id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
11,689 | import argparse
import csv
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import transformers
from transf... | Train the model |
11,690 | import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,... | null |
11,691 | import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,... | Output a list of tuples(story, 1st continuation, 2nd continuation, label) |
11,692 | import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,... | Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label) To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation: input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf... |
11,693 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import transformers
from seq2seq_trainer import Seq2SeqTrainer
from seq2seq_training_args import Seq2SeqTrainingArguments
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
... | Log and save metrics Args: - split: one of train, val, test - metrics: metrics dict - output_dir: where to save the metrics |
11,694 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import transformers
from seq2seq_trainer import Seq2SeqTrainer
from seq2seq_training_args import Seq2SeqTrainingArguments
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
... | null |
11,695 | import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def pack_data_dir(tok, data_dir: Path, max_tokens, save_path):
save_path = Path(save_path)
save_path.mkdir(exist_ok=True)
for split in ["train"]:
src_path, tgt_path = data_dir / f"{spl... | null |
11,696 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | From fairseq |
11,697 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,698 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Remove columns that are populated exclusively by pad_token_id |
11,699 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Go through the text data by order of src length with a bit of randomness. From fastai repo. |
11,700 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | pickle.load(path) |
11,701 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,702 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Save git information to output_dir/git_log.json |
11,703 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Freeze token embeddings and positional embeddings for bart, just token embeddings for t5. |
11,704 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,705 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,706 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,707 | import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import Seq2SeqDataset, pickle_save
class Seq2SeqDataset(AbstractSeq2SeqDataset):
"""A dataset that calls prepare_seq2seq_batch."""
def __getitem__(self, index) -> Dict[str, str]:
... | Save max(src_len, tgt_len) for each example to allow dynamic batching. |
11,708 | import fire
from utils import calculate_rouge, save_json
def save_json(content, path, indent=4, **json_dump_kwargs):
with open(path, "w") as f:
json.dump(content, f, indent=indent, sort_keys=True, **json_dump_kwargs)
def calculate_rouge(
pred_lns: List[str],
tgt_lns: List[str],
use_st... | Kwargs will be passed to calculate_rouge |
11,709 | from pathlib import Path
import fire
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `download_wmt_dataset` function. Write a Python function `def download_wmt_dataset(src_lang="ro", tgt_lang="en", dataset="wmt16", save_dir=None) -> None` to solve the following prob... | Download a dataset using the datasets package and save it to the format expected by finetune.py Format of save_dir: train.source, train.target, val.source, val.target, test.source, test.target. Args: src_lang: <str> source language tgt_lang: <str> target language dataset: <str> wmt16, wmt17, etc. wmt16 is a good start ... |
11,710 | from pathlib import Path
import fire
The provided code snippet includes necessary dependencies for implementing the `minify` function. Write a Python function `def minify(src_dir: str, dest_dir: str, n: int)` to solve the following problem:
Write first n lines of each file f in src_dir to dest_dir/f
Here is the funct... | Write first n lines of each file f in src_dir to dest_dir/f |
11,711 | from typing import Union
import fire
import torch
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `convert` function. Write a Python function `def convert(src_path: str, map_location: str = "cpu", save_path: Union[str, None] = None) -> None` to solve the following p... | Convert a pytorch_model.bin or model.pt file to torch.float16 for faster downloads, less disk space. |
11,712 | import argparse
import itertools
import operator
import sys
from collections import OrderedDict
from run_eval import datetime_now, run_generate
from utils import ROUGE_KEYS
task_score_names = {
"translation": ["bleu"],
"summarization": ROUGE_KEYS,
}
def parse_search_arg(search):
groups = search.split()
... | Run parametric search over the desired hparam space with help of ``run_eval.py``. All the arguments except ``--search`` are passed to ``run_eval.py`` as is. The values inside of "--search" are parsed, reformatted and fed to ``run_eval.py`` as additional args. The format for the ``--search`` value is a simple string wit... |
11,713 | import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import (
... | null |
11,714 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
The provided code snippet includes necessary dependencies for implementing the `parse_args` function. Write a Python function `def parse_args()` to solve the follo... | Helper function parsing the command line options @retval ArgumentParser |
11,715 | import fire
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
The provided code snippet includes necessary dependencies for implementing the `save_randomly_initialized_version` function. Write a Python function `def save_randomly_initialized_version(config_name: str, save_dir: str, **config_kwa... | Save a randomly initialized version of a model using a pretrained config. Args: config_name: which config to use save_dir: where to save the resulting model and tokenizer config_kwargs: Passed to AutoConfig Usage:: save_randomly_initialized_version("facebook/bart-large-cnn", "distilbart_random_cnn_6_3", encoder_layers=... |
11,716 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassi... | null |
11,717 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
... | null |
11,718 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
... | null |
11,719 | import csv
import glob
import json
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional
import tqdm
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
logger = logging.getLogger(__name__)
class Inp... | Loads a data file into a list of `InputFeatures` |
11,720 | import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
from torch import nn
import transformers
from transformer... | null |
11,721 | import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def fill_mask(masked_input, model, tokenizer, topk=5):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("<mask>") == 1
input_ids = torch.tensor(token... | null |
11,722 | import argparse
import glob
import logging
import os
import random
import timeit
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import transformers
from transformers impor... | Train the model |
11,723 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import transformers
from transformers import (
AutoConfig,
AutoModelForQuestionAnswering,
AutoTokenizer,
DataCollatorWithPadding,
HfArgumentParser,
SquadDataset,
)
from transformers import Sq... | null |
11,724 | import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _is_chinese_char(cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/C... | null |
11,725 | import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoT... | null |
11,726 | import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoT... | null |
11,727 | import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
Au... | null |
11,728 | import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
Au... | null |
11,729 | argparse
import json
import logging
import math
import os
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
... | null |
11,730 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCro... | null |
11,731 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCro... | null |
11,732 | import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
from datasets import load_dataset
import evaluate
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoToken... | null |
11,733 | argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelerator
from accelerate.loggin... | null |
11,734 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.io import ImageReadMode, read_image
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torc... | null |
11,735 | import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import numpy as np
from datasets import DatasetDict, load_dataset
import evaluate
import transformers
from transformers import (
AutoConfig,
AutoFea... | Randomly sample chunks of `max_length` seconds from the input audio |
11,736 | import argparse
import json
import logging
import math
import os
import random
from dataclasses import dataclass
from itertools import chain
from pathlib import Path
from typing import Optional, Union
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto i... | null |
11,737 | import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | null |
11,738 | import json
import logging
import os
import re
import sys
import warnings
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
import datasets
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
import evaluate
import transformers
from transformers import (
... | null |
11,739 | functools
import json
import logging
import os
import re
import sys
import warnings
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
import datasets
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
import evaluate
import transformers
from transformers... | null |
11,740 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
from datasets import ClassLabel, load_dataset
import evaluate
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,... | null |
11,741 | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import torch
from datasets import ClassLabel, load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelerator
fro... | null |
11,742 | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelera... | Save results while prefixing metric names. Args: results: (:obj:`dict`): A dictionary of results. output_dir: (:obj:`str`): An output directory. file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`): An output file name. metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`): A metric n... |
11,743 | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelera... | null |
11,744 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional, Tuple
import datasets
from datasets import load_dataset
import evaluate
import transformers
from trainer_seq2seq_qa import QuestionAnsweringSeq2SeqTrainer
from transformers import (
AutoConfig,
AutoMo... | null |
11,745 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset
import evaluate
import transformers
from trainer_qa import QuestionAnsweringTrainer
from transformers import (
DataCollatorWithPadding,
EvalPrediction,
H... | null |
11,746 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset
import evaluate
import transformers
from trainer_qa import QuestionAnsweringTrainer
from transformers import (
AutoConfig,
AutoModelForQuestionAnswering,
... | null |
11,747 | import collections
import json
import logging
import os
from typing import Optional, Tuple
import numpy as np
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `postprocess_qa_predictions` function. Write a Python function `de... | Post-processes the predictions of a question-answering model to convert them to answers that are substrings of the original contexts. This is the base postprocessing functions for models that only return start and end logits. Args: examples: The non-preprocessed dataset (see the main script for more information). featu... |
11,749 | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelera... | Save results while prefixing metric names. Args: results: (:obj:`dict`): A dictionary of results. output_dir: (:obj:`str`): An output directory. file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`): An output file name. metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`): A metric n... |
11,750 | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelera... | null |
11,751 | import logging
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional
import datasets
from datasets import load_dataset
import evaluate
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoC... | null |
11,752 | import logging
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional
import datasets
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForPermutationLanguageModel... | null |
11,753 | import logging
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional
import datasets
from datasets import load_dataset
import evaluate
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
AutoC... | null |
11,754 | import argparse
import json
import logging
import math
import os
import random
from itertools import chain
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import transformers
from accelerate import Accelerator, Di... | null |
11,755 | import argparse
import json
import logging
import math
import os
import random
from itertools import chain
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import transformers
from accelerate import Accelerator, Di... | null |
11,757 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
from datasets import load_dataset
import evaluate
import transformers
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
DataCollatorForSe... | null |
11,758 | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate import Accelera... | null |
11,759 | argparse
import math
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Union
import datasets
import torch
from datasets import DatasetDict, concatenate_datasets, load_dataset
from torch.utils.data.dataloader import DataLoader
from tqdm.auto import tqdm
import ... | null |
11,760 | import math
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Union
import datasets
import torch
from datasets import DatasetDict, concatenate_datasets, load_dataset
from torch.utils.data.dataloader import DataLoader
from tqdm.auto import tqdm
import transform... | Multiplies grads by a constant *c*. |
11,761 | import math
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Union
import datasets
import torch
from datasets import DatasetDict, concatenate_datasets, load_dataset
from torch.utils.data.dataloader import DataLoader
from tqdm.auto import tqdm
import transform... | Compute grad norm given a gradient scale. |
11,762 | import json
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.transforms import functional
from tqdm.auto import ... | null |
11,763 | argparse
import json
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.transforms import functional
from tqdm.aut... | null |
11,764 | import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional... | null |
11,768 | import argparse
import logging
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def set_seed(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed) | null |
11,769 | import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNe... | null |
11,770 | import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNe... | null |
11,771 | import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNe... | null |
11,772 | import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNe... | null |
11,773 | import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNe... | null |
11,774 | import argparse
import logging
import numpy as np
import torch
from transformers import (
CTRLLMHeadModel,
CTRLTokenizer,
GPT2LMHeadModel,
GPT2Tokenizer,
OpenAIGPTLMHeadModel,
OpenAIGPTTokenizer,
TransfoXLLMHeadModel,
TransfoXLTokenizer,
XLMTokenizer,
XLMWithLMHeadModel,
XLNe... | null |
11,775 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformer... | null |
11,776 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Interpola... | null |
11,777 | import argparse
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import nltk
import numpy as np
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import evaluate
import transformers
from accelerate imp... | null |
11,778 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import nltk
import numpy as np
from datasets import load_dataset
import evaluate
import transformers
from filelock import FileLock
from transformers import (
AutoConfig,
AutoModelForSeq2SeqL... | null |
11,779 | import logging
import math
import os
import random
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import evaluate
import jax
import... | Create initial training state. |
11,780 | import logging
import math
import os
import random
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import evaluate
import jax
import... | Returns a linear warmup, linear_decay learning rate function. |
11,781 | import logging
import math
import os
import random
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import evaluate
import jax
import... | Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices. |
11,782 | import logging
import math
import os
import random
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import evaluate
import jax
import... | Returns batches of size `batch_size` from `eval dataset`. Sharding handled by `pad_shard_unpad` in the eval loop. |
11,783 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk
import numpy as np
from datasets import Dataset, load_dataset
from ... | Shift input ids one token to the right. |
11,784 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk
import numpy as np
from datasets import Dataset, load_dataset
from ... | Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices. Shuffle batches if `shuffle` is `True`. |
11,785 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk
import numpy as np
from datasets import Dataset, load_dataset
from ... | null |
11,786 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk
import numpy as np
from datasets import Dataset, load_dataset
from ... | Returns a linear warmup, linear_decay learning rate function. |
11,787 | import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import ClassLabel... | Create initial training state. |
11,788 | import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import ClassLabel... | Returns a linear warmup, linear_decay learning rate function. |
11,789 | import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import ClassLabel... | Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices. |
11,790 | import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import ClassLabel... | Returns batches of size `batch_size` from `eval dataset`. Sharding handled by `pad_shard_unpad` in the eval loop. |
11,791 | import json
import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm imp... | Create initial training state. |
11,792 | import json
import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm imp... | Returns a linear warmup, linear_decay learning rate function. |
11,793 | import json
import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm imp... | Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices. |
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