repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/baseline_stackprop/utils/._loader.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/baseline_stackprop/utils/._module.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/data/._dialogue_data.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._baseline_multi.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._CHAN.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._bert_model_context.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/.___init__.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._transformer_new.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._transformer.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._torchcrf.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._mia.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/__MACOSX/CaBERT-SLU-main/model/._baseline_eca.py | Mac OS X 2 ATTR < < com.apple.quarantine q/0081;6067ce05;Chrome;EB6190ED-75B9-4A04-87B2-88BD2FC5C689 | 207 | 207 | 207 | py |
CaBERT-SLU | CaBERT-SLU-main/baseline_stackprop/utils_bert.py | import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.optim import Adam, RMSprop
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import pickle
import copy
import numpy as np
import collections
from tqdm import tqdm
fr... | 5,829 | 34.120482 | 115 | py |
CaBERT-SLU | CaBERT-SLU-main/baseline_stackprop/train.py | """
@Author : Lee, Qin
@StartTime : 2018/08/13
@Filename : train.py
@Software : Pycharm
@Framework : Pytorch
@LastModify : 2019/05/07
"""
from utils.module import ModelManager
from utils.loader import DatasetManager ######
from utils.process import Processo... | 3,585 | 36.747368 | 103 | py |
CaBERT-SLU | CaBERT-SLU-main/baseline_stackprop/utils/module.py | """
@Author : Lee, Qin
@StartTime : 2018/08/13
@Filename : module.py
@Software : Pycharm
@Framework : Pytorch
@LastModify : 2019/05/07
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_pa... | 17,569 | 38.483146 | 111 | py |
CaBERT-SLU | CaBERT-SLU-main/baseline_stackprop/utils/miulab.py | """
Copy file (including metric) from MiuLab:
https://github.com/MiuLab/SlotGated-SLU
"""
# compute f1 score is modified from conlleval.pl
def __startOfChunk(prevTag, tag, prevTagType, tagType, chunkStart=False):
if prevTag == 'B' and tag == 'B':
chunkStart = True
if prevTag == 'I' and tag == 'B':
chunkStart ... | 4,131 | 25.487179 | 92 | py |
CaBERT-SLU | CaBERT-SLU-main/baseline_stackprop/utils/__init__.py | __author__ = "Lie Pleased" | 26 | 26 | 26 | py |
CaBERT-SLU | CaBERT-SLU-main/baseline_stackprop/utils/process.py | """
@Author : Lee, Qin
@StartTime : 2018/08/13
@Filename : process.py
@Software : Pycharm
@Framework : Pytorch
@LastModify : 2019/05/07
"""
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import pickle
imp... | 18,042 | 39.364653 | 123 | py |
CaBERT-SLU | CaBERT-SLU-main/baseline_stackprop/utils/loader.py | """
@Author : Lee, Qin
@StartTime : 2018/08/13
@Filename : loader.py
@Software : Pycharm
@Framework : Pytorch
@LastModify : 2019/05/07
"""
import os
import numpy as np
from copy import deepcopy
from collections import Counter
from collections import Ordered... | 13,758 | 32.31477 | 112 | py |
CaBERT-SLU | CaBERT-SLU-main/data/dialogue_data.py | import torch as t
from torch.autograd import Variable
import numpy as np
import pandas as pd
import re
import pickle
import h5py
import json
import os
import csv
import spacy
from nltk.tokenize import word_tokenize
from transformers import BertTokenizer, BertModel, BertForMaskedLM
import time
class Data:
def __... | 22,069 | 38.837545 | 145 | py |
CaBERT-SLU | CaBERT-SLU-main/model/transformer_new.py | """Transformer module with masks"""
import torch
import torch.nn as nn
import numpy as np
class ScaledDotProductAttention(nn.Module):
"""Scaled dot-product attention mechanism.
"""
def __init__(self, attention_dropout=0.0):
super(ScaledDotProductAttention, self).__init__()
self.dropout = n... | 3,804 | 32.672566 | 76 | py |
CaBERT-SLU | CaBERT-SLU-main/model/baseline_multi.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from transformers import BertTokenizer, BertModel
class MULTI(nn.Module):
def __init__(self, opt, num_labels=2, num_slot_labels=10):
super(MULTI, self).__init__()
self.device = tor... | 4,648 | 39.426087 | 107 | py |
CaBERT-SLU | CaBERT-SLU-main/model/CHAN.py | import os.path
import math
import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from torch.nn import CosineEmbeddingLoss
ffscores = []
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
s... | 6,881 | 33.238806 | 104 | py |
CaBERT-SLU | CaBERT-SLU-main/model/baseline_eca.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from transformers import BertTokenizer, BertModel
class ECA(nn.Module):
def __init__(self, opt, num_labels=2, num_slot_labels=10):
super(ECA, self).__init__()
self.device = torch.d... | 4,601 | 37.033058 | 122 | py |
CaBERT-SLU | CaBERT-SLU-main/model/transformer.py | """Transformer module with masks"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer
class TransformerModel(nn.Module):
def __init__(self, ninp, nhead, nhid, nlayers, dropout=0.5):
super(TransformerModel, self).... | 1,979 | 36.358491 | 100 | py |
CaBERT-SLU | CaBERT-SLU-main/model/mia.py | import os.path
import math
import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from torch.nn import CosineEmbeddingLoss
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__... | 4,445 | 32.428571 | 103 | py |
CaBERT-SLU | CaBERT-SLU-main/model/__init__.py | from .bert_model_context import BertContextNLU
from .baseline_multi import MULTI
from .baseline_eca import ECA
| 111 | 27 | 46 | py |
CaBERT-SLU | CaBERT-SLU-main/model/torchcrf.py | """SOURCE CODE FROM PYTORCH-CRF
"""
from typing import List, Optional
import torch
import torch.nn as nn
class CRF(nn.Module):
"""Conditional random field.
This module implements a conditional random field [LMP01]_. The forward computation
of this class computes the log likelihood of the given sequence of... | 14,331 | 43.098462 | 95 | py |
CaBERT-SLU | CaBERT-SLU-main/model/bert_model_context.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from transformers import BertTokenizer, BertModel
from model.transformer import TransformerModel
from model.transformer_new import Transformer
from model.CHAN import ContextAttention
from model.torchcrf imp... | 8,750 | 41.072115 | 127 | py |
conversational-datasets | conversational-datasets-master/tools/tfrutil.py | # -*- coding: utf-8 -*-
"""Command line utilities for manipulating tfrecords files.
Usage:
To count the number of examples in a tfrecord file:
python tfrutil.py size train-00999-of-01000.tfrecords
To sample 10000 examples from a file pattern to an output file:
python tfrutil.py sample 10000 train-*-of-0100... | 4,832 | 28.469512 | 77 | py |
conversational-datasets | conversational-datasets-master/tools/__init__.py | 0 | 0 | 0 | py | |
conversational-datasets | conversational-datasets-master/reddit/create_data.py | """A Dataflow script for creating datasets from reddit.
For usage see README.md.
"""
import argparse
import hashlib
import json
import logging
import os
import re
import uuid
from collections import defaultdict, namedtuple
from functools import partial
import apache_beam as beam
import tensorflow as tf
from apache_... | 10,906 | 30.341954 | 79 | py |
conversational-datasets | conversational-datasets-master/reddit/__init__.py | 0 | 0 | 0 | py | |
conversational-datasets | conversational-datasets-master/reddit/create_data_test.py | """Tests for create_data.py."""
import copy
import json
import shutil
import tempfile
import unittest
from glob import glob
from os import path
import tensorflow as tf
from reddit import create_data
class CreateDataPipelineTest(unittest.TestCase):
"""Test running the pipeline end-to-end."""
def setUp(self... | 12,907 | 33.148148 | 79 | py |
conversational-datasets | conversational-datasets-master/amazon_qa/create_data.py | """A Dataflow script for creating Amazon question/answer data.
For usage see README.md.
"""
import argparse
import ast
import hashlib
import json
import logging
import os
import uuid
from functools import partial
import apache_beam as beam
import tensorflow as tf
from apache_beam import pvalue
from apache_beam.io.t... | 7,954 | 31.206478 | 79 | py |
conversational-datasets | conversational-datasets-master/amazon_qa/__init__.py | 0 | 0 | 0 | py | |
conversational-datasets | conversational-datasets-master/amazon_qa/create_data_test.py | """Tests for create_data.py."""
import json
import shutil
import tempfile
import unittest
from glob import glob
from os import path
import tensorflow as tf
from amazon_qa import create_data
_TEST_DATA = [
{
'question': "A A A",
'answer': "B B B",
'asin': "3", # gets put in test set.
... | 6,788 | 30.576744 | 79 | py |
conversational-datasets | conversational-datasets-master/baselines/run_baseline.py | """Evaluate baseline models on conversational datasets.
For usage see README.md.
"""
import argparse
import csv
import enum
import random
import glog
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from baselines import keyword_based, vector_based
def _parse_args():
"""Parse command-line args... | 9,760 | 36.398467 | 76 | py |
conversational-datasets | conversational-datasets-master/baselines/method.py | """Abstract class to define a baseline response selection method."""
import abc
class BaselineMethod(object):
"""Abstract class to define a baseline response selection method."""
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def train(self, examples):
"""Perform any training steps using th... | 1,289 | 33.864865 | 79 | py |
conversational-datasets | conversational-datasets-master/baselines/vector_based_test.py | """Tests for vector_based.py."""
import os
import tempfile
import unittest
import mock
import numpy as np
import tensorflow as tf
from mock import patch
from baselines import vector_based
class TfHubEncoderTest(unittest.TestCase):
@patch("tensorflow_hub.Module")
def test_encode_context(self, mock_module_cl... | 8,741 | 34.681633 | 79 | py |
conversational-datasets | conversational-datasets-master/baselines/keyword_based.py | """Baseline response ranking methods using keyword matching."""
import numpy as np
import scipy.sparse as sp
from sklearn.feature_extraction.text import (HashingVectorizer,
TfidfTransformer,
_document_frequency)
from sklearn.util... | 4,593 | 38.947826 | 89 | py |
conversational-datasets | conversational-datasets-master/baselines/vector_based.py | """Methods for conversational response ranking based on vector comparisons."""
import abc
import itertools
import shutil
import tempfile
import glog
import numpy as np
import tensorflow as tf
import tensorflow_hub
import tensorflow_text # NOQA: required for PolyAI encoders.
import tf_sentencepiece # NOQA: it is use... | 21,348 | 37.605787 | 79 | py |
conversational-datasets | conversational-datasets-master/baselines/__init__.py | 0 | 0 | 0 | py | |
conversational-datasets | conversational-datasets-master/baselines/keyword_based_test.py | """Tests for keyword_based.py."""
import unittest
from baselines import keyword_based
class TfIdfMethodTest(unittest.TestCase):
def test_train_test(self):
"""Check that it can correctly rank a simple example."""
method = keyword_based.TfIdfMethod()
method.train(
["hello how a... | 1,722 | 26.790323 | 69 | py |
conversational-datasets | conversational-datasets-master/opensubtitles/create_data.py | """A Dataflow script for creating sentence pair data from text files.
For usage see README.md.
"""
import argparse
import hashlib
import json
import logging
import os
import re
import uuid
from functools import partial
from os import path
import apache_beam as beam
import tensorflow as tf
from apache_beam import pv... | 8,838 | 31.141818 | 78 | py |
conversational-datasets | conversational-datasets-master/opensubtitles/__init__.py | 0 | 0 | 0 | py | |
conversational-datasets | conversational-datasets-master/opensubtitles/create_data_test.py | """Tests for create_data.py."""
import json
import shutil
import tempfile
import unittest
from glob import glob
from os import path
import tensorflow as tf
from opensubtitles import create_data
_TRAIN_FILE = "\n".join([
"matt: AAAA", # words followed by colons are stripped.
"[skip]", # text in brackets is... | 6,519 | 32.435897 | 76 | py |
mcbeth | mcbeth-master/mcbeth/python/setup.py | import os
from setuptools import find_packages, setup
name = 'mcl'
version = '0.1.0.dev'
description = 'A programming language for the measurement calculus'
long_description = open('README.md', encoding='utf-8').read()
reqs = open('requirements.txt').readlines()
requirements = [r.strip() for r in reqs]
packs = (['m... | 563 | 22.5 | 67 | py |
mcbeth | mcbeth-master/mcbeth/python/CirqBuilder.py | import cirq
import sys
import json
import numpy as np
class XBasisMeasurementSignal(cirq.SingleQubitGate):
"""Custom measurement signal to measure in the X-basis
with specified angle.
These will signal to the program where measurements need
to go at the end of the program. This method DOES NOT
handle any m... | 9,250 | 31.233449 | 92 | py |
mcbeth | mcbeth-master/mcbeth/python/ZXBuilder.py | import pyzx as zx
import sys
import json
"""NOTE: ZXBuilder class should be read-only now. Using Cirq
to handle all QASM input and output should suffice, and we can
read in that QASM input to build ZX diagrams.
"""
class ZXBuilder:
CMD_MAP = {
'Prep': zx.gates.HAD,
'XCorrect': zx.gates.NOT,
'ZCorrect': ... | 2,538 | 27.52809 | 84 | py |
mcbeth | mcbeth-master/mcbeth/python/mcl/__init__.py | import os
import sys
import shutil
# Rough way to add to the Python path.
# TODO: find a cleaner way to implement this. You have to switch
# directories currently to find it.
for src in ["ocaml.so", "../_build/default/python/ocaml.so"]:
if os.path.exists(src):
shutil.copyfile(src, "mcl/ocaml.so")
sys.path.append... | 757 | 30.583333 | 64 | py |
marmot | marmot-master/marmot/__init__.py | 0 | 0 | 0 | py | |
marmot | marmot-master/marmot/evaluation/evaluation_metrics.py | from __future__ import division
# return the f1 for (y_predicted, y_actual)
# use sklearn.metrics.f1_score with average='weighted' for evaluation
from sklearn.metrics import f1_score, accuracy_score
import logging
import numpy as np
from marmot.experiment.import_utils import list_of_lists
from marmot.experiment.prepr... | 11,031 | 39.410256 | 175 | py |
marmot | marmot-master/marmot/evaluation/evaluate.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function, division
import sys, codecs
import numpy as np
from sklearn import metrics
from marmot.evaluation.evaluation_metrics import weighted_fmeasure
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', lev... | 8,375 | 41.30303 | 142 | py |
marmot | marmot-master/marmot/evaluation/evaluate_task2_WMT2014.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, codecs
import numpy as np
issues_accuracy = ['Terminology', 'Mistranslation', 'Omission', 'Addition', 'Untranslated', 'Accuracy']
issues_fluency = ['Agreement', 'Capitalization', 'Fluency', 'Function_words',
'Grammar', 'Morphology_(wor... | 10,974 | 34.633117 | 128 | py |
marmot | marmot-master/marmot/evaluation/evaluate_task1_WMT2016.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
evaluate_wmt16_task1
~~~~~~~~~~~~~~~~~~~~
Evaluation script for Task 1 of the WMT Quality Estimation challenge.
:copyright: (c) 2016 by Fabio Kepler
:licence: MIT
Usage:
evaluate_wmt16_task1 [options] REFERENCE_FILE SUBMISSION_FILE...
evaluate_wmt16_task1 (-h | -... | 6,708 | 38.233918 | 144 | py |
marmot | marmot-master/marmot/evaluation/evaluation_utils.py | from __future__ import print_function, division
from sklearn.metrics import f1_score
def write_res_to_file(test_file, test_predictions, output_file=''):
if output_file == '':
output_file = test_file+'.predictions'
output = open(output_file, 'w')
try:
for idx, line in enumerate(open(test_... | 2,264 | 36.131148 | 104 | py |
marmot | marmot-master/marmot/evaluation/__init__.py | 0 | 0 | 0 | py | |
marmot | marmot-master/marmot/evaluation/check_error_types.py |
from __future__ import division, print_function
import codecs
import argparse
from itertools import groupby
def get_error_distribution(error_file):
with codecs.open(error_file, encoding='utf8') as tsv:
# remove newlines
row_data = [ l.rstrip().split('\t') for l in tsv ]
class_data = [ {'pr... | 1,124 | 42.269231 | 108 | py |
marmot | marmot-master/marmot/evaluation/tests/test_evaluate.py | # test the evaluation functions
import unittest
import os
from marmot.evaluation.evaluate import significance_test, get_scores, read_wmt_annotation, evaluate_wmt, evaluate_hashed_predictions
class TestEvaluate(unittest.TestCase):
def setUp(self):
module_path = os.path.dirname(__file__)
gold_stand... | 2,221 | 39.4 | 132 | py |
marmot | marmot-master/marmot/evaluation/tests/test_evaluation_utils.py | # test the evaluation functions
import unittest
import os
from marmot.evaluation.evaluation_utils import compare_vocabulary
class TestEvaluate(unittest.TestCase):
def test_compare_vocabulary(self):
dataset1 = [['this', 'is', 'sentence', 'number', 'one'], ['another', 'list', 'comes', 'next', '.']]
... | 794 | 32.125 | 109 | py |
marmot | marmot-master/marmot/evaluation/tests/test_evaluation_metrics.py | import unittest
from marmot.evaluation.evaluation_metrics import get_spans, intersect_spans, sequence_correlation
class TestEvaluationUtils(unittest.TestCase):
def setUp(self):
self.predictions = []
cur_pred = []
for line in open('test_data/hyp'):
if line.strip() == '':
... | 2,260 | 35.467742 | 118 | py |
marmot | marmot-master/marmot/features/target_token_left_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
class TargetTokenLeftFeatureExtractor(FeatureExtractor):
'''
Target features:
- target token
- left and right windows of the target token
'''
def __ini... | 697 | 30.727273 | 125 | py |
marmot | marmot-master/marmot/features/gaussian_feature_extractor.py | import numpy as np
import sys
from marmot.features.feature_extractor import FeatureExtractor
from scipy.stats import norm
#generate features from distributions of some existing set of examples
class GaussianFeatureExtractor(FeatureExtractor):
def __init__(self, features):
# the distribution for each feat... | 981 | 41.695652 | 140 | py |
marmot | marmot-master/marmot/features/google_translate_feature_extractor.py | from goslate import Goslate
from nltk import word_tokenize
import ipdb
from marmot.features.feature_extractor import FeatureExtractor
from marmot.exceptions.no_data_error import NoDataError
class GoogleTranslateFeatureExtractor(FeatureExtractor):
def __init__(self, lang='en'):
self.lang = lang
def ... | 893 | 29.827586 | 97 | py |
marmot | marmot-master/marmot/features/pos_feature_extractor.py | import sys
from subprocess import Popen, PIPE
from marmot.features.feature_extractor import FeatureExtractor
from marmot.exceptions.no_data_error import NoDataError
from marmot.exceptions.no_resource_error import NoResourceError
class POSFeatureExtractor(FeatureExtractor):
"""
POS for source and target words... | 2,564 | 40.370968 | 118 | py |
marmot | marmot-master/marmot/features/feature_extractor.py | # an abstract class representing a feature extractor
# a feature extractor takes an object like
# { 'token': <token>, index: <idx>, 'source': [<source toks>]', 'target': [<target toks>], 'tag': <tag>}
# and extracts features for that instance
# IMPORTANT - different feature extractors require different fields in the c... | 1,259 | 38.375 | 140 | py |
marmot | marmot-master/marmot/features/word2vec_feature_extractor.py | import numpy as np
import sys
import logging
from gensim.models import Word2Vec
from marmot.features.feature_extractor import FeatureExtractor
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger('experiment_logger')
def left_context(token_list, toke... | 4,647 | 41.642202 | 144 | py |
marmot | marmot-master/marmot/features/lm_left_feature_extractor.py | from __future__ import print_function
import codecs
from subprocess import call
import os
from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import extract_window, left_context, right_context
from marmot.experiment.import_utils imp... | 3,824 | 36.135922 | 139 | py |
marmot | marmot-master/marmot/features/tfidf_feature_extractor.py | # build a tfidf index in def __init__(corpus_file_name) | 55 | 55 | 55 | py |
marmot | marmot-master/marmot/features/token_count_feature_extractor.py | from __future__ import print_function, division
import numpy as np
from marmot.features.feature_extractor import FeatureExtractor
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger('testlogger')
class TokenCountFeatureExtractor(Featur... | 1,542 | 38.564103 | 118 | py |
marmot | marmot-master/marmot/features/previous_tag_feature_extractor.py | from __future__ import print_function
from marmot.features.feature_extractor import FeatureExtractor
from marmot.exceptions.no_data_error import NoDataError
class PreviousTagFeatureExtractor(FeatureExtractor):
'''
Extracts the tag of the previous token
'''
def __init__(self):
pass
def g... | 721 | 25.740741 | 90 | py |
marmot | marmot-master/marmot/features/pseudo_reference_feature_extractor.py | from nltk import word_tokenize
from marmot.features.feature_extractor import FeatureExtractor
from marmot.exceptions.no_data_error import NoDataError
class PseudoReferenceFeatureExtractor(FeatureExtractor):
'''
A feature that extracts the pseudo-reference feature
for pseudo-references provided in a file... | 953 | 33.071429 | 100 | py |
marmot | marmot-master/marmot/features/lm_feature_extractor.py | from __future__ import print_function
import codecs
from subprocess import call
import os
from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import extract_window, left_context, right_context
from marmot.experiment.import_utils imp... | 4,560 | 40.09009 | 150 | py |
marmot | marmot-master/marmot/features/dictionary_feature_extractor.py | import string
import nltk
from marmot.features.feature_extractor import FeatureExtractor
class DictionaryFeatureExtractor(FeatureExtractor):
'''
Extract binary features indicating that the word belongs to a list of special tokens:
- stopwords
- punctuation symbols
- proper names
- numbers
... | 1,159 | 37.666667 | 152 | py |
marmot | marmot-master/marmot/features/__init__.py | 0 | 0 | 0 | py | |
marmot | marmot-master/marmot/features/target_token_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
class TargetTokenFeatureExtractor(FeatureExtractor):
'''
Target features:
- target token
- left and right windows of the target token
'''
def __init__(... | 845 | 35.782609 | 127 | py |
marmot | marmot-master/marmot/features/syntactic_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
import sys
# get word's parent
def get_parent(dependencies, word_id, sentence_length, context_obj):
for head in dependencies:
if len(dependencies[head]) == 0:
continue
for dep in dependencies[head]:
if dep['id']... | 4,907 | 44.444444 | 168 | py |
marmot | marmot-master/marmot/features/source_word2vec_feature_extractor.py | import numpy as np
import sys
import logging
from gensim.models import Word2Vec
from marmot.features.feature_extractor import FeatureExtractor
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger('experiment_logger')
def left_context(token_list, toke... | 6,892 | 47.886525 | 152 | py |
marmot | marmot-master/marmot/features/source_lm_feature_extractor.py | import codecs
from subprocess import call
import os
from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
from marmot.experiment.import_utils import mk_tmp_dir
from marmot.exceptions.no_data_error im... | 3,413 | 40.634146 | 119 | py |
marmot | marmot-master/marmot/features/wordnet_feature_extractor.py | from __future__ import print_function
import sys
from nltk import wordnet as wn
from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
class WordnetFeatureExtractor(FeatureExtractor):
def __init__(self, src_lang, tg_lang, wordnet_pos=None):
# there's a bug in... | 2,330 | 42.166667 | 156 | py |
marmot | marmot-master/marmot/features/alignment_feature_extractor.py | from __future__ import print_function
import os
import sys
import errno
from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.alignments import train_alignments, align_sentence
from marmot.util.ngram_window_extractor import left_context, right_context
from marmot.exceptions.no_data_error imp... | 3,353 | 44.945205 | 162 | py |
marmot | marmot-master/marmot/features/unbabel/bias_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
class BiasFeatureExtractor(FeatureExtractor):
def get_features(self, context_obj):
return [1]
def get_feature_names(self):
return ['bias']
| 230 | 20 | 62 | py |
marmot | marmot-master/marmot/features/unbabel/paired_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
class PairedFeatureExtractor(FeatureExtractor):
'''
Paired features:
- target token + left context
- target token + right context
- target token + s... | 1,309 | 39.9375 | 111 | py |
marmot | marmot-master/marmot/features/unbabel/trilexical_left_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context
class TrilexicalLeftFeatureExtractor(FeatureExtractor):
'''
Trilexical features:
- target token + left context + source token
- target token + right context + source token... | 858 | 32.038462 | 109 | py |
marmot | marmot-master/marmot/features/unbabel/paired_left_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
class PairedLeftFeatureExtractor(FeatureExtractor):
'''
Paired features:
- target token + left context
- target token + right context
- target token... | 1,165 | 36.612903 | 109 | py |
marmot | marmot-master/marmot/features/unbabel/token_count_feature_extractor.py | from __future__ import print_function, division
import numpy as np
from marmot.features.feature_extractor import FeatureExtractor
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger('testlogger')
class TokenCountFeatureExtractor(Featur... | 1,542 | 38.564103 | 118 | py |
marmot | marmot-master/marmot/features/unbabel/trilexical_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
class TrilexicalFeatureExtractor(FeatureExtractor):
'''
Trilexical features:
- target token + left context + source token
- target token + right context + s... | 1,044 | 37.703704 | 111 | py |
marmot | marmot-master/marmot/features/unbabel/__init__.py | 0 | 0 | 0 | py | |
marmot | marmot-master/marmot/features/phrase/num_translations_feature_extractor.py | from __future__ import division
import sys
import numpy as np
from collections import defaultdict
from nltk import FreqDist
from gensim.corpora import TextCorpus
from marmot.features.feature_extractor import FeatureExtractor
class NumTranslationsFeatureExtractor(FeatureExtractor):
# .f2e file
def __init__(... | 2,228 | 41.865385 | 91 | py |
marmot | marmot-master/marmot/features/phrase/nouns_bigram_feature_extractor.py | from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
def get_nouns(language):
nouns = defaultdict(str)
nouns['english'] = ['NN']
nouns['spanish'] = ['NC', 'NMEA', 'NMON', 'NP']
return nouns[language]
class VerbsBigramFeatureExtractor(FeatureExtractor):
... | 1,434 | 28.285714 | 72 | py |
marmot | marmot-master/marmot/features/phrase/pos_context_left_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.exceptions.no_data_error import NoDataError
from marmot.util.ngram_window_extractor import left_context, right_context
class POSContextLeftFeatureExtractor(FeatureExtractor):
'''
same as POSContextFeatureExtractor, but without right co... | 1,329 | 50.153846 | 175 | py |
marmot | marmot-master/marmot/features/phrase/ngram_frequencies_feature_extractor.py | from __future__ import division
import os
import sys
from subprocess import call
from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
class NgramFrequenciesFeatureExtractor(FeatureExtractor):
def __init__(self, tmp_dir, ngram_count_file=None, corpus=None, srilm=None)... | 4,810 | 46.633663 | 181 | py |
marmot | marmot-master/marmot/features/phrase/pos_feature_extractor.py | from __future__ import division
from collections import defaultdict
import logging
from marmot.features.feature_extractor import FeatureExtractor
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger('testlogger')
# collections of content tags for so... | 6,043 | 44.104478 | 137 | py |
marmot | marmot-master/marmot/features/phrase/context_left_feature_extractor.py | import sys
from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
class ContextLeftFeatureExtractor(FeatureExtractor):
'''
Same as ContextFeatureExtractor, but without right target context
'''
def get_features(self, co... | 1,163 | 43.769231 | 147 | py |
marmot | marmot-master/marmot/features/phrase/oov_feature_extractor.py | import sys
from marmot.features.feature_extractor import FeatureExtractor
from gensim.corpora import TextCorpus
class OOVFeatureExtractor(FeatureExtractor):
'''
Feature that indicates presence of OOV words in the source phrase.
Values:
0 -- no OOV words
1 -- 1 or more OOV words
'''
... | 1,079 | 29.857143 | 86 | py |
marmot | marmot-master/marmot/features/phrase/context_lm_left_feature_extractor.py | import os
import sys
import codecs
from subprocess import call
from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
from marmot.experiment.import_utils import mk_tmp_dir
class ContextLMLeftFeatureE... | 3,745 | 35.368932 | 150 | py |
marmot | marmot-master/marmot/features/phrase/context_lm_feature_extractor.py | import os
import codecs
from subprocess import call
from collections import defaultdict
from marmot.features.feature_extractor import FeatureExtractor
from marmot.util.ngram_window_extractor import left_context, right_context
from marmot.experiment.import_utils import mk_tmp_dir
class ContextLMFeatureExtractor(Featur... | 4,057 | 38.398058 | 155 | py |
marmot | marmot-master/marmot/features/phrase/ne_feature_extractor.py | import sys
from marmot.features.feature_extractor import FeatureExtractor
class NEFeatureExtractor(FeatureExtractor):
'''
Presence/absence of named entities in source and target phrases.
Named entity = word with 1st capital letter
'''
def get_features(self, context_obj):
#sys.stderr.write... | 786 | 30.48 | 68 | py |
marmot | marmot-master/marmot/features/phrase/pos_context_feature_extractor.py | from marmot.features.feature_extractor import FeatureExtractor
from marmot.exceptions.no_data_error import NoDataError
from marmot.util.ngram_window_extractor import left_context, right_context
class POSContextFeatureExtractor(FeatureExtractor):
def get_features(self, context_obj):
if 'target_pos' not in... | 1,445 | 59.25 | 175 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.