id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
20,594 | import argparse
import json
import os
import random
import time
import shortuuid
import torch
from tqdm import tqdm
from fastchat.llm_judge.common import load_questions, temperature_config
from fastchat.model import load_model, get_conversation_template
from fastchat.utils import str_to_torch_dtype
def get_model_answer... | null |
20,595 | import argparse
import json
import os
import random
import time
import shortuuid
import torch
from tqdm import tqdm
from fastchat.llm_judge.common import load_questions, temperature_config
from fastchat.model import load_model, get_conversation_template
from fastchat.utils import str_to_torch_dtype
The provided code s... | Sort by question id and de-duplication |
20,596 | import argparse
import json
import os
import time
import concurrent.futures
import openai
import shortuuid
import tqdm
from fastchat.llm_judge.common import (
load_questions,
temperature_config,
chat_completion_openai,
chat_completion_anthropic,
chat_completion_palm,
)
from fastchat.llm_judge.gen_mo... | null |
20,597 | import argparse
import json
import os
import numpy as np
def get_mt_bench_votes_data(raw_votes):
data = [{}, {}]
for judge_votes in raw_votes:
for vote in judge_votes:
turn = vote["turn"] - 1
if vote["model_a"] < vote["model_b"]:
key = (vote["question_id"], vote["... | null |
20,598 | import argparse
import pandas as pd
def display_result_single(args):
if args.input_file is None:
input_file = (
f"data/{args.bench_name}/model_judgment/{args.judge_model}_single.jsonl"
)
else:
input_file = args.input_file
print(f"Input file: {input_file}")
df_all = ... | null |
20,599 | import argparse
import pandas as pd
def display_result_pairwise(args):
if args.input_file is None:
input_file = (
f"data/{args.bench_name}/model_judgment/{args.judge_model}_pair.jsonl"
)
else:
input_file = args.input_file
print(f"Input file: {input_file}")
df_all = ... | null |
20,600 | import argparse
from concurrent.futures import ThreadPoolExecutor
import json
import numpy as np
from tqdm import tqdm
from fastchat.llm_judge.common import (
load_questions,
load_model_answers,
load_judge_prompts,
check_data,
play_a_match_pair,
play_a_match_single,
get_model_list,
Judge... | null |
20,601 | import argparse
from concurrent.futures import ThreadPoolExecutor
import json
import numpy as np
from tqdm import tqdm
from fastchat.llm_judge.common import (
load_questions,
load_model_answers,
load_judge_prompts,
check_data,
play_a_match_pair,
play_a_match_single,
get_model_list,
Judge... | null |
20,602 | import argparse
from concurrent.futures import ThreadPoolExecutor
import json
import numpy as np
from tqdm import tqdm
from fastchat.llm_judge.common import (
load_questions,
load_model_answers,
load_judge_prompts,
check_data,
play_a_match_pair,
play_a_match_single,
get_model_list,
Judge... | null |
20,603 | import argparse
from concurrent.futures import ThreadPoolExecutor
import json
import numpy as np
from tqdm import tqdm
from fastchat.llm_judge.common import (
load_questions,
load_model_answers,
load_judge_prompts,
check_data,
play_a_match_pair,
play_a_match_single,
get_model_list,
Judge... | null |
20,604 | import argparse
from concurrent.futures import ThreadPoolExecutor
import json
import numpy as np
from tqdm import tqdm
from fastchat.llm_judge.common import (
load_questions,
load_model_answers,
load_judge_prompts,
check_data,
play_a_match_pair,
play_a_match_single,
get_model_list,
Judge... | null |
20,605 | import argparse
from concurrent.futures import ThreadPoolExecutor
import json
import numpy as np
from tqdm import tqdm
from fastchat.llm_judge.common import (
load_questions,
load_model_answers,
load_judge_prompts,
check_data,
play_a_match_pair,
play_a_match_single,
get_model_list,
Judge... | null |
20,606 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
The provided code snippet includes necessary depe... | Load model answers. The return value is a python dict of type: Dict[model_name: str -> Dict[question_id: int -> answer: dict]] |
20,607 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
The provided code snippet includes necessary depe... | Load judge prompts. The return value is a python dict of type: Dict[judge_name: str -> dict] |
20,608 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
class MatchPair:
question: dict
model_1: s... | null |
20,609 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
TIE_DELTA = 0.1
class MatchPair:
question: dic... | null |
20,610 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
API_MAX_RETRY = 16
API_RETRY_SLEEP = 10
API_ERROR_... | null |
20,611 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
def normalize_game_key_dict(judgment_dict):
""... | Load model judgments. The return value is a dict of type: Dict[judge: Tuple -> Dict[game_key: tuple -> game_result: dict] |
20,612 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
The provided code snippet includes necessary depe... | Load model judgments. The return value is a dict of type: Dict[judge: Tuple -> Dict[game_key: tuple -> game_result: dict] |
20,613 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
NEED_REF_CATS = ["math", "reasoning", "coding", "a... | null |
20,614 | import ast
import dataclasses
import glob
import json
import os
import re
import time
from typing import Optional
import openai
import anthropic
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
OPENAI_MODEL_LIST,
)
def get_model_list(answer_dir):
file_paths = ... | null |
20,615 | import argparse
from collections import defaultdict
import re
import gradio as gr
from fastchat.llm_judge.common import (
load_questions,
load_model_answers,
load_single_model_judgments,
load_pairwise_model_judgments,
resolve_single_judgment_dict,
resolve_pairwise_judgment_dict,
get_single_j... | null |
20,616 | from dataclasses import dataclass, field
import json
import math
import jsonlines
import pathlib
from multiprocessing import Pool
from typing import Dict, Optional, Sequence
import numpy as np
import torch
from torch.utils.data import Dataset
import transformers
from transformers import Trainer
from transformers.traine... | null |
20,617 | from dataclasses import dataclass, field
import json
import math
import jsonlines
import pathlib
from multiprocessing import Pool
from typing import Dict, Optional, Sequence
import numpy as np
import torch
from torch.utils.data import Dataset
import transformers
from transformers import Trainer
from transformers.traine... | null |
20,618 | from dataclasses import dataclass, field
import json
import math
import pathlib
from typing import Dict, Optional, Sequence
import numpy as np
import torch
from torch.utils.data import Dataset
import transformers
from transformers import Trainer
from transformers.trainer_pt_utils import LabelSmoother
from fastchat.conv... | null |
20,619 | from dataclasses import dataclass, field
import json
import math
import pathlib
from typing import Dict, Optional, Sequence
import numpy as np
import torch
from torch.utils.data import Dataset
import transformers
from transformers import Trainer
from transformers.trainer_pt_utils import LabelSmoother
from fastchat.conv... | null |
20,620 | import logging
import math
from typing import Optional, Tuple
import torch
import transformers.models.llama.modeling_llama
from torch import nn
def xformers_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
pas... | null |
20,621 | from collections import defaultdict
import copy
import os
from dataclasses import dataclass, field
import random
import json
import logging
import pathlib
from typing import Dict, Optional, Sequence, List
import torch
import torch.distributed as dist
from deepspeed import zero
from deepspeed.runtime.zero.partition_para... | null |
20,622 | from dataclasses import dataclass, field
import logging
import pathlib
import typing
import os
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
import transformers
from transformers import Trai... | null |
20,623 | from collections import defaultdict
import copy
import os
from dataclasses import dataclass, field
import random
import json
import logging
import pathlib
from typing import Dict, Optional, Sequence
import torch
import torch.distributed as dist
import transformers
from torch.utils.data import Dataset
from transformers ... | Given a list of sources, each is a conversation list. This transform: 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; 2. Concatenate conversations together; 3. Tokenize the concatenated conversation; 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. |
20,624 | from collections import defaultdict
import copy
import os
from dataclasses import dataclass, field
import random
import json
import logging
import pathlib
from typing import Dict, Optional, Sequence
import torch
import torch.distributed as dist
import transformers
from torch.utils.data import Dataset
from transformers ... | null |
20,625 | import warnings
from typing import Optional, Tuple
import torch
from flash_attn import __version__ as flash_attn_version
from flash_attn.bert_padding import pad_input, unpad_input
from flash_attn.flash_attn_interface import (
flash_attn_func,
flash_attn_varlen_kvpacked_func,
)
from transformers.models.llama.mod... | null |
20,626 | import argparse
import tempfile
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def upload_hub(model_path, hub_repo_id, component, private):
if component == "all":
components = ["model", "tokenizer"]
else:
components = [component]
kwargs = {"push_to_hub": True, "r... | null |
20,627 | import math
import os
import re
import sys
from typing import Dict, List, Optional
import warnings
import psutil
import torch
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
LlamaTokenizer,
LlamaForCausalLM,
T5Tokenizer,
)
fro... | Register a model adapter. |
20,628 | import math
import os
import re
import sys
from typing import Dict, List, Optional
import warnings
import psutil
import torch
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
LlamaTokenizer,
LlamaForCausalLM,
T5Tokenizer,
)
fro... | Remove parent directory name. |
20,629 | import dataclasses
import gc
import glob
import os
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from huggingface_hub import snapshot_download
import torch
from torch import Tensor
from torch.nn import functional as F
import torch.nn as nn
from tqdm import tqdm
from ... | null |
20,630 | import dataclasses
import gc
import glob
import os
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from huggingface_hub import snapshot_download
import torch
from torch import Tensor
from torch.nn import functional as F
import torch.nn as nn
from tqdm import tqdm
from ... | Simulate group-wise dequantization. |
20,631 | from functools import partial
import torch
import transformers
import transformers.models.llama.modeling_llama
class CondenseRotaryEmbedding(torch.nn.Module):
def __init__(
self, dim, ratio, max_position_embeddings=2048, base=10000, device=None
):
super().__init__()
inv_freq = 1.0 / (bas... | null |
20,632 | import argparse
import torch
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM
def apply_lora(base_model_path, target_model_path, lora_path):
print(f"Loading the base model from {base_model_path}")
base = AutoModelForCausalLM.from_pretrained(
base_model_path, torch... | null |
20,633 | import argparse
import gc
import glob
import json
import os
import shutil
import tempfile
from huggingface_hub import snapshot_download
import torch
from torch import nn
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
GB = 1 << 30
def split_files(model_path, tmp_path, spli... | null |
20,634 | import argparse
import gc
import glob
import json
import os
import shutil
import tempfile
from huggingface_hub import snapshot_download
import torch
from torch import nn
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
def apply_delta(base_model_path, target_model_path, de... | null |
20,635 | from collections import namedtuple, OrderedDict
from typing import List
ModelInfo = namedtuple("ModelInfo", ["simple_name", "link", "description"])
model_info = OrderedDict()
def register_model_info(
full_names: List[str], simple_name: str, link: str, description: str
):
info = ModelInfo(simple_name, link, des... | null |
20,636 | import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
def make_delta(base_model_path, target_model_path, delta_path):
print(f"Loading the base model from {base_model_path}")
base = AutoModelForCausalLM.from_pretrained(
base_model_path, torch_dty... | null |
20,637 | import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
def convert_fp16(in_checkpoint, out_checkpoint):
tokenizer = AutoTokenizer.from_pretrained(in_checkpoint, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
in_checkpoint, torch_dtype=torch.float16, low... | null |
20,638 |
def send_request(ques):
# url = 'https://qanything-test.site.youdao.com/api/local_doc_qa/local_doc_chat'
url = 'http://localhost:8777/api/local_doc_qa/local_doc_chat'
headers = {
'content-type': 'application/json'
}
data = {
"user_id": "liujx_265",
"kb_ids": ["KBf652e9e379c... | null |
20,639 | import os
import sys
import aiohttp
import asyncio
import time
import re
def remove_full_width_characters(s):
# 匹配全角字符的正则表达式
pattern = re.compile(r'[\uFF00-\uFFEF]')
# 替换字符串中的全角字符为空字符串
return pattern.sub('', s) | null |
20,640 | import os
import sys
import aiohttp
import asyncio
import time
import re
file_folder = sys.argv[1]
kb_id = sys.argv[2]
support_end = ('.md', '.txt', '.pptx', '.jpg', '.jpeg', '.png', '.docx', '.xlsx', '.eml', '.csv', '.pdf')
files = []
for root, dirs, file_names in os.walk(file_folder):
for file_name in file_names:... | null |
20,641 | import os
import json
import requests
import time
import random
import string
import hashlib
import argparse
import concurrent.futures
import numpy as np
import pandas as pd
from tqdm import tqdm
import random
import threading
print("response", response)
print(response.iter_lines)
def test_stream():
data_r... | null |
20,642 | import os
import json
import requests
import time
import random
import string
import hashlib
import argparse
import concurrent.futures
import numpy as np
import pandas as pd
from tqdm import tqdm
import random
import threading
def measure_latency(ques, output_file, is_stream=False):
start_time = time.time()
if ... | null |
20,643 | ls import cycle
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, ProgbarLogger
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoade... | null |
20,644 | ls import cycle
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, ProgbarLogger
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoade... | null |
20,645 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, Logger, Tensorboard, text_segmentate, ListDataset, Evaluator, EarlyStopping, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as... | null |
20,646 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, Logger, Tensorboard, text_segmentate, ListDataset, Evaluator, EarlyStopping, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as... | null |
20,647 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, Logger, Tensorboard, text_segmentate, ListDataset, Evaluator, EarlyStopping, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as... | 单条样本推理 |
20,648 | AutoModelForSequenceClassification
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.... | null |
20,649 | AutoModelForSequenceClassification
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.... | null |
20,650 | import os
import json
import shutil
def replace_file(local_path, convert_path, replace=False):
if replace:
shutil.copy(convert_path, local_path) | null |
20,651 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.layers import TplinkerHandshakingKernel
from tqdm import tqdm
import torch
import torch.nn ... | null |
20,652 | json
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.layers import TplinkerHandshakingKernel
from tqdm import tqdm
import torch
import torc... | 评估函数，计算f1、precision、recall |
20,653 | import build_transformer_model
from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
from bert4torch.optimizers import get_linear_schedule_with_warmup
from torch.utils.data import Dataset
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import Dat... | null |
20,654 | import json, glob, re
from tqdm import tqdm
import collections
import gc
import shelve
import time
import os
import random
import jieba
The provided code snippet includes necessary dependencies for implementing the `some_texts` function. Write a Python function `def some_texts()` to solve the following problem:
挑选语料
... | 挑选语料 |
20,655 | import json, glob, re
from tqdm import tqdm
import collections
import gc
import shelve
import time
import os
import random
import jieba
jieba.initialize()
def word_segment(text):
return jieba.lcut(text) | null |
20,656 | import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, truncate_sequences, get_pool_emb
from bert4... | null |
20,657 | import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, truncate_sequences, get_pool_emb
from bert4... | 随机观察一些样本的效果 |
20,658 | from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate
from bert4torch.snippets import truncate_sequences, get_pool_emb
from bert4t... | 分割句子 |
20,659 | from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate
from bert4torch.snippets import truncate_sequences, get_pool_emb
from bert4t... | null |
20,660 | import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, get_pool_emb, truncate_sequences
from bert4... | null |
20,661 | import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, get_pool_emb, truncate_sequences
from bert4... | 随机观察一些样本的效果 |
20,662 | rch.models import build_transformer_model
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from bert4torch.models import build_transformer_model
from... | 单条样本格式：[CLS]篇章[SEP]答案[SEP]问题[SEP] |
20,663 | rch.models import build_transformer_model
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from bert4torch.models import build_transformer_model
from... | null |
20,664 | import torch
import torch.nn as nn
import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from torch.optim import Adam
from bert4torch.snippets import sequence_padding, ListDataset, log_warn_once
from bert4torch.callbacks import Callback
from torc... | null |
20,665 | import torch
import torch.nn as nn
import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from torch.optim import Adam
from bert4torch.snippets import sequence_padding, ListDataset, log_warn_once
from bert4torch.callbacks import Callback
from torc... | 对输入进行随机mask |
20,666 | import build_transformer_model, BaseModel
from torch.utils.data import DataLoader
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback, EarlyStopping, AdversarialTraining
from bert4torch.tokenizers import Tokenizer
import torch.nn.functional as F
from sklearn.metrics i... | null |
20,667 | from torch import device
from training import Model, collate_fn
import torch
from torch.utils.data import DataLoader
from bert4torch.snippets import ListDataset
import pandas as pd
from tqdm import tqdm
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `load_data` functi... | 加载数据。 |
20,668 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data i... | null |
20,669 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data i... | 单条样本推理 |
20,670 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch... | null |
20,671 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch... | null |
20,672 |
def collate_fn(batch):
batch_token_ids, batch_segment_ids, batch_labels = [], [], []
for text1, text2, label in batch:
token_ids, segment_ids = tokenizer.encode(text1, text2, maxlen=maxlen)
batch_token_ids.append(token_ids)
batch_segment_ids.append(segment_ids)
batch_labels.app... | null |
20,673 |
def evaluate(data):
total, right = 0., 0.
for x_true, y_true in data:
y_pred = model.predict(x_true).argmax(axis=1)
total += len(y_true)
right += (y_true == y_pred).sum().item()
return right / total | null |
20,674 | as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
from bert4torch.callbacks import Callback
from bert4torch.optimizers import Lion
import torch.n... | null |
20,675 | as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
from bert4torch.callbacks import Callback
from bert4torch.optimizers import Lion
import torch.n... | null |
20,676 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as op... | null |
20,677 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as op... | 单条样本推理 |
20,678 | t from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, seed_everything
from bert4torch.callbacks import AdversarialTraining
from ... | null |
20,679 | torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, seed_everything
from bert4torch.callbacks import AdversarialTraining
f... | null |
20,680 | torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, seed_everything
from bert4torch.callbacks import AdversarialTraining
f... | null |
20,681 | from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, seed_everything
from bert4torch.callbacks import AdversarialTraining
from be... | null |
20,682 | torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, seed_everything
from bert4torch.callbacks import AdversarialTraining
f... | null |
20,683 | torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, seed_everything
from bert4torch.callbacks import AdversarialTraining
f... | null |
20,684 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate
from bert4torch.callbacks import AdversarialTraining
from bert4t... | null |
20,685 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate
from bert4torch.callbacks import AdversarialTraining
from bert4t... | null |
20,686 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate
from bert4torch.callbacks import AdversarialTraining
from bert4t... | null |
20,687 | from bert4torch.tokenizers import SpTokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
import r... | null |
20,688 | from bert4torch.tokenizers import SpTokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
import r... | null |
20,689 | import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
impor... | null |
20,690 | import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
impor... | null |
20,691 | import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim ... | null |
20,692 | import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim ... | null |
20,693 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data i... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.