id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
11,998 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | null |
11,999 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | Remove columns that are populated exclusively by pad_token_id |
12,000 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | null |
12,001 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | Save git information to output_dir/git_log.json |
12,002 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | null |
12,003 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | list(map(f, x)) |
12,004 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | pickle.dump(obj, path) |
12,005 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | null |
12,006 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | null |
12,007 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | null |
12,008 | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import (
DPRCont... | Split documents into passages |
12,009 | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import (
DPRCont... | Compute the DPR embeddings of document passages |
12,010 | import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def consolidate(
model_type,
generator_name_or_path: str,
question_encoder_name_or_path: str,
dest_dir: Path,
config_name_or_path: str = None,
... | null |
12,011 | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def count_trainable_parameters(model):
model_parameter... | null |
12,012 | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
The provided code snippet includes necessary dependencies ... | Saves the best model by validation EM score. |
12,013 | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def get_early_stopping_callback(metric, patience):
ret... | null |
12,014 | import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
from utils_rag import exact_... | null |
12,015 | import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
from utils_rag import exact_... | null |
12,016 | import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
from utils_rag import exact_... | null |
12,017 | import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
from utils_rag import exact_... | null |
12,018 | import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
from utils_rag import exact_... | null |
12,019 | import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
from utils_rag import exact_... | null |
12,021 | 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 |
12,029 | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transform... | null |
12,033 | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import DPRContextEnc... | Split documents into passages |
12,034 | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import DPRContextEnc... | Compute the DPR embeddings of document passages |
12,036 | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
The provided code snippet includes necessary dependencies ... | Saves the best model by validation EM score. |
12,044 | import os
from functools import partial
from glob import glob
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk
import faiss
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def split_documents(documents):
"""Split documents into passages"""... | null |
12,045 | import os
from functools import partial
from glob import glob
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk
import faiss
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def add_index(shard_dir, index_path):
data_shard_list = []
fo... | null |
12,046 | 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 |
12,047 | 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 |
12,048 | import argparse
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
import joblib
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpt2,
recopy_gpt2,
set_seed,
train_secondary_learn... | Collecting *n* pairs for training the secondary learner Args: context_len: The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded max_steps: To calculate training epochs of secondary learner size_objective_set: size of objective data se... |
12,049 | import argparse
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
import joblib
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpt2,
recopy_gpt2,
set_seed,
train_secondary_learn... | Train the secondary learner Args: secondary_learner_train_data: Data set with (X,IG(X)) pairs to train secondary learner where IG(X) - measure of informativeness and X- context secondary_learner_max_epochs: Number of epochs to train secondary learner secondary_learner_batch_size: Batch size to train secondary learner e... |
12,050 | import argparse
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
import joblib
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpt2,
recopy_gpt2,
set_seed,
train_secondary_learn... | fine-tune with IGF if secondary_learner is not None, else standard fine-tuning Args: model: pre-trained GPT-2 model train_dataset: Data set to train GPT-2 model test_dataset: Evaluate GPT-2 model context_len: The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, seque... |
12,051 | import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def padding_tensor(sequen... | null |
12,052 | import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def is_punctuation(char):... | null |
12,053 | import argparse
import logging
import math
import os
import random
from pathlib import Path
import datasets
import torch
from datasets import ClassLabel, load_dataset, load_metric
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import transformers
from accelerate import Accelerator, DistributedDataPa... | null |
12,054 | import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from datasets import load_dataset
from tqdm.auto import tqdm
import transformers
from accelerate import Accelerator
from finetuning import finetune
from transformers import AutoConfig,... | Self-training a pre-trained model on a downstream task. Args: model_name_or_path: Path to pretrained model or model identifier from huggingface.co/models. train_file: A csv or a json file containing the training data. infer_file: A csv or a json file containing the data to predict on. output_dir: The output directory w... |
12,055 | import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
Au... | null |
12,056 | import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
Au... | null |
12,057 | import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert 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:
# h... | null |
12,058 | import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPT2LMHeadModel
logger = logging.getLogger(__name__)
def print_2d_tensor(tensor... | This method shows how to mask head (set some heads to zero), to test the effect on the network, based on the head importance scores, as described in Michel et al. (http://arxiv.org/abs/1905.10650) |
12,059 | import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPT2LMHeadModel
logger = logging.getLogger(__name__)
def save_model(model, dirp... | This method shows how to prune head (remove heads weights) based on the head importance scores as described in Michel et al. (http://arxiv.org/abs/1905.10650) |
12,060 | import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
import transformers
from transformers import (
... | This method shows how to mask head (set some heads to zero), to test the effect on the network, based on the head importance scores, as described in Michel et al. (http://arxiv.org/abs/1905.10650) |
12,061 | import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
import transformers
from transformers import (
... | This method shows how to prune head (remove heads weights) based on the head importance scores as described in Michel et al. (http://arxiv.org/abs/1905.10650) |
12,062 | import abc
import functools
from collections.abc import Iterable
import numpy as onp
from absl import logging
import jax
import jax.numpy as jnp
from jax import lax, random
def nonnegative_softmax_kernel_feature_creator(
data, projection_matrix, attention_dims_t, batch_dims_t, precision, is_query, normalize_data=T... | Construct a fast softmax attention method. |
12,063 | import abc
import functools
from collections.abc import Iterable
import numpy as onp
from absl import logging
import jax
import jax.numpy as jnp
from jax import lax, random
def generalized_kernel_feature_creator(
data, projection_matrix, batch_dims_t, precision, kernel_fn, kernel_epsilon, normalize_data
):
"""... | Construct a fast generalized attention menthod. |
12,064 | import abc
import functools
from collections.abc import Iterable
import numpy as onp
from absl import logging
import jax
import jax.numpy as jnp
from jax import lax, random
def _numerator(z_slice_shape, precision, unroll=1):
def fwd(qs, ks, vs):
def body(p, qkv):
(q, k, v) = qkv
p ... | null |
12,065 | import abc
import functools
from collections.abc import Iterable
import numpy as onp
from absl import logging
import jax
import jax.numpy as jnp
from jax import lax, random
def _denominator(t_slice_shape, precision, unroll=1):
def fwd(qs, ks):
def body(p, qk):
q, k = qk
p += k
... | null |
12,066 | import abc
import functools
from collections.abc import Iterable
import numpy as onp
from absl import logging
import jax
import jax.numpy as jnp
from jax import lax, random
def _invert_perm(perm):
perm_inv = [0] * len(perm)
for i, j in enumerate(perm):
perm_inv[j] = i
return tuple(perm_inv) | null |
12,067 | import logging
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import jax
import jax.numpy as jnp
from flax import jax_utils
from flax.optim import Adam
from f... | Creates learning rate schedule. Interprets factors in the factors string which can consist of: * constant: interpreted as the constant value, * linear_warmup: interpreted as linear warmup until warmup_steps, * rsqrt_decay: divide by square root of max(step, warmup_steps) * rsqrt_normalized_decay: divide by square root ... |
12,068 | import logging
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import jax
import jax.numpy as jnp
from flax import jax_utils
from flax.optim import Adam
from f... | null |
12,069 | import logging
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import jax
import jax.numpy as jnp
from flax import jax_utils
from flax.optim import Adam
from f... | Calculate evaluation metrics on a batch. |
12,070 | import logging
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import jax
import jax.numpy as jnp
from flax import jax_utils
from flax.optim import Adam
from f... | null |
12,071 | import logging
import os
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import jax
import jax.numpy as jnp
from flax import jax_utils
from flax.optim import Adam
from f... | null |
12,072 | import argparse
import json
from operator import add
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from tqdm import trange
from pplm_classification_head import ClassificationHead
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from transformers.file_utils i... | null |
12,073 | import argparse
import csv
import json
import math
import time
import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm... | null |
12,078 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
12,088 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
12,093 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
12,098 | import os
import sys
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
The provided code snippet includes necessary dependencies for impl... | r""" # Using torch.hub ! import torch config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased') # Download configuration from huggingface.co and cache. config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretra... |
12,099 | import os
import sys
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
The provided code snippet includes necessary dependencies for impl... | r""" # Using torch.hub ! import torch tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased') # Download vocabulary from huggingface.co and cache. tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', './test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretr... |
12,100 | import os
import sys
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
The provided code snippet includes necessary dependencies for impl... | r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test... |
12,101 | import os
import sys
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
The provided code snippet includes necessary dependencies for impl... | r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', 'gpt2') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', './test/saved_model/') # E.g. model was saved using `save_pretrain... |
12,102 | import os
import sys
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
The provided code snippet includes necessary dependencies for impl... | r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', 'bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', './test/bert_model/') # E.g. model was saved using `s... |
12,103 | import os
import sys
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
The provided code snippet includes necessary dependencies for impl... | r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/') # E... |
12,104 | import os
import sys
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
The provided code snippet includes necessary dependencies for impl... | r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased') # Download model and configuration from huggingface.co and cache. model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/') # E.g. model ... |
12,105 | import os
from pathlib import Path
def write_model_card(model_card_dir, src_lang, tgt_lang, model_name):
texts = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, nicht wahr?",
}
# BLUE sc... | null |
12,106 | import os
from pathlib import Path
def write_model_card(model_card_dir, src_lang, tgt_lang, model_name):
texts = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, nicht wahr?",
}
# BLUE sc... | null |
12,107 | import os
from pathlib import Path
def write_model_card(model_card_dir, src_lang, tgt_lang):
texts = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
... | null |
12,108 | import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
def get_base_command(args, ou... | null |
12,109 | import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
nan = float("nan")
def process... | null |
12,110 | import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
nan = float("nan")
def get_ori... | null |
12,111 | from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from transformers.utils import logging
dataset = datasets.load_dataset("xnli", split="test+validation")
def test_string(slow, fast, text):
global perfect
global imperfect
... | null |
12,112 | import importlib
import inspect
import os
import re
CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING
CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
"DecisionTransformerConfig",
"EncoderDecoderConfig",
"RagConfig",
"SpeechEncoderDecoderConfig",
"VisionEncoderDe... | null |
12,113 | import importlib
import inspect
import os
import re
import warnings
from collections import OrderedDict
from difflib import get_close_matches
from pathlib import Path
from transformers import is_flax_available, is_tf_available, is_torch_available
from transformers.models.auto import get_values
from transformers.utils i... | Check all models are properly tested and documented. |
12,114 | import argparse
import collections.abc
import importlib
import inspect
import json
import os
import shutil
import sys
from pathlib import Path
from datasets import load_dataset
from check_config_docstrings import get_checkpoint_from_config_class
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPI... | Return a tuple of all possible architectures attributed to a configuration class `config_class`. For example, BertConfig -> [BertModel, BertForMaskedLM, ..., BertForQuestionAnswering]. |
12,115 | import argparse
import collections.abc
import importlib
import inspect
import json
import os
import shutil
import sys
from pathlib import Path
from datasets import load_dataset
from check_config_docstrings import get_checkpoint_from_config_class
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPI... | null |
12,116 | import argparse
import collections.abc
import importlib
import inspect
import json
import os
import shutil
import sys
from pathlib import Path
from datasets import load_dataset
from check_config_docstrings import get_checkpoint_from_config_class
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPI... | null |
12,117 | import argparse
import collections.abc
import importlib
import inspect
import json
import os
import shutil
import sys
from pathlib import Path
from datasets import load_dataset
from check_config_docstrings import get_checkpoint_from_config_class
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPI... | null |
12,118 | import collections
import importlib.util
import os
import re
from pathlib import Path
PATH_TO_TRANSFORMERS = "src/transformers"
def parse_init(init_file):
"""
Read an init_file and parse (per backend) the _import_structure objects defined and the TYPE_CHECKING objects
defined
"""
with open(init_file... | Check all inits in the transformers repo and raise an error if at least one does not define the same objects in both halves. |
12,119 | import collections
import importlib.util
import os
import re
from pathlib import Path
PATH_TO_TRANSFORMERS = "src/transformers"
def get_transformers_submodules():
"""
Returns the list of Transformers submodules.
"""
submodules = []
for path, directories, files in os.walk(PATH_TO_TRANSFORMERS):
... | null |
12,120 | import argparse
import json
import subprocess
def get_runner_status(target_runners, token):
offline_runners = []
cmd = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
outpu... | null |
12,121 | import argparse
import json
import subprocess
def list_str(values):
return values.split(",") | null |
12,122 | import argparse
from collections import defaultdict
import yaml
PATH_TO_TOC = "docs/source/en/_toctree.yml"
def clean_model_doc_toc(model_doc):
"""
Cleans the table of content of the model documentation by removing duplicates and sorting models alphabetically.
"""
counts = defaultdict(int)
for doc i... | null |
12,123 | import argparse
import math
import dateutil.parser as date_parser
import requests
def extract_time_from_single_job(job):
"""Extract time info from a single job in a GitHub Actions workflow run"""
job_info = {}
start = job["started_at"]
end = job["completed_at"]
start_datetime = date_parser.parse(sta... | Extract time info for all jobs in a GitHub Actions workflow run |
12,124 | import argparse
import os
import re
import packaging.version
def global_version_update(version, patch=False):
"""Update the version in all needed files."""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(fname, version, pattern)
if not patch:
update_version_in_examples(ve... | Do all the necessary pre-release steps. |
12,125 | import argparse
import os
import re
import packaging.version
def global_version_update(version, patch=False):
"""Update the version in all needed files."""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(fname, version, pattern)
if not patch:
update_version_in_examples(ve... | Do all the necesarry post-release steps. |
12,126 | import ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union
import requests
from slack_sdk import WebClient
def handle_test_results(test_results):
expressions = test_results.split(" ")
failed =... | null |
12,127 | import ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union
import requests
from slack_sdk import WebClient
def handle_stacktraces(test_results):
# These files should follow the following architectu... | null |
12,128 | import ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union
import requests
from slack_sdk import WebClient
def dicts_to_sum(objects: Union[Dict[str, Dict], List[dict]]):
if isinstance(objects, dict... | null |
12,129 | import ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union
import requests
from slack_sdk import WebClient
def get_job_links():
run_id = os.environ["GITHUB_RUN_ID"]
url = f"https://api.github.c... | null |
12,130 | import ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union
import requests
from slack_sdk import WebClient
def retrieve_artifact(name: str, gpu: Optional[str]):
if gpu not in [None, "single", "mult... | null |
12,131 | import ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union
import requests
from slack_sdk import WebClient
def retrieve_available_artifacts():
class Artifact:
def __init__(self, name: str, ... | null |
12,132 | import ast
import collections
import functools
import json
import math
import operator
import os
import re
import sys
import time
from typing import Dict, List, Optional, Union
import requests
from slack_sdk import WebClient
def prepare_reports(title, header, reports, to_truncate=True):
report = ""
MAX_ERROR_... | null |
12,133 | import argparse
import os
import sys
import urllib.request
import zipfile
TASK2PATH = {
"CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
"SST": "https://firebasestorage.googleapis.com/v0/b/mtl-s... | null |
12,134 | import argparse
import os
import sys
import urllib.request
import zipfile
TASK2PATH = {
"CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
"SST": "https://firebasestorage.googleapis.com/v0/b/mtl-s... | null |
12,135 | import argparse
import os
import sys
import urllib.request
import zipfile
TASK2PATH = {
"CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
"SST": "https://firebasestorage.googleapis.com/v0/b/mtl-s... | null |
12,136 | import argparse
import os
import sys
import urllib.request
import zipfile
TASKS = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
def get_tasks(task_names):
task_names = task_names.split(",")
if "all" in task_names:
tasks = TASKS
else:
tasks = []
... | null |
12,137 | import argparse
import json
import math
import os
import subprocess
import time
import zipfile
from collections import Counter
import requests
The provided code snippet includes necessary dependencies for implementing the `get_job_links` function. Write a Python function `def get_job_links(workflow_run_id)` to solve t... | Extract job names and their job links in a GitHub Actions workflow run |
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