python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Replabel transforms for use with wav2letter's ASG criterion.
"""
def replabel_symbol(i):
"""
Replabel sy... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/data/replabels.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .asr_dataset import AsrDataset
__all__ = [
'AsrDataset',
]
| EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/data/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
This module contains collection of classes which implement
collate functionalities for various tasks.
Collaters should know wh... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/data/collaters.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
def calc_mean_invstddev(feature):
if len(feature.size()) != 2:
raise ValueError("We expect the input feature to be ... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/data/data_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
from fairseq.data import FairseqDataset
from . import data_utils
from .collaters import Seq2SeqCollater
class ... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/data/asr_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import torch
import torch.nn.f... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/criterions/cross_entropy_acc.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import numpy as np
import torch
from fairseq import utils
from fairseq.criterions import FairseqCriterion... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/criterions/ASG_loss.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import math
from itertools import groupby
import torch
import torch.nn.functional as F
from fairseq im... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/criterions/CTC_loss.py |
import importlib
import os
# ASG loss requires wav2letter
blacklist = set()
try:
import wav2letter
except ImportError:
blacklist.add("ASG_loss.py")
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_") and file not in blacklist:
criterion_name = f... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/speech_recognition/criterions/__init__.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import contextlib
import sys
from collections import Counter
from multiprocessing im... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/multiprocessing_bpe_encoder.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import os
import re
class InputExample:
def __init__(self, paragrap... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/preprocess_RACE.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from functools import lru_cache
import json
def convert_sentence_to_json(sentence):
if '_' in sentence:
prefix, rest = sentence.... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/wsc/wsc_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.data import encoders
from fairseq.criterions... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/wsc/wsc_criterion.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import wsc_criterion # noqa
from . import wsc_task # noqa
| EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/wsc/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import tempfile
import numpy as np
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/wsc/wsc_task.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import commonsense_qa_task # noqa
| EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/commonsense_qa/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import numpy as np
import torch
from fairseq.data import (
data_utils,
Dictionary,
encoders,
IdDataset... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/examples/roberta/commonsense_qa/commonsense_qa_task.py |
#!/usr/bin/env python
"""Helper script to compare two argparse.Namespace objects."""
from argparse import Namespace # noqa
def main():
ns1 = eval(input('Namespace 1: '))
ns2 = eval(input('Namespace 2: '))
def keys(ns):
ks = set()
for k in dir(ns):
if not k.startswith('_'):
... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/compare_namespaces.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Split a large file into a train and valid set while respecting document
boundaries. Documents should be separated by... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/split_train_valid_docs.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Helper script to pre-compute embeddings for a wav2letter++ dataset
"""
import argparse
import glob
import os
from ... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/wav2vec_featurize.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Use this script in order to build symmetric alignments for your translation
dataset.
This script depends on fast_align and mosesdecoder too... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/build_sym_alignment.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/spm_decode.py |
EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/__init__.py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import re
import shutil
import sys
pt_regexp = re.compile(r'checkpoint(\d+|_\d+_\d+|_[a-z]+... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/rm_pt.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Count the number of documents and average number of lines and tokens per
document in a large file. Documents should ... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/count_docs.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
i... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/spm_encode.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Split a large file into shards while respecting document boundaries. Documents
should be separated by a single empty... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/shard_docs.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import sys
impor... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/spm_train.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import collections
import torch
import os
import re
def average_checkpoints(inputs):
"""Loads che... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/average_checkpoints.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Data pre-processing: build vocabularies and binarize training data.
"""
import argparse
import glob
import os
impor... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/wav2vec_manifest.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from fairseq.data import data_utils, Dictionary, indexed_dataset
def get_parser():
parser = argp... | EXA-1-master | exa/models/unilm-master/infoxlm/fairseq/scripts/read_binarized.py |
import setuptools
setuptools.setup(
name="infoxlm",
version="0.0.1",
author="Zewen",
author_email="chizewen@outlook.com",
description="infoxlm",
url="https://github.com/CZWin32768/XLM-Align",
packages=setuptools.find_packages(),
install_requires=[],
classifiers=(
"Programming Language :: Python :... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/setup.py |
import infoxlm
from fairseq_cli.train import cli_main
if __name__ == "__main__":
cli_main() | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/train.py |
import infoxlm.tasks
import infoxlm.models
import infoxlm.criterions | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/__init__.py |
import torch
from fairseq import utils
@torch.no_grad()
def concat_all_gather(tensor):
"""
Performs all_gather operation on the provided tensors.
*** Warning ***: torch.distributed.all_gather has no gradient.
"""
if torch.cuda.device_count() > 1:
return varsize_tensor_all_gather(tensor)
else:
outp... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/utils.py |
import os
from fairseq.tasks import register_task, FairseqTask
from fairseq.data.dictionary import Dictionary
from infoxlm.data import mlm_utils
from infoxlm.data.dict_dataset import DictDataset
from infoxlm.tasks.mlm import Mlm
@register_task("tlm")
class Tlm(Mlm):
@staticmethod
def add_args(parser):
Mlm.a... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/tasks/tlm.py |
import os
from functools import lru_cache
import numpy as np
import torch
from fairseq import utils
from fairseq.data.data_utils import process_bpe_symbol
from fairseq.data.dictionary import Dictionary
from fairseq.tasks import FairseqTask, register_task
from infoxlm.data import mlm_utils
from infoxlm.data.dict_data... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/tasks/xlm_align.py |
import os
from fairseq.tasks import register_task, FairseqTask
from fairseq.data.dictionary import Dictionary
from infoxlm.data import mlm_utils
@register_task("mlm")
class Mlm(FairseqTask):
@staticmethod
def add_args(parser):
mlm_utils.add_mlm_args(parser)
parser.add_argument('data', help='colon separa... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/tasks/mlm.py |
import argparse
import importlib
import os
from fairseq.tasks import TASK_REGISTRY
# automatically import any Python files in the tasks/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
task_name = file[:file.find('.py')]
importlib.import_m... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/tasks/__init__.py |
import os
import torch
from functools import lru_cache
from fairseq.tasks import register_task, FairseqTask
from fairseq.data.dictionary import Dictionary
from fairseq.data import FairseqDataset
from fairseq import utils
from infoxlm.data import mlm_utils
from infoxlm.data.dict_dataset import DictDataset
from infoxlm... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/tasks/infoxlm.py |
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
RobertaEncoder,
... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/models/roberta.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
roberta_base_architecture,
robe... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/models/xlm_align.py |
import argparse
import importlib
import os
from fairseq.models import MODEL_REGISTRY, ARCH_MODEL_INV_REGISTRY
# automatically import any Python files in the models/ directory
models_dir = os.path.dirname(__file__)
for file in os.listdir(models_dir):
path = os.path.join(models_dir, file)
if not file.startswith('_... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/models/__init__.py |
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
register_model,
register_model_architecture,
)
from fairseq.models.roberta import (
RobertaModel,
roberta_base_arch... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/models/infoxlm.py |
import torch
from fairseq.data import FairseqDataset
class TLMDataset(FairseqDataset):
def __init__(self, src_dataset, tgt_dataset, bos, eos):
assert len(src_dataset) == len(tgt_dataset)
self.src_dataset = src_dataset
self.tgt_dataset = tgt_dataset
self.bos = bos
self.eos = eos
self._sizes... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/data/tlm_dataset.py |
import torch
from fairseq.data import BaseWrapperDataset
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from infoxlm.data.mlm_utils import get_mlm_datas... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/data/offset_dataset.py |
import torch
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from fairseq.data.encoders.utils import get_whole_word_mask
def get_mlm_dataset(args, datas... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/data/mlm_utils.py |
import torch
from fairseq.data import (data_utils,
TokenBlockDataset, PrependTokenDataset, PadDataset, TruncateDataset,
NumelDataset, NumSamplesDataset, NestedDictionaryDataset,
MaskTokensDataset, AppendTokenDataset, )
from fairseq.data.encoders.utils import get_whole_word_mask
from infoxlm.data.mlm_utils impo... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/data/xlm_align.py |
import numpy as np
import os
import torch
from threading import Thread
from fairseq.data import data_utils, FairseqDataset, FairseqIterableDataset
class DictIterDataset(FairseqIterableDataset):
def __init__(self, defn, sizes=None):
self.defn = defn
for v in self.defn.values():
if not isinstance(v, (F... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/data/dict_dataset.py |
EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/data/__init__.py | |
import numpy as np
import torch
from fairseq.data import data_utils, FairseqDataset, MaskTokensDataset, TruncateDataset, BaseWrapperDataset
from infoxlm.data.dict_dataset import DictDataset
def get_xlco_dataset(args, dataset_path, vocab, mask_idx, combine=False):
dataset = data_utils.load_indexed_dataset(
data... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/data/xlco_dataset.py |
import collections
import logging
import math
import torch
import numpy as np
from torch import nn
from torch.nn import functional as F
from torch import distributed
from fairseq import utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.data.data_utils import process_bpe_symbol
fr... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/criterions/xlm_align.py |
import os
import importlib
# automatically import any Python files in the criterions/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('infoxlm.criterions.' + module) | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/criterions/__init__.py |
import collections
import logging
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch import distributed
from fairseq import utils
from fairseq.criterions import FairseqCriterion, register_criterion
logger = logging.getLogger(__name__)
@register_criterion('xlco')
class Xl... | EXA-1-master | exa/models/unilm-master/infoxlm/src-infoxlm/infoxlm/criterions/xlco.py |
import deltalm
from fairseq_cli.preprocess import cli_main
if __name__ == "__main__":
cli_main() | EXA-1-master | exa/models/unilm-master/deltalm/preprocess.py |
import deltalm
from fairseq_cli.generate import cli_main
if __name__ == "__main__":
cli_main() | EXA-1-master | exa/models/unilm-master/deltalm/generate.py |
import deltalm
from fairseq_cli.interactive import cli_main
if __name__ == "__main__":
cli_main() | EXA-1-master | exa/models/unilm-master/deltalm/interactive.py |
import deltalm
from fairseq_cli.train import cli_main
if __name__ == "__main__":
cli_main() | EXA-1-master | exa/models/unilm-master/deltalm/train.py |
import deltalm.models | EXA-1-master | exa/models/unilm-master/deltalm/deltalm/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import... | EXA-1-master | exa/models/unilm-master/deltalm/deltalm/models/deltalm.py |
import argparse
import importlib
import os
from fairseq.models import MODEL_REGISTRY, ARCH_MODEL_INV_REGISTRY
# automatically import any Python files in the models/ directory
models_dir = os.path.dirname(__file__)
for file in os.listdir(models_dir):
path = os.path.join(models_dir, file)
if not file.startswith('_'... | EXA-1-master | exa/models/unilm-master/deltalm/deltalm/models/__init__.py |
#!/usr/bin/env python3
from setuptools import find_packages, setup
setup(
name="markuplmft",
version="0.1",
author="MarkupLM Team",
packages=find_packages(),
python_requires=">=3.7",
extras_require={"dev": ["flake8", "isort", "black"]},
) | EXA-1-master | exa/models/unilm-master/markuplm/setup.py |
from __future__ import absolute_import, division, print_function
import argparse
import logging
import os
import random
import glob
import timeit
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
from... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_websrc/run.py |
import csv
import json
import argparse
import os.path as osp
import os
from operator import itemgetter
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--root_dir", default=None, type=str, required=True,
help="The root directory of the raw WebSRC dataset; The o... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_websrc/dataset_generation.py |
from __future__ import absolute_import, division, print_function
import json
import logging
import math
import collections
from io import open
from os import path as osp
from tqdm import tqdm
import bs4
from bs4 import BeautifulSoup as bs
from transformers.models.bert.tokenization_bert import BasicTokenizer, whitespac... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_websrc/utils.py |
import argparse
import collections
import json
import os
import re
import string
import sys
from copy import deepcopy
from bs4 import BeautifulSoup
class EvalOpts:
r"""
The options which the matrix evaluation process needs.
Arguments:
data_file (str): the SQuAD-style json file of the dataset... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_websrc/utils_evaluate.py |
from __future__ import absolute_import, division, print_function
import argparse
import logging
import os
import random
import glob
import timeit
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
from... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_websrc/draft.py |
from __future__ import absolute_import, division, print_function
import argparse
import logging
import os
import random
import glob
import numpy as np
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
from tensorboardX import Summar... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_swde/run.py |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_swde/prepare_data.py |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_swde/pack_data.py |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_swde/constants.py |
import tqdm
from torch.utils.data import Dataset
from markuplmft.data.tag_utils import tags_dict
import pickle
import os
import constants
class SwdeFeature(object):
def __init__(self,
html_path,
input_ids,
token_type_ids,
attention_mask,
... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_swde/utils.py |
import os
import sys
import constants
def page_hits_level_metric(
vertical,
target_website,
sub_output_dir,
prev_voted_lines
):
"""Evaluates the hit level prediction result with precision/recall/f1."""
all_precisions = []
all_recall = []
all_f1 = []
lines = prev_v... | EXA-1-master | exa/models/unilm-master/markuplm/examples/fine_tuning/run_swde/eval_utils.py |
from transformers import CONFIG_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, \
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_NAMES_MAPPING, TOKENIZER_MAPPING
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, RobertaConverter
from transformers.file_utils import PRESET_MIRROR_DICT
from .mode... | EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/__init__.py |
EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/models/__init__.py | |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia MarkupLM Team Authors and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/... | EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/models/markuplm/modeling_markuplm.py |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia MarkupLM Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/models/markuplm/tokenization_markuplm.py |
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use thi... | EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/models/markuplm/__init__.py |
# coding=utf-8
# Copyright 2010, The Microsoft Research Asia MarkupLM Team authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/models/markuplm/configuration_markuplm.py |
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia MarkupLM Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/models/markuplm/tokenization_markuplm_fast.py |
tags_dict = {'a': 0, 'abbr': 1, 'acronym': 2, 'address': 3, 'altGlyph': 4, 'altGlyphDef': 5, 'altGlyphItem': 6,
'animate': 7, 'animateColor': 8, 'animateMotion': 9, 'animateTransform': 10, 'applet': 11, 'area': 12,
'article': 13, 'aside': 14, 'audio': 15, 'b': 16, 'base': 17, 'basefont': 18, '... | EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/data/tag_utils.py |
EXA-1-master | exa/models/unilm-master/markuplm/markuplmft/data/__init__.py | |
#!/usr/bin/env python3
from setuptools import find_packages, setup
setup(
name="layoutlmft",
version="0.1",
author="LayoutLM Team",
url="https://github.com/microsoft/unilm/tree/master/layoutlmft",
packages=find_packages(),
python_requires=">=3.7",
extras_require={"dev": ["flake8", "isort", "... | EXA-1-master | exa/models/unilm-master/layoutlmft/setup.py |
import os
import re
import numpy as np
from transformers.utils import logging
logger = logging.get_logger(__name__)
PREFIX_CHECKPOINT_DIR = "checkpoint"
_re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)$")
def get_last_checkpoint(folder):
content = os.listdir(folder)
checkpoints = [
... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/evaluation.py |
from collections import OrderedDict
from transformers import CONFIG_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_NAMES_MAPPING, TOKENIZER_MAPPING
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, BertConverter, XLMRobertaConverter
from transformers.models.auto.modeling_auto import auto... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/__init__.py |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
import torch
from transformers.file_utils import ModelOutput
@dataclass
class ReOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = No... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/utils.py |
EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/__init__.py | |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier f... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/model_args.py |
# coding=utf-8
from transformers.models.layoutlm.tokenization_layoutlm import LayoutLMTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/layoutlmv2-base-uncased":... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutlmv2/tokenization_layoutlmv2.py |
from .configuration_layoutlmv2 import LayoutLMv2Config
from .modeling_layoutlmv2 import LayoutLMv2ForRelationExtraction, LayoutLMv2ForTokenClassification, LayoutLMv2Model
from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
from .tokenization_layoutlmv2_fast import LayoutLMv2TokenizerFast
| EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutlmv2/__init__.py |
# -*- coding: utf-8 -*-
def add_layoutlmv2_config(cfg):
_C = cfg
# -----------------------------------------------------------------------------
# Config definition
# -----------------------------------------------------------------------------
_C.MODEL.MASK_ON = True
# When using pre-trained m... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py |
# coding=utf-8
import math
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
import detectron2
from detectron2.modeling import META_ARCH_REGISTRY
from transformers import PreTrainedModel
from transformers.modeling_outputs import (
... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutlmv2/modeling_layoutlmv2.py |
# coding=utf-8
from transformers.models.layoutlm.tokenization_layoutlm_fast import LayoutLMTokenizerFast
from transformers.utils import logging
from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutlmv2/tokenization_layoutlmv2_fast.py |
# coding=utf-8
from transformers.models.layoutlm.configuration_layoutlm import LayoutLMConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutlmv2/configuration_layoutlmv2.py |
# coding=utf-8
from transformers.utils import logging
from ..layoutlmv2 import LayoutLMv2Config
logger = logging.get_logger(__name__)
LAYOUTXLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"layoutxlm-base": "https://huggingface.co/layoutxlm-base/resolve/main/config.json",
"layoutxlm-large": "https://huggingface.co/lay... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutxlm/configuration_layoutxlm.py |
# coding=utf-8
from transformers import XLMRobertaTokenizerFast
from transformers.file_utils import is_sentencepiece_available
from transformers.utils import logging
if is_sentencepiece_available():
from .tokenization_layoutxlm import LayoutXLMTokenizer
else:
LayoutXLMTokenizer = None
logger = logging.get_l... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutxlm/tokenization_layoutxlm_fast.py |
# coding=utf-8
from transformers import XLMRobertaTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"layoutxlm-base": "https://huggin... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutxlm/tokenization_layoutxlm.py |
from .configuration_layoutxlm import LayoutXLMConfig
from .modeling_layoutxlm import LayoutXLMForRelationExtraction, LayoutXLMForTokenClassification, LayoutXLMModel
from .tokenization_layoutxlm import LayoutXLMTokenizer
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
| EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutxlm/__init__.py |
# coding=utf-8
from transformers.utils import logging
from ..layoutlmv2 import LayoutLMv2ForRelationExtraction, LayoutLMv2ForTokenClassification, LayoutLMv2Model
from .configuration_layoutxlm import LayoutXLMConfig
logger = logging.get_logger(__name__)
LAYOUTXLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"layoutxlm-base... | EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutxlm/modeling_layoutxlm.py |
from transformers.models.layoutlm import *
| EXA-1-master | exa/models/unilm-master/layoutlmft/layoutlmft/models/layoutlm/__init__.py |
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