python_code stringlengths 0 187k | repo_name stringlengths 8 46 | file_path stringlengths 6 135 |
|---|---|---|
"""Slightly modified subclass of the AllenNLP conll2003 dataset reader.
Allows pruning negative sentences given a percent value and a limiting
by a max length
"""
from typing import Dict, Sequence, Iterable, List
import itertools
import logging
logging.basicConfig(level=logging.ERROR)
from overrides import overrides
... | coleridge-rich-context-ai2-master | project/ner_rcc/rcc_ner.py |
"""This script splits up the papers such that the datasets in train, dev, and test are disjoint.
Note: It assumes that the old splits and their file structure remain the same as before"""
import os
import json
from collections import defaultdict
import numpy as np
import random
def build_dataset_id_to_papers(old_t... | coleridge-rich-context-ai2-master | project/ner_retraining/create_splits.py |
import sys
import os
import argparse
sys.path.append(os.path.abspath(os.path.join("project")))
import ner_model
import json
def main(conll_path, output_path, model_path):
ner = ner_model.NerModel(conll_path, model_path)
citations_list = ner.predict_from_publication_list()
with open(output_path, "w") as fp:... | coleridge-rich-context-ai2-master | project/ner_retraining/generate_ner_output.py |
from overrides import overrides
from allennlp.common.util import JsonDict
from allennlp.data import Instance
from allennlp.predictors.predictor import Predictor
@Predictor.register('classifier')
class ClassifierPredictor(Predictor):
"""
Predictor for the :class:`~allennlp.models.text_classification.Seq2Seq` m... | coleridge-rich-context-ai2-master | project/field_classifier/predictor.py |
from allennlp.models.archival import load_archive
from allennlp.service.predictors import Predictor
from field_classifier.classifier import Classifier
from field_classifier.predictor import ClassifierPredictor
from field_classifier.textcat import TextCatReader
import os
import json
import numpy as np
l0_archive = load... | coleridge-rich-context-ai2-master | project/field_classifier/eval_field_classifier.py |
from typing import Any, Dict, List, Optional
import torch
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import FeedForward, Seq2SeqEncoder, TextFieldEmbedder
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from allennlp.nn.util import (get_final... | coleridge-rich-context-ai2-master | project/field_classifier/classifier.py |
coleridge-rich-context-ai2-master | project/field_classifier/__init__.py | |
import json
import logging
from typing import Dict, List
import numpy as np
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import (Field, LabelField, ListField, MetadataField,
TextFiel... | coleridge-rich-context-ai2-master | project/field_classifier/textcat.py |
coleridge-rich-context-ai2-master | project/s2base/__init__.py | |
import spacy
class SciSpaCyParser(object):
def __init__(self):
self.nlp = spacy.load('en_scispacy_core_web_sm')
def remove_new_lines(self, text):
"""Used to preprocess away new lines in the middle of words. This function
is intended to be called on a raw string before it is passed t... | coleridge-rich-context-ai2-master | project/s2base/scispacy_util.py |
import argparse
import itertools
import ir_datasets
import abnirml
_logger = ir_datasets.log.easy()
def flush(fout, qid, docs):
scored_docs = [(score, did) for did, score in docs.items()]
for i, (score, did) in enumerate(sorted(scored_docs, reverse=True)):
fout.write(f'{qid} 0 {did} {i+1} {score} ru... | abnirml-master | abnirml/rerank.py |
import os
import atexit
import shutil
import tempfile
from contextlib import contextmanager
import threading
import pyterrier as pt
import ir_datasets
_logger = ir_datasets.log.easy()
class _JavaInterface:
def __init__(self):
self._autoclass = None
self.cast = None
self.JavaException = No... | abnirml-master | abnirml/java.py |
import ir_datasets
_logger = ir_datasets.log.easy()
_logger.logger().setLevel(20) # INFO
from . import datasets
from . import probes
from . import scorers
from . import indices
from . import eval as ev
from . import util
from pathlib import Path
import pyterrier as pt
from abnirml.java import J
J.initialize()
ProbeExp... | abnirml-master | abnirml/__init__.py |
import traceback
import os
import fcntl
from fnmatch import fnmatch
import json
import ir_datasets
import abnirml
_logger = ir_datasets.log.easy()
class Locker:
def __init__(self, file):
self.file = file
self.fp = None
def __enter__ (self):
self.fp = open(self.file, 'w')
fcn... | abnirml-master | abnirml/__main__.py |
import os
import shutil
import json
from pytools import memoize_method
import pyterrier
import ir_datasets
from abnirml.java import J
_logger = ir_datasets.log.easy()
class TerrierIndex:
def __init__(self, path):
self._path = path
def path(self):
return self._path
def _index_ref(self)... | abnirml-master | abnirml/indices/terrier.py |
from .terrier import TerrierIndex, PtIndexWrapper
| abnirml-master | abnirml/indices/__init__.py |
import random
import spacy
import ir_datasets
import abnirml
from .base import Probe
class BiasProbe(Probe):
def __init__(self, dataset='abnirml:nbias', doc_field='text', query_inferer=None):
self.dataset = ir_datasets.load(dataset)
self.doc_field = doc_field
self.query_inferer = query_inf... | abnirml-master | abnirml/probes/bias.py |
import itertools
from .base import Probe
class ConstVar:
def __init__(self, axiom, epsilon=0):
self.axiom = axiom
self.epsilon = epsilon
def score(self, query, doc_id, rel):
raise NotImplementedError
def is_const(self, a, b):
return abs(a - b) <= self.epsilon
def is_... | abnirml-master | abnirml/probes/const_var.py |
import random
import spacy
import ir_datasets
import abnirml
from .base import Probe
class JflegProbe(Probe):
def __init__(self, source='abnirml:jfleg', query_inferer=None):
self.dataset = ir_datasets.load(source)
self.query_inferer = query_inferer or abnirml.util.CommonNounChunk()
def pairs_... | abnirml-master | abnirml/probes/fluency.py |
from . import base
from .base import Probe
from . import const_var
from . import transform
from .bias import BiasProbe
from .fluency import JflegProbe
from .formality import GyafcProbe
from .factuality import FactualityProbe
from .summarization import XSumProbe, CnnDmProbe
from .paraphrase import ParaphraseProbe
from .... | abnirml-master | abnirml/probes/__init__.py |
import os
import gzip
import pickle
import lz4.frame
from pathlib import Path
from .base import Probe
import ir_datasets
class CachedProbe(Probe):
def __init__(self, probe, path):
self.probe = probe
self.path = Path(path)
def pair_symmetry(self):
return self.probe.pair_symmetry()
... | abnirml-master | abnirml/probes/cached.py |
import spacy
import ir_datasets
from .base import Probe
class GyafcProbe(Probe):
def __init__(self, spacy_model='en_core_web_sm', genre_filter=None, yahoo_l6_dataset=None, gyafc_dataset=None):
self.spacy_model = spacy_model
self.genre_filter = genre_filter
self.yahoo_l6_dataset = yahoo_l6_... | abnirml-master | abnirml/probes/formality.py |
from .base import Probe
class NLAugProbe(Probe):
def __init__(self, dataset, generator, rel_range=None, query_field='text', doc_field='text'):
self.dataset = dataset
self.generator = generator
if rel_range is not None:
if isinstance(rel_range, (tuple, list)):
as... | abnirml-master | abnirml/probes/nlaug.py |
import re
import random
import spacy
import ir_datasets
import abnirml
from .base import Probe
_logger = ir_datasets.log.easy()
class FactualityProbe(Probe):
def __init__(self, dataset='dpr-w100/natural-questions/dev', spacy_model='en_core_web_sm', random_seed=42, valid_entities=('PERSON', 'NORP', 'FAC', 'ORG',... | abnirml-master | abnirml/probes/factuality.py |
import re
import itertools
import random
import string
import spacy
from ..scorers import doctttttquery
from .base import Probe
from abnirml.java import J
class TransformProbe(Probe):
def __init__(self, dataset, transform, rel_range=None, query_field='text', doc_field='text'):
self.dataset = dataset
... | abnirml-master | abnirml/probes/transform.py |
import ir_datasets
import os
import hashlib
from glob import glob
import spacy
from .base import Probe
class XSumProbe(Probe):
def __init__(self, spacy_model='en_core_web_sm', dataset='abnirml:xsum'):
super().__init__()
self.spacy_model = spacy_model
self.dataset = ir_datasets.load(datase... | abnirml-master | abnirml/probes/summarization.py |
import random
import spacy
import ir_datasets
import abnirml
from .base import Probe
class SimplificationProbe(Probe):
def __init__(self, dataset='abnirml:wikiturk', query_inferer=None):
self.dataset = ir_datasets.load(dataset)
self.query_inferer = query_inferer or abnirml.util.CommonNounChunk()
... | abnirml-master | abnirml/probes/simplification.py |
import random
import spacy
import ir_datasets
import abnirml
from .base import Probe
class ParaphraseProbe(Probe):
def __init__(self, dataset='abnirml:mspc', doc_field='text', paraphrase_label=True, query_inferer=None):
self.dataset = ir_datasets.load(dataset)
self.doc_field = doc_field
se... | abnirml-master | abnirml/probes/paraphrase.py |
class Probe:
def pair_symmetry(self):
return 'asymmetric' # most probes are asymmetric
def pairs_iter(self):
raise NotImplementedError
| abnirml-master | abnirml/probes/base.py |
import ir_datasets
import random
import spacy
__all__ = ['QueryInferer', 'CommonNounChunk', 'SelectAll', 'RandomSelector']
class QueryInferer:
def infer_queries(self, text_a, text_b):
raise NotImplementedError()
class CommonNounChunk(QueryInferer):
def __init__(self, spacy_model='en_core_web_sm', ... | abnirml-master | abnirml/util/query_inference.py |
from .query_inference import *
| abnirml-master | abnirml/util/__init__.py |
from collections import namedtuple
import io
import contextlib
import ir_datasets
from . import DownloadConfig
from ir_datasets import Dataset
NAME = 'abnirml:jfleg'
BASE_PATH = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, BASE_PATH)
JflegDoc = namedtuple('JflegDoc', ['doc_id', 'nonfluent',... | abnirml-master | abnirml/datasets/jfleg.py |
from typing import NamedTuple, Tuple
import io
import contextlib
import ir_datasets
from ir_datasets.indices import PickleLz4FullStore
from . import DownloadConfig
from ir_datasets import Dataset
# Text simplification dataset from <https://github.com/cocoxu/simplification>
# Wei Xu and Courtney Napoles and Ellie Pavli... | abnirml-master | abnirml/datasets/wikiturk.py |
import os
import yaml
from ir_datasets.util.download import _DownloadConfig
DownloadConfig = _DownloadConfig(contents=yaml.load(open('./abnirml/etc/downloads.yaml'), Loader=yaml.BaseLoader))
from . import cnn_dailymail
from . import gyafc
from . import jfleg
from . import mspc
from . import nbias
from . import wikitu... | abnirml-master | abnirml/datasets/__init__.py |
import tarfile
import io
import os
from collections import namedtuple
import ir_datasets
from ir_datasets.indices import PickleLz4FullStore
from ir_datasets.util import ZipExtract
from . import DownloadConfig
from ir_datasets import Dataset
NAME = 'abnirml:xsum'
BASE_PATH = ir_datasets.util.home_path() / NAME
dlc = D... | abnirml-master | abnirml/datasets/xsum.py |
from collections import namedtuple
import io
import ir_datasets
from ir_datasets.util import GzipExtract, LocalDownload
from ir_datasets.indices import PickleLz4FullStore
from . import DownloadConfig
from ir_datasets import Dataset
NAME = 'abnirml:yahoo-l6'
BASE_PATH = ir_datasets.util.home_path() / NAME
dlc = Downlo... | abnirml-master | abnirml/datasets/yahoo_l6.py |
import io
import os
from collections import namedtuple
import ir_datasets
from ir_datasets.util import ZipExtract
from ir_datasets import Dataset
from . import DownloadConfig
NAME = 'abnirml:nbias'
BASE_PATH = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, BASE_PATH)
NBiasDoc = namedtuple('N... | abnirml-master | abnirml/datasets/nbias.py |
import os
from collections import namedtuple
import ir_datasets
from ir_datasets.util import ZipExtractCache
from . import DownloadConfig
from ir_datasets import Dataset
NAME = 'abnirml:gyafc'
BASE_PATH = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, BASE_PATH)
GyafcDoc = namedtuple('GyafcDo... | abnirml-master | abnirml/datasets/gyafc.py |
import tarfile
import io
import os
from collections import namedtuple
import ir_datasets
from ir_datasets.indices import PickleLz4FullStore
from ir_datasets.util import ZipExtract
from ir_datasets import Dataset
from . import DownloadConfig
NAME = 'abnirml:cnn_dailymail'
BASE_PATH = ir_datasets.util.home_path() / NAM... | abnirml-master | abnirml/datasets/cnn_dailymail.py |
from collections import namedtuple
from typing import NamedTuple
import io
import contextlib
import ir_datasets
from ir_datasets.indices import PickleLz4FullStore
from . import DownloadConfig
from ir_datasets import Dataset
NAME = 'abnirml:mspc'
BASE_PATH = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.cont... | abnirml-master | abnirml/datasets/mspc.py |
import pyterrier
import pandas as pd
from abnirml.java import J
from .base import Scorer
class _TerrerRetriever(Scorer):
def __init__(self, name, index, batch_size=100):
super().__init__(name)
self.index = index
self.batch_size = batch_size
def batch_score(self, queries, texts):
... | abnirml-master | abnirml/scorers/terrier.py |
import hashlib
import gzip
import torch
import transformers
import ir_datasets
from .base import Scorer
from .cached import SimpleFsCache
_logger = ir_datasets.log.easy()
class DocTTTTTQuery(Scorer):
def __init__(self, scorer, count=4, delta=0):
super().__init__(None)
self.scorer = scorer
... | abnirml-master | abnirml/scorers/doctttttquery.py |
import os
import re
import pickle
import hashlib
from pathlib import Path
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from nltk import word_tokenize
import ir_datasets
from .base import NeuralScorer
_logger = ir_datasets.log.easy()
class ConvKNRM(NeuralScorer):
def __ini... | abnirml-master | abnirml/scorers/conv_knrm.py |
# import os
# import torch
# import torch.nn.functional as F
# from transformers import BertTokenizerFast, BertForNextSentencePrediction
# from pytorch_transformers.modeling_bert import BertForPreTraining, BertPreTrainedModel, BertEmbeddings, BertEncoder, BertPreTrainingHeads
# from .base import NeuralScorer
# class ... | abnirml-master | abnirml/scorers/epic.py |
import torch
import torch.nn.functional as F
import transformers
import ir_datasets
from .base import NeuralScorer
_logger = ir_datasets.log.easy()
class Seq2seqT5(NeuralScorer):
"""
From:
> Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. Document Ranking with a Pretrained
> Sequence-to-Sequence Mod... | abnirml-master | abnirml/scorers/seq2seq_t5.py |
from . import base
from .base import PyTerrierScorer, Scorer, NeuralScorer
from .seq2seq_t5 import Seq2seqT5
from .terrier import TerrierBM25
from .cached import CachedScorer
from .vanilla_bert import VanillaBERT
from .doctttttquery import DocTTTTTQuery
from .conv_knrm import ConvKNRM, KNRM
from .s2 import S2
| abnirml-master | abnirml/scorers/__init__.py |
from pathlib import Path
import gzip
import zlib
import itertools
import hashlib
import pickle
import ir_datasets
_logger = ir_datasets.log.easy()
class SimpleFsCache:
def __init__(self, path, open_fn=open):
self._memcache = {}
if Path(path).exists():
with open_fn(path, 'r+b') as f:
... | abnirml-master | abnirml/scorers/cached.py |
import os
import math
import torch
import torch.nn.functional as F
from transformers import BertTokenizerFast, BertForNextSentencePrediction
from pytorch_transformers.modeling_bert import BertForPreTraining, BertPreTrainedModel, BertEmbeddings, BertEncoder, BertPreTrainingHeads
from .base import NeuralScorer
class Va... | abnirml-master | abnirml/scorers/vanilla_bert.py |
from typing import Dict, List, Tuple
import pickle
import datetime
from collections import Counter
import re
import numpy as np
import pandas as pd
import kenlm
from nlpre import unidecoder
from nltk.util import ngrams
from blingfire import text_to_words
import ir_datasets
from .base import NeuralScorer
class S2(Neu... | abnirml-master | abnirml/scorers/s2.py |
import torch
import pandas as pd
import pyterrier as pt
class Scorer:
def __init__(self, name):
self.name = name
def delta(self):
return 0.
def score_iter(self, it):
raise NotImplementedError
class NeuralScorer(Scorer):
def __init__(self, name, batch_size=8):
super(... | abnirml-master | abnirml/scorers/base.py |
import json
import itertools
import hashlib
import scipy
import ir_datasets
_logger = ir_datasets.log.easy()
class AxiomEvaluator:
def __init__(self, scorer, axiom, hash_fn=hashlib.md5, epsilon=1e-6):
self.scorer = scorer
self.axiom = axiom
self.hash_fn = hash_fn
self.epsilon = e... | abnirml-master | abnirml/eval/axiom_eval.py |
from .axiom_eval import AxiomEvaluator
from .probe_experiment import ProbeExperiment, ProbeDist, rerank, topk_diffs, CutoffCompare
| abnirml-master | abnirml/eval/__init__.py |
import json
import itertools
import hashlib
import scipy
from collections import Counter
import ir_datasets
import abnirml
import seaborn as sns
_logger = ir_datasets.log.easy()
def _asymmetric_probe_scorer(score_a, score_b):
return score_a - score_b
def _symmetric_probe_scorer(score_a, score_b):
return a... | abnirml-master | abnirml/eval/probe_experiment.py |
import openai
import argparse
import json
import csv
import random
import os
from annoy import AnnoyIndex
from sklearn.metrics import accuracy_score
from utils import chunks, read_lines
from baselines.utils import gpt3_completion, write_items
from sentence_transformers import SentenceTransformer
from sklearn.neighbor... | csqa2-master | baselines/gpt3.py |
csqa2-master | baselines/__init__.py | |
import openai
import os
import time
import sys
from typing import List
openai.api_key = os.environ["OPENAI_API_KEY"]
def gpt3_completion(prompt, model_name, max_tokens, temperature, logprobs, echo, num_outputs, top_p, best_of):
# call GPT-3 API until result is provided and then return it
response = None
... | csqa2-master | baselines/utils.py |
import argparse
import os
import sys
import logging
from typing import Tuple
from gevent.pywsgi import WSGIServer
from flask import Flask, Response, request, jsonify
from app.ui import create_ui
from app.utils import StackdriverJsonFormatter
from werkzeug.middleware.proxy_fix import ProxyFix
from datetime import date
... | allennlp-gallery-main | app/start.py |
from flask import Blueprint, render_template
from .projects import load_all_projects, Project
from typing import Optional
from werkzeug.exceptions import NotFound
from markdown import markdown
def create_ui() -> Blueprint:
app = Blueprint("app", __name__)
@app.app_template_filter()
def md_to_html(md: str)... | allennlp-gallery-main | app/app/ui.py |
allennlp-gallery-main | app/app/__init__.py | |
from pythonjsonlogger import jsonlogger
class StackdriverJsonFormatter(jsonlogger.JsonFormatter):
"""
Custom log JSON log formatter that adds the severity member, allowing
end users to filter logs by the level of the log message emitted.
TODO: Parse request logs and add fields for each request element... | allennlp-gallery-main | app/app/utils.py |
import json
from dataclasses import dataclass, field
from typing import Optional, List, Set
from datetime import date, datetime
from pathlib import Path
from os import listdir
from logging import getLogger
@dataclass(frozen=True)
class Author:
name: str
affiliation: Optional[str] = None
email: Optional[st... | allennlp-gallery-main | app/app/projects.py |
# -*- coding: utf-8 -*-
import requests
import time
import math
import signal
def is_ok(url: str) -> bool:
"""
Returns True if the provided URL responds with a 2XX when fetched via
a HTTP GET request.
"""
try:
resp = requests.get(url)
except:
return False
return True if mat... | allennlp-gallery-main | sonar/ping.py |
'''
This script is the source code for a project that
Field Cady and Oren Etzioni are working on.
'''
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
from scipy import stats
import pys2 # library internal to Allen Institute
# The field we use to tell rank paper importance
CITATION_COUNT_FI... | china_ai-master | main.py |
from setuptools import find_packages, setup
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# release markers:
# X.Y
# X.Y.Z # For bugfix releases
#
# pre-release markers:
# X.YaN # Alpha release
# X.YbN # Beta release
# X.YrcN # Release Candidate
# X.Y ... | allennlp-server-master | setup.py |
import os
_MAJOR = "1"
_MINOR = "0"
# 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 = os.environ.get("ALLENN... | allennlp-server-master | allennlp_server/version.py |
import allennlp_server.commands
| allennlp-server-master | allennlp_server/__init__.py |
from allennlp_server.commands.server_simple import SimpleServer
| allennlp-server-master | allennlp_server/commands/__init__.py |
"""
A `Flask <https://palletsprojects.com/p/flask/>`_ server for serving predictions
from a single AllenNLP model. It also includes a very, very bare-bones
web front-end for exploring predictions (or you can provide your own).
$ allennlp serve --help
usage: allennlp serve [-h] --archive-path ARCHIVE_PATH --pre... | allennlp-server-master | allennlp_server/commands/server_simple.py |
allennlp-server-master | tests/__init__.py | |
allennlp-server-master | tests/commands/__init__.py | |
import importlib
import io
import json
import os
import sys
from contextlib import redirect_stdout
import flask.testing
from allennlp.commands import main
from allennlp.common.testing import AllenNlpTestCase
from allennlp.common.util import JsonDict
from allennlp.models.archival import load_archive
from allennlp.predi... | allennlp-server-master | tests/commands/server_simple_test.py |
#!/usr/bin/env python3
import argparse
from typing import Dict
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("version_type", choices=["stable", "latest", "current"])
parser.add_argument("--minimal", action="store_true", default=False)
parser.add_argument("--as-range", actio... | allennlp-server-master | scripts/get_version.py |
import json
import pickle
import logging
from collections import defaultdict
from typing import Any, Dict, List, Iterable, Text
from overrides import overrides
import torch
from allennlp.data.fields import (
MetadataField,
TextField,
IndexField,
ListField,
)
from allennlp.data.dataset_readers.dataset... | data-efficient-finetuning-main | attribution/p3_jsonl_reader.py |
import json
import logging
import random
from collections import defaultdict
from typing import List, Iterable, Optional, Tuple, Dict
import torch
from overrides import overrides
import datasets
from allennlp.data.fields import (
MetadataField,
TextField,
)
from allennlp.data.dataset_readers.dataset_reader im... | data-efficient-finetuning-main | attribution/ni_reader.py |
import json
import logging
import random
from collections import defaultdict
from typing import List, Iterable, Optional, Tuple, Dict
import torch
from overrides import overrides
import datasets
from allennlp.data.fields import (
MetadataField,
TextField,
IndexField,
ListField,
)
from allennlp.data.da... | data-efficient-finetuning-main | attribution/huggingface_readers.py |
data-efficient-finetuning-main | attribution/__init__.py | |
from typing import Any, Dict, List
from overrides import overrides
import logging
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
from torch.nn import CrossEntropyLoss
from allennlp.nn import util
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models import Model
from ... | data-efficient-finetuning-main | attribution/model.py |
"""
Adapted from t-few repo:
https://github.com/r-three/t-few/blob/master/src/models/lora.py
"""
import re
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from allennlp.models import Model
from attribution.model import BasicSeq2Seq
logger = l... | data-efficient-finetuning-main | attribution/ia3.py |
from typing import Dict, List
from overrides import overrides
import logging
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
from allennlp.nn import util
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models import Model
from allennlp.training.metrics import Average
l... | data-efficient-finetuning-main | attribution/ni_model.py |
'''
Mix and Match Adapter using recommended settings from https://arxiv.org/abs/2110.04366
'''
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.t5.modeling_t5 import (
T5LayerCrossAttention,
T5LayerSelfAttention,
T5Block
)
from transformers import P... | data-efficient-finetuning-main | attribution/mam.py |
from gzip import READ
import json
import logging
import random
from collections import defaultdict
from typing import List, Iterable, Optional, Tuple, Dict
from overrides import overrides
import datasets
from allennlp.data.fields import (
MetadataField,
TextField,
IndexField,
ListField,
)
from allennl... | data-efficient-finetuning-main | attribution/icl_readers.py |
from typing import Dict, List
from overrides import overrides
import logging
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
from allennlp.nn import util
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models import Model
from allennlp.training.metrics import Average
l... | data-efficient-finetuning-main | attribution/drop_model.py |
import logging
from collections import defaultdict
from typing import Iterable, Optional, Tuple
from overrides import overrides
import datasets
from allennlp.data.fields import (
MetadataField,
TextField,
)
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.instance impo... | data-efficient-finetuning-main | attribution/drop_reader.py |
import json
import pickle
import logging
from collections import defaultdict
from typing import Any, Dict, List, Iterable
import random
from overrides import overrides
import torch
from allennlp.common.util import JsonDict
from allennlp.data.fields import (
MetadataField,
TextField,
IndexField,
ListF... | data-efficient-finetuning-main | attribution/qasper_reader.py |
import json
import pickle
import logging
from collections import defaultdict
from typing import Any, Dict, List, Iterable
from overrides import overrides
import torch
from allennlp.data.fields import (
MetadataField,
TextField,
IndexField,
ListField,
)
from allennlp.data.dataset_readers.dataset_reade... | data-efficient-finetuning-main | attribution/p3_cluster_reader.py |
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import json
from tqdm import tqdm
import random
random.seed(23103)
text_data = []
clusters_data = json.load(open("./t0_cluster_data.json"))
for cluster_id, cluster_data in clusters_dat... | data-efficient-finetuning-main | scripts/analyze_surface_similarity.py |
import json
import sys
from transformers import T5TokenizerFast
import re
from datasets import load_dataset
from sklearn.metrics import f1_score
from sklearn.preprocessing import MultiLabelBinarizer
file = sys.argv[1]
tokenizer = T5TokenizerFast.from_pretrained('t5-base')
mlb = MultiLabelBinarizer()
concept_dict = js... | data-efficient-finetuning-main | scripts/evaluate_eurlex_preds.py |
import tqdm
import sys
import json
import argparse
datasets = [
"rte",
"anli_r1",
"anli_r2",
"anli_r3",
"wic",
"copa",
"wsc",
"winogrande",
"hellaswag",
"cb",
"story_cloze",
"casehold",
"drop",
"qasper"
]
parser = argparse.ArgumentParser()
parser.add_argument("-... | data-efficient-finetuning-main | scripts/indices_to_file.py |
import torch
import json
import os
import pickle
from tqdm import tqdm
from collections import defaultdict
import numpy
from sklearn.decomposition import PCA
from sklearn import mixture
from sklearn.metrics.pairwise import cosine_distances
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "goo... | data-efficient-finetuning-main | scripts/make_gradient_clusters.py |
import json
import os
import tqdm
import gzip
import argparse
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import faiss
import numpy
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, required=True)
parser.add_argument("--output_prefix", type=str, required=True)
p... | data-efficient-finetuning-main | scripts/index_p3_train_reps.py |
import faiss
import argparse
import torch
import numpy
import json
import tqdm
from scipy.stats import entropy
from sklearn.cluster import kmeans_plusplus
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
numpy.random.seed(20389)
parser = argparse.ArgumentParser()
parser.add_argument("--training_data", ... | data-efficient-finetuning-main | scripts/select_few_shots.py |
import json
import sys
stuff = {}
file = open(sys.argv[1], 'r')
for line in file:
sample = json.loads(line)
stuff[sample['query_id'][0]] = sample['answer'][0]
with open('drop_preds.json', 'w') as f:
json.dump(stuff, f)
| data-efficient-finetuning-main | scripts/convert_allennlp_pred_to_drop_eval_format.py |
"""
A script to construct balanced random p3 sets. We fully balance this and uniformly sample from the list of tasks,
then sample a random input from this task. Note that this does not fully match t0 training (which only lightly balances
dataset sizes).
"""
import torch
import json
import os
import pickle
from tqdm imp... | data-efficient-finetuning-main | scripts/construct_balanced_sample.py |
import argparse
import random
import faiss
import numpy
from sklearn.cluster import kmeans_plusplus
import torch
import json
import gzip
import tqdm
from collections import defaultdict
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(o... | data-efficient-finetuning-main | scripts/retrieve_training_data.py |
#!/usr/bin/python
from collections import defaultdict
from typing import Any, Dict, List, Set, Tuple, Union, Optional
import json
import argparse
import string
import re
import numpy as np
from scipy.optimize import linear_sum_assignment
# From here through _normalize_answer was originally copied from:
# https://wo... | data-efficient-finetuning-main | scripts/drop_eval_script.py |
import argparse
import faiss
import numpy
from sklearn.cluster import kmeans_plusplus
import torch
import json
import gzip
import tqdm
from collections import defaultdict
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath... | data-efficient-finetuning-main | scripts/retrieve_training_data_ni.py |
import gzip
import json
import random
from tqdm import tqdm
import sys
file = sys.argv[1]
outfile = open(sys.argv[1][:-6] + '_index.txt', 'w')
# start by loading the file into memory
with open(file) as f:
for sample in f:
outfile.write(f'{json.loads(sample)["index_id"]}\n')
| data-efficient-finetuning-main | scripts/grab_instance_idxes.py |
import json
from collections import defaultdict
data = json.load(open("./t0_cluster_data.json"))
cluster_stats = {}
dataset_to_clusters = defaultdict(list)
for cluster_id, cluster_data in data.items():
input_set = set([x["input"] for x in cluster_data])
dataset_set = set([x["dataset"] for x in cluster_data])
... | data-efficient-finetuning-main | scripts/compute_stats.py |
import json
import os
import tqdm
import gzip
import argparse
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import faiss
import numpy
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, required=True)
parser.add_argument("--output_prefix", type=str, required=True)
p... | data-efficient-finetuning-main | scripts/index_ni_train_reps.py |
import gzip
import json
import random
from tqdm import tqdm
import sys
outfile_path = sys.argv[1]
p3_data = sys.argv[2]
# fill in these values with yours.
outfiles = ['qasper']#'anli_r1', 'anli_r2', 'anli_r3', 'casehold', 'cb', 'copa', 'drop', 'hellaswag', 'rte', 'story_cloze', 'wic', 'winogrande', 'wsc']
indices = ... | data-efficient-finetuning-main | scripts/get_random_indices.py |
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