id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
163,233 | import sys
from typing import List, Optional, Tuple
def preprocess_race_batch(
examples,
question_column: str,
context_column: str,
answer_column: str,
) -> Tuple[List[str], List[str]]:
contexts = examples['article']
questions = examples['question']
all_options = examples['o... | null |
163,234 | import sys
from typing import List, Optional, Tuple
def preprocess_newsqa_batch(
examples,
question_column: str,
context_column: str,
answer_column: str,
) -> Tuple[List[str], List[str]]:
questions = examples[question_column]
contexts = examples[context_column]
answers = exa... | null |
163,235 | import sys
from typing import List, Optional, Tuple
def preprocess_ropes_batch(
examples,
question_column: str,
context_column: str,
answer_column: str,
) -> Tuple[List[str], List[str]]:
questions = examples[question_column]
backgrounds = examples["background"]
situations = ... | null |
163,236 | import sys
from typing import List, Optional, Tuple
def preprocess_openbookqa_batch(
examples,
question_column: str,
context_column: str,
answer_column: str,
) -> Tuple[List[str], List[str]]:
questions = examples['question_stem']
all_options = examples['choices']
answers = e... | null |
163,237 | import sys
from typing import List, Optional, Tuple
def preprocess_social_iqa_batch(
examples,
question_column: str,
context_column: str,
answer_column: str,
) -> Tuple[List[str], List[str]]:
contexts = examples['article']
questions = examples['question']
all_options = examp... | null |
163,238 | import sys
from typing import List, Optional, Tuple
def preprocess_dream_batch(
examples,
question_column: str,
context_column: str,
answer_column: str,
) -> Tuple[List[str], List[str]]:
contexts = [" ".join(dialogue) for dialogue in examples['dialogue']]
questions = examples['q... | null |
163,239 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,240 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,241 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,242 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,243 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,244 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,245 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,246 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,247 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,248 | dataset_files =["squad1_1","squad2","narrativeqa_dev","mctest_corrected_the_separator","race_string","arc_hard","arc_easy","boolq","openbookqa"]+["newsqa","quoref","ropes","drop","natural_questions_with_dpr_para","commonsenseqa","qasc","physical_iqa","social_iqa","winogrande_xl","multirc","boolq_np"]
format2id = {"extr... | null |
163,250 | import json
def load_from_rer_(devices,datasets):
pp_total = {}
dataset2id = {}
selected = {"squad1_1":8164,"squad2":130319,"narrativeqa_dev":3567,"mctest_corrected_the_separator":342,"race_string":14536,"arc_hard":317,"arc_easy":395,"boolq":765,"openbookqa":580,"newsqa":445,"quoref":1574,"ropes":1272... | null |
163,251 | import json
def load_from_ret_(devices,datasets):
pp_total = {}
dataset2id = {}
selected = {"squad1_1":8164,"squad2":130319,"narrativeqa_dev":3567,"mctest_corrected_the_separator":342,"race_string":14536,"arc_hard":317,"arc_easy":395,"boolq":765,"openbookqa":580,"newsqa":445,"quoref":1574,"ropes":1272... | null |
163,252 | import json
def load_from_ret(devices,datasets):
pp_total = {}
dataset2id = {}
dataset_files =["squad1_1","squad2","narrativeqa_dev","mctest_corrected_the_separator","race_string","arc_hard","arc_easy","boolq","openbookqa"]+["newsqa","quoref","ropes","drop","natural_questions_with_dpr_para","commonsenseq... | null |
163,253 | import json
def load_from_rer(devices,datasets):
pp_total = {}
dataset2id = {}
dataset_files =["squad1_1","squad2","narrativeqa_dev","mctest_corrected_the_separator","race_string","arc_hard","arc_easy","boolq","openbookqa"]+["newsqa","quoref","ropes","drop","natural_questions_with_dpr_para","commonsenseqa"... | null |
163,254 | import json
def load_from_qa(devices,datasets):
pp_total = {}
dataset2id = {}
selected = {"squad1_1":8164,"squad2":130319,"narrativeqa_dev":3567,"mctest_corrected_the_separator":342,"race_string":14536,"arc_hard":317,"arc_easy":395,"boolq":765,"openbookqa":580,"newsqa":445,"quoref":1574,"ropes":1272,"... | null |
163,255 | import json
def load_all_select_ids(devices,datasets):
qa_total = {}
rt_total = {}
rr_total = {}
dataset2id = {}
selected = {"squad1_1":8164,"squad2":130319,"narrativeqa_dev":3567,"mctest_corrected_the_separator":342,"race_string":14536,"arc_hard":317,"arc_easy":395,"boolq":765,"openbookqa":58... | null |
163,256 | import json
def load_level(devices):
format2size={"bool":1,"multichoice":5,"extractive":29,"abstractive":9}
priority_level = {}
for k_ in format2size.keys():
priority_level[k_]={"ret":0,"rer":0,"qa":0}
for device in devices:
# print("device:",device)
fid = open("./mem_scores/prio... | null |
163,257 | import json
def load_hints(devices):
total = {}
for device in devices:
fid = open("./mem_scores/format_hints-{}.json".format(device),'r',encoding='utf-8')
pp = json.load(fid)
for k_ in pp.keys():
total[int(float(k_))]=pp[k_]
return total | null |
163,258 | import json
def load_hints_dev(devices,epoch_id):
total = {}
for device in devices:
fid = open("./mem_scores/format_hintsdev-{}{}.json".format(device,str(epoch_id)),'r',encoding='utf-8')
pp = json.load(fid)
for k_ in pp.keys():
total[int(float(k_))]=pp[k_]
return tota... | null |
163,259 | import json
def load_hints_test(devices,epoch_id):
total = {}
for device in devices:
fid = open("./mem_scores/format_hintstest-{}{}.json".format(device,str(epoch_id)),'r',encoding='utf-8')
pp = json.load(fid)
for k_ in pp.keys():
total[int(float(k_))]=pp[k_]
return to... | null |
163,260 | import logging
from typing import Tuple
import torch
from torch import Tensor as T
from torch import nn
from transformers.models.bert import BertConfig, BertModel
from transformers.optimization import AdamW
from transformers.models.bert import BertTokenizer
from transformers.models.roberta import RobertaTokenizer
from ... | null |
163,261 | import logging
from typing import Tuple
import torch
from torch import Tensor as T
from torch import nn
from transformers.models.bert import BertConfig, BertModel
from transformers.optimization import AdamW
from transformers.models.bert import BertTokenizer
from transformers.models.roberta import RobertaTokenizer
from ... | null |
163,262 | import logging
from typing import Tuple
import torch
from torch import Tensor as T
from torch import nn
from transformers.models.bert import BertConfig, BertModel
from transformers.optimization import AdamW
from transformers.models.bert import BertTokenizer
from transformers.models.roberta import RobertaTokenizer
from ... | null |
163,263 | import logging
from typing import Tuple
import torch
from torch import Tensor as T
from torch import nn
from transformers.models.bert import BertConfig, BertModel
from transformers.optimization import AdamW
from transformers.models.bert import BertTokenizer
from transformers.models.roberta import RobertaTokenizer
from ... | null |
163,264 | import logging
from typing import Tuple
import torch
from pytext.models.representations.transformer_sentence_encoder import TransformerSentenceEncoder
from pytext.optimizer.optimizers import AdamW
from torch import Tensor as T
from torch import nn
from .biencoder import BiEncoder
def get_optimizer(model: nn.Module, lea... | null |
163,266 | from torch.cuda import amp
import collections
import logging
import random
from typing import Tuple, List
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor as T
from torch import nn
from dpr.data.biencoder_data import BiEncoderSample
from dpr.utils.data_utils import Tensorizer
fro... | calculates q->ctx scores for every row in ctx_vector :param q_vector: :param ctx_vector: :return: |
163,267 | from torch.cuda import amp
import collections
import logging
import random
from typing import Tuple, List
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor as T
from torch import nn
from dpr.data.biencoder_data import BiEncoderSample
from dpr.utils.data_utils import Tensorizer
fro... | null |
163,268 | from torch.cuda import amp
import collections
import logging
import random
from typing import Tuple, List
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor as T
from torch import nn
from dpr.data.biencoder_data import BiEncoderSample
from dpr.utils.data_utils import Tensorizer
fro... | null |
163,269 | import logging
from typing import Tuple
from fairseq.models.roberta.hub_interface import RobertaHubInterface
from fairseq.models.roberta.model import RobertaModel as FaiseqRobertaModel
from fairseq.optim.adam import FairseqAdam
from torch import Tensor as T
from torch import nn
from dpr.models.hf_models import get_robe... | null |
163,272 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,273 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,274 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,275 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,276 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,277 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,278 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,279 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,280 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,281 | import datasets
import collections
import math
def _get_ngrams(segment, max_order):
"""Extracts all n-grams upto a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
methods.... | Computes BLEU score of translated segments against one or more references. Args: reference_corpus: list of lists of references for each translation. Each reference should be tokenized into a list of tokens. translation_corpus: list of translations to score. Each translation should be tokenized into a list of tokens. ma... |
163,284 | import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
return re.s... | null |
163,290 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
from absl import logging
import nltk
import numpy as np
import six
from six.moves import map
from six.moves import range
from .scoring import *
from .rouge_tokenizers import *
impor... | Creates ngrams from the given list of tokens. Args: tokens: A list of tokens from which ngrams are created. n: Number of tokens to use, e.g. 2 for bigrams. Returns: A dictionary mapping each bigram to the number of occurrences. |
163,291 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
from absl import logging
import nltk
import numpy as np
import six
from six.moves import map
from six.moves import range
from .scoring import *
from .rouge_tokenizers import *
def _l... | Computes LCS (Longest Common Subsequence) rouge scores. Args: target_tokens: Tokens from the target text. prediction_tokens: Tokens from the predicted text. Returns: A Score object containing computed scores. |
163,292 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
from absl import logging
import nltk
import numpy as np
import six
from six.moves import map
from six.moves import range
from .scoring import *
from .rouge_tokenizers import *
def _u... | ROUGE: Summary-level LCS, section 3.2 in ROUGE paper. Args: ref_sent: list of tokenized reference sentences can_sent: list of tokenized candidate sentences Returns: summary level ROUGE score |
163,293 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
from absl import logging
import nltk
import numpy as np
import six
from six.moves import map
from six.moves import range
from .scoring import *
from .rouge_tokenizers import *
impor... | Compute n-gram based rouge scores. Args: target_ngrams: A Counter object mapping each ngram to number of occurrences for the target text. prediction_ngrams: A Counter object mapping each ngram to number of occurrences for the prediction text. Returns: A Score object containing computed scores. |
163,294 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
from nltk.stem import porter
import re
import six
NON_ALPHANUM_RE = re.compile(NON_ALPHANUM_PATTERN)
SPACES_RE = re.compile(SPACES_PATTERN)
VALID_TOKEN_RE = re.compile(VALID_TOKEN_PATTERN)
The provid... | Tokenize input text into a list of tokens. This approach aims to replicate the approach taken by Chin-Yew Lin in the original ROUGE implementation. Args: text: A text blob to tokenize. stemmer: An optional stemmer. Returns: A list of string tokens extracted from input text. |
163,295 | import argparse
import json
import re
import string
import sys
from collections import Counter
def f1_score(prediction, ground_truth):
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_tr... | null |
163,296 | import logging
import t5
import os
import json
import functools
import tensorflow as tf
import tensorflow_datasets as tfds
def dataset_preprocessor(ds):
def normalize_text(text):
"""Lowercase and remove quotes from a TensorFlow string."""
text = tf.strings.lower(text)
text = tf.strings.rege... | null |
163,297 | import logging
import t5
import os
import json
import functools
import tensorflow as tf
import tensorflow_datasets as tfds
DATA_DIR = f"gs://unifiedqa/data/"
def get_path(data_dir1, split):
tsv_path = {
"train": os.path.join(data_dir1, "train.tsv"),
"dev": os.path.join(data_dir1, "dev.tsv"),
... | null |
163,298 | import glob
import json
import logging
import pickle
import time
from typing import List, Tuple, Dict, Iterator
import numpy as np
import torch
from torch import Tensor as T
from torch import nn
from datasets import load_from_disk
from transformers import (
AutoTokenizer,
AdamW,
get_linear_schedule_with_war... | null |
163,299 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,300 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,301 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,302 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,303 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,304 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,305 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,306 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,307 | import os
import time
import torch
import copy,random
import sys
import gc
import json
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from dataclasses import dataclass, field
from typing import Optional
from typin... | null |
163,308 | import copy
import math
import os
import warnings
import time
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint
import torch.nn.functional as F
from transformers.activations import ACT2FN
from transformers.file_utils import (
DUMMY_INPUTS,
DUMM... | Load tf checkpoints in a pytorch model. |
163,313 | import json
import re
import torch
import transformers
import transformers.models.llama.modeling_llama
from functools import partial
def process_system_message(system_message, functions):
assert "with a function call to actually excute your step." in system_message
# we find that following ReACT format and mer... | null |
163,314 | import json
import re
import torch
import transformers
import transformers.models.llama.modeling_llama
from functools import partial
The provided code snippet includes necessary dependencies for implementing the `get_gpu_memory` function. Write a Python function `def get_gpu_memory(max_gpus=None)` to solve the followi... | Get available memory for each GPU. |
163,315 | import json
import re
import torch
import transformers
import transformers.models.llama.modeling_llama
from functools import partial
def standardize_category(category):
save_category = category.replace(" ", "_").replace(",", "_").replace("/", "_")
while " " in save_category or "," in save_category:
sav... | null |
163,316 | import json
import re
import torch
import transformers
import transformers.models.llama.modeling_llama
from functools import partial
def standardize(string):
res = re.compile("[^\\u4e00-\\u9fa5^a-z^A-Z^0-9^_]")
string = res.sub("_", string)
string = re.sub(r"(_)\1+","_", string).lower()
while True:
... | null |
163,317 | import json
import re
import torch
import transformers
import transformers.models.llama.modeling_llama
from functools import partial
def change_name(name):
change_list = ["from", "class", "return", "false", "true", "id", "and"]
if name in change_list:
name = "is_" + name
return name | null |
163,318 | import json
import re
import torch
import transformers
import transformers.models.llama.modeling_llama
from functools import partial
class CondenseRotaryEmbedding(torch.nn.Module):
def __init__(self, dim, ratio, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
inv_freq = 1.... | null |
163,319 | import json
import re
import torch
import transformers
import transformers.models.llama.modeling_llama
from functools import partial
def process_retrieval_ducoment(documents_df):
ir_corpus = {}
corpus2tool = {}
for row in documents_df.itertuples():
doc = json.loads(row.document_content)
ir_... | null |
163,320 | import gc
import abc
import numpy as np
import math
from typing import Iterable
import torch
from transformers.generation.logits_process import (
LogitsProcessorList,
RepetitionPenaltyLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
)
from config import base_list, bsz
d... | null |
163,321 | import gc
import abc
import numpy as np
import math
from typing import Iterable
import torch
from transformers.generation.logits_process import (
LogitsProcessorList,
RepetitionPenaltyLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
)
from config import base_list, bsz
T... | 根据公式换算delta |
163,322 | import gc
import abc
import numpy as np
import math
from typing import Iterable
import torch
from transformers.generation.logits_process import (
LogitsProcessorList,
RepetitionPenaltyLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
)
from config import base_list, bsz
d... | null |
163,323 | import gc
import abc
import numpy as np
import math
from typing import Iterable
import torch
from transformers.generation.logits_process import (
LogitsProcessorList,
RepetitionPenaltyLogitsProcessor,
TemperatureLogitsWarper,
TopKLogitsWarper,
TopPLogitsWarper,
)
from config import base_list, bsz
de... | null |
163,324 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWit... | Make causal mask used for bi-directional self-attention. |
163,325 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWit... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
163,326 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWit... | null |
163,327 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWit... | This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) |
163,328 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
d = 128
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def theta(i, base):
return base ** (-2 * i / d)
def diff_qm... | null |
163,329 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def get_tokenizer():
from transformers import AutoTokenizer
tok... | null |
163,330 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def get_kv_retrieval_prompt(
data,
key: str,
query_aware_con... | null |
163,331 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def get_kv_retrieval_prompt(
data,
key: str,
query_aware_con... | null |
163,332 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def caculate_mid_mse(sample, target):
total = 0
idx = 0
idx... | null |
163,333 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def merge_peak(ori_peak, new_peak):
# new_peak = [k for k in new_pea... | null |
163,334 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def qmkn(m, base):
result = 0
for j in range(int(d / 2)):
... | null |
163,335 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def qmkn(m, base):
result = 0
for j in range(int(d / 2)):
... | null |
163,336 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def qmkn(m, base):
def plot_introduction():
plt.figure(figsize=(7,8... | null |
163,337 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def qmkn(m, base):
def plot_base_selection_sample():
import mpl_too... | null |
163,338 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def qmkn(m, base):
result = 0
for j in range(int(d / 2)):
... | null |
163,339 | import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
from xopen import xopen
import random
from scipy.signal import argrelextrema
from matplotlib.pyplot import MultipleLocator
import seaborn as sns
import palettable
def plot_ablation_heat():
f = [float(k.strip()) for k in open('heat... | null |
163,340 | import os
import json
import argparse
import regex
import unicodedata
import string
def normalize_answer(s):
def remove_articles(text):
return regex.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def lower(text):
return text.lower()
... | null |
163,341 | import numpy as np
import torch
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `unsqueeze` function. Write a Python function `def unsqueeze(input, dims)` to solve the following problem:
Implement multi-dimension unsqueeze function.
Here is the function:
... | Implement multi-dimension unsqueeze function. |
163,342 | import numpy as np
import torch
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `gumbel_softmax` function. Write a Python function `def gumbel_softmax(input, tau=1, eps=1e-10, use_gpu=False)` to solve the following problem:
Basic implement of gumbel_softma... | Basic implement of gumbel_softmax. |
163,343 | import numpy as np
import torch
import torch.nn.functional as F
def equal(x, y, dtype=None):
""" Implement equal in dy-graph mode. """
if dtype is None:
dtype = "float32"
if isinstance(x, torch.Tensor):
x = x.numpy()
if isinstance(y, torch.Tensor):
y = y.numpy()
out = np.equa... | Implement not_equal in dy-graph mode. |
163,344 | from collections import Counter
from nltk.translate import bleu_score
from nltk.translate.bleu_score import SmoothingFunction
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `distinct` function. Write a Python function `def distinct(seqs)` to solve the following proble... | Calculate intra/inter distinct 1/2. |
163,345 | from collections import Counter
from nltk.translate import bleu_score
from nltk.translate.bleu_score import SmoothingFunction
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `bleu` function. Write a Python function `def bleu(hyps, refs)` to solve the following problem:... | Calculate bleu 1/2. |
163,346 |
The provided code snippet includes necessary dependencies for implementing the `batch` function. Write a Python function `def batch(reader, batch_size, drop_last=False)` to solve the following problem:
This operator creates a batched reader which combines the data from the input reader to batched data. Args: reader(g... | This operator creates a batched reader which combines the data from the input reader to batched data. Args: reader(generator): the data reader to read from. batch_size(int): size of each mini-batch. drop_last(bool, optional): If set to True, the last batch is dropped when the size of last batch is not equal to batch_si... |
163,347 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import json
import logging
import os
import regex as re
import sys
import unicodedata
def clean_string(string):
replace_mp = {
" - ": "-",
" ' ": "'",
" n't": "n't",
" 'm": "'m",
... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.