id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
11,898 | import logging
import re
import torch
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
logger = logging.getLogger(__name__)
def fuse_qkv(model, args):
"""Adjust quantization ranges to match an ... | Function called before the training loop. |
11,899 | import logging
import re
import torch
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementi... | Enable calibration of all *_input_quantizer modules in model. |
11,900 | import logging
import re
import torch
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
logger = logging.getLogger(__name__)
def print_quant_summary(model):
"""Print summary of all quantizer mod... | Disable calibration and load amax for all "*_input_quantizer modules in model. |
11,901 | import logging
import re
import torch
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
The provided code snippet includes necessary dependencies for implementing the `expand_amax` function. Write ... | Expand per-tensor amax to be per channel, where each channel is assigned the per-tensor amax. |
11,902 | import logging
import re
import torch
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementi... | Print model quantization configuration. |
11,903 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import torch
from absl import logging as absl_logging
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
i... | null |
11,904 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import torch
from absl import logging as absl_logging
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
i... | null |
11,905 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import torch
from absl import logging as absl_logging
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
i... | null |
11,906 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import torch
from absl import logging as absl_logging
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
i... | null |
11,907 | 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... |
11,908 | 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_with_beam_search` function. Write a Py... | Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the original contexts. This is the postprocessing functions for models that return start and end logits, indices, as well as cls token predictions. Args: examples: The non-preprocessed dataset... |
11,909 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset, load_metric
import quant_trainer
import transformers
from trainer_quant_qa import QuestionAnsweringTrainer
from transformers import (
AutoTokenizer,
DataCol... | null |
11,910 | 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, load_metric
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoProcessor,
... | null |
11,911 | import numpy as np
import torch
import gym
from mujoco_py import GlfwContext
from transformers import DecisionTransformerModel
state_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
device = "cuda"
def get_action(model, states, actions, rewards, returns_to_go, timesteps):
# we don't care ab... | null |
11,912 | import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union
import datasets
import numpy as np
import torch
from packaging import version
from torch import nn
import librosa
from lang_trans import arabic
from transformers... | null |
11,913 | import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
H... | null |
11,914 | import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
import librosa
from transformers import (
HfArgumentParser,
Trainer,
TrainingA... | null |
11,915 | import argparse
import glob
import json
import logging
import os
import random
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import tr... | Train the model |
11,916 | import logging
import sys
import time
from dataclasses import field
from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
from datasets import DatasetDict, load_dataset
from tqdm import tqdm
import flax
import jax
import jax.numpy as jnp
import librosa
import optax
from flax import ... | null |
11,917 | import logging
import sys
import time
from dataclasses import field
from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
from datasets import DatasetDict, load_dataset
from tqdm import tqdm
import flax
import jax
import jax.numpy as jnp
import librosa
import optax
from flax import ... | null |
11,918 | import logging
import sys
import time
from dataclasses import field
from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
from datasets import DatasetDict, load_dataset
from tqdm import tqdm
import flax
import jax
import jax.numpy as jnp
import librosa
import optax
from flax import ... | null |
11,919 | import logging
import sys
import time
from dataclasses import field
from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
from datasets import DatasetDict, load_dataset
from tqdm import tqdm
import flax
import jax
import jax.numpy as jnp
import librosa
import optax
from flax import ... | null |
11,920 | import logging
import sys
import time
from dataclasses import field
from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
from datasets import DatasetDict, load_dataset
from tqdm import tqdm
import flax
import jax
import jax.numpy as jnp
import librosa
import optax
from flax import ... | null |
11,921 | import os
import numpy as np
from tqdm import tqdm
import jsonlines
def get_strided_contexts_and_ans(example, tokenizer, doc_stride=2048, max_length=4096, assertion=True):
# overlap will be of doc_stride - q_len
out = get_context_and_ans(example, assertion=assertion)
answer = out["answer"]
# later, remo... | null |
11,922 | import os
import numpy as np
from tqdm import tqdm
import jsonlines
CATEGORY_MAPPING = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def save_to_disk(hf_data, file_name):
with jsonlines.open(file_name, "a") as writer:
for example in tqdm(hf_data, total=len(hf_data), desc="Saving samples ... "):
... | null |
11,923 | import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
from tqdm.auto import tqdm
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import... | null |
11,924 | import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
from tqdm.auto import tqdm
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import... | null |
11,925 | import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
from tqdm.auto import tqdm
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import... | null |
11,926 | import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
from tqdm.auto import tqdm
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import... | null |
11,927 | import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
from tqdm.auto import tqdm
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import... | null |
11,928 | import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
from tqdm.auto import tqdm
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import... | null |
11,929 | from datasets import load_from_disk
import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from transformers import BigBirdTokenizerFast
PUNCTUATION_SET_TO_EXCLUDE = set("".join(["‘", "’", "´", "`", ".", ",", "-", '"']))
def get_sub_answers(answers, begin=0, end=None):
return [" ... | null |
11,930 | from datasets import load_from_disk
import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from transformers import BigBirdTokenizerFast
def get_best_valid_start_end_idx(start_scores, end_scores, top_k=1, max_size=100):
best_start_scores, best_start_idx = jax.lax.top_k(start_sco... | null |
11,931 | from datasets import load_from_disk
import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from transformers import BigBirdTokenizerFast
def format_dataset(sample):
question = sample["question"]["text"]
context = sample["document"]["tokens"]["token"]
is_html = sample["do... | null |
11,932 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | null |
11,933 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | The training iterator is advanced so that after groupifying the samples, `num_samples` of length `max_seq_length` are returned. |
11,934 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | null |
11,935 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | null |
11,936 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | null |
11,937 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | null |
11,938 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | null |
11,939 | import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import flax
import jax
import ja... | null |
11,940 | import logging
import math
import os
import sys
import time
from dataclasses import dataclass, field
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqdm import tqdm
import jax
import jax.numpy ... | Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices. Shuffle batches if `shuffle` is `True`. |
11,941 | import logging
import math
import os
import sys
import time
from dataclasses import dataclass, field
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqdm import tqdm
import jax
import jax.numpy ... | null |
11,942 | import logging
import math
import os
import sys
import time
from dataclasses import dataclass, field
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqdm import tqdm
import jax
import jax.numpy ... | null |
11,943 | import logging
import math
import os
import sys
import time
from dataclasses import dataclass, field
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqdm import tqdm
import jax
import jax.numpy ... | Returns a linear warmup, linear_decay learning rate function. |
11,944 | import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
_unmatched = object()
def _replacement_rules(rules):
def replace(key, val):
for rule, replacement in rules:
if _match(rule, key):
... | null |
11,945 | import json
import logging
import os
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Callable, Optional
import torch
from torchvision.datasets import VisionDataset
from torchvision.io import ImageReadMode, read_image
from torchvision.transforms import CenterCr... | null |
11,946 | import json
import logging
import os
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Callable, Optional
import torch
from torchvision.datasets import VisionDataset
from torchvision.io import ImageReadMode, read_image
from torchvision.transforms import CenterCr... | Returns a linear warmup, linear_decay learning rate function. |
11,947 | import argparse
import glob
import json
import logging
import os
import random
import numpy as np
import torch
from sklearn.metrics import f1_score
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import ... | Train the model |
11,948 | 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, load_metric
import bitsandbytes as bnb
import transformers
fro... | null |
11,949 | 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, load_metric
import bitsandbytes as bnb
import transf... | null |
11,950 | 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 IterableDatasetDict, interleave_datasets, load_dataset, load_metric
from torch.utils.data import IterableDataset
imp... | null |
11,951 | import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
The provided code snippet includes necessary dependencies for implementing the `log_results` function. Write a Python function `def log_r... | DO NOT CHANGE. This function computes and logs the result metrics. |
11,952 | import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
The provided code snippet includes necessary dependencies for implementing the `normalize_text` function. Write a Python function `def no... | DO ADAPT FOR YOUR USE CASE. this function normalizes the target text. |
11,953 | import os
from collections import deque
import torch
from torch.utils.data import Dataset
def _add_missing_period(line):
END_TOKENS = [".", "!", "?", "...", "'", "`", '"', "\u2019", "\u2019", ")"]
if line.startswith("@highlight"):
return line
if line[-1] in END_TOKENS:
return line
return... | Extract the story and summary from a story file. Arguments: raw_story (str): content of the story file as an utf-8 encoded string. Raises: IndexError: If the story is empty or contains no highlights. |
11,954 | import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from modeling_bertabs import BertAbs, build_predictor
from transformers import BertTokenizer
from .utils_summarization import (
CNNDMDa... | null |
11,955 | import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from modeling_bertabs import BertAbs, build_predictor
from transformers import BertTokenizer
from .utils_summarization import (
CNNDMDa... | Decode the summary and return it in a format suitable for evaluation. |
11,956 | import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from modeling_bertabs import BertAbs, build_predictor
from transformers import BertTokenizer
from .utils_summarization import (
CNNDMDa... | null |
11,957 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
BertAbsConfig = namedtuple(
"BertAbsConfig",
[
"temp_d... | Copy/paste and tweak the pre-trained weights provided by the creators of BertAbs for the internal architecture. |
11,958 | import copy
import math
import numpy as np
import torch
from torch import nn
from torch.nn.init import xavier_uniform_
from configuration_bertabs import BertAbsConfig
from transformers import BertConfig, BertModel, PreTrainedModel
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715... | null |
11,959 | import copy
import math
import numpy as np
import torch
from torch import nn
from torch.nn.init import xavier_uniform_
from configuration_bertabs import BertAbsConfig
from transformers import BertConfig, BertModel, PreTrainedModel
The provided code snippet includes necessary dependencies for implementing the `tile` fu... | Tiles x on dimension dim count times. |
11,960 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional
import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
Trainer... | null |
11,961 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional
import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
Trainer... | Gets predictions by the same method as the zero-shot pipeline but with DataParallel & more efficient batching |
11,962 | import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
... | null |
11,963 | import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
... | null |
11,964 | import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from accelerate import Accelera... | Generate multiple codes for each task in the dataset. This function leverage accelerator to distribute the processing to multiple GPUs. dataloader, a wrapper around a TokenizeDataset objectm is supposed to send all the prompts from the evalution dataset to the modelm as the following: [p_0_0, p_0_1, ..., p_0_nc-1, p_1_... |
11,965 | import logging
import torch
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from accelerate import Accelerator
from arguments import EvaluationArguments
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_s... | null |
11,966 | import logging
import torch
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from accelerate import Accelerator
from arguments import EvaluationArguments
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_s... | null |
11,967 | import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from datasets import load_dataset
from arguments import PreprocessingArguments
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokeni... | Chain all preprocessing steps into one function to not fill cache. |
11,968 | import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from datasets import load_dataset
from arguments import PreprocessingArguments
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokeni... | Filter dataset with heuristics. Config, test and has_no_keywords files are removed with a given probability. |
11,969 | import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from datasets import load_dataset
from arguments import PreprocessingArguments
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokeni... | Compress a file with g-zip. |
11,970 | from datasets import load_dataset
from tqdm import tqdm
from arguments import TokenizerTrainingArguments
from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
args = parser.parse_args()
iter_dataset = iter(dataset)
def batch_iterator(batch_size... | null |
11,971 | import multiprocessing
import time
from datasets import load_dataset
from arguments import PretokenizationArguments
from transformers import AutoTokenizer, HfArgumentParser
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_dir)
def tokenize(example):
output = dict()
output["input_ids"] = tokenizer(examp... | null |
11,972 | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.c... | null |
11,973 | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.c... | null |
11,974 | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.c... | null |
11,975 | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.c... | null |
11,976 | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.c... | null |
11,977 | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.c... | null |
11,978 | import logging
import os
import time
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.c... | null |
11,979 | import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from tqdm import tqdm
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedItera... | Deduplicate the dataset using minhash and jaccard similarity. This function first generate duplicate clusters, then each cluster is reduced to the extremes that are similar to the other elements in the cluster. Codes are called similar if their Jaccard similarity is greater than jaccard_threshold (0.85 default). Args: ... |
11,980 | import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | Calculate entropy of a pre-softmax logit Tensor |
11,981 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import time
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed ... | Train the model |
11,982 | import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollat... | null |
11,983 | import logging
import os
import sys
from collections import defaultdict
from dataclasses import dataclass, field
from functools import partial
from typing import List, Optional
import nltk
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from filelock import FileLock
from tr... | null |
11,984 | import logging
import os
import sys
from collections import defaultdict
from copy import deepcopy
from dataclasses import dataclass, field
from functools import partial
from typing import List, Optional
import nltk
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from filelo... | null |
11,985 | import dataclasses
import enum
import functools
import math
import re
from typing import Any, List, Text
def convert_to_float(value):
"""Converts value to a float using a series of increasingly complex heuristics.
Args:
value: object that needs to be converted. Allowed types include
float/int/stri... | null |
11,986 | import dataclasses
import enum
import functools
import math
import re
from typing import Any, List, Text
EMPTY_ANSWER = "none"
def _get_float_answer(table, answer_coordinates, aggregation_op):
def _get_answer_coordinates(table, sql_query):
def _get_answer_text(table, answer_coordinates, float_answer):
def retrieve_wik... | null |
11,987 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import numpy as np
import torch
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
... | Data collator that removes the "pairID" key if present. |
11,988 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import numpy as np
import torch
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
... | null |
11,989 | import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTok... | Loads a data file into a list of ``InputFeatures`` Args: examples: List of ``InputExamples`` containing the examples. label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method. max_length: Maximum example length. tokenizer: Instance of a tokenizer that will tokenize the ... |
11,990 | import argparse
import glob
import json
import logging
import os
import random
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from emme... | Train the model |
11,991 | import argparse
import glob
import logging
import os
import random
import timeit
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from emmental import M... | Train the model |
11,992 | import copy
import itertools
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers import BartConfig
from transformers.generation_utils import GenerationMixin
The provided code snippet includes necessary dependencies for implementing the `_convert_past_list_to_tuple` f... | In Bart model, the type of past_key_values is tuple(tuple(torch.FloatTensor)) which is not TorchScript-compatible. To support this, we have to convert it during the export process. This function will convert past values from a list to tuple(tuple(torch.FloatTensor)) for the inner decoder. According to the definition of... |
11,993 | import copy
import itertools
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers import BartConfig
from transformers.generation_utils import GenerationMixin
class EncoderForONNX(torch.nn.Module):
def __init__(self, encoder):
def forward(self, input_ids, atte... | null |
11,994 | import copy
import itertools
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from transformers import BartConfig
from transformers.generation_utils import GenerationMixin
class DecoderForONNX(torch.nn.Module):
def __init__(self, decoder):
def forward(self, input_ids, enco... | null |
11,995 | import argparse
import logging
import os
import sys
import numpy as np
import torch
import onnxruntime
import transformers
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
from transformers import BartForConditionalGeneration, BartTokenizer
de... | null |
11,996 | import argparse
import logging
import os
import sys
import numpy as np
import torch
import onnxruntime
import transformers
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
from transformers import BartForConditionalGeneration, BartTokenizer
mod... | null |
11,997 | import argparse
import logging
import os
import sys
import numpy as np
import torch
import onnxruntime
import transformers
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
from transformers import BartForConditionalGeneration, BartTokenizer
log... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.