {
**named_combination,
**{"rc": completed_process.returncode, "experiment_id": experiment_id},
"details": experiment_details,
"experiment_named_id": experiment_named_id,
}
def on_task_done(x):
with lock:
# result_list is modified only by the main process, not the pool workers.
completed_tasks[x["experiment_named_id"]] = x
completed_tasks.sync_to_disk()
global completed_tasks_in_this_run_count
completed_tasks_in_this_run_count += 1
def on_task_error(x):
with lock:
# result_list is modified only by the main process, not the pool workers.
completed_tasks[x["experiment_named_id"]] = x
completed_tasks.sync_to_disk()
global completed_tasks_in_this_run_count
completed_tasks_in_this_run_count += 1
def get_experiment_result_detailed(experiment_path):
experiment_results_path = os.path.join(experiment_path, "experiment_results.jsonl")
try:
with jsonlines.open(experiment_results_path, "r") as reader:
lines = []
for line in reader:
lines.append(line)
assert len(lines) == 1
return lines[0]
except FileNotFoundError:
return None
class SetupController:
def __init__(self, options):
self.logger = get_logger()
self.opt = options
if self.opt.cuda_devices:
# to run on SCC
if self.opt.cuda_devices == "SGE_GPU":
self.cuda_devices = os.environ.get("SGE_GPU")
else:
self.cuda_devices = self.opt.cuda_devices
if self.cuda_devices:
self.logger.info("cuda devices:" + self.cuda_devices)
self.cuda_devices = self.cuda_devices.split(",")
self.logger.info(
f"was assigned {len(self.cuda_devices)} cuda devices: "
f"{self.cuda_devices}"
)
if self.opt.num_workers < 0:
self.logger.info(
f"num_workers < 0: using cuda device count. setting "
f"num_workers={len(self.cuda_devices)}"
)
self.opt.num_workers = len(self.cuda_devices)
else:
# do not use CUDA
self.cuda_devices = None
self.snem = "f1_macro"
self.experiment_base_path = self.opt.experiments_path
args_names_ordered = [
"own_model_name",
"optimizer",
"initializer",
"learning_rate",
"batch_size",
"balancing",
"num_epoch",
"loss",
"eval_only_after_last_epoch",
"devmode",
"local_context_focus",
"SRD",
"pretrained_model_name",
"state_dict",
"single_targets",
"multi_targets",
"targetclasses",
"knowledgesources",
"is_use_natural_target_phrase_for_spc",
"default_lm",
"coref_mode_in_training",
]
combinations = None
if self.opt.combi_mode == "default":
combinations = combinations_default_0
elif self.opt.combi_mode == "top":
combinations = combinations_top
elif self.opt.combi_mode == "top_coref8":
combinations = combinations_top_coref8
elif self.opt.combi_mode == "top_coref8_stanceno":
combinations = combinations_top_coref8_stanceno
elif self.opt.combi_mode == "top_coref16":
combinations = combinations_top_coref16
elif self.opt.combi_mode == "top_finetuned":
combinations = combinations_top_finetuned
elif self.opt.combi_mode == "top4otherdomains":
combinations = combinations_top4otherdomains
elif self.opt.combi_mode == "ensemble":
combinations = combinations_ensemble
elif self.opt.combi_mode == "final_stance0":
combinations = combinations_final_stance0
elif self.opt.combi_mode == "final_stance1":
combinations = combinations_final_stance1
elif self.opt.combi_mode == "final_nostance":
combinations = combinations_final_nostance
elif self.opt.combi_mode == "final_tdbert":
combinations = combinations_final_tdbert
elif self.opt.combi_mode == "final_lcf2":
combinations = combinations_final_lcf2
elif self.opt.combi_mode == "final_spc":
combinations = combinations_final_spc
if not combinations:
raise ValueError(
"combination(mode={}, id={}) not defined".format(
self.opt.combi_mode, self.opt.combi_id
)
)
# key: name of parameter that is only applied if its conditions are met
# pad_value: list of tuples, consisting of parameter name and the pad_value it needs to have in order for the
# condition to be satisfied
# Note that all tuples in this list are OR connected, so if at least one is satisfied, the conditions are met.
# If we need AND connected conditions, my idea is to add an outer list, resulting in a list of lists (of
# tuples) where all lists are AND connected.
# If a condition is not satisfied, the corresponding parameter will still be pass
# if a key is not listed here, there are no conditions and thus the key / parameter will be used always
conditions = {
"is_use_natural_target_phrase_for_spc": [("own_model_name", "spc_bert")],
"spc_lm_representation": [("own_model_name", "spc_bert"),],
"spc_input_order": [("own_model_name", "spc_bert"),],
"aen_lm_representation": [("own_model_name", "aen_bert"),],
"use_early_stopping": [("num_epoch", "10"),],
"local_context_focus": [
("own_model_name", "lcf_bert"),
("own_model_name", "lcf_bert2"),
("own_model_name", "lcfs_bert"),
("own_model_name", "lcfst_bert"),
],
"SRD": [
("own_model_name", "lcf_bert"),
("own_model_name", "lcf_bert2"),
("own_model_name", "lcfs_bert"),
("own_model_name", "lcfst_bert"),
],
"single_targets": [
# we need single_targets mode only for models that are single target
("own_model_name", "lcf_bert"),
("own_model_name", "lcf_bert2"),
("own_model_name", "lcfs_bert"),
("own_model_name", "lcft_bert"),
("own_model_name", "lcfst_bert"),
("own_model_name", "aen_bert"),
("own_model_name", "spc_bert"),
("own_model_name", "tdbert"),
("own_model_name", "EnsembleTopA"),
("own_model_name", "EnsembleTopB"),
("own_model_name", "tdbert-qa-mul"),
("own_model_name", "tdbert-qa-con"),
("own_model_name", "tdbertlikesingle"),
("own_model_name", "notargetclsbert"),
("own_model_name", "random_single"),
("own_model_name", "grutsc"),
],
"multi_targets": [
("own_model_name", "tdbertlikemulti"),
("own_model_name", "tdbertlikemulti_dense"),
("own_model_name", "seq2seq"),
("own_model_name", "seq2seq_withouttargetmask"),
("own_model_name", "random_multi"),
("own_model_name", "contrasting"),
],
"knowledgesources": [
("own_model_name", "EnsembleTopA"),
("own_model_name", "EnsembleTopB"),
("own_model_name", "grutsc"),
],
"pretrained_model_name": [("default_lm", "bert-base-uncased"),],
}
assert set(args_names_ordered) == set(combinations.keys()), (
f"mismatch args_names_ordered vs. combinations: "
f"{set(args_names_ordered).symmetric_difference(combinations)}"
)
self.experiment_base_id = (
self.opt.dataset + "_" + datetime.today().strftime("%Y%m%d-%H%M%S")
)
self.basecmd = ["python", "train.py"]
self.basepath = "controller_data"
self.basepath_data = os.path.join(self.basepath, "datasets")
combination_count = 1
_combination_values = []
for arg_name in args_names_ordered:
arg_values = list(combinations[arg_name])
combination_count = combination_count * len(arg_values)
_combination_values.append(arg_values)
combinations = list(product(*_combination_values))
assert len(combinations) == combination_count
self.logger.info(
"{} arguments, totaling in {} combinations".format(
len(args_names_ordered), combination_count
)
)
# apply conditions
self.logger.info("applying conditions...")
self.named_combinations, count_duplicates = self._apply_conditions(
combinations, args_names_ordered, conditions
)
self.logger.info(
"applied conditions. removed {} combinations. {} -> {}".format(
count_duplicates, combination_count, len(self.named_combinations)
)
)
self.combination_count = len(self.named_combinations)
# expand by number of runs per experiment
expanded_named_combinations = []
for named_combination in self.named_combinations:
for run_id in range(self.opt.num_runs_per_experiment):
expanded_named_combination = named_combination.copy()
expanded_named_combination["run_id"] = run_id
expanded_named_combinations.append(expanded_named_combination)
self.logger.info(
"expanded to %s runs per experiment. combination count: %s -> %s",
self.opt.num_runs_per_experiment,
self.combination_count,
len(expanded_named_combinations),
)
self.named_combinations = expanded_named_combinations
self.combination_count = len(self.named_combinations)
if self.opt.dataset == "semeval14restaurants":
(
self.dataset_preparer,
self.datasetname,
self.task_format,
) = DatasetPreparer.semeval14restaurants(self.basepath_data)
elif self.opt.dataset == "semeval14laptops":
(
self.dataset_preparer,
self.datasetname,
self.task_format,
) = DatasetPreparer.semeval14laptops(self.basepath_data)
elif self.opt.dataset == "acl14twitter":
(
self.dataset_preparer,
self.datasetname,
self.task_format,
) = DatasetPreparer.acl14twitter(self.basepath_data)
elif (
self.opt.dataset
== "newsmtsc_devtest_rw"
):
(
self.dataset_preparer,
self.datasetname,
self.task_format,
) = DatasetPreparer.newsmtsc_devtest_rw(
self.basepath_data
)
elif (
self.opt.dataset
== "newsmtsc_devtest_mt"
):
(
self.dataset_preparer,
self.datasetname,
self.task_format,
) = DatasetPreparer.newsmtsc_devtest_mt(
self.basepath_data
)
else:
raise Exception("unknown dataset: {}".format(self.opt.dataset))
def _apply_conditions(self, combinations, args_names_ordered, conditions):
named_combinations = []
seen_experiment_ids = set()
count_duplicates = 0
with tqdm(total=len(combinations)) as pbar:
for combination in combinations:
named_combination = {}
full_named_combination = self._args_combination_to_single_arg_values(
combination, args_names_ordered
)
# for a parameter combination, pass only those parameters that are valid
# for that combination
for arg_index, arg_name in enumerate(args_names_ordered):
# iterate each parameter and validate - using the other parameter
# names and values - whether its conditions are met
if self._check_conditions(
arg_name, full_named_combination, conditions
):
# if yes, pass it
named_combination[arg_name] = combination[arg_index]
self.logger.debug(
"using '{}' in combination {}".format(arg_name, combination)
)
else:
self.logger.debug(
"not using '{}' in combination {}".format(
arg_name, combination
)
)
# check if experiment_id of named combination was already seen
experiment_id = self._experiment_named_id_from_named_combination(
named_combination
)
if experiment_id not in seen_experiment_ids:
seen_experiment_ids.add(experiment_id)
named_combinations.append(named_combination)
else:
count_duplicates += 1
pbar.update(1)
return named_combinations, count_duplicates
def _check_conditions(self, arg_name, full_named_combination, conditions):
"""
For a given parameter, checks whether its conditions are satisfied. If so, returns True, else False.
:param arg_name:
:param arg_value:
:return:
"""
if arg_name in conditions and len(conditions[arg_name]) >= 1:
# at this point we know that there are conditions for the given parameters
or_connected_conditions = conditions[arg_name]
for cond_tup in or_connected_conditions:
cond_param_name = cond_tup[0]
cond_param_value = cond_tup[1]
# get parameter and its pad_value in current combination
if full_named_combination[cond_param_name] == cond_param_value:
return True
# since there was at least one condition due to our check above, we return
# False here, since the for loop did not return True
return False
else:
# if there is no condition associated with arg_name just return true
return True
def _build_args(self, named_args):
args_list = []
for arg_name, arg_val in named_args.items():
self._add_arg(args_list, arg_name, arg_val)
return args_list
def _add_arg(self, args_list, name, value):
args_list.append("--" + name)
args_list.append(str(value))
return args_list
def _prepare_experiment_env(self, experiment_path):
os.makedirs(experiment_path, exist_ok=True)
self.dataset_preparer.export(os.path.join(experiment_path, "datasets"))
def _args_combination_to_single_arg_values(
self, args_combination, args_names_ordered
):
args_names_values = {}
for arg_index, arg_name in enumerate(args_names_ordered):
args_names_values[arg_name] = args_combination[arg_index]
return args_names_values
def _experiment_named_id_from_named_combination(self, named_combination):
return "__".join(["{}={}".format(k, v) for (k, v) in named_combination.items()])
def prepare_single_setup(self, named_combination, experiment_id, pool_index):
experiment_path = "{}/{}/".format(self.experiment_base_id, experiment_id)
experiment_path = os.path.join(self.experiment_base_path, experiment_path)
self._prepare_experiment_env(experiment_path)
args = self._build_args(named_combination)
self._add_arg(args, "snem", self.snem)
self._add_arg(args, "dataset_name", self.datasetname)
self._add_arg(args, "experiment_path", experiment_path)
self._add_arg(args, "data_format", self.task_format)
if self.cuda_devices:
# we use the pool_index to get the respective pool's cuda device
cuda_device_id = self.cuda_devices[pool_index]
cuda_device_name = "cuda:" + str(cuda_device_id)
self._add_arg(args, "device", cuda_device_name)
cmd = self.basecmd + args
human_cmd = " ".join(cmd)
with open(os.path.join(experiment_path, "experiment_cmd.sh"), "w") as writer:
writer.write(human_cmd)
return cmd, human_cmd, experiment_path
def run(self):
global completed_tasks
if not self.opt.results_path:
results_path = "results/default_results_{}".format(self.datasetname)
else:
results_path = self.opt.results_path
if not self.opt.continue_run:
self.logger.info("not continuing")
os.remove(results_path)
else:
self.logger.info("continuing previous run(s)")
completed_tasks = DiskDict(results_path)
self.logger.info("found {} previous results".format(len(completed_tasks)))
self.logger.info("preparing experiment setups...")
experiment_descs = defaultdict(list)
previous_tasks = Counter()
with tqdm(total=self.combination_count) as pbar:
for i, named_combination in enumerate(self.named_combinations):
_experiment_named_id = self._experiment_named_id_from_named_combination(
named_combination
)
# i modulo the number of workers determines which pool this experiment
# will run in. the pool_index will also later be used to determine
# which cuda device (if any) to use, thereby making sure, that all tasks
# of one pool use the same device, whereas each device will be used
# solely by only one pool (this way, we make sure that at no time two
# or more tasks will run that use the same cuda device)
pool_index = i % self.opt.num_workers
if _experiment_named_id in completed_tasks:
task_desc = completed_tasks[_experiment_named_id]
if task_desc["rc"] != 0:
if self.opt.rerun_non_rc0:
self.logger.debug(
"task {} was already executed, "
"but with rc={}. rerunning.".format(
_experiment_named_id, task_desc["rc"]
)
)
(
cmd,
human_cmd,
experiment_path,
) = self.prepare_single_setup(
named_combination, i, pool_index
)
experiment_descs[pool_index].append(
(
i,
_experiment_named_id,
named_combination,
cmd,
human_cmd,
experiment_path,
)
)
previous_tasks["new"] += 1
del completed_tasks[_experiment_named_id]
else:
self.logger.debug(
"task {} was already executed, but with rc={}. not "
"rerunning.".format(
_experiment_named_id, task_desc["rc"]
)
)
previous_tasks["rcnon0"] += 1
else:
# rerun tasks where the rc != 0 (always rerun tasks that have
# not been executed at all, yet)
self.logger.debug(
"skipping experiment: {}".format(_experiment_named_id)
)
self.logger.debug(
"previous result: {}".format(
completed_tasks[_experiment_named_id]
)
)
previous_tasks["rc0"] += 1
else:
(cmd, human_cmd, experiment_path,) = self.prepare_single_setup(
named_combination, i, pool_index
)
experiment_descs[pool_index].append(
(
i,
_experiment_named_id,
named_combination,
cmd,
human_cmd,
experiment_path,
)
)
previous_tasks["new"] += 1
pbar.update(1)
self.logger.info(
"summary (new is also increased for tasks that were executed previously "
"but yielded rc!=0 - if rerun_non_rc0==True"
)
self.logger.info("{}".format(previous_tasks))
self.logger.info("starting {} experiments".format(self.combination_count))
self.logger.info(
"creating {} process pools with each 1 worker".format(self.opt.num_workers)
)
if self.cuda_devices and len(self.cuda_devices) != self.opt.num_workers:
self.logger.warning(
"number of cuda devices does not match number of workers: "
"{} vs. {}".format(len(self.cuda_devices), self.opt.num_workers)
)
manager = multiprocessing.Manager()
running_processes = manager.dict()
for pool_index in range(self.opt.num_workers):
pool = multiprocessing.Pool(processes=1, maxtasksperchild=1)
for desc in experiment_descs[pool_index]:
proc_args = desc + (running_processes,) # must be a tuple
pool.apply_async(
start_worker,
args=proc_args,
callback=on_task_done,
error_callback=on_task_error,
)
self.logger.info(
f"created pool with {len(experiment_descs[pool_index])} tasks, each "
f"processed by 1 worker"
)
self.logger.info("waiting for workers to complete all jobs...")
prev_count_done = 0
with tqdm(total=previous_tasks["new"], initial=prev_count_done) as pbar:
while completed_tasks_in_this_run_count < previous_tasks["new"]:
time.sleep(10)
update_inc = completed_tasks_in_this_run_count - prev_count_done
if update_inc > 0:
pbar.update(update_inc)
prev_count_done = completed_tasks_in_this_run_count
completed_tasks.sync_to_disk()
best_dev_snem, best_experiment_id = self._get_best_dev_snem()
sorted_running_procs = sorted(
self._get_running_processes(running_processes),
key=lambda running_device: running_device[1],
)
pbar.set_postfix_str(
f"dev-snem: {best_dev_snem:.3f} ({best_experiment_id}); prcs: {sorted_running_procs}"
)
# save to disk
completed_tasks.sync_to_disk()
self.logger.info(
f"finished all tasks ({completed_tasks_in_this_run_count} of "
f"{previous_tasks['new']})"
)
# clone the results so that we can process them while not modifying the original
processed_results = dict(completed_tasks)
experiments_rc_overview = Counter()
non_okay_experiment_ids = []
for experiment_named_id, experiment_result in processed_results.items():
rc = experiment_result["rc"]
experiments_rc_overview[rc] += 1
if rc != 0:
if rc == 99:
pass
else:
non_okay_experiment_ids.append(experiment_result["experiment_id"])
self.logger.warning(
f"{len(non_okay_experiment_ids)} experiments did not return 0: "
f"{sorted(non_okay_experiment_ids)}"
)
# snem-based performance sort
sorted_results = list(dict(processed_results).values())
for result in sorted_results:
if result["details"]:
result["dev_snem"] = result["details"]["dev_stats"][self.snem]
del result["details"]
else:
result["dev_snem"] = -1.0
sorted_results.sort(key=lambda x: x["dev_snem"], reverse=True)
headers = list(sorted_results[0].keys())
rows = [x.values() for x in sorted_results]
self.logger.info("all experiments finished. statistics:")
self.logger.debug("snem-based performances:")
self.logger.debug("\n" + tabulate(rows, headers))
self.logger.info("return codes: {}".format(experiments_rc_overview))
self.logger.info(
"best dev-snem: %s (id: %s)",
sorted_results[0]["dev_snem"],
sorted_results[0]["experiment_id"],
)
def _get_best_dev_snem(self):
best_dev_snem = -1.0
experiment_id = -1
# to avoid that the dict is modified while iterating it, copy it for temporary use
# if we're unlucky this could happen otherwise: RuntimeError: dictionary changed size during iteration
tmp_tasks = completed_tasks.copy()
for task in tmp_tasks.values():
if task.get("details") and task["details"].get("dev_stats"):
best_dev_snem = max(
best_dev_snem, task["details"]["dev_stats"][self.snem]
)
experiment_id = task["experiment_id"]
return best_dev_snem, experiment_id
def _get_running_processes(self, running_processes):
return [
(exp_id, running_device[1])
for exp_id, running_device in running_processes.items()
if running_device[0]
]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", default=None, type=str)
parser.add_argument("--experiments_path", default="./experiments", type=str)
parser.add_argument(
"--continue_run", type=str2bool, nargs="?", const=True, default=True
)
parser.add_argument(
"--rerun_non_rc0", type=str2bool, nargs="?", const=True, default=True
)
parser.add_argument("--num_workers", type=int, default=1)
parser.add_argument("--results_path", type=str, default=None)
parser.add_argument("--combi_mode", type=str, required=True)
parser.add_argument("--combi_id", type=int, default=0)
parser.add_argument(
"--cuda_devices",
type=str,
default=None,
help="Comma separated list of cuda device IDs, e.g., "
"0,1,2,3; or SGE_GPU to read this list from "
"the environment variable SGE_GPU. If not defined,"
"CPU will be used instead of GPU.",
)
parser.add_argument("--num_runs_per_experiment", type=int, default=1)
parser.add_argument(
"--only_calc_combinations", type=str2bool, nargs="?", const=True, default=False
)
opt = parser.parse_args()
if opt.only_calc_combinations:
SetupController(opt)
else:
SetupController(opt).run()
================================================
FILE: NewsSentiment/controller_data/datasets/NewsMTSC-dataset/devtest_mt.jsonl
================================================
{"primary_gid": "allsides_1002_402_12_former FBI director James B. Comey_51_56", "sentence_normalized": "While White House officials said in the days after Comey's dismissal that it was largely the result of a memo written by Deputy Attorney General Rod J. Rosenstein criticizing the FBI director's handling of the investigation into Hillary Clinton’s use of a private email server when she was secretary of state, Trump suggested in the NBC interview that the Russian investigation played a role in his decision.", "targets": [{"Input.gid": "allsides_1002_402_12_Hillary Clinton’s_229_246", "from": 229, "to": 246, "mention": "Hillary Clinton’s", "polarity": 2.0, "further_mentions": [{"from": 282, "to": 285, "mention": "she"}]}, {"Input.gid": "allsides_1002_402_12_former FBI director James B. Comey_51_56", "from": 51, "to": 56, "mention": "Comey", "polarity": 2.0}]}
{"primary_gid": "allsides_112_45_4_Stedman Graham_0_6", "sentence_normalized": "Graham's comment only reinforced growing rumors that Winfrey is seriously considering a presidential bid.", "targets": [{"Input.gid": "allsides_112_45_4_Stedman Graham_0_6", "from": 0, "to": 6, "mention": "Graham", "polarity": 4.0}, {"Input.gid": "allsides_112_45_4_Winfrey_53_60", "from": 53, "to": 60, "mention": "Winfrey", "polarity": 4.0}]}
{"primary_gid": "allsides_115_46_35_White House adviser Stephen Miller_66_72", "sentence_normalized": "\"I think I've wasted enough of my viewers' time,\" Tapper said, as Miller attempted to keep talking.", "targets": [{"Input.gid": "allsides_115_46_35_White House adviser Stephen Miller_66_72", "from": 66, "to": 72, "mention": "Miller", "polarity": 2.0}, {"Input.gid": "allsides_115_46_35_host Jake Tapper_50_56", "from": 50, "to": 56, "mention": "Tapper", "polarity": 4.0}]}
{"primary_gid": "allsides_1164_465_5_President Trump_31_46", "sentence_normalized": "Pence had been in contact with President Trump about the test.", "targets": [{"Input.gid": "allsides_1164_465_5_Pence_0_5", "from": 0, "to": 5, "mention": "Pence", "polarity": 4.0}, {"Input.gid": "allsides_1164_465_5_President Trump_31_46", "from": 31, "to": 46, "mention": "President Trump", "polarity": 4.0}]}
{"primary_gid": "allsides_1166_466_62_Gervais_216_223", "sentence_normalized": "“It may reflect any combination of genuine innumeracy [lack of math skills], incomprehension of an oddly phrased item, participant inattentiveness or jesting, sampling error, or a genuine flaw in the ... technique,” Gervais and Najle write in the paper.", "targets": [{"Input.gid": "allsides_1166_466_62_Gervais_216_223", "from": 216, "to": 223, "mention": "Gervais", "polarity": 4.0}, {"Input.gid": "allsides_1166_466_62_Najle_228_233", "from": 228, "to": 233, "mention": "Najle", "polarity": 4.0}]}
{"primary_gid": "allsides_1183_473_1_Assad_150_182", "sentence_normalized": "U.S. Ambassador to the United Nations Nikki Haley and Secretary of State Rex Tillerson on Sunday appeared to offer differing views on the future of Syrian President Bashar al-Assad.", "targets": [{"Input.gid": "allsides_1183_473_1_Haley_46_51", "from": 44, "to": 49, "mention": "Haley", "polarity": 4.0}, {"Input.gid": "allsides_1183_473_1_Assad_150_182", "from": 148, "to": 180, "mention": "Syrian President Bashar al-Assad", "polarity": 4.0}]}
{"primary_gid": "allsides_1183_473_22_Assad_201_206", "sentence_normalized": "In a separate interview on CBS's Face the Nation, Tillerson said it was the goal of the U.S. and its allies to \"navigate a political outcome in which the Syrian people in fact will determine Bashar al-Assad's fate and his legitimacy,\" citing in part \"the United States' own founding principles are self-determination.\"", "targets": [{"Input.gid": "allsides_1183_473_22_Tillerson_50_59", "from": 50, "to": 59, "mention": "Tillerson", "polarity": 6.0}, {"Input.gid": "allsides_1183_473_22_Assad_201_206", "from": 201, "to": 206, "mention": "Assad", "polarity": 2.0}]}
{"primary_gid": "allsides_1183_473_3_Assad_237_242", "sentence_normalized": "[Despite U.S. missile barrage, Syria continues airstrikes against rebels] Haley indicated in an interview on CNN's “State of the Union” that the United States does not see a peaceful political resolution for Syria's civil war as long as Assad remains in power.”", "targets": [{"Input.gid": "allsides_1183_473_3_Assad_237_242", "from": 237, "to": 242, "mention": "Assad", "polarity": 2.0}, {"Input.gid": "allsides_1183_473_3_Haley_74_79", "from": 74, "to": 79, "mention": "Haley", "polarity": 4.0}]}
{"primary_gid": "allsides_1185_473_12_U.S. Ambassador to the United Nations Nikki Haley_171_173", "sentence_normalized": "Tillerson conceded that removing Assad from power could \"ultimately\" require greater pressure, including military action, from the U.S. or an international coalition, but he said that isn't the preferred choice.", "targets": [{"Input.gid": "allsides_1185_473_12_Assad_33_38", "from": 33, "to": 38, "mention": "Assad", "polarity": 2.0}, {"Input.gid": "allsides_1185_473_12_U.S. Ambassador to the United Nations Nikki Haley_171_173", "from": 171, "to": 173, "mention": "he", "polarity": 4.0}]}
{"primary_gid": "allsides_1197_477_133_Yazan al-Saadi_29_43", "sentence_normalized": "AMY GOODMAN: But last night— YAZAN AL-SAADI: He’s playing a game.", "targets": [{"Input.gid": "allsides_1197_477_133_AMY GOODMAN_0_11", "from": 0, "to": 11, "mention": "AMY GOODMAN", "polarity": 4.0}, {"Input.gid": "allsides_1197_477_133_Yazan al-Saadi_29_43", "from": 29, "to": 43, "mention": "YAZAN AL-SAADI", "polarity": 4.0}]}
{"primary_gid": "allsides_1197_477_24_Yazan al-Saadi_70_84", "sentence_normalized": "AMY GOODMAN: For more, we are going to Lebanon, where we’re joined by Yazan al-Saadi, Syrian-Canadian writer, researcher based in Beirut.", "targets": [{"Input.gid": "allsides_1197_477_24_AMY GOODMAN_0_11", "from": 0, "to": 11, "mention": "AMY GOODMAN", "polarity": 4.0}, {"Input.gid": "allsides_1197_477_24_Yazan al-Saadi_70_84", "from": 70, "to": 84, "mention": "Yazan al-Saadi", "polarity": 4.0}]}
{"primary_gid": "allsides_1280_512_28_Senate Minority Leader Chuck Schumer (D-N.Y.)_19_26", "sentence_normalized": "Bharara was once a Schumer aide.", "targets": [{"Input.gid": "allsides_1280_512_28_Bharara_0_7", "from": 0, "to": 7, "mention": "Bharara", "polarity": 4.0}, {"Input.gid": "allsides_1280_512_28_Senate Minority Leader Chuck Schumer (D-N.Y.)_19_26", "from": 19, "to": 26, "mention": "Schumer", "polarity": 4.0}]}
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{"primary_gid": "allsides_4083_1696_17_Michelle Obama_53_58", "sentence_normalized": "Fox Business Network’s Lou Dobbs openly scoffed that Obama has saved anything.", "targets": [{"Input.gid": "allsides_4083_1696_17_Lou Dobbs_23_32", "from": 23, "to": 32, "mention": "Lou Dobbs", "polarity": 2.0}, {"Input.gid": "allsides_4083_1696_17_Michelle Obama_53_58", "from": 53, "to": 58, "mention": "Obama", "polarity": 2.0}]}
{"primary_gid": "allsides_4156_1731_4_girlfriend Reeva Steenkamp_97_106", "sentence_normalized": "Masipa said prosecutors had not proven beyond a reasonable doubt that Pistorius intended to kill Steenkamp.", "targets": [{"Input.gid": "allsides_4156_1731_4_girlfriend Reeva Steenkamp_97_106", "from": 97, "to": 106, "mention": "Steenkamp", "polarity": 4.0}, {"Input.gid": "allsides_4156_1731_4_Judge Thokozile Masipa_0_6", "from": 0, "to": 6, "mention": "Masipa", "polarity": 4.0}]}
{"primary_gid": "allsides_4209_1754_19_Benton_45_51", "sentence_normalized": "In 2013, a phone recording surfaced in which Benton said that he was holding his nose while working for McConnell and thinking of the long-term best interests of Sen.", "targets": [{"Input.gid": "allsides_4209_1754_19_Benton_45_51", "from": 45, "to": 51, "mention": "Benton", "polarity": 2.0}, {"Input.gid": "allsides_4209_1754_19_McConnell_104_113", "from": 104, "to": 113, "mention": "McConnell", "polarity": 2.0}]}
{"primary_gid": "allsides_4209_1754_2_Mitch McConnell's_30_39", "sentence_normalized": "The announcement comes as the McConnell campaign wrestles with Benton's possible ties to a bribery scandal when he was political director for then-Rep.", "targets": [{"Input.gid": "allsides_4209_1754_2_Benton_63_69", "from": 63, "to": 69, "mention": "Benton", "polarity": 2.0}, {"Input.gid": "allsides_4209_1754_2_Mitch McConnell's_30_39", "from": 30, "to": 39, "mention": "McConnell", "polarity": 2.0}]}
{"primary_gid": "allsides_4255_1774_8_Michael Brown_17_22", "sentence_normalized": "But they believe Brown died from a final bullet fired through the top of his skull because all of the other gunshot wounds were survivable, Baden said.", "targets": [{"Input.gid": "allsides_4255_1774_8_Dr. Michael Baden, the former New York City chief medical examiner who conducted the autopsy_4_8", "from": 4, "to": 8, "mention": "they", "polarity": 4.0}, {"Input.gid": "allsides_4255_1774_8_Michael Brown_17_22", "from": 17, "to": 22, "mention": "Brown", "polarity": 4.0, "further_mentions": [{"from": 73, "to": 76, "mention": "his"}]}]}
{"primary_gid": "allsides_425_169_8_President Trump_0_15", "sentence_normalized": "President Trump is to blame, he said without mentioning him by name.", "targets": [{"Input.gid": "allsides_425_169_8_This Flake_56_59", "from": 56, "to": 59, "mention": "him", "polarity": 2.0}, {"Input.gid": "allsides_425_169_8_President Trump_0_15", "from": 0, "to": 15, "mention": "President Trump", "polarity": 2.0, "further_mentions": [{"from": 29, "to": 31, "mention": "he"}]}]}
{"primary_gid": "allsides_4265_1779_1_Ferguson_15_23", "sentence_normalized": "Residents in Ferguson, Missouri, on Wednesday night were once again demonstrating in protest of the controversial shooting death of 18-year-old Michael Brown.", "targets": [{"Input.gid": "allsides_4265_1779_1_18-year-old Michael Brown_134_159", "from": 132, "to": 157, "mention": "18-year-old Michael Brown", "polarity": 4.0}, {"Input.gid": "allsides_4265_1779_1_Ferguson_15_23", "from": 13, "to": 21, "mention": "Ferguson", "polarity": 4.0}]}
{"primary_gid": "allsides_4284_1787_21_Ms. Hanabusa_74_86", "sentence_normalized": "In the last days of his life in 2012, he asked Mr. Abercrombie to appoint Ms. Hanabusa to succeed him.", "targets": [{"Input.gid": "allsides_4284_1787_21_Ms. Hanabusa_74_86", "from": 74, "to": 86, "mention": "Ms. Hanabusa", "polarity": 6.0}, {"Input.gid": "allsides_4284_1787_21_Neil Abercrombie_47_62", "from": 47, "to": 62, "mention": "Mr. Abercrombie", "polarity": 4.0}]}
{"primary_gid": "allsides_431_172_9_Vladimir Putin_1_6", "sentence_normalized": "\"Putin is so rattled by the spreading Magnitsky sanctions around the world that he’s ready to run roughshod over all rules and western norms,\" Browder told The Guardian.", "targets": [{"Input.gid": "allsides_431_172_9_Vladimir Putin_1_6", "from": 1, "to": 6, "mention": "Putin", "polarity": 2.0, "further_mentions": [{"from": 143, "to": 150, "mention": "Browder"}, {"from": 80, "to": 82, "mention": "he"}]}, {"Input.gid": "allsides_431_172_9_Magnitsky_38_47", "from": 38, "to": 47, "mention": "Magnitsky", "polarity": 4.0}]}
{"primary_gid": "allsides_433_172_3_Browder_0_7", "sentence_normalized": "Browder adds that the U.S. government wouldn’t want to be seen as “helping Putin.”", "targets": [{"Input.gid": "allsides_433_172_3_Vladimir Putin's_75_80", "from": 75, "to": 80, "mention": "Putin", "polarity": 2.0}, {"Input.gid": "allsides_433_172_3_Browder_0_7", "from": 0, "to": 7, "mention": "Browder", "polarity": 4.0}]}
{"primary_gid": "allsides_4341_1813_0_Obama_36_41", "sentence_normalized": "Senate Hopeful Joe Miller Calls For Obama's Impeachment, Blasts Mark Begich Over Border", "targets": [{"Input.gid": "allsides_4341_1813_0_Obama_36_41", "from": 36, "to": 41, "mention": "Obama", "polarity": 2.0}, {"Input.gid": "allsides_4341_1813_0_Blasts Mark Begich Over Border_57_87", "from": 57, "to": 87, "mention": "Blasts Mark Begich Over Border", "polarity": 2.0}]}
{"primary_gid": "allsides_4365_1823_35_Cantor_65_71", "sentence_normalized": "The conventional wisdom is that Boehner has been strengthened by Cantor's defeat, as his strongest potential challenger for the speaker's gavel has been removed.", "targets": [{"Input.gid": "allsides_4365_1823_35_Cantor_65_71", "from": 65, "to": 71, "mention": "Cantor", "polarity": 2.0}, {"Input.gid": "allsides_4365_1823_35_\" Boehner_32_39", "from": 32, "to": 39, "mention": "Boehner", "polarity": 6.0, "further_mentions": [{"from": 85, "to": 88, "mention": "his"}]}]}
{"primary_gid": "allsides_436_174_5_George H.W. Bush_29_45", "sentence_normalized": "“To Presidents Jimmy Carter, George H.W. Bush, Bill Clinton, George W. Bush and Barack Obama, Melania and I want to express our deep gratitude for your tremendous assistance,” Trump said in the message.", "targets": [{"Input.gid": "allsides_436_174_5_George H.W. Bush_29_45", "from": 29, "to": 45, "mention": "George H.W. Bush", "polarity": 6.0, "further_mentions": [{"from": 61, "to": 75, "mention": "George W. Bush"}]}, {"Input.gid": "allsides_436_174_5_current President Donald Trump — sort of_176_181", "from": 176, "to": 181, "mention": "Trump", "polarity": 4.0}]}
{"primary_gid": "allsides_4387_1831_23_Bergdahl_17_25", "sentence_normalized": "The aftermath of Bergdahl's release has been consumed by controversy, with many service members claiming that Bergdahl deserted prior to his capture in 2009.", "targets": [{"Input.gid": "allsides_4387_1831_23_their leader Mullah Mohammad Omar_137_140", "from": 137, "to": 140, "mention": "his", "polarity": 2.0}, {"Input.gid": "allsides_4387_1831_23_Bergdahl_17_25", "from": 17, "to": 25, "mention": "Bergdahl", "polarity": 2.0, "further_mentions": [{"from": 110, "to": 118, "mention": "Bergdahl"}]}]}
{"primary_gid": "allsides_4438_1855_4_Karl Rove_0_4", "sentence_normalized": "Rove said his other point was that Clinton’s health will be an issue if she runs in 2016 “whether she likes it or not.”", "targets": [{"Input.gid": "allsides_4438_1855_4_Karl Rove_0_4", "from": 0, "to": 4, "mention": "Rove", "polarity": 4.0, "further_mentions": [{"from": 10, "to": 13, "mention": "his"}]}, {"Input.gid": "allsides_4438_1855_4_Hillary Clinton_35_42", "from": 35, "to": 42, "mention": "Clinton", "polarity": 2.0, "further_mentions": [{"from": 98, "to": 101, "mention": "she"}, {"from": 72, "to": 75, "mention": "she"}]}]}
{"primary_gid": "allsides_4462_1865_4_Monica Lewinsky_154_157", "sentence_normalized": "\"The Clinton administration, the special prosecutor's minions, the political operatives on both sides of the aisle, and the media were able to brand me,\" she wrote.", "targets": [{"Input.gid": "allsides_4462_1865_4_Monica Lewinsky_154_157", "from": 154, "to": 157, "mention": "she", "polarity": 4.0}, {"Input.gid": "allsides_4462_1865_4_Clinton_5_12", "from": 5, "to": 12, "mention": "Clinton", "polarity": 2.0}]}
{"primary_gid": "allsides_4468_1868_5_Trey Gowdy_72_75", "sentence_normalized": "His background as a federal prosecutor and his zeal for the truth make him the ideal person to lead this panel,\" Boehner said in a statement on Monday afternoon.", "targets": [{"Input.gid": "allsides_4468_1868_5_House Speaker John A. Boehner (R-Ohio)_114_121", "from": 113, "to": 120, "mention": "Boehner", "polarity": 6.0}, {"Input.gid": "allsides_4468_1868_5_Trey Gowdy_72_75", "from": 71, "to": 74, "mention": "him", "polarity": 6.0, "further_mentions": [{"from": 0, "to": 3, "mention": "His"}, {"from": 43, "to": 46, "mention": "his"}]}]}
{"primary_gid": "allsides_4481_1873_14_Clayton Lockett_1_16", "sentence_normalized": "Clayton Lockett tried to rob a house Miss Neiman was at.", "targets": [{"Input.gid": "allsides_4481_1873_14_Stephanie Neiman_38_49", "from": 37, "to": 48, "mention": "Miss Neiman", "polarity": 4.0, "further_mentions": [{"from": 42, "to": 48, "mention": "Neiman"}]}, {"Input.gid": "allsides_4481_1873_14_Clayton Lockett_1_16", "from": 0, "to": 15, "mention": "Clayton Lockett", "polarity": 2.0}]}
{"primary_gid": "allsides_4546_1901_3_Yanukovych_103_113", "sentence_normalized": "Kremlin spokesman Dmitry Peskov made the envoy announcement after Putin and Ukrainian President Viktor Yanukovych talked over the phone, state-run news agency RIA Novosti reported, according to Reuters.", "targets": [{"Input.gid": "allsides_4546_1901_3_Yanukovych_103_113", "from": 103, "to": 113, "mention": "Yanukovych", "polarity": 4.0}, {"Input.gid": "allsides_4546_1901_3_Russian President Vladimir Putin_66_71", "from": 66, "to": 71, "mention": "Putin", "polarity": 4.0}]}
{"primary_gid": "allsides_4605_1928_30_Begich_82_84", "sentence_normalized": "The new report comes only days after Obama's State of the Union address, in which he reiterated his support for an \"all-of-the-above\" energy strategy that embraces a wide range of sources, from oil and natural gas to renewables such as wind and solar power.", "targets": [{"Input.gid": "allsides_4605_1928_30_Obama_37_42", "from": 37, "to": 42, "mention": "Obama", "polarity": 6.0}, {"Input.gid": "allsides_4605_1928_30_Begich_82_84", "from": 82, "to": 84, "mention": "he", "polarity": 6.0, "further_mentions": [{"from": 96, "to": 99, "mention": "his"}]}]}
{"primary_gid": "allsides_4605_1928_7_Obama_28_33", "sentence_normalized": "Mary Landrieu in calling on Obama to approve the project, proposed back in 2010.", "targets": [{"Input.gid": "allsides_4605_1928_7_Mary Landrieu_0_13", "from": 0, "to": 13, "mention": "Mary Landrieu", "polarity": 4.0}, {"Input.gid": "allsides_4605_1928_7_Obama_28_33", "from": 28, "to": 33, "mention": "Obama", "polarity": 4.0}]}
{"primary_gid": "allsides_4630_1939_31_Mr. Cropper_0_3", "sentence_normalized": "His mother said she did not believe that Mr. Aguilar knew Ms. Benlolo, as some online reports had suggested.", "targets": [{"Input.gid": "allsides_4630_1939_31_Mr. Cropper_0_3", "from": 0, "to": 3, "mention": "His", "polarity": 4.0}, {"Input.gid": "allsides_4630_1939_31_Aguilar_41_52", "from": 41, "to": 52, "mention": "Mr. Aguilar", "polarity": 4.0}]}
{"primary_gid": "allsides_4643_1945_4_Christie_64_72", "sentence_normalized": "Also on Monday, nine-time Olympic gold medalist Carl Lewis said Christie dropped a plan to appoint him the state's first physical fitness ambassador when he launched a political campaign against a Christie friend.", "targets": [{"Input.gid": "allsides_4643_1945_4_Christie_64_72", "from": 64, "to": 72, "mention": "Christie", "polarity": 2.0, "further_mentions": [{"from": 197, "to": 205, "mention": "Christie"}]}, {"Input.gid": "allsides_4643_1945_4_nine-time Olympic gold medalist Carl Lewis_16_58", "from": 16, "to": 58, "mention": "nine-time Olympic gold medalist Carl Lewis", "polarity": 6.0, "further_mentions": [{"from": 154, "to": 156, "mention": "he"}, {"from": 99, "to": 102, "mention": "him"}]}]}
{"primary_gid": "allsides_4658_1951_2_Obama_345_350", "sentence_normalized": "“I think what we are going to see in the speech, Joe, is all of the language in the speech kind of fading left about transformation and transparency and checks and balances, but, frankly, I think the substance of the speech is going to be holding his ground,” Hayden told MSNBC’s “Morning Joe” host Joe Scarborough based on news reports of what Obama will unveil on Friday.", "targets": [{"Input.gid": "allsides_4658_1951_2_Obama_345_350", "from": 345, "to": 350, "mention": "Obama", "polarity": 4.0}, {"Input.gid": "allsides_4658_1951_2_Hayden_260_266", "from": 260, "to": 266, "mention": "Hayden", "polarity": 4.0}]}
{"primary_gid": "allsides_4661_1953_17_Defense MSNBC President Phil Griffin_0_7", "sentence_normalized": "Griffin spoke the Hollywood Reporter’s Marisa Guthrie about the controversies that prompted Martin Bashir and Alec Baldwin to leave the network and Melissa Harris-Perry to apologize.", "targets": [{"Input.gid": "allsides_4661_1953_17_Martin Bashir_92_105", "from": 92, "to
gitextract_ate4vaz2/ ├── .gitignore ├── .idea/ │ ├── .gitignore │ ├── NewsMTSC.iml │ ├── misc.xml │ ├── modules.xml │ └── vcs.xml ├── CODE_OF_CONDUCT.md ├── LICENSE ├── MANIFEST.in ├── NewsSentiment/ │ ├── .gitignore │ ├── DatasetPreparer.py │ ├── SentimentClasses.py │ ├── __init__.py │ ├── __main__.py │ ├── combinations_default.py │ ├── consts.py │ ├── controller.py │ ├── controller_data/ │ │ └── datasets/ │ │ ├── NewsMTSC-dataset/ │ │ │ ├── devtest_mt.jsonl │ │ │ ├── devtest_rw.jsonl │ │ │ ├── readme.md │ │ │ └── train.jsonl │ │ ├── acl14twitter/ │ │ │ ├── test.raw.jsonl │ │ │ └── train.raw.jsonl │ │ ├── newsmtsc-train-and-test-mt/ │ │ │ ├── devtest_mtsc_only.jsonl │ │ │ └── train.jsonl │ │ ├── newsmtsc-train-and-test-rw/ │ │ │ ├── devtest_mtsc_and_single_primaries.jsonl │ │ │ └── train.jsonl │ │ ├── semeval14laptops/ │ │ │ ├── Laptops_Test_Gold.xml.seg.jsonl │ │ │ └── Laptops_Train.xml.seg.jsonl │ │ └── semeval14restaurants/ │ │ ├── Restaurants_Test_Gold.xml.seg.jsonl │ │ └── Restaurants_Train.xml.seg.jsonl │ ├── converter_huggingface.py │ ├── createoverview.py │ ├── customexceptions.py │ ├── dataset.py │ ├── diskdict.py │ ├── download.py │ ├── earlystopping.py │ ├── evaluator.py │ ├── experiments/ │ │ └── default/ │ │ └── datasets/ │ │ ├── newsmtsc-mt/ │ │ │ ├── dev.jsonl │ │ │ ├── test.jsonl │ │ │ └── train.jsonl │ │ ├── newsmtsc-mt-hf/ │ │ │ ├── dev.jsonl │ │ │ ├── test.jsonl │ │ │ └── train.jsonl │ │ ├── newsmtsc-rw/ │ │ │ ├── dev.jsonl │ │ │ ├── test.jsonl │ │ │ └── train.jsonl │ │ └── newsmtsc-rw-hf/ │ │ ├── dev.jsonl │ │ ├── test.jsonl │ │ └── train.jsonl │ ├── fxlogger.py │ ├── infer.py │ ├── inferrest.py │ ├── knowledge/ │ │ ├── __init__.py │ │ ├── bingliuopinion/ │ │ │ ├── bingliuopinion.py │ │ │ ├── converter.py │ │ │ ├── license.txt │ │ │ ├── negative-words.txt │ │ │ ├── opinion_polarity.ddict │ │ │ └── positive-words.txt │ │ ├── knowledgeutils.py │ │ ├── liwc/ │ │ │ ├── data/ │ │ │ │ ├── .gitignore │ │ │ │ └── readme.txt │ │ │ ├── dic.py │ │ │ ├── liwc.py │ │ │ ├── liwchelper.py │ │ │ └── trie.py │ │ ├── mpqasubjectivity/ │ │ │ ├── converter.py │ │ │ ├── mpqasubjectivity.py │ │ │ ├── subjclueslen1-HLTEMNLP05.tff │ │ │ └── subjclueslen1-HLTEMNLP05.tff.ddict │ │ ├── nrcemolex/ │ │ │ ├── NRC-Emotion-Lexicon-Wordlevel-v0.92.txt │ │ │ ├── NRC-Emotion-Lexicon-Wordlevel-v0.92.txt.ddict │ │ │ ├── converter.py │ │ │ └── nrcemolex.py │ │ └── zeros/ │ │ └── zerosknowledge.py │ ├── layers/ │ │ ├── AggregatorForBert.py │ │ ├── __init__.py │ │ ├── attention.py │ │ ├── dynamic_rnn.py │ │ ├── point_wise_feed_forward.py │ │ ├── pytorchnlpattention.py │ │ └── squeeze_embedding.py │ ├── losses/ │ │ ├── __init__.py │ │ ├── crossentropycrossweight.py │ │ ├── crossentropylosslsr.py │ │ ├── crossentropylosswithconfidence.py │ │ └── seq2seqloss.py │ ├── models/ │ │ ├── FXBaseModel.py │ │ ├── FXEnsemble.py │ │ ├── __init__.py │ │ ├── ensemble.py │ │ ├── ensembleb.py │ │ ├── multitargets/ │ │ │ ├── __init__.py │ │ │ ├── contrasting.py │ │ │ ├── random_multi.py │ │ │ ├── seq2seq.py │ │ │ ├── seq2seq_without_targetmask.py │ │ │ ├── tdbertlikemultitarget.py │ │ │ └── tdbertlikemultitarget_dense.py │ │ └── singletarget/ │ │ ├── __init__.py │ │ ├── aen.py │ │ ├── grutscsingle.py │ │ ├── lcf.py │ │ ├── lcf2.py │ │ ├── lcfs.py │ │ ├── lcfst.py │ │ ├── lcft.py │ │ ├── notargetcls.py │ │ ├── random_single.py │ │ ├── spc.py │ │ ├── td_bert.py │ │ ├── td_bert_qa.py │ │ └── tdbertlikesingle.py │ ├── plotter_utils.py │ ├── pretrained_models/ │ │ ├── .gitignore │ │ └── state_dicts/ │ │ └── .gitignore │ ├── results/ │ │ └── .gitignore │ └── train.py ├── README.md ├── READMEpypi.md ├── hubconf.py ├── pyproject.toml ├── pythoninfo.md └── setup.cfg
SYMBOL INDEX (416 symbols across 62 files)
FILE: NewsSentiment/DatasetPreparer.py
class DatasetPreparer (line 20) | class DatasetPreparer:
method __init__ (line 21) | def __init__(self, name, basepath_datasets, task_format, human, non_hu...
method get_filepath_by_name (line 64) | def get_filepath_by_name(self, filename):
method file_to_dictlst (line 67) | def file_to_dictlst(self, filepath):
method files_to_dictlst (line 75) | def files_to_dictlst(self, list_filepaths):
method create_slices (line 81) | def create_slices(self, data_type):
method merge_slices (line 122) | def merge_slices(self):
method _get_label_counts (line 138) | def _get_label_counts(self, tasks):
method print_set_info (line 153) | def print_set_info(self):
method init_set (line 193) | def init_set(self, sets_info):
method export (line 250) | def export(self, savepath):
method poltsanews_rel801010_allhuman (line 275) | def poltsanews_rel801010_allhuman(cls, basepath):
method poltsanews_crossval8010_allhuman (line 299) | def poltsanews_crossval8010_allhuman(cls, basepath):
method acl14twitter (line 322) | def acl14twitter(cls, basepath):
method semeval14laptops (line 346) | def semeval14laptops(cls, basepath):
method semeval14restaurants (line 370) | def semeval14restaurants(cls, basepath):
method newsmtsc_devtest_mt (line 394) | def newsmtsc_devtest_mt(cls, basepath):
method newsmtsc_devtest_rw (line 417) | def newsmtsc_devtest_rw(cls, basepath):
FILE: NewsSentiment/SentimentClasses.py
class SentimentClasses (line 4) | class SentimentClasses:
method initialize (line 10) | def initialize(sentiment_classes: dict):
method get_num_classes (line 14) | def get_num_classes():
method __evaluate_boundary (line 18) | def __evaluate_boundary(given_value: float, boundary: tuple):
method __evaluate_boundaries_of_class (line 35) | def __evaluate_boundaries_of_class(
method __get_legacy_information (line 46) | def __get_legacy_information():
method get_sorted_expected_label_names (line 73) | def get_sorted_expected_label_names():
method get_sorted_expected_label_values (line 79) | def get_sorted_expected_label_values():
method get_polarity_associations (line 85) | def get_polarity_associations():
method get_polarity_associations_inverse (line 89) | def get_polarity_associations_inverse():
method __find_sentiment_class (line 93) | def __find_sentiment_class(polarity: float):
method polarity2label (line 114) | def polarity2label(polarity: float) -> str:
method polarity2normalized_polarity (line 123) | def polarity2normalized_polarity(polarity: float) -> int:
method Sentiment3ForNewsMtsc (line 132) | def Sentiment3ForNewsMtsc():
method SentimentStrong3ForNewsMtsc (line 147) | def SentimentStrong3ForNewsMtsc():
method SentimentWeak3ForNewsMtsc (line 162) | def SentimentWeak3ForNewsMtsc():
FILE: NewsSentiment/consts.py
function set_default_lm (line 11) | def set_default_lm(new_name: str):
function get_default_lm (line 16) | def get_default_lm():
FILE: NewsSentiment/controller.py
function start_worker (line 59) | def start_worker(
function on_task_done (line 94) | def on_task_done(x):
function on_task_error (line 103) | def on_task_error(x):
function get_experiment_result_detailed (line 112) | def get_experiment_result_detailed(experiment_path):
class SetupController (line 125) | class SetupController:
method __init__ (line 126) | def __init__(self, options):
method _apply_conditions (line 379) | def _apply_conditions(self, combinations, args_names_ordered, conditio...
method _check_conditions (line 425) | def _check_conditions(self, arg_name, full_named_combination, conditio...
method _build_args (line 452) | def _build_args(self, named_args):
method _add_arg (line 459) | def _add_arg(self, args_list, name, value):
method _prepare_experiment_env (line 464) | def _prepare_experiment_env(self, experiment_path):
method _args_combination_to_single_arg_values (line 468) | def _args_combination_to_single_arg_values(
method _experiment_named_id_from_named_combination (line 476) | def _experiment_named_id_from_named_combination(self, named_combination):
method prepare_single_setup (line 479) | def prepare_single_setup(self, named_combination, experiment_id, pool_...
method run (line 505) | def run(self):
method _get_best_dev_snem (line 712) | def _get_best_dev_snem(self):
method _get_running_processes (line 726) | def _get_running_processes(self, running_processes):
FILE: NewsSentiment/converter_huggingface.py
function convert_polarity (line 12) | def convert_polarity(polarity):
function convert_target (line 23) | def convert_target(obj, target):
function convert_obj (line 36) | def convert_obj(obj):
function convert (line 46) | def convert(path):
FILE: NewsSentiment/createoverview.py
function rename_flatten (line 15) | def rename_flatten(dictionary, key_prefix):
function without_keys (line 25) | def without_keys(d, keys):
function non_scalar_to_str (line 29) | def non_scalar_to_str(d):
function _find_run_ids (line 43) | def _find_run_ids(completed_tasks: dict):
function _aggregate_and_mean (line 92) | def _aggregate_and_mean(df: pd.DataFrame):
function _dfs_to_excel (line 117) | def _dfs_to_excel(pathname, name2df):
function shelve2xlsx (line 128) | def shelve2xlsx(opt, ignore_graceful_exit_experiments):
function jsonl2xlsx (line 168) | def jsonl2xlsx(opt):
FILE: NewsSentiment/customexceptions.py
class TooLongTextException (line 1) | class TooLongTextException(Exception):
class TargetNotFoundException (line 5) | class TargetNotFoundException(Exception):
FILE: NewsSentiment/dataset.py
class RandomOversampler (line 60) | class RandomOversampler(torch.utils.data.sampler.Sampler):
method __init__ (line 61) | def __init__(self, dataset: Dataset, random_seed=None):
method __len__ (line 85) | def __len__(self):
method __iter__ (line 88) | def __iter__(self):
class FXEasyTokenizer (line 92) | class FXEasyTokenizer:
method __init__ (line 98) | def __init__(
method _get_labels (line 112) | def _get_labels(cls):
method create_entire_text (line 127) | def create_entire_text(
method create_entire_text (line 137) | def create_entire_text(
method create_entire_text (line 146) | def create_entire_text(
method prepare_left_segment (line 181) | def prepare_left_segment(text_left: str):
method prepare_target_mention (line 194) | def prepare_target_mention(target_mention: str):
method prepare_right_segment (line 203) | def prepare_right_segment(text_right: str):
method _create_word_to_wordpiece_mapping (line 214) | def _create_word_to_wordpiece_mapping(
method _batch_create_word_to_wordpiece_mapping (line 222) | def _batch_create_word_to_wordpiece_mapping(
method _calculate_dep_matrix (line 293) | def _calculate_dep_matrix(self, text_tokens, text_tokens_as_str):
method _calculate_dep_distance (line 339) | def _calculate_dep_distance(self, text_tokens, text_left_len, target):
method _create_target_mask (line 414) | def _create_target_mask(
method _batch_create_target_mask (line 449) | def _batch_create_target_mask(
method _encode_for_target_mask (line 481) | def _encode_for_target_mask(
method _create_target_mask_on_encoding (line 503) | def _create_target_mask_on_encoding(
method _create_knowledge_source_tensor (line 608) | def _create_knowledge_source_tensor(
method _create_mapping_from_tokenbased_to_wordpiece_based (line 652) | def _create_mapping_from_tokenbased_to_wordpiece_based(self, text, tok...
method _batch_create_mapping_from_tokenbased_to_wordpiece_based (line 658) | def _batch_create_mapping_from_tokenbased_to_wordpiece_based(self, tex...
method _convert_non_null_to_one (line 688) | def _convert_non_null_to_one(self, lst):
method _map_token_index_to_wordpiece_index (line 697) | def _map_token_index_to_wordpiece_index(self, a, b):
method _create_dependency_tensor (line 703) | def _create_dependency_tensor(
method _create_dependency_tree_hop_distances_of_tokens_to_target (line 765) | def _create_dependency_tree_hop_distances_of_tokens_to_target(
method create_model_input_seqs (line 819) | def create_model_input_seqs(
method batch_create_model_input_seqs (line 847) | def batch_create_model_input_seqs(
method batch_create_model_input_seqs (line 855) | def batch_create_model_input_seqs(
method batch_create_model_input_seqs (line 862) | def batch_create_model_input_seqs(
method _create_coreferential_target_masks (line 1120) | def _create_coreferential_target_masks(
method _batch_create_coreferential_target_masks (line 1127) | def _batch_create_coreferential_target_masks(
method _merge_coref_target_masks_into_preferred_target_mask (line 1154) | def _merge_coref_target_masks_into_preferred_target_mask(
class FXDataset (line 1184) | class FXDataset(Dataset):
method __init__ (line 1189) | def __init__(
method __prepare_text_field (line 1258) | def __prepare_text_field(self, text: str):
method __create_target_text_components (line 1267) | def __create_target_text_components(
method __parse_dataset_row (line 1293) | def __parse_dataset_row(self, task, ignore_parsing_errors):
method _create_target_inputs (line 1335) | def _create_target_inputs(
method _create_fillup_target (line 1430) | def _create_fillup_target(self):
method _get_as_list (line 1442) | def _get_as_list(self, lst_inputs_of_targets: List[dict], key: str):
method _get_values_as_lists_in_dict (line 1455) | def _get_values_as_lists_in_dict(
method _convert_multi_targets_in_single_item_to_single_target_in_multi_items (line 1463) | def _convert_multi_targets_in_single_item_to_single_target_in_multi_it...
method _convert_multi_targets_in_single_item_to_k_targets_in_multi_items (line 1482) | def _convert_multi_targets_in_single_item_to_k_targets_in_multi_items(
method _create_item_for_model (line 1549) | def _create_item_for_model(self, example_id, text, targets):
method create_virtual_target_from_target_mention (line 1614) | def create_virtual_target_from_target_mention(self, target, mention, t...
method _expand_coref_mentions_of_targets_to_multiple_targets (line 1636) | def _expand_coref_mentions_of_targets_to_multiple_targets(
method task_to_dataset_item (line 1652) | def task_to_dataset_item(self, task, coref_mode: str, ignore_parsing_e...
method __getitem__ (line 1704) | def __getitem__(self, index):
method __len__ (line 1707) | def __len__(self):
method set_params2index (line 1711) | def set_params2index(params2index: dict):
method get_input_by_params (line 1719) | def get_input_by_params(inputs: List, weight_name: str, field_name: str):
method get_all_inputs_for_model (line 1739) | def get_all_inputs_for_model(inputs: List, model_class: FXBaseModel):
method _stack_target_inputs (line 1747) | def _stack_target_inputs(self, lst_inputs_of_targets: List[dict]) -> d...
method _stack_special_field (line 1793) | def _stack_special_field(self, key: str, lst_inputs_of_targets: List[d...
method _stack_or_create_tensor (line 1821) | def _stack_or_create_tensor(self, lst_values: List):
FILE: NewsSentiment/diskdict.py
class DiskDict (line 10) | class DiskDict(dict):
method __init__ (line 11) | def __init__(self, sync_path: Union[Path, str]):
method sync_to_disk (line 24) | def sync_to_disk(self):
FILE: NewsSentiment/download.py
class Download (line 12) | class Download:
method __init__ (line 14) | def __init__(
method list_versions (line 40) | def list_versions(model_cls, own_model_name=""):
method download (line 53) | def download(cls, model_cls, version=None, force=False, skip_if_exists...
method model_filename (line 67) | def model_filename(model_cls, version=None):
method model_path (line 76) | def model_path(cls, model_cls, version=None):
method add_subparser (line 83) | def add_subparser(subparser):
method run_from_parser (line 105) | def run_from_parser(cls, args_namespace):
FILE: NewsSentiment/earlystopping.py
class EarlyStopping (line 8) | class EarlyStopping:
method __init__ (line 11) | def __init__(self, patience=2, delta=0.01):
method __call__ (line 29) | def __call__(self, dev_score):
FILE: NewsSentiment/evaluator.py
class Evaluator (line 14) | class Evaluator:
method __init__ (line 15) | def __init__(
method mean_from_all_statistics (line 36) | def mean_from_all_statistics(self, all_test_stats):
method calc_statistics (line 59) | def calc_statistics(self, y_true, y_pred, y_pred_confidence):
method print_stats (line 180) | def print_stats(self, stats, description):
method write_error_table (line 198) | def write_error_table(self, y_true, y_pred, texts_list, filepath):
FILE: NewsSentiment/fxlogger.py
function get_logger (line 6) | def get_logger():
FILE: NewsSentiment/infer.py
class TargetSentimentClassifier (line 21) | class TargetSentimentClassifier:
method __init__ (line 22) | def __init__(
method infer_from_text (line 73) | def infer_from_text(self, left, target, right):
method infer (line 89) | def infer(
method infer (line 104) | def infer(
method infer (line 119) | def infer(
method infer (line 133) | def infer(
method batch_infer (line 221) | def batch_infer(
method get_info_for_label (line 308) | def get_info_for_label(self, classification_result, label):
function parse_arguments (line 315) | def parse_arguments(override_args=False):
FILE: NewsSentiment/inferrest.py
function index (line 11) | def index():
function start_rest_server (line 22) | def start_rest_server(port=13273):
FILE: NewsSentiment/knowledge/bingliuopinion/bingliuopinion.py
function get_num_bingliu_polarities (line 14) | def get_num_bingliu_polarities():
function get_bingliu_polarities_as_tensor (line 18) | def get_bingliu_polarities_as_tensor(term: str):
FILE: NewsSentiment/knowledge/bingliuopinion/converter.py
function convert_single_txt_to_dict (line 21) | def convert_single_txt_to_dict(path: Path):
function convert_txt_to_dict (line 32) | def convert_txt_to_dict():
FILE: NewsSentiment/knowledge/knowledgeutils.py
function find_key_original_or_lc (line 1) | def find_key_original_or_lc(data: dict, data_keys_lower: dict, key: str):
FILE: NewsSentiment/knowledge/liwc/dic.py
function _parse_categories (line 1) | def _parse_categories(lines):
function _parse_lexicon (line 17) | def _parse_lexicon(lines, category_mapping):
function read_dic (line 29) | def read_dic(filepath):
FILE: NewsSentiment/knowledge/liwc/liwc.py
function get_num_liwc_categories (line 12) | def get_num_liwc_categories():
function get_liwc_categories_as_tensor (line 16) | def get_liwc_categories_as_tensor(term: str):
FILE: NewsSentiment/knowledge/liwc/liwchelper.py
function load_token_parser (line 12) | def load_token_parser(filepath="knowledge/liwc/data/LIWC2015_English.dic"):
FILE: NewsSentiment/knowledge/liwc/trie.py
function build_trie (line 1) | def build_trie(lexicon):
function search_trie (line 22) | def search_trie(trie, token, token_i=0):
FILE: NewsSentiment/knowledge/mpqasubjectivity/converter.py
function get_value (line 22) | def get_value(entry_key_value: str):
function convert_txt_to_dict (line 26) | def convert_txt_to_dict():
FILE: NewsSentiment/knowledge/mpqasubjectivity/mpqasubjectivity.py
function get_num_mpqa_subjectivity_polarities (line 14) | def get_num_mpqa_subjectivity_polarities():
function get_mpqa_subjectivity_polarities_as_tensor (line 18) | def get_mpqa_subjectivity_polarities_as_tensor(term: str):
FILE: NewsSentiment/knowledge/nrcemolex/converter.py
function convert_txt_to_dict (line 27) | def convert_txt_to_dict():
FILE: NewsSentiment/knowledge/nrcemolex/nrcemolex.py
function get_num_nrc_emotions (line 14) | def get_num_nrc_emotions():
function get_nrc_emotions_as_tensor (line 18) | def get_nrc_emotions_as_tensor(term: str):
FILE: NewsSentiment/knowledge/zeros/zerosknowledge.py
function get_num_zero_dimensions (line 4) | def get_num_zero_dimensions():
function get_zeros_as_tensor (line 13) | def get_zeros_as_tensor(term: str):
FILE: NewsSentiment/layers/AggregatorForBert.py
class AggregatorForBert (line 5) | class AggregatorForBert(nn.Module):
method __init__ (line 12) | def __init__(self, spc_lm_representation: str):
method forward (line 16) | def forward(self, last_hidden_state, pooler_output, all_hidden_states):
FILE: NewsSentiment/layers/attention.py
class Attention (line 13) | class Attention(nn.Module):
method __init__ (line 14) | def __init__(
method reset_parameters (line 52) | def reset_parameters(self):
method forward (line 57) | def forward(self, k, q):
class NoQueryAttention (line 103) | class NoQueryAttention(Attention):
method __init__ (line 106) | def __init__(
method reset_q (line 123) | def reset_q(self):
method forward (line 127) | def forward(self, k, **kwargs):
class FXBertSelfAttention (line 133) | class FXBertSelfAttention(nn.Module):
method __init__ (line 139) | def __init__(self, hidden_size, num_attention_heads, attention_probs_d...
method transpose_for_scores (line 157) | def transpose_for_scores(self, x):
method forward (line 162) | def forward(
FILE: NewsSentiment/layers/dynamic_rnn.py
class DynamicLSTM (line 11) | class DynamicLSTM(nn.Module):
method __init__ (line 12) | def __init__(self, input_size, hidden_size, num_layers=1, bias=True, b...
method forward (line 51) | def forward(self, x, x_len):
FILE: NewsSentiment/layers/point_wise_feed_forward.py
class PositionwiseFeedForward (line 9) | class PositionwiseFeedForward(nn.Module):
method __init__ (line 11) | def __init__(self, d_hid, d_inner_hid=None, dropout=0):
method forward (line 20) | def forward(self, x):
FILE: NewsSentiment/layers/pytorchnlpattention.py
class ClassicAttention (line 5) | class ClassicAttention(nn.Module):
method __init__ (line 31) | def __init__(self, dimensions, attention_type="general"):
method forward (line 45) | def forward(self, query, context):
FILE: NewsSentiment/layers/squeeze_embedding.py
class SqueezeEmbedding (line 11) | class SqueezeEmbedding(nn.Module):
method __init__ (line 15) | def __init__(self, batch_first=True):
method forward (line 19) | def forward(self, x, x_len):
FILE: NewsSentiment/losses/crossentropycrossweight.py
class CrossEntropyLoss_CrossWeight (line 8) | class CrossEntropyLoss_CrossWeight(nn.Module):
method __init__ (line 9) | def __init__(self, device, ignore_index, weight=None, crossloss_weight...
method forward (line 19) | def forward(
FILE: NewsSentiment/losses/crossentropylosslsr.py
class CrossEntropyLoss_LSR (line 9) | class CrossEntropyLoss_LSR(nn.Module):
method __init__ (line 10) | def __init__(self, device, smoothing_value=0.2, weight=None):
method _create_smooth_one_hot_for_true_targets (line 17) | def _create_smooth_one_hot_for_true_targets(
method forward (line 67) | def forward(
FILE: NewsSentiment/losses/crossentropylosswithconfidence.py
class CrossEntropyLossWithConfidence (line 5) | class CrossEntropyLossWithConfidence(nn.Module):
method __init__ (line 6) | def __init__(self, weight, ignore_index):
method forward (line 14) | def forward(self, predictions: torch.Tensor, labels: torch.Tensor):
FILE: NewsSentiment/losses/seq2seqloss.py
class SequenceLoss (line 7) | class SequenceLoss(nn.Module):
method __init__ (line 12) | def __init__(self, device, weight):
method forward (line 22) | def forward(
FILE: NewsSentiment/models/FXBaseModel.py
class FXBaseModel (line 20) | class FXBaseModel(PreTrainedModel):
method __init__ (line 21) | def __init__(self, *args, **kwargs):
method get_language_models (line 26) | def get_language_models():
method get_input_field_ids (line 31) | def get_input_field_ids():
method invoke_language_model (line 34) | def invoke_language_model(self, lm, input_ids, token_type_ids=None):
function provide_pretrained (line 59) | def provide_pretrained(version, pretrained_url):
function default_pretrained (line 80) | def default_pretrained(version):
function model_includes_pretrained (line 104) | def model_includes_pretrained(model):
function __get_pretrained_wrapper_class (line 122) | def __get_pretrained_wrapper_class(base_class):
FILE: NewsSentiment/models/FXEnsemble.py
class FXEnsemble (line 12) | class FXEnsemble(FXBaseModel):
method get_language_models (line 14) | def get_language_models():
method get_input_field_ids (line 22) | def get_input_field_ids():
method __init__ (line 32) | def __init__(self, transformer_models: Dict, opt: Namespace):
method _combine_text_out_with_target_mask (line 56) | def _combine_text_out_with_target_mask(
method forward (line 68) | def forward(self, inputs: List):
FILE: NewsSentiment/models/ensemble.py
class EnsembleTopA (line 11) | class EnsembleTopA(FXBaseModel):
method get_language_models (line 13) | def get_language_models():
method get_input_field_ids (line 17) | def get_input_field_ids():
method __init__ (line 32) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 50) | def forward(self, inputs: List):
FILE: NewsSentiment/models/ensembleb.py
class EnsembleTopB (line 11) | class EnsembleTopB(FXBaseModel):
method get_language_models (line 13) | def get_language_models():
method get_input_field_ids (line 17) | def get_input_field_ids():
method __init__ (line 32) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 46) | def forward(self, inputs: List):
FILE: NewsSentiment/models/multitargets/contrasting.py
class Contrasting (line 12) | class Contrasting(FXBaseModel):
method get_language_models (line 25) | def get_language_models():
method get_input_field_ids (line 29) | def get_input_field_ids():
method __init__ (line 35) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 45) | def forward(self, inputs):
FILE: NewsSentiment/models/multitargets/random_multi.py
class RandomMulti (line 12) | class RandomMulti(FXBaseModel):
method get_language_models (line 18) | def get_language_models():
method get_input_field_ids (line 27) | def get_input_field_ids():
method __init__ (line 33) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 37) | def forward(self, inputs):
FILE: NewsSentiment/models/multitargets/seq2seq.py
class SeqTwoSeq (line 12) | class SeqTwoSeq(FXBaseModel):
method get_language_models (line 19) | def get_language_models():
method get_input_field_ids (line 23) | def get_input_field_ids():
method __init__ (line 29) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 42) | def forward(self, inputs):
FILE: NewsSentiment/models/multitargets/seq2seq_without_targetmask.py
class SeqTwoSeqWithoutTargetMask (line 12) | class SeqTwoSeqWithoutTargetMask(FXBaseModel):
method get_language_models (line 19) | def get_language_models():
method get_input_field_ids (line 23) | def get_input_field_ids():
method __init__ (line 29) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 41) | def forward(self, inputs):
FILE: NewsSentiment/models/multitargets/tdbertlikemultitarget.py
class TDBertLikeMultiTarget (line 11) | class TDBertLikeMultiTarget(FXBaseModel):
method get_language_models (line 24) | def get_language_models():
method get_input_field_ids (line 28) | def get_input_field_ids():
method __init__ (line 38) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 46) | def forward(self, inputs):
FILE: NewsSentiment/models/multitargets/tdbertlikemultitarget_dense.py
class TDBertLikeMultiTargetDense (line 11) | class TDBertLikeMultiTargetDense(FXBaseModel):
method get_language_models (line 24) | def get_language_models():
method get_input_field_ids (line 28) | def get_input_field_ids():
method __init__ (line 34) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 45) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/aen.py
class AEN_Base (line 20) | class AEN_Base(FXBaseModel):
method get_language_models (line 22) | def get_language_models():
method get_input_field_ids (line 26) | def get_input_field_ids():
method __init__ (line 33) | def __init__(self, transformer_models: Dict, opt: Namespace):
method apply_lm (line 70) | def apply_lm(self, _input, _input_attention=None):
method forward (line 113) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/grutscsingle.py
class GRUTSCSingle (line 21) | class GRUTSCSingle(FXBaseModel):
method get_language_models (line 37) | def get_language_models():
method get_input_field_ids (line 41) | def get_input_field_ids():
method __init__ (line 60) | def __init__(
method forward (line 96) | def forward(self, inputs, is_return_ensemble_values: bool = False):
FILE: NewsSentiment/models/singletarget/lcf.py
class GlobalContext (line 15) | class GlobalContext(nn.Module):
method __init__ (line 16) | def __init__(self, global_context_seqs_per_doc):
method forward (line 20) | def forward(self, inputs):
class SelfAttention (line 24) | class SelfAttention(nn.Module):
method __init__ (line 25) | def __init__(self, config, opt):
method forward (line 32) | def forward(self, inputs):
class LCF_BERT (line 41) | class LCF_BERT(FXBaseModel):
method get_language_models (line 43) | def get_language_models():
method get_input_field_ids (line 47) | def get_input_field_ids():
method __init__ (line 59) | def __init__(self, transformer_models: Dict, opt: Namespace):
method feature_dynamic_mask (line 86) | def feature_dynamic_mask(self, text_local_indices, aspect_indices):
method feature_dynamic_weighted (line 119) | def feature_dynamic_weighted(self, text_local_indices, aspect_indices):
method forward (line 152) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/lcf2.py
class GlobalContext (line 18) | class GlobalContext(nn.Module):
method __init__ (line 19) | def __init__(self, global_context_seqs_per_doc):
method forward (line 23) | def forward(self, inputs):
class SelfAttention (line 27) | class SelfAttention(nn.Module):
method __init__ (line 28) | def __init__(self, config, opt):
method forward (line 39) | def forward(self, inputs):
class LCF_BERT2Dual (line 48) | class LCF_BERT2Dual(FXBaseModel):
method get_language_models (line 57) | def get_language_models():
method get_input_field_ids (line 61) | def get_input_field_ids():
method __init__ (line 73) | def __init__(self, transformer_models: Dict, opt: Namespace):
method feature_dynamic_mask (line 94) | def feature_dynamic_mask(self, text_local_indices, aspect_indices):
method feature_dynamic_weighted (line 127) | def feature_dynamic_weighted(self, text_local_indices, aspect_indices):
method forward (line 160) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/lcfs.py
class PointwiseFeedForward (line 16) | class PointwiseFeedForward(nn.Module):
method __init__ (line 21) | def __init__(self, d_hid, d_inner_hid=None, d_out=None, dropout=0):
method forward (line 32) | def forward(self, x):
class SelfAttention (line 39) | class SelfAttention(nn.Module):
method __init__ (line 40) | def __init__(self, config, opt):
method forward (line 47) | def forward(self, inputs):
class LCFS_BERT (line 56) | class LCFS_BERT(FXBaseModel):
method get_language_models (line 58) | def get_language_models():
method get_input_field_ids (line 62) | def get_input_field_ids():
method __init__ (line 75) | def __init__(self, transformer_models: Dict, opt: Namespace):
method feature_dynamic_mask (line 102) | def feature_dynamic_mask(self, text_local_indices, aspect_indices, dis...
method feature_dynamic_weighted (line 148) | def feature_dynamic_weighted(
method forward (line 201) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/lcfst.py
class PointwiseFeedForward (line 17) | class PointwiseFeedForward(nn.Module):
method __init__ (line 22) | def __init__(self, d_hid, d_inner_hid=None, d_out=None, dropout=0):
method forward (line 33) | def forward(self, x):
class SelfAttention (line 40) | class SelfAttention(nn.Module):
method __init__ (line 41) | def __init__(self, config, opt):
method forward (line 48) | def forward(self, inputs):
class LCFST_BERT (line 57) | class LCFST_BERT(FXBaseModel):
method get_language_models (line 59) | def get_language_models():
method get_input_field_ids (line 63) | def get_input_field_ids():
method __init__ (line 77) | def __init__(self, transformer_models: Dict, opt: Namespace):
method feature_dynamic_mask (line 108) | def feature_dynamic_mask(self, text_local_indices, aspect_indices, dis...
method feature_dynamic_weighted (line 154) | def feature_dynamic_weighted(
method forward (line 207) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/lcft.py
class PointwiseFeedForward (line 16) | class PointwiseFeedForward(nn.Module):
method __init__ (line 21) | def __init__(self, d_hid, d_inner_hid=None, d_out=None, dropout=0):
method forward (line 32) | def forward(self, x):
class SelfAttention (line 39) | class SelfAttention(nn.Module):
method __init__ (line 40) | def __init__(self, config, opt):
method forward (line 47) | def forward(self, inputs):
class LCFT_BERT (line 56) | class LCFT_BERT(FXBaseModel):
method get_language_models (line 58) | def get_language_models():
method get_input_field_ids (line 62) | def get_input_field_ids():
method __init__ (line 75) | def __init__(self, transformer_models: Dict, opt: Namespace):
method feature_dynamic_mask (line 106) | def feature_dynamic_mask(self, text_local_indices, aspect_indices, dis...
method feature_dynamic_weighted (line 152) | def feature_dynamic_weighted(
method forward (line 205) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/notargetcls.py
class NoTargetClsBert (line 12) | class NoTargetClsBert(FXBaseModel):
method get_language_models (line 14) | def get_language_models():
method get_input_field_ids (line 18) | def get_input_field_ids():
method __init__ (line 24) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 33) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/random_single.py
class RandomSingle (line 12) | class RandomSingle(FXBaseModel):
method get_language_models (line 18) | def get_language_models():
method get_input_field_ids (line 27) | def get_input_field_ids():
method __init__ (line 33) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 37) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/spc.py
class SPC_Base (line 12) | class SPC_Base(FXBaseModel):
method get_language_models (line 14) | def get_language_models():
method get_input_field_ids (line 18) | def get_input_field_ids():
method __init__ (line 28) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 37) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/td_bert.py
class TD_BERT (line 18) | class TD_BERT(FXBaseModel):
method get_language_models (line 20) | def get_language_models():
method get_input_field_ids (line 24) | def get_input_field_ids():
method __init__ (line 30) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 37) | def forward(self, inputs, is_return_ensemble_values: bool = False):
FILE: NewsSentiment/models/singletarget/td_bert_qa.py
class TD_BERT_QA_MUL (line 14) | class TD_BERT_QA_MUL(FXBaseModel):
method get_language_models (line 16) | def get_language_models():
method get_input_field_ids (line 20) | def get_input_field_ids():
method __init__ (line 26) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 34) | def forward(self, inputs):
class TD_BERT_QA_CON (line 75) | class TD_BERT_QA_CON(FXBaseModel):
method get_language_models (line 77) | def get_language_models():
method get_input_field_ids (line 81) | def get_input_field_ids():
method __init__ (line 87) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 95) | def forward(self, inputs):
FILE: NewsSentiment/models/singletarget/tdbertlikesingle.py
class TDBertLikeSingle (line 12) | class TDBertLikeSingle(FXBaseModel):
method get_language_models (line 27) | def get_language_models():
method get_input_field_ids (line 31) | def get_input_field_ids():
method __init__ (line 37) | def __init__(self, transformer_models: Dict, opt: Namespace):
method forward (line 45) | def forward(self, inputs):
FILE: NewsSentiment/plotter_utils.py
function create_save_plotted_confusion_matrix (line 12) | def create_save_plotted_confusion_matrix(conf_matrix, expected_labels, b...
function plot_confusion_matrix (line 19) | def plot_confusion_matrix(cm, classes, normalize=False, title=None, cmap...
FILE: NewsSentiment/train.py
class Instructor (line 117) | class Instructor:
method __init__ (line 118) | def __init__(self, opt):
method _load_dataset (line 183) | def _load_dataset(self, path, coref_mode):
method load_datasets (line 197) | def load_datasets(self):
method _print_args (line 223) | def _print_args(self):
method create_transformer_model (line 240) | def create_transformer_model(
method _reset_params_of_own_model (line 279) | def _reset_params_of_own_model(self):
method _create_prepare_model_path_to_store_state_dict (line 292) | def _create_prepare_model_path_to_store_state_dict(self, snem, epoch):
method select_inputs (line 308) | def select_inputs(self, sample_batched, is_single_item=False):
method _train (line 344) | def _train(self, criterion, optimizer, train_data_loader, dev_data_loa...
method _intraining_evaluation_and_model_save (line 528) | def _intraining_evaluation_and_model_save(
method save_pretrained_model (line 555) | def save_pretrained_model(
method _save_model_state_dict (line 573) | def _save_model_state_dict(self, early_stopping, epoch: int, dev_stats):
method _get_classes_from_sequence_output (line 654) | def _get_classes_from_sequence_output(
method _evaluate (line 701) | def _evaluate(self, data_loader, get_examples=False, basepath=None):
method get_normalized_inv_class_frequencies (line 787) | def get_normalized_inv_class_frequencies(self):
method _is_random_model (line 799) | def _is_random_model(self):
method run (line 802) | def run(self):
method get_serializable_stats (line 892) | def get_serializable_stats(self, stats):
method get_serializable_opts (line 898) | def get_serializable_opts(self):
method perform_post_training_actions (line 906) | def perform_post_training_actions(
function str2bool (line 968) | def str2bool(v):
function _setup_cuda (line 979) | def _setup_cuda(opt):
function _die_gracefully (line 1008) | def _die_gracefully(reason: str):
function check_arguments (line 1015) | def check_arguments(opt):
function post_process_arguments (line 1080) | def post_process_arguments(opt):
function prepare_and_start_instructor (line 1118) | def prepare_and_start_instructor(opt):
function parse_arguments (line 1222) | def parse_arguments(override_args=False, overwrite_logging_level=None):
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Condensed preview — 126 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (34,819K chars).
[
{
"path": ".gitignore",
"chars": 3266,
"preview": "# Created by .ignore support plugin (hsz.mobi)\n### Python template\n# Byte-compiled / optimized / DLL files\n__pycache__/\n"
},
{
"path": ".idea/.gitignore",
"chars": 176,
"preview": "# Default ignored files\n/shelf/\n/workspace.xml\n# Datasource local storage ignored files\n/dataSources/\n/dataSources.local"
},
{
"path": ".idea/NewsMTSC.iml",
"chars": 521,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<module type=\"JAVA_MODULE\" version=\"4\">\n <component name=\"NewModuleRootManager\" "
},
{
"path": ".idea/misc.xml",
"chars": 268,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"PhpWorkspaceProjectConfiguration\" backwa"
},
{
"path": ".idea/modules.xml",
"chars": 268,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ProjectModuleManager\">\n <modules>\n "
},
{
"path": ".idea/vcs.xml",
"chars": 167,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"VcsDirectoryMappings\">\n <mapping dire"
},
{
"path": "CODE_OF_CONDUCT.md",
"chars": 3226,
"preview": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, w"
},
{
"path": "LICENSE",
"chars": 1394,
"preview": "This project uses code from various projects, who may have their own license or copyright.\n- ABSA-PyTorch by Yury Soong "
},
{
"path": "MANIFEST.in",
"chars": 44,
"preview": "include READMEpypi.md\ninclude pythoninfo.md\n"
},
{
"path": "NewsSentiment/.gitignore",
"chars": 0,
"preview": ""
},
{
"path": "NewsSentiment/DatasetPreparer.py",
"chars": 15097,
"preview": "\"\"\"\nUsed by controller.py to prepare datasets for an experiment, thereby most importantly considering whether the experi"
},
{
"path": "NewsSentiment/SentimentClasses.py",
"chars": 6379,
"preview": "from typing import Iterable\n\n\nclass SentimentClasses:\n FILLUP_POLARITY_VALUE = -100\n FILLUP_POLARITY_LABEL = \"fill"
},
{
"path": "NewsSentiment/__init__.py",
"chars": 58,
"preview": "from NewsSentiment.infer import TargetSentimentClassifier\n"
},
{
"path": "NewsSentiment/__main__.py",
"chars": 492,
"preview": "import argparse\n\nfrom NewsSentiment.download import Download\n\nif __name__ == '__main__':\n parser = argparse.ArgumentP"
},
{
"path": "NewsSentiment/combinations_default.py",
"chars": 3495,
"preview": "from NewsSentiment.consts import BERT_BASE_UNCASED\n\ncombinations_default_0 = {\n \"own_model_name\": [\n # baselin"
},
{
"path": "NewsSentiment/consts.py",
"chars": 1737,
"preview": "BERT_BASE_UNCASED = \"bert-base-uncased\"\nROBERTA_BASE = \"roberta-base\"\nXLNET_BASE_CASED = \"xlnet-base-cased\"\nALBERT_BASE "
},
{
"path": "NewsSentiment/controller.py",
"chars": 31676,
"preview": "\"\"\"\nDefinitions:\nsetup: represents a fixed, unique combination of for instance:\n* model, e.g., BERT, DISTILBERT\n* hyper-"
},
{
"path": "NewsSentiment/controller_data/datasets/NewsMTSC-dataset/devtest_mt.jsonl",
"chars": 409897,
"preview": "{\"primary_gid\": \"allsides_1002_402_12_former FBI director James B. Comey_51_56\", \"sentence_normalized\": \"While White Hou"
},
{
"path": "NewsSentiment/controller_data/datasets/NewsMTSC-dataset/devtest_rw.jsonl",
"chars": 444226,
"preview": "{\"primary_gid\": \"allsides_703_283_55_Mr. Trump_124_133\", \"sentence_normalized\": \"A group of congressional Democrats said"
},
{
"path": "NewsSentiment/controller_data/datasets/NewsMTSC-dataset/readme.md",
"chars": 3744,
"preview": "# Welcome\n\nThe files contained in this archive are part of the dataset \"NewsMTSC\" described in our paper \"NewsMTSC: (Mul"
},
{
"path": "NewsSentiment/controller_data/datasets/NewsMTSC-dataset/train.jsonl",
"chars": 3277842,
"preview": "{\"primary_gid\": \"allsides_1000_401_25_Reality Leigh Winner_0_6\", \"sentence_normalized\": \"Winner wrote that she had a 30-"
},
{
"path": "NewsSentiment/controller_data/datasets/acl14twitter/test.raw.jsonl",
"chars": 170068,
"preview": "{\"primary_gid\": \"-1\", \"sentence_normalized\": \" shaquille o'neal to miss 3rd straight playoff game | the ... : $t$ will m"
},
{
"path": "NewsSentiment/controller_data/datasets/acl14twitter/train.raw.jsonl",
"chars": 1530068,
"preview": "{\"primary_gid\": \"-1\", \"sentence_normalized\": \"i agree about arafat . i mean , shit , they even gave one to jimmy carter "
},
{
"path": "NewsSentiment/controller_data/datasets/newsmtsc-train-and-test-mt/devtest_mtsc_only.jsonl",
"chars": 409897,
"preview": "{\"primary_gid\": \"allsides_1002_402_12_former FBI director James B. Comey_51_56\", \"sentence_normalized\": \"While White Hou"
},
{
"path": "NewsSentiment/controller_data/datasets/newsmtsc-train-and-test-mt/train.jsonl",
"chars": 3277842,
"preview": "{\"primary_gid\": \"allsides_1000_401_25_Reality Leigh Winner_0_6\", \"sentence_normalized\": \"Winner wrote that she had a 30-"
},
{
"path": "NewsSentiment/controller_data/datasets/newsmtsc-train-and-test-rw/devtest_mtsc_and_single_primaries.jsonl",
"chars": 444226,
"preview": "{\"primary_gid\": \"allsides_703_283_55_Mr. Trump_124_133\", \"sentence_normalized\": \"A group of congressional Democrats said"
},
{
"path": "NewsSentiment/controller_data/datasets/newsmtsc-train-and-test-rw/train.jsonl",
"chars": 3277842,
"preview": "{\"primary_gid\": \"allsides_1000_401_25_Reality Leigh Winner_0_6\", \"sentence_normalized\": \"Winner wrote that she had a 30-"
},
{
"path": "NewsSentiment/controller_data/datasets/semeval14laptops/Laptops_Test_Gold.xml.seg.jsonl",
"chars": 149845,
"preview": "{\"primary_gid\": \"-1\", \"sentence_normalized\": \" boot time is super fast , around anywhere from 35 seconds to 1 minute .\","
},
{
"path": "NewsSentiment/controller_data/datasets/semeval14laptops/Laptops_Train.xml.seg.jsonl",
"chars": 590995,
"preview": "{\"primary_gid\": \"-1\", \"sentence_normalized\": \"i charge it at night and skip taking the cord with me because of the good "
},
{
"path": "NewsSentiment/controller_data/datasets/semeval14restaurants/Restaurants_Test_Gold.xml.seg.jsonl",
"chars": 268913,
"preview": "{\"primary_gid\": \"-1\", \"sentence_normalized\": \"the bread is top notch as well .\", \"targets\": [{\"Input.gid\": \"-1\", \"mentio"
},
{
"path": "NewsSentiment/controller_data/datasets/semeval14restaurants/Restaurants_Train.xml.seg.jsonl",
"chars": 877363,
"preview": "{\"primary_gid\": \"-1\", \"sentence_normalized\": \"but the staff was so horrible to us .\", \"targets\": [{\"Input.gid\": \"-1\", \"m"
},
{
"path": "NewsSentiment/converter_huggingface.py",
"chars": 1709,
"preview": "\"\"\"\nThis file converts the dataset files (3 splits) into the format we'll use on\nHuggingface Hub, i.e., where examples w"
},
{
"path": "NewsSentiment/createoverview.py",
"chars": 6007,
"preview": "import argparse\nimport json\nfrom collections import defaultdict\n\nfrom diskdict import DiskDict\n\nimport jsonlines\nimport "
},
{
"path": "NewsSentiment/customexceptions.py",
"chars": 101,
"preview": "class TooLongTextException(Exception):\n pass\n\n\nclass TargetNotFoundException(Exception):\n pass\n"
},
{
"path": "NewsSentiment/dataset.py",
"chars": 74246,
"preview": "import random\nfrom collections import Counter\nfrom typing import (\n List,\n Iterable,\n Set,\n Union,\n Dict,"
},
{
"path": "NewsSentiment/diskdict.py",
"chars": 841,
"preview": "from pathlib import Path\nimport pickle\nfrom typing import Union\n\nfrom NewsSentiment.fxlogger import get_logger\n\nlogger ="
},
{
"path": "NewsSentiment/download.py",
"chars": 3935,
"preview": "\"\"\"\nDownload a specific version of a finetuned model and place it in pretrained_models.\n\"\"\"\nimport os\nimport string\n\nimp"
},
{
"path": "NewsSentiment/earlystopping.py",
"chars": 1927,
"preview": "# taken from https://github.com/Bjarten/early-stopping-pytorch\n# Copyright by Bjarten\n# License: MIT\n\nfrom NewsSentiment"
},
{
"path": "NewsSentiment/evaluator.py",
"chars": 8248,
"preview": "from collections import Counter\nfrom statistics import mean\n\nimport jsonlines\nimport numpy as np\nfrom sklearn import met"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-mt/dev.jsonl",
"chars": 123624,
"preview": "{\"primary_gid\": \"allsides_6250_2689_12_Barack Obama_73_78\", \"sentence_normalized\": \"Both the Gallup and Rasmussen tracki"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-mt/test.jsonl",
"chars": 286273,
"preview": "{\"primary_gid\": \"allsides_1811_738_0_Hillary_25_32\", \"sentence_normalized\": \"Colin Powell: Everything Hillary Touches Sh"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-mt/train.jsonl",
"chars": 3277842,
"preview": "{\"primary_gid\": \"allsides_1000_401_25_Reality Leigh Winner_0_6\", \"sentence_normalized\": \"Winner wrote that she had a 30-"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-mt-hf/dev.jsonl",
"chars": 136179,
"preview": "{\"mention\": \"Obama\", \"polarity\": 1, \"from\": 73, \"to\": 78, \"sentence\": \"Both the Gallup and Rasmussen tracking surveys ha"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-mt-hf/test.jsonl",
"chars": 313577,
"preview": "{\"mention\": \"Hillary\", \"polarity\": -1, \"from\": 25, \"to\": 32, \"sentence\": \"Colin Powell: Everything Hillary Touches She S"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-mt-hf/train.jsonl",
"chars": 2506388,
"preview": "{\"mention\": \"Winner\", \"polarity\": 0, \"from\": 0, \"to\": 6, \"sentence\": \"Winner wrote that she had a 30-minute private meet"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-rw/dev.jsonl",
"chars": 135641,
"preview": "{\"primary_gid\": \"allsides_3447_1422_11_Ted Cruz_88_92\", \"sentence_normalized\": \"In the 2015 Conservative Political Actio"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-rw/test.jsonl",
"chars": 308585,
"preview": "{\"primary_gid\": \"polusa_v1_4307505_-1_11_Seth_39_43\", \"sentence_normalized\": \"Though we do not know what other items Set"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-rw/train.jsonl",
"chars": 3277842,
"preview": "{\"primary_gid\": \"allsides_1000_401_25_Reality Leigh Winner_0_6\", \"sentence_normalized\": \"Winner wrote that she had a 30-"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-rw-hf/dev.jsonl",
"chars": 99078,
"preview": "{\"mention\": \"Cruz\", \"polarity\": 0, \"from\": 88, \"to\": 92, \"sentence\": \"In the 2015 Conservative Political Action Conferen"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-rw-hf/test.jsonl",
"chars": 227869,
"preview": "{\"mention\": \"Seth\", \"polarity\": 0, \"from\": 39, \"to\": 43, \"sentence\": \"Though we do not know what other items Seth may ha"
},
{
"path": "NewsSentiment/experiments/default/datasets/newsmtsc-rw-hf/train.jsonl",
"chars": 2506388,
"preview": "{\"mention\": \"Winner\", \"polarity\": 0, \"from\": 0, \"to\": 6, \"sentence\": \"Winner wrote that she had a 30-minute private meet"
},
{
"path": "NewsSentiment/fxlogger.py",
"chars": 586,
"preview": "import logging\n\nlogger = None\n\n\ndef get_logger():\n global logger\n\n if not logger:\n formatter = logging.Form"
},
{
"path": "NewsSentiment/infer.py",
"chars": 13527,
"preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom math import ceil\nfrom jsonlines import jsonlines\nfrom"
},
{
"path": "NewsSentiment/inferrest.py",
"chars": 641,
"preview": "from flask import Flask, request\n\nfrom NewsSentiment import TargetSentimentClassifier\n\napp = Flask(__name__)\n\ntsc = Targ"
},
{
"path": "NewsSentiment/knowledge/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "NewsSentiment/knowledge/bingliuopinion/bingliuopinion.py",
"chars": 861,
"preview": "import torch\n\nfrom NewsSentiment.diskdict import DiskDict\nfrom NewsSentiment.knowledge.bingliuopinion.converter import P"
},
{
"path": "NewsSentiment/knowledge/bingliuopinion/converter.py",
"chars": 1782,
"preview": "from collections import defaultdict, Counter\nfrom pathlib import Path\nimport csv\nimport os\n\nfrom tqdm import tqdm\n\nfrom "
},
{
"path": "NewsSentiment/knowledge/bingliuopinion/license.txt",
"chars": 1191,
"preview": ";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n;\n; Both files (negative- and positive-word"
},
{
"path": "NewsSentiment/knowledge/bingliuopinion/negative-words.txt",
"chars": 44757,
"preview": "2-faced\n2-faces\nabnormal\nabolish\nabominable\nabominably\nabominate\nabomination\nabort\naborted\naborts\nabrade\nabrasive\nabrupt"
},
{
"path": "NewsSentiment/knowledge/bingliuopinion/positive-words.txt",
"chars": 19092,
"preview": "a+\nabound\nabounds\nabundance\nabundant\naccessable\naccessible\nacclaim\nacclaimed\nacclamation\naccolade\naccolades\naccommodativ"
},
{
"path": "NewsSentiment/knowledge/knowledgeutils.py",
"chars": 388,
"preview": "def find_key_original_or_lc(data: dict, data_keys_lower: dict, key: str):\n # try to get the key as it is from the dic"
},
{
"path": "NewsSentiment/knowledge/liwc/data/.gitignore",
"chars": 83,
"preview": "# Ignore everything in this directory\n*\n# Except this file\n!.gitignore\n!readme.txt\n"
},
{
"path": "NewsSentiment/knowledge/liwc/data/readme.txt",
"chars": 63,
"preview": "In order to use LIWC, you need to acquire a license from them.\n"
},
{
"path": "NewsSentiment/knowledge/liwc/dic.py",
"chars": 1859,
"preview": "def _parse_categories(lines):\n \"\"\"\n Read (category_id, category_name) pairs from the categories section.\n Each "
},
{
"path": "NewsSentiment/knowledge/liwc/liwc.py",
"chars": 939,
"preview": "import torch\n\nfrom NewsSentiment.knowledge.liwc.liwchelper import load_token_parser\n\nparse, category_names = load_token_"
},
{
"path": "NewsSentiment/knowledge/liwc/liwchelper.py",
"chars": 812,
"preview": "from .dic import read_dic\nfrom .trie import build_trie, search_trie\n\ntry:\n import pkg_resources\n\n __version__ = pk"
},
{
"path": "NewsSentiment/knowledge/liwc/trie.py",
"chars": 1002,
"preview": "def build_trie(lexicon):\n \"\"\"\n Build a character-trie from the plain pattern_string -> categories_list\n mapping"
},
{
"path": "NewsSentiment/knowledge/mpqasubjectivity/converter.py",
"chars": 2197,
"preview": "import os\nfrom collections import defaultdict, Counter\nfrom pathlib import Path\nimport csv\n\nfrom tqdm import tqdm\n\nfrom "
},
{
"path": "NewsSentiment/knowledge/mpqasubjectivity/mpqasubjectivity.py",
"chars": 888,
"preview": "import torch\n\nfrom NewsSentiment.diskdict import DiskDict\nfrom NewsSentiment.knowledge.knowledgeutils import find_key_or"
},
{
"path": "NewsSentiment/knowledge/mpqasubjectivity/subjclueslen1-HLTEMNLP05.tff",
"chars": 662572,
"preview": "type=weaksubj len=1 word1=abandoned pos1=adj stemmed1=n priorpolarity=negative\ntype=weaksubj len=1 word1=abandonment pos"
},
{
"path": "NewsSentiment/knowledge/nrcemolex/NRC-Emotion-Lexicon-Wordlevel-v0.92.txt",
"chars": 2579144,
"preview": "aback\tanger\t0\naback\tanticipation\t0\naback\tdisgust\t0\naback\tfear\t0\naback\tjoy\t0\naback\tnegative\t0\naback\tpositive\t0\naback\tsadn"
},
{
"path": "NewsSentiment/knowledge/nrcemolex/converter.py",
"chars": 1530,
"preview": "import os\nfrom collections import defaultdict, Counter\nfrom pathlib import Path\nimport csv\n\nfrom NewsSentiment.fxlogger "
},
{
"path": "NewsSentiment/knowledge/nrcemolex/nrcemolex.py",
"chars": 813,
"preview": "import torch\n\nfrom NewsSentiment.knowledge.knowledgeutils import find_key_original_or_lc\nfrom NewsSentiment.knowledge.nr"
},
{
"path": "NewsSentiment/knowledge/zeros/zerosknowledge.py",
"chars": 665,
"preview": "import torch\n\n\ndef get_num_zero_dimensions():\n # while one dimension would suffice, using only one can cause (especia"
},
{
"path": "NewsSentiment/layers/AggregatorForBert.py",
"chars": 1903,
"preview": "import torch.nn as nn\nimport torch\n\n\nclass AggregatorForBert(nn.Module):\n \"\"\"\n According to https://huggingface.co"
},
{
"path": "NewsSentiment/layers/__init__.py",
"chars": 130,
"preview": "# -*- coding: utf-8 -*-\n# file: __init__.py\n# author: songyouwei <youwei0314@gmail.com>\n# Copyright (C) 2018. All Rights"
},
{
"path": "NewsSentiment/layers/attention.py",
"chars": 8505,
"preview": "# -*- coding: utf-8 -*-\n# file: attention.py\n# author: songyouwei <youwei0314@gmail.com> for top two classes, huggingfac"
},
{
"path": "NewsSentiment/layers/dynamic_rnn.py",
"chars": 3936,
"preview": "# -*- coding: utf-8 -*-\n# file: dynamic_rnn.py\n# author: songyouwei <youwei0314@gmail.com>\n# Copyright (C) 2018. All Rig"
},
{
"path": "NewsSentiment/layers/point_wise_feed_forward.py",
"chars": 841,
"preview": "# -*- coding: utf-8 -*-\n# file: point_wise_feed_forward.py\n# author: songyouwei <youwei0314@gmail.com>\n# Copyright (C) 2"
},
{
"path": "NewsSentiment/layers/pytorchnlpattention.py",
"chars": 3823,
"preview": "import torch\nimport torch.nn as nn\n\n# from https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/nn/attention.ht"
},
{
"path": "NewsSentiment/layers/squeeze_embedding.py",
"chars": 1207,
"preview": "# -*- coding: utf-8 -*-\n# file: squeeze_embedding.py\n# author: songyouwei <youwei0314@gmail.com>\n# Copyright (C) 2018. A"
},
{
"path": "NewsSentiment/losses/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "NewsSentiment/losses/crossentropycrossweight.py",
"chars": 5185,
"preview": "import torch\nimport torch.nn as nn\n\n# CrossEntropyLoss with additional cross weight loss for two targets\nfrom NewsSentim"
},
{
"path": "NewsSentiment/losses/crossentropylosslsr.py",
"chars": 5355,
"preview": "# adapted from ABSA-PyTorch\nimport torch\nimport torch.nn as nn\n\n# CrossEntropyLoss for Label Smoothing Regularization\nfr"
},
{
"path": "NewsSentiment/losses/crossentropylosswithconfidence.py",
"chars": 1359,
"preview": "import torch.nn as nn\nimport torch\n\n\nclass CrossEntropyLossWithConfidence(nn.Module):\n def __init__(self, weight, ign"
},
{
"path": "NewsSentiment/losses/seq2seqloss.py",
"chars": 4719,
"preview": "import torch\nimport torch.nn as nn\n\nfrom NewsSentiment.SentimentClasses import SentimentClasses\n\n\nclass SequenceLoss(nn."
},
{
"path": "NewsSentiment/models/FXBaseModel.py",
"chars": 4715,
"preview": "import os\nfrom copy import copy\n\nimport torch.nn as nn\n\nfrom abc import abstractmethod\n\nfrom transformers import (\n X"
},
{
"path": "NewsSentiment/models/FXEnsemble.py",
"chars": 5708,
"preview": "from abc import ABC\nfrom argparse import Namespace\nfrom typing import List, Dict\nimport torch.nn as nn\nimport torch\n\nfro"
},
{
"path": "NewsSentiment/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "NewsSentiment/models/ensemble.py",
"chars": 2698,
"preview": "from argparse import Namespace\nfrom typing import List, Dict\nfrom NewsSentiment.consts import *\nimport torch.nn as nn\nim"
},
{
"path": "NewsSentiment/models/ensembleb.py",
"chars": 2324,
"preview": "from argparse import Namespace\nfrom typing import List, Dict\nfrom NewsSentiment.consts import *\nimport torch.nn as nn\nim"
},
{
"path": "NewsSentiment/models/multitargets/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "NewsSentiment/models/multitargets/contrasting.py",
"chars": 3531,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\n\nfrom NewsSentiment.consts im"
},
{
"path": "NewsSentiment/models/multitargets/random_multi.py",
"chars": 1308,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\n\nfrom NewsSentiment.consts im"
},
{
"path": "NewsSentiment/models/multitargets/seq2seq.py",
"chars": 2901,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch.nn as nn\n\nfrom NewsSentiment.SentimentClasses impor"
},
{
"path": "NewsSentiment/models/multitargets/seq2seq_without_targetmask.py",
"chars": 2360,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch.nn as nn\n\nfrom NewsSentiment.SentimentClasses impor"
},
{
"path": "NewsSentiment/models/multitargets/tdbertlikemultitarget.py",
"chars": 5300,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch.nn as nn\n\nfrom NewsSentiment.consts import *\nfrom N"
},
{
"path": "NewsSentiment/models/multitargets/tdbertlikemultitarget_dense.py",
"chars": 3234,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch.nn as nn\n\nfrom NewsSentiment.consts import *\nfrom N"
},
{
"path": "NewsSentiment/models/singletarget/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "NewsSentiment/models/singletarget/aen.py",
"chars": 5945,
"preview": "# adapted from absa-pytorch\nfrom argparse import Namespace\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\n\n"
},
{
"path": "NewsSentiment/models/singletarget/grutscsingle.py",
"chars": 7188,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\nfrom transformers import Pret"
},
{
"path": "NewsSentiment/models/singletarget/lcf.py",
"chars": 8184,
"preview": "# adapted from https://github.com/yangheng95/LCF-ABSA\nfrom argparse import Namespace\nfrom typing import Dict\n\nimport num"
},
{
"path": "NewsSentiment/models/singletarget/lcf2.py",
"chars": 8313,
"preview": "# adapted from https://github.com/yangheng95/LC-ABSA/blob/c945a94e0f86116c5578245aa9ad36c46c7b9c4a/models/lc_apc/lcf_ber"
},
{
"path": "NewsSentiment/models/singletarget/lcfs.py",
"chars": 10011,
"preview": "# adapted from https://github.com/yangheng95/LCF-ABSA and\n# https://github.com/StevePhan101/LCFS-BERT/\nfrom argparse imp"
},
{
"path": "NewsSentiment/models/singletarget/lcfst.py",
"chars": 13491,
"preview": "# adapted from https://github.com/yangheng95/LCF-ABSA and\n# https://github.com/StevePhan101/LCFS-BERT/\nfrom argparse imp"
},
{
"path": "NewsSentiment/models/singletarget/lcft.py",
"chars": 12285,
"preview": "# adapted from https://github.com/yangheng95/LCF-ABSA\nfrom argparse import Namespace\nfrom typing import Dict\n\nimport num"
},
{
"path": "NewsSentiment/models/singletarget/notargetcls.py",
"chars": 1617,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch.nn as nn\n\nfrom NewsSentiment.consts import *\nfrom N"
},
{
"path": "NewsSentiment/models/singletarget/random_single.py",
"chars": 1310,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\n\nfrom NewsSentiment.consts im"
},
{
"path": "NewsSentiment/models/singletarget/spc.py",
"chars": 2336,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch.nn as nn\n\nfrom NewsSentiment.consts import *\nfrom N"
},
{
"path": "NewsSentiment/models/singletarget/td_bert.py",
"chars": 3238,
"preview": "# this file re-implements TD-BERT by Gao Zhengjie et al.\n# while this file aims to be conceptually identical to TD-BERT,"
},
{
"path": "NewsSentiment/models/singletarget/td_bert_qa.py",
"chars": 5184,
"preview": "# this file re-implements TD-BERT-QA by Gao Zhengjie et al.\nfrom argparse import Namespace\nfrom typing import Dict\n\nimpo"
},
{
"path": "NewsSentiment/models/singletarget/tdbertlikesingle.py",
"chars": 3881,
"preview": "from argparse import Namespace\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\n\nfrom NewsSentiment.consts im"
},
{
"path": "NewsSentiment/plotter_utils.py",
"chars": 2615,
"preview": "import os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix\n\nfrom NewsSen"
},
{
"path": "NewsSentiment/pretrained_models/.gitignore",
"chars": 84,
"preview": "# Ignore everything in this directory\n*\n# Except this file\n!.gitignore\n!state_dicts\n"
},
{
"path": "NewsSentiment/pretrained_models/state_dicts/.gitignore",
"chars": 71,
"preview": "# Ignore everything in this directory\n*\n# Except this file\n!.gitignore\n"
},
{
"path": "NewsSentiment/results/.gitignore",
"chars": 71,
"preview": "# Ignore everything in this directory\n*\n# Except this file\n!.gitignore\n"
},
{
"path": "NewsSentiment/train.py",
"chars": 55410,
"preview": "import argparse\nimport math\nimport os\nimport random\nimport sys\nimport time\nfrom collections import Counter\nfrom typing i"
},
{
"path": "README.md",
"chars": 6214,
"preview": "# NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles\nNewsMTSC is a dataset for target-dependen"
},
{
"path": "READMEpypi.md",
"chars": 3877,
"preview": "# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles\nNewsSentiment is "
},
{
"path": "hubconf.py",
"chars": 72,
"preview": "from NewsSentiment.models.singletarget.grutscsingle import GRUTSCSingle\n"
},
{
"path": "pyproject.toml",
"chars": 100,
"preview": "[build-system]\nrequires = [\n \"setuptools>=45\",\n \"wheel\"\n]\nbuild-backend = \"setuptools.build_meta\"\n"
},
{
"path": "pythoninfo.md",
"chars": 632,
"preview": "This step is optional if you have Python >=3.8, <3.12 installed (run `python --version`\nin a terminal and check the vers"
},
{
"path": "setup.cfg",
"chars": 1797,
"preview": "[metadata]\nname = NewsSentiment\nversion = 1.2.28\nauthor = Felix Hamborg\nauthor_email = felix.hamborg@uni-konstanz.de\ndes"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the fhamborg/NewsMTSC GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 126 files (30.6 MB), approximately 8.0M tokens, and a symbol index with 416 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.