id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
1,313 | import os
import copy
import logging
import torch
import numpy as np
import time
from filelock import FileLock
import json
import itertools
import random
import transformers
from src.processors import processors_mapping, num_labels_mapping, output_modes_mapping, compute_metrics_mapping, median_mapping
from transformers... | null |
1,314 | import os
import copy
import logging
import torch
import numpy as np
import time
from filelock import FileLock
import json
import itertools
import random
import transformers
from src.processors import processors_mapping, num_labels_mapping, output_modes_mapping, compute_metrics_mapping, median_mapping
from transformers... | null |
1,315 | import os
import copy
import logging
import torch
import numpy as np
import time
from filelock import FileLock
import json
import itertools
import random
import transformers
from src.processors import processors_mapping, num_labels_mapping, output_modes_mapping, compute_metrics_mapping, median_mapping
from transformers... | null |
1,316 | import os
import copy
import logging
import torch
import numpy as np
import time
from filelock import FileLock
import json
import itertools
import random
import transformers
from transformers.data.processors.utils import InputFeatures
from transformers import DataProcessor, InputExample
from transformers.data.processor... | null |
1,317 | import argparse
import pandas as pd
import json
import numpy as np
import torch
import os
from torch import device
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction, GlueDataset
from transformers import GlueDataTrainingArguments, glue_compute_metrics
from transformer... | null |
1,318 | import os
import pandas as pd
import numpy as np
import os
def merge(table1, table2, table3):
t1 = pd.read_csv(table1, header=None, dtype=np.float64)
t2 = pd.read_csv(table2, header=None, dtype=np.float64)
for i in range(8):
t1.at[8, i] = t1[i].to_numpy().mean()
t2.at[8, i] = t2[i].to_numpy... | null |
1,319 | import argparse
import os
import numpy as np
import pandas as pd
from pandas import DataFrame
def get_label(task, line):
if task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SST-2", "STS-B", "CoLA"]:
# GLUE style
line = line.strip().split('\t')
if task == 'CoLA':
return line[1]
... | null |
1,320 | import argparse
import os
import numpy as np
import pandas as pd
from pandas import DataFrame
def load_datasets(data_dir, tasks):
datasets = {}
for task in tasks:
if task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SST-2", "STS-B", "CoLA"]:
# GLUE style (tsv)
dataset = {}
... | null |
1,321 | import argparse
import os
import numpy as np
import pandas as pd
from pandas import DataFrame
The provided code snippet includes necessary dependencies for implementing the `split_header` function. Write a Python function `def split_header(task, lines)` to solve the following problem:
Returns if the task file has a he... | Returns if the task file has a header or not. Only for GLUE tasks. |
1,322 | import transformers
from transformers import T5ForConditionalGeneration, T5Tokenizer
import argparse
import torch
import os
from tqdm import tqdm
import json
import argparse
import pandas as pd
def generate(dataset, template, model, tokenizer, target_number, mapping, beam, label=None, length_limit=None, truncate=None):... | null |
1,323 | from sentence_transformers import SentenceTransformer, util
import argparse
import os
import numpy as np
from tqdm import tqdm
import pandas as pd
def get_sentence(task, line):
if task in ['mr', 'sst-5', 'subj', 'trec', 'cr', 'mpqa']:
# Text classification tasks
if line[1] is None or pd.isna(line[1... | null |
1,324 | from sentence_transformers import SentenceTransformer, util
import argparse
import os
import numpy as np
from tqdm import tqdm
import pandas as pd
def split_header(task, lines):
"""Returns if the task file has a header or not."""
if task in ["CoLA"]:
return [], lines
elif task in ["MNLI", "MRPC", "Q... | null |
1,326 | import numpy as np
import scipy
import math
import sklearn
import collections
from logging import getLogger
from .qa_utils import normalize_squad, qa_metrics
import sklearn.metrics
The provided code snippet includes necessary dependencies for implementing the `perplexity` function. Write a Python function `def perplex... | Computes the perplexity accuracy. |
1,330 | import numpy as np
import scipy
import math
import sklearn
import collections
from logging import getLogger
from .qa_utils import normalize_squad, qa_metrics
import sklearn.metrics
def transform_for_generation(predictions, targets):
mapping = {k: i for i, k in enumerate(set(targets))}
targets = np.asarray([m... | null |
1,337 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelF... | null |
1,338 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelF... | null |
1,339 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelF... | null |
1,340 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelF... | null |
1,341 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelF... | null |
1,342 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelF... | null |
1,343 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def get_remove_columns(d... | null |
1,344 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def preprocess_function(... | null |
1,345 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def get_backbone(model_a... | null |
1,346 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def mask_token_func(token... | null |
1,347 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
CLIPConfig,
CLIPProcessor,
CLIPModel,
)
from... | null |
1,348 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
CLIPConfig,
CLIPProcessor,
CLIPModel,
)
from... | null |
1,349 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
CLIPConfig,
CLIPProcessor,
CLIPModel,
)
from... | null |
1,350 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
CLIPConfig,
CLIPProcessor,
CLIPModel,
)
from... | null |
1,351 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
CLIPConfig,
CLIPProcessor,
CLIPModel,
)
from... | null |
1,352 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoModelForMaskedLM,
AutoTokeni... | null |
1,353 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoModelForMaskedLM,
AutoTokeni... | null |
1,354 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoModelForMaskedLM,
AutoTokeni... | null |
1,355 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoModelForMaskedLM,
AutoTokeni... | null |
1,356 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoModelForMaskedLM,
AutoTokeni... | null |
1,359 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
import numpy as np
from transformers import (
AutoConfig,
AutoModelForMaskedLM,
AutoTokeni... | null |
1,362 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import numpy as np
... | null |
1,363 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import numpy as np
... | null |
1,364 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import numpy as np
... | null |
1,365 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import numpy as np
... | null |
1,366 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import numpy as np
... | null |
1,369 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def get_backbone(model_a... | null |
1,370 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def mask_token_func(token... | null |
1,374 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import numpy as np
... | null |
1,375 | from openpromptu.data_utils import InputExample
import torch
from transformers.data.data_collator import torch_default_data_collator
from transformers.data.data_collator import DataCollatorMixin as HfDataCollatorMixin
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import numpy as np
... | null |
1,376 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
BlenderbotForConditionalGeneration,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def get_rem... | null |
1,377 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
BlenderbotForConditionalGeneration,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def preproc... | null |
1,378 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
BlenderbotForConditionalGeneration,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def get_bac... | null |
1,379 | from openpromptu.data_utils import InputExample
from transformers import Seq2SeqTrainer as HfSeq2SeqTrainer
from transformers import (
AutoConfig,
BlenderbotForConditionalGeneration,
AutoTokenizer,
)
from transformers.data.data_collator import DataCollatorForSeq2Seq as DataCollator
import torch
def mask_tok... | null |
1,380 | import numpy as np
import re
The provided code snippet includes necessary dependencies for implementing the `round_stsb_target` function. Write a Python function `def round_stsb_target(label)` to solve the following problem:
STSB maps two sentences to a floating point number between 1 and 5 representing their semantic... | STSB maps two sentences to a floating point number between 1 and 5 representing their semantic similarity. Since we are treating all tasks as text-to-text tasks we need to convert this floating point number to a string. The vast majority of the similarity score labels in STSB are in the set [0, 0.2, 0.4, ..., 4.8, 5.0]... |
1,381 | import numpy as np
import re
The provided code snippet includes necessary dependencies for implementing the `pad_punctuation` function. Write a Python function `def pad_punctuation(text)` to solve the following problem:
Re-implementation of _pad_punctuation in t5. This function adds spaces around punctuation. While th... | Re-implementation of _pad_punctuation in t5. This function adds spaces around punctuation. While this pads punctuation as expected, it has the unexpected effected of padding certain unicode characters with accents, with spaces as well. For instance: "François" becomes "Fran ç ois |
1,382 | import os
import argparse
import random
import json
from examples_prompt.search_space import AllBackboneSearchSpace, AllDeltaSearchSpace, BaseSearchSpace, DatasetSearchSpace
import optuna
from functools import partial
from optuna.samplers import TPESampler
import shutil
import time
import subprocess
def objective_singl... | null |
1,383 | import json
import os
import re
The provided code snippet includes necessary dependencies for implementing the `create_dir` function. Write a Python function `def create_dir(output_dir)` to solve the following problem:
Checks whether to the output_dir already exists and creates it if not. Args: output_dir: path to the... | Checks whether to the output_dir already exists and creates it if not. Args: output_dir: path to the output_dir |
1,384 | import json
import os
import re
def get_last_checkpoint(output_dir):
if os.path.exists(os.path.join(output_dir, 'pytorch_model.bin')):
return output_dir
return None | null |
1,385 | import json
import os
import re
def save_json(filepath, dictionary):
with open(filepath, "w") as outfile:
json.dump(dictionary, outfile) | null |
1,386 | import json
import os
import re
def read_json(filepath):
f = open(filepath,)
return json.load(f) | null |
1,387 | import time
import os
import torch
import numpy as np
from sklearn.metrics import accuracy_score, recall_score, f1_score
import bmtrain as bmt
from model_center.arguments import add_model_config_args, add_training_args, argparse
from model_center.model import Bert
from model_center.tokenizer import BertTokenizer
from m... | null |
1,388 | import time
import os
import torch
import numpy as np
from sklearn.metrics import accuracy_score, recall_score, f1_score
import bmtrain as bmt
from model_center.arguments import add_model_config_args, add_training_args, argparse
def get_args():
parser = argparse.ArgumentParser()
parser = add_model_config_args(p... | null |
1,389 | import time
import os
import torch
import numpy as np
from sklearn.metrics import accuracy_score, recall_score, f1_score
import bmtrain as bmt
from model_center.arguments import add_model_config_args, add_training_args, argparse
from model_center.model import Bert
from model_center.tokenizer import BertTokenizer
from m... | null |
1,390 | import time
import os
import torch
import numpy as np
from sklearn.metrics import accuracy_score, recall_score, f1_score
import bmtrain as bmt
from model_center.arguments import add_model_config_args, add_training_args, argparse
from model_center.model import Bert
from model_center.tokenizer import BertTokenizer
from m... | null |
1,391 |
def SetConsoleTextAttribute(stream_id, attrs):
handle = _GetStdHandle(stream_id)
return _SetConsoleTextAttribute(handle, attrs) | null |
1,392 | STDOUT = -11
try:
import ctypes
from ctypes import LibraryLoader
windll = LibraryLoader(ctypes.WinDLL)
from ctypes import wintypes
except (AttributeError, ImportError):
windll = None
SetConsoleTextAttribute = lambda *_: None
winapi_test = lambda *_: None
else:
from ctypes import byref, S... | null |
1,393 |
def FillConsoleOutputCharacter(stream_id, char, length, start):
handle = _GetStdHandle(stream_id)
char = c_char(char.encode())
length = wintypes.DWORD(length)
num_written = wintypes.DWORD(0)
# Note that this is hard-coded for ANSI (vs wide) bytes.
success = _FillConsole... | null |
1,394 |
The provided code snippet includes necessary dependencies for implementing the `FillConsoleOutputAttribute` function. Write a Python function `def FillConsoleOutputAttribute(stream_id, attr, length, start)` to solve the following problem:
FillConsoleOutputAttribute( hConsole, csbi.wAttributes, dwConSize, coordScreen,... | FillConsoleOutputAttribute( hConsole, csbi.wAttributes, dwConSize, coordScreen, &cCharsWritten ) |
1,395 |
def SetConsoleTitle(title):
return _SetConsoleTitleW(title) | null |
1,396 | CSI = '\033['
def code_to_chars(code):
return CSI + str(code) + 'm' | null |
1,397 | OSC = '\033]'
BEL = '\a'
def set_title(title):
return OSC + '2;' + title + BEL | null |
1,398 | CSI = '\033['
def clear_screen(mode=2):
return CSI + str(mode) + 'J' | null |
1,399 | CSI = '\033['
def clear_line(mode=2):
return CSI + str(mode) + 'K' | null |
1,400 | import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
def reset_all():
if AnsiToWin32 is not None: # Issue #74: objects might become None at exit
AnsiToWin32(orig_stdout).reset_all()
def _wipe_internal_state_for_tests():
global orig_stdout, orig_stderr
orig_stdout = Non... | null |
1,401 | import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
class AnsiToWin32(object):
'''
Implements a 'write()' method which, on Windows, will strip ANSI character
sequences from the text, and if outputting to a tty, will convert them into
win32 function calls.
'''
ANSI_CS... | null |
1,402 | import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
def init(autoreset=False, convert=None, strip=None, wrap=True):
if not wrap and any([autoreset, convert, strip]):
raise ValueError('wrap=False conflicts with any other arg=True')
global wrapped_stdout, wrapped_stderr
glo... | null |
1,403 | import atexit
import contextlib
import sys
from .ansitowin32 import AnsiToWin32
def reinit():
if wrapped_stdout is not None:
sys.stdout = wrapped_stdout
if wrapped_stderr is not None:
sys.stderr = wrapped_stderr | null |
1,404 | try:
from msvcrt import get_osfhandle
except ImportError:
def get_osfhandle(_):
from . import win32
def enable_vt_processing(fd):
if win32.windll is None or not win32.winapi_test():
return False
try:
handle = get_osfhandle(fd)
mode = win32.GetConsoleMode(handle)
win32.S... | null |
1,405 | import datetime
import os
import signal
import sys
import warnings
from typing import Optional
import requests
import openai
from openai.upload_progress import BufferReader
from openai.validators import (
apply_necessary_remediation,
apply_validators,
get_validators,
read_any_format,
write_out_file,... | null |
1,406 | import datetime
import os
import signal
import sys
import warnings
from typing import Optional
import requests
import openai
from openai.upload_progress import BufferReader
from openai.validators import (
apply_necessary_remediation,
apply_validators,
get_validators,
read_any_format,
write_out_file,... | null |
1,407 | import datetime
import os
import signal
import sys
import warnings
from typing import Optional
import requests
import openai
from openai.upload_progress import BufferReader
from openai.validators import (
apply_necessary_remediation,
apply_validators,
get_validators,
read_any_format,
write_out_file,... | null |
1,408 | import datetime
import os
import signal
import sys
import warnings
from typing import Optional
import requests
import openai
from openai.upload_progress import BufferReader
from openai.validators import (
apply_necessary_remediation,
apply_validators,
get_validators,
read_any_format,
write_out_file,... | null |
1,409 | import datetime
import os
import signal
import sys
import warnings
from typing import Optional
import requests
import openai
from openai.upload_progress import BufferReader
from openai.validators import (
apply_necessary_remediation,
apply_validators,
get_validators,
read_any_format,
write_out_file,... | null |
1,410 | import logging
import os
import re
import sys
from enum import Enum
from typing import Optional
import openai
logger = logging.getLogger("openai")
def _console_log_level():
def logfmt(props):
def log_debug(message, **params):
msg = logfmt(dict(message=message, **params))
if _console_log_level() == "debug":
... | null |
1,411 | import logging
import os
import re
import sys
from enum import Enum
from typing import Optional
import openai
logger = logging.getLogger("openai")
def _console_log_level():
if openai.log in ["debug", "info"]:
return openai.log
elif OPENAI_LOG in ["debug", "info"]:
return OPENAI_LOG
else:
... | null |
1,412 | import logging
import os
import re
import sys
from enum import Enum
from typing import Optional
import openai
logger = logging.getLogger("openai")
def logfmt(props):
def fmt(key, val):
# Handle case where val is a bytes or bytesarray
if hasattr(val, "decode"):
val = val.decode("utf-8")
... | null |
1,413 | import logging
import os
import re
import sys
from enum import Enum
from typing import Optional
import openai
The provided code snippet includes necessary dependencies for implementing the `convert_to_dict` function. Write a Python function `def convert_to_dict(obj)` to solve the following problem:
Converts a OpenAIOb... | Converts a OpenAIObject back to a regular dict. Nested OpenAIObjects are also converted back to regular dicts. :param obj: The OpenAIObject to convert. :returns: The OpenAIObject as a dict. |
1,414 | import logging
import os
import re
import sys
from enum import Enum
from typing import Optional
import openai
def merge_dicts(x, y):
z = x.copy()
z.update(y)
return z | null |
1,415 | import logging
import os
import re
import sys
from enum import Enum
from typing import Optional
import openai
def default_api_key() -> str:
if openai.api_key_path:
with open(openai.api_key_path, "rt") as k:
api_key = k.read().strip()
if not api_key.startswith("sk-"):
... | null |
1,416 | import io
def progress(total, desc):
import tqdm # type: ignore
meter = tqdm.tqdm(total=total, unit_scale=True, desc=desc)
def incr(progress):
meter.n = progress
if progress == total:
meter.close()
else:
meter.refresh()
return incr | null |
1,417 | import io
def MB(i):
return int(i // 1024**2) | null |
1,418 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | null |
1,419 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | null |
1,420 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | null |
1,421 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | null |
1,422 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | null |
1,423 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | Precision-Recall plotting for a multiclass problem. It plots average precision-recall, per class precision recall and reference f1 contours. Code slightly modified, but heavily based on https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html |
1,424 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | Return the distances between a query embedding and a list of embeddings. |
1,425 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | Return a list of indices of nearest neighbors from a list of distances. |
1,426 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | Return the PCA components of a list of embeddings. |
1,427 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | Returns t-SNE components of a list of embeddings. |
1,428 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | Return an interactive 2D chart of embedding components. |
1,429 | import textwrap as tr
from typing import List, Optional
import matplotlib.pyplot as plt
import plotly.express as px
from scipy import spatial
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import average_precision_score, precision_recall_curve
from tenacity import retry, st... | Return an interactive 3D chart of embedding components. |
1,430 | import asyncio
import json
import platform
import sys
import threading
import warnings
from contextlib import asynccontextmanager
from json import JSONDecodeError
from typing import (
AsyncGenerator,
AsyncIterator,
Dict,
Iterator,
Optional,
Tuple,
Union,
overload,
)
from urllib.parse imp... | null |
1,431 | import asyncio
import json
import platform
import sys
import threading
import warnings
from contextlib import asynccontextmanager
from json import JSONDecodeError
from typing import (
AsyncGenerator,
AsyncIterator,
Dict,
Iterator,
Optional,
Tuple,
Union,
overload,
)
from urllib.parse imp... | Returns a value suitable for the 'proxies' argument to 'aiohttp.ClientSession.request. |
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