python_code stringlengths 0 187k | repo_name stringlengths 8 46 | file_path stringlengths 6 135 |
|---|---|---|
dnw-master | data/__init__.py | |
import os
import torch
import torchvision
from torchvision import transforms
from genutil.config import FLAGS
class CIFAR10:
def __init__(self):
super(CIFAR10, self).__init__()
data_root = os.path.join(FLAGS.data_dir, "cifar10")
use_cuda = torch.cuda.is_available()
# Data loadi... | dnw-master | data/cifar10.py |
import os
import json
def load_aokvqa(aokvqa_dir, split, version='v1p0'):
assert split in ['train', 'val', 'test', 'test_w_ans']
dataset = json.load(open(
os.path.join(aokvqa_dir, f"aokvqa_{version}_{split}.json")
))
return dataset
def get_coco_path(split, image_id, coco_dir):
return os.p... | aokvqa-main | load_aokvqa.py |
import os
import json
import argparse
import pathlib
from collections import Counter
from load_aokvqa import load_aokvqa
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'... | aokvqa-main | heuristics/most_common_answer.py |
import os
import json
from random import seed, sample
import argparse
import pathlib
from load_aokvqa import load_aokvqa
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'... | aokvqa-main | heuristics/random_unweighted.py |
import os
import json
import numpy as np
import argparse
import pathlib
from collections import Counter
from load_aokvqa import load_aokvqa
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--split', type=str, choices=['tr... | aokvqa-main | heuristics/random_weighted.py |
import os
import argparse
from collections import Counter
import pathlib
from load_aokvqa import load_aokvqa
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--out', type=pathlib.Path, required=True, dest='output_file')
a... | aokvqa-main | data_scripts/build_vocab.py |
import os
import argparse
import pathlib
from tqdm import tqdm
from PIL import Image
import torch
import torch.nn as nn
from torchvision import models
from torchvision import transforms as T
from load_aokvqa import load_aokvqa, get_coco_path
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', typ... | aokvqa-main | data_scripts/extract_resnet_features.py |
import os
import argparse
import pathlib
from tqdm import tqdm
import torch
from transformers import AutoTokenizer, AutoModel
from load_aokvqa import load_aokvqa
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--split',... | aokvqa-main | data_scripts/extract_bert_features.py |
import json
from tqdm import tqdm
import argparse
import pathlib
import torch
import clip
parser = argparse.ArgumentParser()
parser.add_argument('--vocab', type=pathlib.Path, required=True, dest='vocab_file')
parser.add_argument('--model-type', type=str, choices=['RN50', 'RN50x4', 'RN50x16', 'RN50x64', 'RN101', 'ViT-... | aokvqa-main | data_scripts/encode_vocab_clip.py |
import os
from PIL import Image
from tqdm import tqdm
import argparse
import pathlib
import torch
import clip
from load_aokvqa import load_aokvqa, get_coco_path
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--coco-dir... | aokvqa-main | data_scripts/extract_clip_features.py |
import argparse
import pathlib
import json
from load_aokvqa import load_aokvqa
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], ... | aokvqa-main | evaluation/prepare_predictions.py |
import argparse
import pathlib
import json
import glob
from load_aokvqa import load_aokvqa
def eval_aokvqa(dataset, preds, multiple_choice=False, strict=True):
if isinstance(dataset, list):
dataset = { dataset[i]['question_id'] : dataset[i] for i in range(len(dataset)) }
if multiple_choice is False... | aokvqa-main | evaluation/eval_predictions.py |
import argparse
import pathlib
import json
from tqdm import tqdm
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
from load_aokvqa import load_aokvqa
def map_to_choices(dataset, predictions, device='cpu'):
if isinstance(dataset, list):
dataset = { data... | aokvqa-main | evaluation/remap_predictions.py |
import os
import json
import argparse
import pathlib
from load_aokvqa import load_aokvqa
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--coco-dir', type=pathlib.Path, required=True, dest='coco_dir')
parser.add_argument... | aokvqa-main | gpt3/caption_inputs.py |
import os
import random
import json
from tqdm import tqdm
import argparse
import pathlib
import openai
openai.organization = os.getenv('OPENAI_ORG')
openai.api_key = os.getenv('OPENAI_API_KEY')
from load_aokvqa import load_aokvqa
random.seed(0)
def main():
parser = argparse.ArgumentParser()
parser.add_arg... | aokvqa-main | gpt3/query_gpt3.py |
import json
import argparse
import pathlib
from load_aokvqa import load_aokvqa
parser = argparse.ArgumentParser()
parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
parser.add_argument('--split', type=str, choices=['train', 'val', 'test_w_ans'], required=True)
parser.add_argumen... | aokvqa-main | gpt3/rationale_inputs.py |
import sys
import os
import argparse
import pathlib
from tqdm import tqdm
import json
import torch
import torch.nn as nn
# https://github.com/PyTorchLightning/pytorch-lightning/issues/11663
import sentencepiece; import pytorch_lightning as pl; import clip
from transfer_experiments.train import LinearClassifier
from ... | aokvqa-main | transfer_experiments/predict.py |
import os
import sys
import json
import argparse
import pathlib
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
# https://github.com/PyTorchLightning/pytorch-lightning/issues/11663
import sentencepiece; import pytorch_lightning as pl
i... | aokvqa-main | transfer_experiments/train.py |
from setuptools import setup, find_packages
def parse_requirements_file(path):
requirements = []
with open(path) as requirements_file:
import re
def fix_url_dependencies(req: str) -> str:
"""Pip and setuptools disagree about how URL dependencies should be handled."""
m... | bettermap-master | setup.py |
import bettermap
def f(x: float) -> float:
return x * x
_INPUT = list(range(100))
_EXPECTED = list(map(f, _INPUT))
def test_map_per_process():
result = list(bettermap.map_per_process(f, _INPUT))
result.sort()
assert result == _EXPECTED
def test_ordered_map_per_process():
result = list(better... | bettermap-master | tests/test_basic_functionality.py |
bettermap-master | tests/__init__.py | |
from .bettermap import *
| bettermap-master | bettermap/__init__.py |
#!/usr/bin/python3
import io
import sys
from concurrent.futures import ThreadPoolExecutor
import itertools
import multiprocessing as mp
from multiprocessing.connection import Connection
from multiprocessing.context import ForkProcess
from typing import Iterable, List, Optional, Any, Dict, Tuple
import dill
from queu... | bettermap-master | bettermap/bettermap.py |
from os import mkdir
from os.path import join, dirname, expanduser, exists
DATA_DIR = expanduser("~/data-dbg")
COCO_SOURCE = join(DATA_DIR, "coco")
COCO_ANNOTATIONS = join(COCO_SOURCE, "annotations")
COCO_IMAGES = join(COCO_SOURCE, "images")
VQAE = join(DATA_DIR, "vqa-e")
VISUAL_NEWS = join(DATA_DIR, "visual_news/or... | close-main | close/file_paths.py |
import gzip
import logging
import tarfile
import tempfile
import zipfile
from os import listdir, makedirs
from os.path import dirname, exists, join
import requests
from tqdm import tqdm
from close import file_paths
from close.utils import py_utils
def ensure_dir_exists(filename):
"""Make sure the parent directory... | close-main | close/download.py |
import argparse
import logging
import os
from transformers import AutoConfig
from l2v.data.visual_news import VisualNews
from l2v.experiments.utils import get_adapter
from l2v.train.optimizer import AdamWBuilder, DelayedWarmupScheduleBuilder
from l2v.train.trainer import TrainerSimple
from l2v.utils import py_utils
... | close-main | close/experiments/train_visual_news.py |
import os
from typing import Union
from close.data.coco_captioning import CocoCaptioningKP
from close.data.dataset import Dataset
from close.data.vqa_e import EVQA
from close.data.vqa_v2 import Vqa2, VqaWithCaptions
from close.data.visual_entailment import VisualEntailment
from close.model.language_adapters import *
f... | close-main | close/experiments/utils.py |
import argparse
import logging
import os
import sys
root_folder = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
sys.path.append(root_folder)
from close.data.coco_captioning import CocoCaptioningKP
from close.data.visual_entailment import VisualEntailment
from close.data... | close-main | close/experiments/train.py |
import json
import nltk
import openai
import random
import numpy as np
from collections import OrderedDict
from tqdm import tqdm
openai.api_key = "YOUR_OPENAI_KEY"
harry_potter_characters = [
"Sirius Black",
"Cho Chang",
"Aberforth Dumbledore",
"Albus Dumbledore",
"Hermione Granger",
"Fenrir G... | close-main | close/experiments/generate_stylistic_captioning.py |
import argparse
import json
import logging
import os
from typing import Union
import numpy as np
import sys
root_folder = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
sys.path.append(root_folder)
from close.data.coco_captioning import CocoCaptioningKP
from close.dat... | close-main | close/experiments/eval.py |
from os import listdir
from os.path import join
from close import file_paths
_IMAGE_ID_TO_SIZE_MAP = {}
IMAGE_SOURCE_MAP = {
"coco": file_paths.COCO_IMAGES,
"flicker30k": file_paths.FLICKER30K,
"visual_news": file_paths.VISUAL_NEWS,
}
def get_image_file(image_id) -> str:
"""Returns the filepath of an image... | close-main | close/utils/image_utils.py |
import logging
from typing import Union
import torch
from torch import nn
def get_device(device_name: Union[None, str, int]=None):
if device_name is None:
if torch.cuda.is_available():
logging.info("cuda found, defaulting to cuda device")
return torch.device('cuda')
else:
logging.info("cu... | close-main | close/utils/pytorch_utils.py |
"""Code from GPV-2 for saving FromParams objects to disk, used for model/trainer saving
AllenNLP recently added their own to_params approach, but there default implementation
does not work for some of our models so we stick with the GPV-2 version.
"""
import enum
import typing
from collections import OrderedDict
from... | close-main | close/utils/to_params.py |
import json
import logging
import pickle
import sys
from collections import defaultdict
from json import JSONEncoder
from os import listdir, remove, walk, makedirs
from os.path import exists, join, isdir, basename, dirname, split, relpath
from shutil import rmtree
from typing import TypeVar, List, Iterable, Dict, Any, ... | close-main | close/utils/py_utils.py |
#!/usr/bin/env python
#
# File Name : ptbtokenizer.py
#
# Description : Do the PTB Tokenization and remove punctuations.
#
# Creation Date : 29-12-2014
# Last Modified : Thu Mar 19 09:53:35 2015
# Authors : Hao Fang <hfang@uw.edu> and Tsung-Yi Lin <tl483@cornell.edu>
# Modified to silence the stederr output
import os... | close-main | close/utils/quiet_ptbtokenizer.py |
import logging
from os import listdir
from os.path import dirname, join, exists
import torch
from allennlp.common import Params
from close.model.model import Model, BEST_STATE_NAME
from close.utils import py_utils
from close.utils.py_utils import load_json_object, import_all, select_run_dir
def load_model(run_dir, ... | close-main | close/model/load_model.py |
import logging
from collections import Counter
from dataclasses import dataclass, field, replace
from typing import Any, Callable, List, Dict, Tuple, Union, Optional
import numpy as np
import clip
import torch
from PIL import Image
from allennlp.common import Registrable, Params
from torch import nn
from transformers ... | close-main | close/model/clip_t5_model.py |
import pickle
from os.path import join, dirname
import torch
from allennlp.common import Params
from torch.distributions import multivariate_normal
from close import file_paths
from close.model.layers import Layer
from close.utils.py_utils import load_json_object
import numpy as np
from close.utils.pytorch_utils imp... | close-main | close/model/language_adapters.py |
from typing import Union, Optional, List, Callable, Any, Dict, Tuple
import torch
from allennlp.common import Registrable, FromParams
from allennlp.nn.beam_search import BeamSearch
from dataclasses import dataclass
from torch import nn
BEST_STATE_NAME = "best-state.pth"
@dataclass
class ExampleOutput:
text: Lis... | close-main | close/model/model.py |
from os.path import join, dirname
from typing import List, Dict, Any
import torch
from allennlp.common import Registrable, FromParams
from torch import nn
from close.utils import pytorch_utils
from close.utils.py_utils import load_json_object
from close.utils.to_params import to_params
class Layer(nn.Module, Regist... | close-main | close/model/layers.py |
import json
from collections import Callable
from dataclasses import dataclass
from typing import Dict, Union
import torch
from allennlp.common import FromParams, Params
from torch.utils.data import DataLoader
from tqdm import tqdm
from close.model.load_model import load_model
from close.model.model import ExampleOut... | close-main | close/train/runner.py |
import torch
from typing import Dict, Tuple, List, Optional, Any, Union
from allennlp.common import Registrable
from dataclasses import dataclass
from torch.optim import AdamW, SGD, Optimizer
class OptimizerBuilder(Registrable):
"""Builds an Optimizer
We use this class rather then using an Optimizer directly si... | close-main | close/train/optimizer.py |
import json
import logging
import os
import socket
from datetime import datetime
from os import makedirs
from os.path import join, exists
from time import perf_counter
from typing import List, Optional, Dict, Union
import numpy as np
import torch
from allennlp.common import FromParams, Params
from dataclasses import d... | close-main | close/train/trainer.py |
import re
from collections import defaultdict, Counter
from numbers import Number
from typing import Optional, List, Dict, Any
import numpy as np
from allennlp.common import FromParams, Registrable, Params
from dataclasses import dataclass, replace
from pycocoevalcap.bleu.bleu import Bleu
from pycocoevalcap.cider.ci... | close-main | close/train/evaluator.py |
"""Provides functions to use T5 in allennlp's BeamSearch
We use this instead of transformer's beam search mostly for legacy reasons since that is
what the GPV-2 models used
"""
import torch
from torch.nn import functional as F
from close.utils import py_utils
def t5_initialize_decoding(tokenizer, model, encoder_ou... | close-main | close/train/allennlp_beamsearch.py |
import json
from datetime import datetime
from os.path import isdir
from typing import Dict, Any, Union
from close.model.model import ExampleOutput
from close.train.evaluator import Evaluator
from close.utils import py_utils
from close.utils.py_utils import load_json_object, dump_json_object
from close.utils.to_params... | close-main | close/eval/evaluation.py |
import argparse
import json
import logging
import os
from typing import Union
import numpy as np
from l2v.data.coco_captioning import CocoCaptioning, CocoSCE
from l2v.data.dataset import Dataset
from l2v.data.visual_news import VisualNews
from l2v.data.vqa_e import EVQA
from l2v.data.vqa_v2 import Vqa2
from l2v.eval.... | close-main | close/eval/compute_predictions.py |
import re
"""VQA evaluation copied from the offical VQA 2.0 eval script"""
contractions = {
"aint": "ain't", "arent": "aren't", "cant": "can't", "couldve": "could've", "couldnt": "couldn't", \
"couldn'tve": "couldn't've", "couldnt've": "couldn't've", "didnt": "didn't", "doesnt": "doesn't", "dont": "don't", "hadn... | close-main | close/eval/vqa_eval.py |
from dataclasses import dataclass, replace
from os.path import join
from typing import List, Optional, Union
import numpy as np
from collections import Counter
from close import file_paths
from close.data.coco_captioning import CocoCaptioning2014
from close.data.dataset import Dataset
from close.utils import image_ut... | close-main | close/data/vqa_v2.py |
from dataclasses import dataclass
from os.path import isfile, join
from typing import List
import numpy as np
from close import file_paths
from close.data.dataset import Dataset
from close.utils.py_utils import int_to_str, load_json_object
@dataclass
class VisualNewsExample:
example_id: str
caption: str
image_... | close-main | close/data/visual_news.py |
from typing import List
from allennlp.common import Registrable
class Dataset(Registrable):
"""Dataset we can train/evaluate on"""
def get_name(self) -> str:
"""Get the name of the dataset that uniquely identifies it"""
raise NotImplementedError()
def load(self) -> List:
"""Loads the examples"""
... | close-main | close/data/dataset.py |
import json
import logging
from collections import defaultdict
from dataclasses import dataclass
from os.path import join
from typing import Optional, Dict, Any, List
from close import file_paths
from close.data.dataset import Dataset
from close.utils import image_utils, py_utils
from close.utils.py_utils import int_t... | close-main | close/data/coco_captioning.py |
import json
from dataclasses import dataclass
from os.path import join
from typing import Dict, List, Any, Optional
from close import file_paths
from close.data.dataset import Dataset
from close.utils import py_utils
from close.utils.py_utils import int_to_str
@dataclass
class VisualEntailmentExample:
example_id: ... | close-main | close/data/visual_entailment.py |
import json
import logging
from collections import Counter
from os.path import join
from typing import List
from close import file_paths
from close.data.dataset import Dataset
from close.data.vqa_v2 import VqaExample
from close.utils import py_utils
from close.utils.image_utils import get_coco_image_id
from close.util... | close-main | close/data/vqa_e.py |
import json
import re
def main():
# todo: @@SEP@@ to ; , @@#@@ to #
predictions_file = "old_data_dev_low_level_preds.json"
traget_file= predictions_file.replace('.json', '.csv')
with open(predictions_file, "r") as fd:
preds = [json.loads(line) for line in fd.readlines()]
preds = [re.sub(r'@... | break-evaluator-master | allennlp_preds_format.py |
import networkx as nx
from queue import Queue, deque
def has_cycle(graph: nx.DiGraph):
try:
nx.find_cycle(graph, orientation='original')
return True
except:
return False
def get_graph_levels(graph: nx.DiGraph):
"""
Find graph level for each node
level[node] := 0 if the no... | break-evaluator-master | utils/graph.py |
from __future__ import print_function
import sys
import threading
try:
import thread
except ImportError:
import _thread as thread
def quit_function(fn_name):
print('{0} took too long'.format(fn_name), file=sys.stderr)
sys.stderr.flush()
# raises KeyboardInterrupt
thread.interrupt_main()
... | break-evaluator-master | utils/timeout.py |
from time import sleep
from utils.timeout import exit_after
@exit_after(5)
def countdown(n):
print('countdown started', flush=True)
for i in range(n, -1, -1):
print(i, end=', ', flush=True)
sleep(1)
print('countdown finished')
if __name__ == "__main__":
try:
countdown(10)
... | break-evaluator-master | utils/timeout_test.py |
from typing import Dict, Tuple
import numbers
from itertools import zip_longest
import argparse
import os
import random
import re
import numpy as np
import pandas as pd
import json
from evaluation.decomposition import Decomposition
from evaluation.graph_matcher import GraphMatchScorer, get_ged_plus_scores
from evalu... | break-evaluator-master | scripts/evaluate_predictions.py |
from pathlib import Path
import os
import argparse
import traceback
import pandas as pd
import re
from enum import Enum
DELIMITER = ';'
REF = '#'
pd.set_option('display.max_colwidth', -1)
def parse_decomposition(qdmr):
"""Parses the decomposition into an ordered list of steps
Parameters
----------
qdmr : st... | break-evaluator-master | scripts/qdmr_to_program.py |
from evaluation.decomposition import Decomposition, draw_decomposition_graph
from evaluation.graph_matcher import AStarSearcher
examples = [
# 0
(Decomposition(["representatives from New York state or Indiana state",
"the life spans of @@1@@"]),
Decomposition(["representatives from n... | break-evaluator-master | evaluation/graph_matcher_tests.py |
import heapq
import networkx as nx
import networkx.algorithms.isomorphism as iso
import numpy as np
from itertools import chain, combinations, permutations
from multiprocessing import Pool
from progressbar import ProgressBar, SimpleProgress
from tqdm import tqdm
from evaluation.sequence_matcher import SequenceMatch... | break-evaluator-master | evaluation/graph_matcher.py |
import matplotlib.pyplot as plt
import networkx as nx
import re
from utils.graph import get_graph_levels
class Decomposition(object):
def __init__(self, decomposition_list):
self.decomposition_list = [str(step) for step in decomposition_list]
def _get_graph_edges(self):
edges = []
fo... | break-evaluator-master | evaluation/decomposition.py |
# coding=utf-8
# Copyright 2019 The Tensor2Tensor 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 applicable... | break-evaluator-master | evaluation/sari_hook.py |
import spacy
from edit_distance import SequenceMatcher
from tqdm import tqdm
class SequenceMatchScorer(object):
def __init__(self, remove_stop_words):
self.parser = spacy.load('en_core_web_sm', disable=['ner'])
self.remove_stop_words = remove_stop_words
# TODO: extend the default stop wo... | break-evaluator-master | evaluation/sequence_matcher.py |
from __future__ import annotations
from typing import Callable
from abc import ABC, abstractmethod
import os
import re
import networkx as nx
import spacy
from spacy.tokens.token import Token
import _pickle as pk
import logging
from evaluation.decomposition import Decomposition
_logger = logging.getLogger(__name__)
... | break-evaluator-master | evaluation/normal_form/normalization_rules.py |
from overrides import overrides
import networkx as nx
from queue import Queue, deque
import logging
import re
import spacy
from evaluation.decomposition import Decomposition, draw_decomposition_graph
from utils.graph import get_graph_levels
from evaluation.normal_form.normalization_rules import prepare_node
import ... | break-evaluator-master | evaluation/normal_form/normalized_graph_matcher.py |
from abc import ABC
import logging
import networkx as nx
from spacy.tokens.token import Token
from scripts.qdmr_to_program import QDMROperation
import scripts.qdmr_to_program as qdmr
from evaluation.normal_form.normalization_rules import DecomposeRule, ReferenceToken, run_tests
_logger = logging.getLogger(__name__)
... | break-evaluator-master | evaluation/normal_form/operations_normalization_rules.py |
#!/usr/bin/env python
import logging
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(os.path.join(__file__, os.pardir))))
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
level=logging.INFO)
from arc_solvers.commands import main # pylint: ... | ARC-Solvers-main | arc_solvers/run.py |
ARC-Solvers-main | arc_solvers/__init__.py | |
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.nn.util import replace_masked_values
from allennlp.nn.util import get_text_field_mask
import allennlp
from typing import Union, Dict
import torch
from allennlp.modules import MatrixAttention, Seq2SeqEncoder
def masked_mean(tensor, dim, m... | ARC-Solvers-main | arc_solvers/nn/util.py |
ARC-Solvers-main | arc_solvers/nn/__init__.py | |
"""
Script to compute the QA score from the entailment predictions for each supporting sentence and
answer choice.
USAGE:
python scripts/evaluate_predictions.py predictions_file qa_file output_file
Minimal expected format of files.
1. predictions_file:
{"id": "Mercury_SC_415702",
"question": {
"choice": {... | ARC-Solvers-main | arc_solvers/processing/evaluate_predictions.py |
from typing import Dict, List
from elasticsearch import Elasticsearch
import re
class EsHit:
def __init__(self, score: float, position: int, text: str, type: str):
"""
Basic information about an ElasticSearch Hit
:param score: score returned by the query
:param position: position ... | ARC-Solvers-main | arc_solvers/processing/es_search.py |
"""
Script to convert the retrieved HITS into an entailment dataset
USAGE:
python scripts/convert_to_entailment.py hits_file output_file
JSONL format of files
1. hits_file:
{
"id": "Mercury_SC_415702",
"question": {
"stem": "George wants to warm his hands quickly by rubbing them. Which skin surface will... | ARC-Solvers-main | arc_solvers/processing/convert_to_entailment.py |
"""
Script to compute the QA score from the scores per choice
USAGE:
python scripts/calculate_scores.py predictions_file
Minimal expected format of predictions_file:
{
"question": {
"stem":"George wants to warm his hands quickly by rubbing them. Which skin surface will
produce the most he... | ARC-Solvers-main | arc_solvers/processing/calculate_scores.py |
"""
Script to convert the retrieved hits into a paragraph comprehension dataset. Questions with no
hits are mapped to a blank paragraph.
USAGE:
python scripts/convert_to_para_comprehension.py hits_file qa_file output_file
JSONL format of files
1. hits_file:
{
"id": "Mercury_SC_415702",
"question": {
"st... | ARC-Solvers-main | arc_solvers/processing/convert_to_para_comprehension.py |
ARC-Solvers-main | arc_solvers/processing/__init__.py | |
"""
Script to retrieve HITS for each answer choice and question
USAGE:
python scripts/add_retrieved_text.py qa_file output_file
JSONL format of files
1. qa_file:
{
"id":"Mercury_SC_415702",
"question": {
"stem":"George wants to warm his hands quickly by rubbing them. Which skin surface will
... | ARC-Solvers-main | arc_solvers/processing/add_retrieved_text.py |
from arc_solvers.models.entailment.tree_attention import TreeAttention
from arc_solvers.models.qa.multi_choice.qa_multi_choice_max_att import QAMultiChoiceMaxAttention | ARC-Solvers-main | arc_solvers/models/__init__.py |
ARC-Solvers-main | arc_solvers/models/entailment/__init__.py | |
"""
=====================================================================
Decomposable Graph Entailment Model code replicated from SciTail repo
https://github.com/allenai/scitail
=====================================================================
"""
from typing import Dict, List, Any, Tuple
import numpy
import tor... | ARC-Solvers-main | arc_solvers/models/entailment/tree_attention.py |
ARC-Solvers-main | arc_solvers/models/qa/__init__.py | |
ARC-Solvers-main | arc_solvers/models/qa/multi_choice/__init__.py | |
from allennlp.modules.matrix_attention import MatrixAttention
from typing import Dict, Optional, AnyStr, List, Any
import torch
from allennlp.common import Params
from allennlp.common.checks import ConfigurationError
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules imp... | ARC-Solvers-main | arc_solvers/models/qa/multi_choice/qa_multi_choice_max_att.py |
ARC-Solvers-main | arc_solvers/training_config/qa/multi_choice/__init__.py | |
from allennlp.commands import main as main_allennlp
def main(prog: str = None) -> None:
predictor_overrides = {
"decomposable_attention": "decompatt",
"tree_attention": "dgem",
"bidaf": "bidaf_qa"
}
main_allennlp(prog,
predictor_overrides=predictor_overrides)
| ARC-Solvers-main | arc_solvers/commands/__init__.py |
ARC-Solvers-main | arc_solvers/service/__init__.py | |
import logging
from allennlp.common.util import JsonDict, sanitize
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.instance import Instance
from allennlp.models.model import Model
from allennlp.service.predictors.predictor import Predictor
from overrides import overrides
logg... | ARC-Solvers-main | arc_solvers/service/predictors/dgem_predictor.py |
import logging
from operator import itemgetter
from typing import List
from allennlp.common.util import JsonDict, sanitize
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.instance import Instance
from allennlp.models.model import Model
from allennlp.service.predictors.predicto... | ARC-Solvers-main | arc_solvers/service/predictors/bidaf_qa_predictor.py |
from arc_solvers.service.predictors.decompatt_qa_predictor import DecompAttPredictor
from arc_solvers.service.predictors.dgem_predictor import DgemPredictor
from arc_solvers.service.predictors.bidaf_qa_predictor import BidafQaPredictor
| ARC-Solvers-main | arc_solvers/service/predictors/__init__.py |
import logging
from allennlp.common.util import JsonDict, sanitize
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.instance import Instance
from allennlp.models.model import Model
from allennlp.service.predictors.predictor import Predictor
from overrides import overrides
logg... | ARC-Solvers-main | arc_solvers/service/predictors/decompatt_qa_predictor.py |
from arc_solvers.modules.single_time_distributed import SingleTimeDistributed
| ARC-Solvers-main | arc_solvers/modules/__init__.py |
"""
=====================================================================
Decomposable Graph Entailment Model code replicated from SciTail repo
https://github.com/allenai/scitail
=====================================================================
"""
import torch
class SingleTimeDistributed(torch.nn.Module):
"... | ARC-Solvers-main | arc_solvers/modules/single_time_distributed.py |
from arc_solvers.data.dataset_readers.arc_multichoice_json_reader import ArcMultiChoiceJsonReader | ARC-Solvers-main | arc_solvers/data/__init__.py |
"""
=====================================================================
Decomposable Graph Entailment Model code replicated from SciTail repo
https://github.com/allenai/scitail
=====================================================================
"""
import logging
from builtins import ValueError
from typing import ... | ARC-Solvers-main | arc_solvers/data/dataset_readers/entailment_tuple_reader.py |
from typing import Dict, List, Any
import json
import logging
from allennlp.data import Dataset
from overrides import overrides
from allennlp.common import Params
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import ... | ARC-Solvers-main | arc_solvers/data/dataset_readers/arc_multichoice_json_reader.py |
from arc_solvers.data.dataset_readers.entailment_tuple_reader import EntailmentTupleReader
| ARC-Solvers-main | arc_solvers/data/dataset_readers/__init__.py |
#!/usr/bin/python3
# This script uses the Python Elasticsearch API to index a user-specified text corpus in an
# ElasticSearch cluster. The corpus is expected to be a text file with a sentence per line.
# Each sentence is indexed as a separate document, and per the mappings defined here, the
# Snowball Stemmer is used... | ARC-Solvers-main | scripts/index-corpus.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.