id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
163,622 | import argparse
import fnmatch
import json
import os
import pdb
import pickle
import re
import sqlite3
from typing import Dict, List, Tuple
import backoff
import openai
import pandas as pd
import sqlparse
from tqdm import tqdm
def question_package(data_json, knowledge=False):
question_list = []
for data in dat... | null |
163,623 | import argparse
import fnmatch
import json
import os
import pdb
import pickle
import re
import sqlite3
from typing import Dict, List, Tuple
import backoff
import openai
import pandas as pd
import sqlparse
from tqdm import tqdm
def knowledge_package(data_json, knowledge=False):
knowledge_list = []
for data in d... | null |
163,624 | import argparse
import fnmatch
import json
import os
import pdb
import pickle
import re
import sqlite3
from typing import Dict, List, Tuple
import backoff
import openai
import pandas as pd
import sqlparse
from tqdm import tqdm
def decouple_question_schema(datasets, db_root_path):
question_list = []
db_path_lis... | null |
163,625 | import argparse
import fnmatch
import json
import os
import pdb
import pickle
import re
import sqlite3
from typing import Dict, List, Tuple
import backoff
import openai
import pandas as pd
import sqlparse
from tqdm import tqdm
def new_directory(path):
if not os.path.exists(path):
os.makedirs(path)
def ... | null |
163,626 | import sys
import json
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
def package_sqls(sql_path, db_root_path, mode='gpt', data_mode='dev'):
clean_sqls = []
db_path_list = []
if mode == 'gpt':
sql_data = json.load(open(sql_path + ... | null |
163,627 | import sys
import json
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
def result_callback(result):
exec_result.append(result)
def execute_model(predicted_sql,ground_truth, db_place, idx, meta_time_out):
try:
res = func_timeout(meta_tim... | null |
163,628 | import sys
import json
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
def sort_results(list_of_dicts):
return sorted(list_of_dicts, key=lambda x: x['sql_idx']) | null |
163,629 | import sys
import json
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
def load_json(dir):
def compute_acc_by_diff(exec_results,diff_json_path):
num_queries = len(exec_results)
results = [res['res'] for res in exec_results]
contents = load... | null |
163,630 | import sys
import json
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
def print_data(score_lists,count_lists):
levels = ['simple', 'moderate', 'challenging', 'total']
print("{:20} {:20} {:20} {:20} {:20}".format("", *levels))
print("{:20}... | null |
163,631 | import os
import pdb
import sys
import json
import numpy as np
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
import time
import math
def package_sqls(sql_path, db_root_path, mode='gpt', data_mode='dev'):
clean_sqls = []
db_path_list = []
... | null |
163,632 | import os
import pdb
import sys
import json
import numpy as np
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
import time
import math
def result_callback(result):
exec_result.append(result)
def execute_model(predicted_sql,ground_truth, db_place, i... | null |
163,633 | import os
import pdb
import sys
import json
import numpy as np
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
import time
import math
def sort_results(list_of_dicts):
return sorted(list_of_dicts, key=lambda x: x['sql_idx']) | null |
163,634 | import os
import pdb
import sys
import json
import numpy as np
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
import time
import math
def compute_ves(exec_results):
num_queries = len(exec_results)
total_ratio = 0
count = 0
for i, resul... | null |
163,635 | import os
import pdb
import sys
import json
import numpy as np
import argparse
import sqlite3
import multiprocessing as mp
from func_timeout import func_timeout, FunctionTimedOut
import time
import math
def print_data(score_lists,count_lists):
levels = ['simple', 'moderate', 'challenging', 'total']
print("{:20... | null |
163,636 | import json
import re
import pprint
import os
import tqdm
import random
def gen_global_index():
index = 0
while True:
yield index
index += 1 | null |
163,637 | import json
import re
import pprint
import os
import tqdm
import random
random.seed(42)
def split_trans(split):
if split == 'train' or split == 'test' or split == 'dev':
return split
elif split == 'valid':
return 'dev'
elif split == 'valid1':
return 'dev'
elif split == 'valid2':
... | null |
163,638 | import os
import sys
import json
import random
import numpy as np
import tqdm
from utils.metrics_summarize import create_reward_fn
def split_trans(split):
if split == 'train' or split == 'test' or split == 'dev':
return split
elif split == 'valid':
return 'dev'
elif split == 'valid1':
... | null |
163,639 | import json
import re
import pprint
import os
import tqdm
def gen_global_index():
index = 0
while True:
yield index
index += 1 | null |
163,640 | import json
import re
import pprint
import os
import tqdm
Roles = {
"Human": "<|prompter|>",
"Assistant": "<|assistant|>"
}
def hhrlhf_preprocess(path,filename,index_generator,split='train'):
with open(os.path.join(path,filename),'r', encoding='utf-8') as f:
raw = f.readlines()
data = []
... | null |
163,641 | import os
import sys
import json
import random
import numpy as np
import tqdm
from utils.metrics_hh import create_reward_fn
def split_trans(split):
def concat_wo_ranker(prefixes, suffixes):
def reward_model_ranker(prefixes, suffixes):
def extract_train_data(root_dir, if_score, if_rerank, training_stage_num = None, spl... | null |
163,642 | import sys
import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from dataclasses import dataclass
import nltk
from nltk.stem.porter import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
import nltk
nltk.download('wordnet')
def get_ble... | null |
163,643 | import sys
import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from dataclasses import dataclass
import nltk
def create_reward_fn_2():
model_name = "OpenAssistant/reward-model-deberta-v3-large-v2"
model_device = "cuda:{}".format(torch.cuda.dev... | null |
163,644 | import sys
import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from dataclasses import dataclass
import nltk
def create_reward_fn_3():
model_name = "OpenAssistant/reward-model-deberta-v3-large"
model_device = "cuda:{}".format(torch.cuda.device... | null |
163,645 | import random
import numpy as np
import torch
import argparse
from transformers import SchedulerType
args = parse_args()
def parse_args():
parser = argparse.ArgumentParser(description="Preference Ranking Optimization For Human Alignment")
parser.add_argument(
"--task",
type=str,
default... | null |
163,646 | import random
import numpy as np
import torch
import argparse
from transformers import SchedulerType
import random
random.seed(33)
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.benchmark=False
torch.b... | null |
163,647 | import sys
import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from dataclasses import dataclass
import utils.reward_model
import nltk
from nltk.stem.porter import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
import nltk
nltk.downlo... | null |
163,648 | import sys
import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from dataclasses import dataclass
import utils.reward_model
import nltk
def create_reward_fn_2():
model_name = "OpenAssistant/oasst-rm-2-pythia-6.9b-epoch-1"
model_device = "cuda:{... | null |
163,649 | import sys
import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from dataclasses import dataclass
import utils.reward_model
import nltk
def create_reward_fn_3():
model_name = "OpenAssistant/oasst-rm-2.1-pythia-1.4b-epoch-2.5"
model_device = "cu... | null |
163,650 | import torch
import torch.nn.functional as F
import tqdm
import numpy as np
import random
def setup_seed(seed=42):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.benchmark=False
torch.backends.cudnn.deterministic=True | null |
163,651 | import torch
import torch.nn.functional as F
import tqdm
import numpy as np
import random
def generate_pipeline(model, tokenizer, prompts, add_special_tokens=False, gen_kwarg={"max_new_tokens": 64, "num_beams": 1, "do_sample": False,}, batch_size = 28):
def pipeline(prompts):
tokenizer.padding_side = "left... | null |
163,652 | import os
import argparse
import json
import tqdm
import torch
import torch.nn.functional as F
import metrics2
from transformers import (
AutoConfig,
AutoTokenizer,
LlamaTokenizer,
AutoModelForCausalLM
)
from infer_func_now import setup_seed, generate_pipeline
from accelerate import Accelerator
from acc... | null |
163,653 | import os
import argparse
import json
import tqdm
import evaluate
def get_args():
parser = argparse.ArgumentParser(description="")
parser.add_argument('--index', type=str)
parser.add_argument('--stage', type=int)
parser.add_argument('--directory', default="best_checkpoint", type=str)
args = parser.... | null |
163,654 | import sys
import os
import math
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXConfig, GPTNeoXModel, GPTNeoXPreTrainedModel
from transformers.utils import ModelOutput
from datacl... | null |
163,655 | import sys
import os
import math
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXConfig, GPTNeoXModel, GPTNeoXPreTrainedModel
from transformers.utils import ModelOutput
from datacl... | null |
163,656 | import sys
import os
import math
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXConfig, GPTNeoXModel, GPTNeoXPreTrainedModel
from transformers.utils import ModelOutput
from datacl... | null |
163,657 | import os
import argparse
import json
import tqdm
import torch
import torch.nn.functional as F
import metrics2
from transformers import (
AutoConfig,
AutoTokenizer,
LlamaTokenizer,
AutoModelForCausalLM
)
from peft import PeftConfig, PeftModel
from infer_func_now import setup_seed
from accelerate import ... | null |
163,659 | import torch
import torch.nn.functional as F
import tqdm
import numpy as np
import random
def generate_pipeline(model, tokenizer, prompts, add_special_tokens=False, gen_kwarg={"max_new_tokens": 128, "num_beams": 1, "do_sample": False,}, batch_size = 28):
def pipeline(prompts):
tokenizer.padding_side = "lef... | null |
163,661 | import os
import argparse
import json
import tqdm
def get_args():
parser = argparse.ArgumentParser(description="")
parser.add_argument('--index', type=str)
parser.add_argument('--stage', type=int)
parser.add_argument('--directory', default="best_checkpoint", type=str)
args = parser.parse_args()
... | null |
163,662 | import sys
import os
import math
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXConfig, GPTNeoXModel, GPTNeoXPreTrainedModel
from transformers.utils import ModelOutput
from datacl... | null |
163,663 | import sys
import os
import math
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXConfig, GPTNeoXModel, GPTNeoXPreTrainedModel
from transformers.utils import ModelOutput
from datacl... | null |
163,664 | import sys
import os
import math
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXConfig, GPTNeoXModel, GPTNeoXPreTrainedModel
from transformers.utils import ModelOutput
from datacl... | null |
163,665 | import os
import argparse
import json
import tqdm
import torch
import torch.nn.functional as F
import metrics2
from transformers import (
AutoConfig,
AutoTokenizer,
LlamaTokenizer,
AutoModelForCausalLM
)
from peft import PeftConfig, PeftModel
from infer_func_now import setup_seed, generate_pipeline, ran... | null |
163,666 | import argparse
import os
import pickle
import time
import torch
from tqdm import tqdm
from data_loader import DataLoader
from config import Config
from ECDMetric import ECDMetric
from model import BertForMatching
from transformers import BertTokenizer, BertConfig
from tensorboardX import SummaryWriter
from transformer... | null |
163,667 | import argparse
import os
import pickle
import time
import torch
from tqdm import tqdm
from data_loader import DataLoader
from config import Config
from ECDMetric import ECDMetric
from model import BertForMatching
from transformers import BertTokenizer, BertConfig
from tensorboardX import SummaryWriter
from transformer... | null |
163,668 | import argparse
import os
import pickle
import time
import torch
from tqdm import tqdm
import numpy as np
from data_cls import DataProcessor
from model import BertForNLU
from transformers import BertTokenizer, BertConfig
from tensorboardX import SummaryWriter
from transformers import AdamW
import json
import random
imp... | null |
163,669 | import argparse
import os
import pickle
import time
import torch
from tqdm import tqdm
import numpy as np
from data_cls import DataProcessor
from model import BertForNLU
from transformers import BertTokenizer, BertConfig
from tensorboardX import SummaryWriter
from transformers import AdamW
import json
import random
imp... | null |
163,670 | import numpy as np
import json
def compute(dialogs):
dial_acc = []
turn_acc_all = []
turn_acc = []
for dial in dialogs.values():
tmp = []
# 基本通用.重听 基本通用.简短词 拒识
for turn in dial:
if 'usr_query' in turn:
true_label = turn['usr_intent']
a... | null |
163,671 | import numpy as np
import json
def compute_with_hard_data(dialogs, robust_ids):
dial_acc = []
turn_acc = []
for dial_id, dial in dialogs.items():
tmp = []
if dial_id not in robust_ids: continue
# 基本通用.重听 基本通用.简短词 拒识
for turn_id, turn in enumerate(dial):
if 'usr_... | null |
163,672 | from transformers.optimization import AdamW, get_linear_schedule_with_warmup
from transformers import GPT2LMHeadModel, BertTokenizer
from reader import RisaWOZReader
from eval import RisaWOZEvaluator
import torch
import torch.nn as nn
import os
import random
import argparse
import time
import logging
import json
import... | null |
163,673 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
The provided code snippet includes necessary dependencies for implementing the `top_k_top_p_filtering` function. Write a Python function `def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filt... | Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (vocabulary size) top_k > 0: keep only top k tokens with highest probability (top-k filtering). top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filterin... |
163,674 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
def py2np(list):
return np.array(list) | null |
163,675 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
def write_dict(fn, dic):
with open(fn, 'w') as f:
json.dump(dic, f, indent=2) | null |
163,676 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
def f1_score(label_list, pred_list):
tp = len([t for t in pred_list if t in label_list])
fp = max(0, len(pred_list) - tp)
fn = max(0, len(label_list) - tp)
precision = tp / (tp + f... | null |
163,677 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
def padSeqs_gpt(sequences, pad_id, maxlen=None):
lengths = []
for x in sequences:
lengths.append(len(x))
num_samples = len(sequences)
seq_mexlen = np.max(lengths)
# m... | null |
163,678 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
def padSeqs(sequences, maxlen=None, truncated = False, pad_method='post',
trunc_method='pre', dtype='int32', value=0.):
if not hasattr(sequences, '__len__'):
rais... | null |
163,679 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
The provided code snippet includes necessary dependencies for implementing the `get_glove_matrix` function. Write a Python function `def get_glove_matrix(glove_path, vocab, initial_embedding_np)` ... | return a glove embedding matrix :param self: :param glove_file: :param initial_embedding_np: :return: np array of [V,E] |
163,680 | import logging
import json
import torch
import numpy as np
from collections import OrderedDict
import ontology as ontology
def position_encoding_init(self, n_position, d_pos_vec):
position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / d_pos_vec) for j in range(d_pos_vec)]
if po... | null |
163,681 | import json, random
import re
def normalize_slot(s):
s = re.sub(r"3\.0\s?[Tt]\s?", "", s)
s = s.lower()
s = s.replace('/', '').replace(' ', '')
s = re.sub(r"\(.*\)$", "", s)
return s.lower() | null |
163,682 | import math, logging, json
from collections import Counter
from nltk.util import ngrams
import copy
import pprint
import numpy as np
import ontology as ontology
The provided code snippet includes necessary dependencies for implementing the `my_lcs` function. Write a Python function `def my_lcs(string, sub)` to solve t... | Calculates longest common subsequence for a pair of tokenized strings :param string : list of str : tokens from a string split using whitespace :param sub : list of str : shorter string, also split using whitespace :returns: length (list of int): length of the longest common subsequence between the two strings Note: my... |
163,683 | import copy
import os, random, argparse, time, logging, json, tqdm
import numpy as np
from copy import deepcopy
from collections import OrderedDict
import torch
import pprint
from utils import RisaWOZT5Reader
from config import global_config as cfg
from transformers import (AdamW, BertTokenizer, WEIGHTS_NAME,CONFIG_NAM... | null |
163,686 | import json
from tool_manager import ToolManager
import re
from rouge import Rouge
import os
from utils import ChatGPTWrapper, DavinciWrapper, GPT4Wrapper
import logging
from tqdm import tqdm
from api_call_extraction import parse_api_call
from datetime import datetime
import numpy as np
class Rouge(datasets.Metric):
... | null |
163,687 | import json
from tool_manager import ToolManager
import re
from rouge import Rouge
import os
from utils import ChatGPTWrapper, DavinciWrapper, GPT4Wrapper
import logging
from tqdm import tqdm
from api_call_extraction import parse_api_call
from datetime import datetime
import numpy as np
def get_api_call(model_output):... | null |
163,688 | import json
from tool_manager import ToolManager
from api_call_extraction import parse_api_call, get_api_call
import logging
from rouge import Rouge
def split_by_uppercase(s):
return ''.join([' ' + c if c.isupper() else c for c in s]).strip()
def calculate_rouge_l_score(reference, hypothesis):
rouge = Rouge()
... | null |
163,689 | import json
from tool_manager import ToolManager
import re
from rouge import Rouge
import os
from utils import ChatGPTWrapper, DavinciWrapper
import logging
from tqdm import tqdm
from api_call_extraction import parse_api_call
from datetime import datetime
import numpy as np
class Rouge(datasets.Metric):
def _info(... | null |
163,690 | import json
from tool_manager import ToolManager
import re
from rouge import Rouge
import os
from utils import ChatGPTWrapper, DavinciWrapper
import logging
from tqdm import tqdm
from api_call_extraction import parse_api_call
from datetime import datetime
import numpy as np
def get_api_call(model_output):
api_call... | null |
163,691 | import json
from tool_manager import ToolManager
import re
from rouge import Rouge
import os
from utils import ChatGPTWrapper, DavinciWrapper
import logging
from tqdm import tqdm
from api_call_extraction import parse_api_call
from datetime import datetime
import numpy as np
def print_error_samples(sample):
pri... | null |
163,692 | import gradio as gr
import os
import json
import requests
from tool_manager import ToolManager
from utils import ChatGPTWrapper, GPT4Wrapper
from api_call_extraction import get_api_call, parse_api_call
from api_call_extraction import parse_api_call
import logging
logging.basicConfig(level=logging.INFO, format='%(asct... | null |
163,693 | import gradio as gr
import os
import json
import requests
from tool_manager import ToolManager
from utils import ChatGPTWrapper, GPT4Wrapper
from api_call_extraction import get_api_call, parse_api_call
from api_call_extraction import parse_api_call
import logging
with gr.Blocks(css = """#col_container { margin-left: ... | null |
163,694 | import gradio as gr
import os
import json
import requests
from tool_manager import ToolManager
from utils import ChatGPTWrapper, GPT4Wrapper
from api_call_extraction import get_api_call, parse_api_call
from api_call_extraction import parse_api_call
import logging
with gr.Blocks(css = """#col_container { margin-left: ... | null |
163,695 | import gradio as gr
import os
import json
import requests
from tool_manager import ToolManager
from utils import ChatGPTWrapper, GPT4Wrapper
from api_call_extraction import get_api_call, parse_api_call
from api_call_extraction import parse_api_call
import logging
with gr.Blocks(css = """#col_container { margin-left: ... | null |
163,696 | import gradio as gr
import os
import json
import requests
from tool_manager import ToolManager
from utils import ChatGPTWrapper, GPT4Wrapper
from api_call_extraction import get_api_call, parse_api_call
from api_call_extraction import parse_api_call
import logging
logging.basicConfig(level=logging.INFO, format='%(asct... | null |
163,697 | import re
def fn(**kwargs):
return kwargs | null |
163,698 | import os
import pickle
import torch.distributed as dist
from transformers import PretrainedConfig, WavLMConfig, RobertaConfig
def get_rank():
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank() | null |
163,699 | import os
import pickle
import torch.distributed as dist
from transformers import PretrainedConfig, WavLMConfig, RobertaConfig
def read_processed_pretrain(combined_path):
if os.path.isdir(combined_path):
datas = None
for r, d, fs in os.walk(combined_path):
if not d:
for ... | null |
163,700 | import os
import pickle
import torch.distributed as dist
from transformers import PretrainedConfig, WavLMConfig, RobertaConfig
def get_ds_config(args, num_gpus):
return {
"train_batch_size": args.batch_size * num_gpus * args.grad_acc,
"train_micro_batch_size_per_gpu": args.batch_size,
"zero... | null |
163,701 | import json
import math
import time
import tqdm
import random
import argparse
import numpy as np
from utils import *
from modeling_spectra.model import *
from dataset import PretrainDataset, DataCollatorForPreTraining, DownstreamDataset, DataCollatorForDownstream
from torch.nn.parallel import DistributedDataParallel as... | null |
163,702 | import json
import math
import time
import tqdm
import random
import argparse
import numpy as np
from utils import *
from modeling_spectra.model import *
from dataset import PretrainDataset, DataCollatorForPreTraining, DownstreamDataset, DataCollatorForDownstream
from torch.nn.parallel import DistributedDataParallel as... | null |
163,703 | import random
import numpy as np
from tqdm import tqdm
import torch
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, f1_score
def get_centroids(embeddings):
centroids = embeddings.mean(dim=1)
return centroids
def get_cossim(embeddings, centroids):
num_utterances = embeddings.shape... | null |
163,704 | import math
import random
import warnings
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from typing import Optional, Tuple
from transformers.activations import ACT2FN
from transformers.deepspeed import is_deepspeed_zero3_enabled
from transformers.modeling_outputs import BaseModelOut... | Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: The shape for which to comp... |
163,705 | import math
import random
import warnings
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from typing import Optional, Tuple
from transformers.activations import ACT2FN
from transformers.deepspeed import is_deepspeed_zero3_enabled
from transformers.modeling_outputs import BaseModelOut... | null |
163,706 | import os
import torch
import pickle
import torch.distributed as dist
from transformers import PretrainedConfig, WavLMConfig, RobertaConfig
def get_rank():
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank() | null |
163,707 | import os
import torch
import pickle
import torch.distributed as dist
from transformers import PretrainedConfig, WavLMConfig, RobertaConfig
def read_processed_pretrain(combined_path):
if os.path.isdir(combined_path):
datas = None
for r, d, fs in os.walk(combined_path):
if not d:
... | null |
163,708 | import glob
import json
import sys
import numpy as np
import re
import os
from tqdm import tqdm
def load_dataset_config(dataset_config):
with open(dataset_config, "r", encoding='utf-8') as f:
raw_config = json.load(f)
return raw_config['class_types'], raw_config['slots'], raw_config['label_maps'] | null |
163,709 | import glob
import json
import sys
import numpy as np
import re
import os
from tqdm import tqdm
def tokenize(text):
if "\u0120" in text:
text = re.sub(" ", "", text)
text = re.sub("\u0120", " ", text)
text = text.strip()
return ' '.join([tok for tok in map(str.strip, re.split("(\W+)", te... | null |
163,710 | import os
import re
import glob
import json
import math
import torch
import pickle
import random
import logging
import argparse
import numpy as np
from apex import amp
from model import DSTModel
from tqdm import tqdm, trange
from utils_dst import InputFeatures
from torch.nn.utils.rnn import pad_sequence
from tensorlist... | null |
163,711 | import os
import re
import glob
import json
import math
import torch
import pickle
import random
import logging
import argparse
import numpy as np
from apex import amp
from model import DSTModel
from tqdm import tqdm, trange
from utils_dst import InputFeatures
from torch.nn.utils.rnn import pad_sequence
from tensorlist... | null |
163,712 | import os
import re
import glob
import json
import math
import torch
import pickle
import random
import logging
import argparse
import numpy as np
from apex import amp
from model import DSTModel
from tqdm import tqdm, trange
from utils_dst import InputFeatures
from torch.nn.utils.rnn import pad_sequence
from tensorlist... | Train the model |
163,713 | import os
import re
import glob
import json
import math
import torch
import pickle
import random
import logging
import argparse
import numpy as np
from apex import amp
from model import DSTModel
from tqdm import tqdm, trange
from utils_dst import InputFeatures
from torch.nn.utils.rnn import pad_sequence
from tensorlist... | null |
163,714 | import os
import re
import glob
import json
import math
import torch
import pickle
import random
import logging
import argparse
import numpy as np
from apex import amp
from model import DSTModel
from tqdm import tqdm, trange
from utils_dst import InputFeatures
from torch.nn.utils.rnn import pad_sequence
from tensorlist... | null |
163,715 | import re
import os
import json
import pickle
import librosa
import argparse
import numpy as np
from tqdm import tqdm
from joblib import Parallel, delayed
from utils_dst import (DSTExample, convert_to_unicode)
def is_in_list(tok, value):
found = False
tok_list = [item for item in map(str.strip, re.split("(\W+)... | null |
163,716 | import re
import os
import json
import pickle
import librosa
import argparse
import numpy as np
from tqdm import tqdm
from joblib import Parallel, delayed
from utils_dst import (DSTExample, convert_to_unicode)
def load_acts(input_file, data_indexs, slot_list):
def normalize_label(slot, value_label):
def get_turn_label(... | null |
163,717 | import six
import json
import torch
import pickle
import logging
import argparse
import numpy as np
from tqdm import tqdm
from collections import defaultdict
from joblib import Parallel, delayed
from transformers import Wav2Vec2Processor, RobertaTokenizerFast, BertTokenizer
def _truncate_seq_pair(tokens_a, tokens_b, hi... | null |
163,718 | import six
import json
import torch
import pickle
import logging
import argparse
import numpy as np
from tqdm import tqdm
from collections import defaultdict
from joblib import Parallel, delayed
from transformers import Wav2Vec2Processor, RobertaTokenizerFast, BertTokenizer
class InputFeatures(object):
"""A single ... | null |
163,719 | import math
import random
import warnings
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from typing import Optional, Tuple
from transformers.activations import ACT2FN
from transformers.deepspeed import is_deepspeed_zero3_enabled
from transformers.modeling_outputs import BaseModelOut... | null |
163,720 | import os
import sys
import json
import pickle
import librosa
import argparse
import numpy as np
from transformers import RobertaTokenizerFast as RTF
max_len = args.max_speech_slice_length * 10 - 1
def cut_by_limit(words):
cut = []
for j, word in enumerate(words):
st = round(float(word['startTime'][:-1... | null |
163,721 | import os
import sys
import json
import pickle
import librosa
import argparse
import numpy as np
from transformers import RobertaTokenizerFast as RTF
def get_path(f):
return "/".join(f.split('/')[:-1]) | null |
163,722 | import os
import sys
import json
import pickle
import librosa
import argparse
import numpy as np
from transformers import RobertaTokenizerFast as RTF
to = RTF.from_pretrained("roberta-base")
audio_path = args.speech_dir
target = args.save_processed_speech_dir
SAMPLE_RATE = args.sample_rate // 10
datas = []
audio_id = 0... | null |
163,723 | import json
import os
import torch
import pickle
import random
import numpy as np
from torch.utils.data import Dataset
def pad(sequence, length, pad_token=0):
seq_len = sequence.shape[0]
if length > seq_len:
padding = torch.ones(length - seq_len, dtype=sequence.dtype) * pad_token
att = torch.ca... | null |
163,724 | import json
import os
import torch
import pickle
import random
import numpy as np
from torch.utils.data import Dataset
def compute_valid(transcript, offset, length):
sv = [0 for _ in range(length)]
ev = [0 for _ in range(length)]
start_labels, end_labels = [], []
for i, item in enumerate(transcript):
... | null |
163,728 | import json
import sqlite3
from nltk import word_tokenize
def tokenize(string):
def get_tables_with_alias(schema, toks):
def parse_sql(toks, start_idx, tables_with_alias, schema):
def get_sql(schema, query):
toks = tokenize(query)
tables_with_alias, toks = get_tables_with_alias(schema.schema, toks)
_, sql ... | null |
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