python_code stringlengths 0 187k | repo_name stringlengths 8 46 | file_path stringlengths 6 135 |
|---|---|---|
print(
'ERROR: stitch_wrapper not yet compiled. Please run `cd /path/to/tensorbox/utils && make`'
)
| deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/stitch_wrapper.py |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: AnnoList.proto
import sys
_b = sys.version_info[0] < 3 and (lambda x: x
) or (lambda x: x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from go... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/AnnoList_pb2.py |
import os
import sys
import string
import matplotlib
matplotlib.use('Agg')
from pylab import *
import numpy as np
class MatPlotter:
fontsize = 15
color = 0
colors = ["r-", "b-", "k-", "c-", "m-", "y-"]
colors += [x + "-" for x in colors]
colors += ["g-", "g--"]
curFigure = []
legendNames =... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/MatPlotter.py |
deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/__init__.py | |
#!/usr/bin/env python
import sys
import os
import random
import re
from AnnotationLib import *
from MatPlotter import *
from optparse import OptionParser
from copy import deepcopy
from math import sqrt
def main(argv):
parser = OptionParser(usage="usage: %prog [options] <datafile> [...]")
parser.add_option(
... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/plotSimple.py |
import os
from math import sqrt
import gzip
import json
import bz2
import numpy as np
from collections import MutableSequence
#import AnnoList_pb2
from . import PalLib
import xml.dom.minidom
xml_dom_ext_available = False
try:
import xml.dom.ext
xml_dom_ext_available = True
except ImportError:
pass
###... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/AnnotationLib.py |
#import AnnoList_pb2
from . import AnnotationLib
def loadPal(filename):
_annolist = AnnoList_pb2.AnnoList()
f = open(filename, "rb")
_annolist.ParseFromString(f.read())
f.close()
return _annolist
def savePal(filename, _annolist):
f = open(filename, "wb")
f.write(_annolist.SerializeToS... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/PalLib.py |
def is_number(s):
try:
float(s)
return True
except ValueError:
return False
| deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/ma_utils.py |
#!/usr/bin/env python
import os, sys
from AnnotationLib import *
from optparse import OptionParser
import copy
import math
# BASED ON WIKIPEDIA VERSION
# n - number of nodes
# C - capacity matrix
# F - flow matrix
# s - source
# t - sink
# sumC - sum over rows of C (too speed up computation)
def edmonds_karp(n, C, ... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/annolist/doRPC.py |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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 ... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/slim_nets/resnet_v1.py |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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 ... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/slim_nets/inception_v1.py |
deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/slim_nets/__init__.py | |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# 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 applica... | deepfigures-open-master | vendor/tensorboxresnet/tensorboxresnet/utils/slim_nets/resnet_utils.py |
from setuptools import setup, find_packages
setup(
name="allennlp_beaker",
version="0.0.1",
description=(
"An interactive AllenNLP plugin for submitting training jobs to beaker"
),
long_description=open("README.md").read(),
long_description_content_type="text/markdown",
classifiers=... | allennlp-beaker-master | setup.py |
allennlp-beaker-master | allennlp_beaker/__init__.py | |
from collections import deque
from datetime import date
import os
import shutil
import subprocess
from tempfile import TemporaryDirectory
from typing import Any, Dict, List, Iterable, Optional, Tuple
import uuid
from allennlp.common.file_utils import cached_path
from allennlp.common.params import Params
import click
i... | allennlp-beaker-master | allennlp_beaker/__main__.py |
allennlp-beaker-master | tests/__init__.py | |
def test_hello():
print("Hello!")
| allennlp-beaker-master | tests/test_hello.py |
from setuptools import setup, find_packages
def read_requirements(filename: str):
with open(filename) as requirements_file:
import re
def fix_url_dependencies(req: str) -> str:
"""Pip and setuptools disagree about how URL dependencies should be handled."""
m = re.match(
... | better-promptability-main | setup.py |
_MAJOR = "0"
_MINOR = "1"
# On main and in a nightly release the patch should be one ahead of the last
# released build.
_PATCH = "0"
# This is mainly for nightly builds which have the suffix ".dev$DATE". See
# https://semver.org/#is-v123-a-semantic-version for the semantics.
_SUFFIX = ""
VERSION_SHORT = "{0}.{1}".for... | better-promptability-main | better_promptability/version.py |
from tango import Step
@Step.register("check_install")
class CheckInstall(Step):
DETERMINISTIC = True
CACHEABLE = False
def run(self) -> None:
import torch
if torch.cuda.is_available():
print("All good! CUDA is available :)")
else:
print("All good! No CUDA... | better-promptability-main | better_promptability/check_install.py |
better-promptability-main | better_promptability/__init__.py | |
"""
Changing T5Attention's forward to support prefix tuning, along with subclassing other classes that
use T5Attention. Changes in T5Attention's forward from are marked with
"# <CHANGE>" and "# </CHANGE>". It's possible that the added logic can be separated as some code
that entirely preceeds the original forward, s.t.... | better-promptability-main | better_promptability/models/t5_with_prefix.py |
from __future__ import annotations
import logging
from typing import Any, Dict
from allennlp.training.metrics import Metric
from learn2learn.utils import clone_module
from tango.common.lazy import Lazy
import torch
import torch.distributed as dist
from tango.common.params import logger as tango_logger
from tango.integ... | better-promptability-main | better_promptability/models/meta_learner.py |
better-promptability-main | better_promptability/models/__init__.py | |
from __future__ import annotations
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union
from allennlp.training.metrics import Metric
import torch
import torch.nn.functional as F
from tango.common.lazy import Lazy
from tango.integrations.pytorch_lightning.model import Lightnin... | better-promptability-main | better_promptability/models/model.py |
from __future__ import annotations
import logging
from typing import Any, Callable, IO, Optional, Union, Dict
import torch
from tango.common.lazy import Lazy
from tango.integrations.torch.optim import Optimizer
from transformers import T5ForConditionalGeneration
from ..data.config import Config
from ..data.prompt_dat... | better-promptability-main | better_promptability/models/prefix_transformer.py |
better-promptability-main | better_promptability/common/__init__.py | |
from contextlib import contextmanager
from copy import deepcopy
import logging
import os
import shutil
import tempfile
from pathlib import Path
from typing import List, Dict, Any, Optional, cast, Union
from tango.common.registrable import Registrable
from tango.common.util import PathOrStr
class BetterPromptabilityT... | better-promptability-main | better_promptability/common/testing.py |
from .process_dataset import ProcessDataset
| better-promptability-main | better_promptability/steps/__init__.py |
import logging
import os
from typing import Dict
from datasets import Dataset, DatasetDict
from tango.step import Step
from allennlp.common import cached_transformers
logger = logging.getLogger(__name__)
@Step.register("process_story_cloze")
class ProcessStoryCloze(Step):
DETERMINISTIC: bool = True
CACHEAB... | better-promptability-main | better_promptability/steps/process_story_cloze.py |
import logging
import os
from typing import Dict
from datasets import Dataset, DatasetDict
from tango.step import Step
logger = logging.getLogger(__name__)
@Step.register("process_dataset")
class ProcessDataset(Step):
DETERMINISTIC: bool = True
CACHEABLE = False # use datasets caching.
def run(
... | better-promptability-main | better_promptability/steps/process_dataset.py |
from collections import defaultdict
from typing import Any, Dict, List, Tuple, Set, Optional
import numpy as np
from tango import Format, JsonFormat, Step
from tango.common import Params
import torch
@Step.register("aggregate_results")
class AggregateResults(Step):
DETERMINISTIC = True
CACHEABLE = True
F... | better-promptability-main | better_promptability/train/aggregate_results.py |
better-promptability-main | better_promptability/train/__init__.py | |
import logging
import os
import sys
from pathlib import Path
from typing import Dict, List, Tuple, Optional
import dill
import pytorch_lightning as pl
import transformers
from pytorch_lightning.plugins import DDPShardedPlugin
from pytorch_lightning.utilities import rank_zero_only
from tango.common.lazy import Lazy
fro... | better-promptability-main | better_promptability/train/train.py |
from typing import Dict, List, Optional, Tuple
import pytorch_lightning as pl
from tango.common.lazy import Lazy
from tango.integrations.pytorch_lightning import LightningTrainer
from tango.format import JsonFormat
from tango.step import Step
from ..data.config import Config
from ..data.prompt_data_module import Prom... | better-promptability-main | better_promptability/train/eval.py |
from __future__ import annotations
from typing import Union
from transformers.optimization import Adafactor as HFAdafactor
from tango.integrations.torch.optim import Optimizer
@Optimizer.register("adafactor")
class Adafactor(HFAdafactor):
"""See https://github.com/huggingface/transformers/issues/14830
Never... | better-promptability-main | better_promptability/train/optim.py |
import sys
import dill
from tango.common.logging import initialize_logging
from tango.common.util import import_extra_module
from better_promptability.train.train import _train_step
def main():
initialize_logging()
_, kwargs_file, results_file = sys.argv
with open(kwargs_file, "rb") as f:
train... | better-promptability-main | better_promptability/train/train_main.py |
better-promptability-main | better_promptability/modules/__init__.py | |
import logging
import torch
from transformers import (
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelForCausalLM,
)
logger = logging.getLogger(__name__... | better-promptability-main | better_promptability/modules/transformer.py |
import logging
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
class WithPrefixEmbedding(nn.Module):
"""
From
https://github.com/shmsw25/Channel-LM-Prompting/blob/cbbb92cc97039c73475ddf0db46896e9efeff3c1/model_util.py#L113
""... | better-promptability-main | better_promptability/modules/with_prefix_embedding.py |
from __future__ import annotations
from typing import Mapping, Optional
from tango.common import PathOrStr, Params
from .config import Config
from .t0_module import T0Module
class T0Mixture:
"""
This class is used to initialize a collection of T0Module.
"""
def __init__(
self,
mixtu... | better-promptability-main | better_promptability/data/t0_mixture.py |
from __future__ import annotations
import random
from typing import Any, Optional, Union
from datasets import Dataset as HFDataset
from torch.utils.data import Dataset
from tango.common import Tqdm
class MixerDataset(Dataset):
"""
This dataset mixes multiple other datasets into a single :class:`Dataset`.
... | better-promptability-main | better_promptability/data/mixer_dataset.py |
from typing import Optional, Union
from tango.common.aliases import PathOrStr
from tango.common.registrable import Registrable
class Config(Registrable):
def __init__(
self,
seed: int = 42,
gpus: int = 1,
precision: Union[int, str] = 32,
output_dir: Optional[PathOrStr] = N... | better-promptability-main | better_promptability/data/config.py |
from __future__ import annotations
from typing import Any, Mapping
from urllib.error import HTTPError
from tango.common.aliases import PathOrStr
from transformers import T5Tokenizer, PreTrainedTokenizerBase
from .data_utils import PAD_TYPE
from .data_module import DataModule
from .config import Config
class PromptD... | better-promptability-main | better_promptability/data/prompt_data_module.py |
from __future__ import annotations
import logging
import os
from abc import abstractmethod, abstractproperty
from collections.abc import ItemsView
from typing import Any, Mapping, Optional, Union
from allennlp.training.metrics import Metric
import datasets
from datasets import Dataset as HFDataset, DatasetDict as HFDa... | better-promptability-main | better_promptability/data/data_module.py |
from __future__ import annotations
import random
from typing import Optional, Mapping
from datasets import Dataset as HFDataset
from tango.common import PathOrStr, Tqdm
import torch
import torch.distributed as dist
from torch.utils.data.dataloader import DataLoader
from transformers.trainer_pt_utils import LengthGroup... | better-promptability-main | better_promptability/data/t0_meta_learning_data_module.py |
from .t0_mixture import T0Mixture
from .t0_module import T0Module
| better-promptability-main | better_promptability/data/__init__.py |
from __future__ import annotations
from typing import Any, List, Mapping, Optional
from pathlib import Path
import pickle
from allennlp.training.metrics import Metric
import numpy as np
from tango.common import Params, PathOrStr
import datasets
from .data_module import DatasetDictType
from .data_utils import md5, PAD... | better-promptability-main | better_promptability/data/t0_module.py |
from __future__ import annotations
from typing import Optional, Mapping, Any
from tango.common import Tqdm, DatasetDict, PathOrStr
from .data_utils import PAD_TYPE
from .config import Config
from .mixer_dataset import MixerDataset, _UndersampledDataset
from .prompt_data_module import PromptDataModule
from .t0_mixture... | better-promptability-main | better_promptability/data/t0_multitask_data_module.py |
from __future__ import annotations
import hashlib
from typing import Iterable, Mapping, Union
import math
import numpy as np
import torch
from torch.utils.data._utils.collate import default_collate
PAD_TYPE = Union[int, float, bool]
def _find_max_shapes(
batch: list[dict[str, np.ndarray]], allow_keys: Iterable... | better-promptability-main | better_promptability/data/data_utils.py |
from __future__ import annotations
import math
import random
from typing import Callable
import torch.distributed as dist
from torch.utils.data.dataloader import DataLoader, _BaseDataLoaderIter
class MixerDataLoader(DataLoader):
"""
A dataloader that encapsulates multiple dataloaders. At each iteration, yie... | better-promptability-main | better_promptability/data/mixer_dataloader.py |
def test_hello():
print("Hello, World!")
| better-promptability-main | tests/hello_test.py |
better-promptability-main | tests/__init__.py | |
import os
from tango.common import Params
def test_few_shot_baseline_all():
os.environ["CKPT"] = "null"
d = Params.from_file("configs/fewshot_eval_all_green.jsonnet").as_dict()
del os.environ["CKPT"]
assert "result_anli_GPT_3_style_r1_score_eval" in d["steps"]
assert "aggregated_results" in d["st... | better-promptability-main | tests/configs_test.py |
better-promptability-main | tests/models/__init__.py | |
better-promptability-main | tests/steps/__init__.py | |
from better_promptability.steps.process_story_cloze import ProcessStoryCloze
from better_promptability.common.testing import BetterPromptabilityTestCase
class ProcessStoryClozeTest(BetterPromptabilityTestCase):
def test_process_story_cloze(self):
step = ProcessStoryCloze()
result = step.run(
... | better-promptability-main | tests/steps/process_story_cloze_test.py |
from better_promptability.steps.process_dataset import ProcessDataset
from better_promptability.common.testing import BetterPromptabilityTestCase
class ProcessDatasetTest(BetterPromptabilityTestCase):
def test_process_dataset(self):
step = ProcessDataset()
result = step.run(
old_data_p... | better-promptability-main | tests/steps/process_dataset_test.py |
import pytest
from transformers.models import t5 as hf_t5
from better_promptability.modules.transformer import Transformer
@pytest.fixture(scope="module")
def model_name():
return "google/t5-small-lm-adapt"
@pytest.fixture(scope="module")
def tokenizer(model_name):
return hf_t5.T5Tokenizer.from_pretrained(m... | better-promptability-main | tests/modules/transformer_test.py |
better-promptability-main | tests/modules/__init__.py | |
better-promptability-main | tests/data/__init__.py | |
from better_promptability.data.config import Config
from better_promptability.data import T0Module
from better_promptability.common.testing import BetterPromptabilityTestCase
class T0ModuleTest(BetterPromptabilityTestCase):
def test_t0_module_green(self):
t0 = T0Module(
config=Config(),
... | better-promptability-main | tests/data/t0_data_module_test.py |
import pytest
from better_promptability.data.mixer_dataset import MixerDataset
@pytest.fixture
def datasets():
return [["a1", "a2", "a3"], ["b1", "b2", "b3", "b4", "b5", "b6", "b7"]]
def test_mixer_dataset(datasets):
mixer = MixerDataset(datasets)
assert len(mixer) == 10
assert [x for x in mixer] =... | better-promptability-main | tests/data/mixer_dataset_test.py |
import random
import sys
from tqdm import tqdm
random.seed(100)
TASKS_METADATA = [ # task name, num templates, random performance
("ANLI", 45, 1/3),
("Hellaswag", 4, 1/4),
("StoryCloze", 5, 1/2),
("CB", 15, 1/3),
("COPA", 12, 1/2),
("RTE", 10, 1/2),
("WIC", 10, 1/2),
("WSC", 10, 1/... | better-promptability-main | scripts/bootstrap.py |
import logging
import os
import sys
from tango.common import Params
from tqdm import tqdm
from better_promptability.steps.process_dataset import ProcessDataset
from better_promptability.steps.process_story_cloze import ProcessStoryCloze
logging.basicConfig(level=logging.INFO)
def process_green_datasets(old_base_p... | better-promptability-main | scripts/process_green_datasets.py |
"""
Download all of the data from the [bigscience/P3](https://huggingface.co/datasets/bigscience/P3)
corresponding to a particular mixture. This script should only be run from the root of this repository.
"""
import importlib
import json
import os
import sys
from pathlib import Path
import datasets
from tango.common ... | better-promptability-main | scripts/download_t0_training_set.py |
"""
Subsamples the training set for each dataset (i.e., for all tepmlates).
Ideally we want to sample the same examples across templates for a given dataset, but unfortunately
this is impossible since the P3 dataset cache does not guarantee the same example order across
templates. Check out, for example, hellaswag_comp... | better-promptability-main | scripts/subsample_t0_training_set.py |
import sys
import os
sys.path.append(os.path.abspath(os.path.join("..", "nla_semparse")))
from nla_semparse.nla_metric import NlaMetric
def test_metric_basic():
metric = NlaMetric()
metric(["2"], ["2"])
assert metric.get_metric() == {
"well_formedness": 1.0,
"denotation_accuracy": 1.0,
... | allennlp-guide-master | nla_semparse/tests/nla_metric_test.py |
allennlp-guide-master | nla_semparse/nla_semparse/__init__.py | |
from allennlp_semparse import DomainLanguage, predicate
class NlaLanguage(DomainLanguage):
def __init__(self):
super().__init__(
start_types={int},
allowed_constants={
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4... | allennlp-guide-master | nla_semparse/nla_semparse/nla_language.py |
from typing import Dict, List, Optional
from allennlp.training.metrics.metric import Metric
from allennlp_semparse.domain_languages.domain_language import ExecutionError
from .nla_language import NlaLanguage
@Metric.register("nla_metric")
class NlaMetric(Metric):
"""
Metric for evaluating prefix arithmetic ... | allennlp-guide-master | nla_semparse/nla_semparse/nla_metric.py |
import sys
import os
import random
import math
import argparse
from typing import List, Dict, Any
sys.path.append(os.path.abspath(os.path.join("..", "nla_semparse")))
from nla_semparse.nla_language import NlaLanguage
class DataGenerator:
"""
Generator for data points for natural language arithmetic.
"""... | allennlp-guide-master | nla_semparse/scripts/generate_data.py |
# Inputs
text: TextField
title: TextField
stars: LabelField
# Outputs
aspect: LabelField
sentiment: LabelField
| allennlp-guide-master | exercises/chapter05/input_output/add_list_source.py |
# Inputs
text: TextField
title: TextField
# Outputs
sentiment: LabelField
| allennlp-guide-master | exercises/chapter05/input_output/add_stars_source.py |
# Inputs
text: TextField
title: TextField
# Outputs
sentiment: LabelField
| allennlp-guide-master | exercises/chapter05/input_output/add_title_solution.py |
# Inputs
text: TextField
# Outputs
sentiment: LabelField
| allennlp-guide-master | exercises/chapter05/input_output/add_title_source.py |
# Inputs
text: TextField
title: TextField
stars: LabelField
aspect: LabelField # either here
# Outputs
sentiment: LabelField
aspect: LabelField # or here
| allennlp-guide-master | exercises/chapter05/input_output/add_aspect_solution.py |
# Inputs
text: TextField
title: TextField
stars: LabelField
# Outputs
aspect: List[LabelField]
sentiment: List[LabelField] # or a SequenceLabelField that depends on `aspect`
| allennlp-guide-master | exercises/chapter05/input_output/add_list_solution.py |
# Inputs
text: TextField
title: TextField
stars: LabelField
# OR stars: ArrayField, if you want to model the numerical value
# Outputs
sentiment: LabelField
| allennlp-guide-master | exercises/chapter05/input_output/add_stars_solution.py |
# Inputs
text: TextField
title: TextField
stars: LabelField
# Outputs
sentiment: LabelField
| allennlp-guide-master | exercises/chapter05/input_output/add_aspect_source.py |
from allennlp.common import Params
from allennlp.data import DatasetReader, Instance, Vocabulary
from allennlp.data.fields import LabelField, TextField
from allennlp.data.iterators import BasicIterator
from allennlp.data.token_indexers import SingleIdTokenIndexer
from allennlp.data.tokenizers import WordTokenizer
from ... | allennlp-guide-master | exercises/chapter05/putting_them_together/config_source.py |
from allennlp.data import DatasetReader, Instance, Vocabulary
from allennlp.data.fields import LabelField, TextField
from allennlp.data.iterators import BasicIterator
from allennlp.data.token_indexers import SingleIdTokenIndexer
from allennlp.data.tokenizers import WordTokenizer
from allennlp.models import Model
from a... | allennlp-guide-master | exercises/chapter05/putting_them_together/code_source.py |
from allennlp.data import DatasetReader, Instance
from allennlp.data.fields import LabelField, TextField
from allennlp.data.token_indexers import SingleIdTokenIndexer
from allennlp.data.tokenizers import WordTokenizer
# Data will be formatted as:
# [title][tab][text][tab][stars][tab][aspect][tab][sentiment]
@Dataset... | allennlp-guide-master | exercises/chapter05/input_output_reader/add_fields_source.py |
from allennlp.data import DatasetReader, Instance
from allennlp.data.fields import LabelField, TextField
from allennlp.data.token_indexers import SingleIdTokenIndexer
from allennlp.data.tokenizers import WordTokenizer
# Data will be formatted as:
# [title][tab][text][tab][stars][tab][aspect][tab][sentiment]
@Dataset... | allennlp-guide-master | exercises/chapter05/input_output_reader/add_fields_solution.py |
def test():
assert len(instances) == 2, "You didn't get two instances"
expected_fields = {"text", "title", "stars", "aspect", "sentiment"}
assert (
instances[0].fields.keys() == expected_fields
), "You don't have the right fields in your Instance"
assert (
instances[0]["sentiment"] =... | allennlp-guide-master | exercises/chapter05/input_output_reader/add_fields_test.py |
inputs = {"sentence": "a very well-made, funny and entertaining picture."}
archive = (
"https://storage.googleapis.com/allennlp-public-models/"
"basic_stanford_sentiment_treebank-2020.06.09.tar.gz"
)
predictor = Predictor.from_path(archive)
interpreter = SimpleGradient(predictor)
interpretation = interpreter.sa... | allennlp-guide-master | exercises/part3/interpret/saliency_source.py |
inputs = {"sentence": "a very well-made, funny and entertaining picture."}
archive = (
"https://storage.googleapis.com/allennlp-public-models/"
"basic_stanford_sentiment_treebank-2020.06.09.tar.gz"
)
predictor = Predictor.from_path(archive)
reducer = InputReduction(predictor) # or Hotflip(predictor)
# if it is... | allennlp-guide-master | exercises/part3/interpret/attacker_source.py |
from allennlp.interpret.saliency_interpreters import SimpleGradient
from allennlp.predictors import Predictor
| allennlp-guide-master | exercises/part3/interpret/saliency_setup.py |
from allennlp.models.archival import load_archive
from allennlp.predictors import Predictor
from allennlp.interpret.attackers import InputReduction
| allennlp-guide-master | exercises/part3/interpret/attacker_setup.py |
inputs = ["one plus three minus four", "five minus six times seven over one"]
for nla_input in inputs:
output = translate_nla(nla_input)
print(f"Input: {nla_input}")
print(f"Prediction: {output}\n")
| allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/predictor_source_medium.py |
import csv
from typing import Dict
from allennlp.common.file_utils import cached_path
from allennlp.common.util import START_SYMBOL, END_SYMBOL
from allennlp.data import DatasetReader, Instance
from allennlp.data.fields import TextField
from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer
from a... | allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/dataset_reader_setup.py |
class Seq2SeqDatasetReader(DatasetReader):
def __init__(
self,
source_tokenizer: Tokenizer = None,
target_tokenizer: Tokenizer = None,
source_token_indexers: Dict[str, TokenIndexer] = None,
target_token_indexers: Dict[str, TokenIndexer] = None,
**kwargs,
) -> None... | allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/dataset_reader_source.py |
from typing import List
from allennlp.predictors import Predictor
from allennlp.models.archival import load_archive
from allennlp_models.generation import (
ComposedSeq2Seq,
) # Need this for loading model archive
from nla_semparse.nla_semparse.nla_metric import (
NlaMetric,
) # Need this for loading model a... | allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/predictor_source_easy.py |
from typing import Dict, List, Optional
from allennlp.training.metrics.metric import Metric
from allennlp_semparse.domain_languages.domain_language import ExecutionError
from nla_semparse.nla_semparse.nla_language import NlaLanguage
@Metric.register("nla_metric")
class NlaMetric(Metric):
"""
Metric for eval... | allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/metric_setup.py |
from typing import List
from allennlp.predictors import Predictor
from allennlp.models.archival import load_archive
from allennlp_models.generation import (
ComposedSeq2Seq,
) # Need this for loading model archive
from nla_semparse.nla_semparse.nla_metric import (
NlaMetric,
) # Need this for loading model a... | allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/predictor_setup.py |
def evaluate(prediction: str, target: str) -> Dict[str, float]:
metric = NlaMetric()
metric([prediction], [target])
return metric.get_metric(reset=True)
target = "(subtract (multiply 7 3) 2)"
predictions = [
"(subtract (multiply 7 3) 2)",
"(subtract (multiply 6 4) 5)",
"subtract () add divide... | allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/metric_source.py |
inputs = [
"eight over nine times six minus three plus seven over five minus one",
"seven times eight plus five minus six plus one plus three plus two over seven",
]
for nla_input in inputs:
output = translate_nla(nla_input)
print(f"Input: {nla_input}")
print(f"Prediction: {output}\n")
| allennlp-guide-master | exercises/part3/semantic-parsing-seq2seq/predictor_source_hard.py |
from collections import Counter, defaultdict
from typing import Dict
from allennlp.data.instance import Instance
from allennlp.data.fields import Field, TextField, LabelField, SequenceLabelField
from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer
from allennlp.data.tokenizers import Token
from ... | allennlp-guide-master | exercises/part2/reading-data/instances_setup.py |
# You can implement your own dataset reader by subclassing DatasetReader.
# At the very least, you need to implement the _read() method, preferably
# text_to_instance() as well.
@DatasetReader.register("classification-tsv")
class ClassificationTsvReader(DatasetReader):
def __init__(
self,
tokenizer:... | allennlp-guide-master | exercises/part2/reading-data/dataset_reader_basic_source.py |
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