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我正在尝试使用SimpleTransformers默认设置来进行多任务学习。

我在这里使用他们网站上的示例

代码如下所示：

import logging

import pandas as pd
from simpletransformers.t5 import T5Model, T5Args

logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)


train_data = [
    ["binary classification", "Anakin was Luke's father" , 1],
    ["binary classification", "Luke was a Sith Lord" , 0],
    ["generate question", "Star Wars is an American epic space-opera media franchise created by George Lucas, which began with the eponymous 1977 film and quickly became a worldwide pop-culture phenomenon", "Who created the Star Wars franchise?"],
    ["generate question", "Anakin was Luke's father" , "Who was Luke's father?"],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["prefix", "input_text", "target_text"]

eval_data = [
    ["binary classification", "Leia was Luke's sister" , 1],
    ["binary classification", "Han was a Sith Lord" , 0],
    ["generate question", "In 2020, the Star Wars franchise's total value was estimated at US$70 billion, and it is currently the fifth-highest-grossing media franchise of all time.", "What is the total value of the Star Wars franchise?"],
    ["generate question", "Leia was Luke's sister" , "Who was Luke's sister?"],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["prefix", "input_text", "target_text"]

model_args = T5Args()
model_args.num_train_epochs = 200
model_args.no_save = True
model_args.evaluate_generated_text = False
model_args.evaluate_during_training = False
model_args.evaluate_during_training_verbose = False
model_args.use_multiprocessing = False
model_args.use_multiprocessing_for_evaluation = False

model = T5Model("t5", "t5-base", args=model_args)


def count_matches(labels, preds):
    print(labels)
    print(preds)
    return sum([1 if label == pred else 0 for label, pred in zip(labels, preds)])


model.train_model(train_df, show_running_loss=True)

目前我什至没有使用eval_df（尽管我计划在我的真实代码中使用它），因为它没有在他们的代码中正确设置。在这个超级简单的设置中，我认为该库可以正常工作。然而，在尝试了两个系统（一个 Windows，一个 Linux，两个最新版本的 SimpleTransformers）后，我收到以下错误：

  File "C:\Users\name\AppData\Local\Programs\Python\Python38\lib\site-packages\simpletransformers\t5\t5_utils.py", line 175, in <listcomp>       
    preprocess_data(d) for d in tqdm(data, disable=args.silent)
  File "C:\Users\name\AppData\Local\Programs\Python\Python38\lib\site-packages\simpletransformers\t5\t5_utils.py", line 81, in preprocess_data   
    batch = tokenizer.prepare_seq2seq_batch(
  File "C:\Users\name\AppData\Local\Programs\Python\Python38\lib\site-packages\transformers\tokenization_utils_base.py", line 3282, in prepare_seq2seq_batch
    labels = self(
  File "C:\Users\name\AppData\Local\Programs\Python\Python38\lib\site-packages\transformers\tokenization_utils_base.py", line 2262, in __call__  
    raise ValueError(
ValueError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).

我正在使用确切的设置，并且所有输入DataFrames都包含字符串。

谁能帮助弄清楚为什么这个基本设置会失败？谢谢。

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1 回答 1

0

在示例代码中，如果您更改

train_data = [
    ["binary classification", "Anakin was Luke's father" , 1],
    ["binary classification", "Luke was a Sith Lord" , 0],
    ["generate question", "Star Wars is an American epic space-opera media franchise created by George Lucas, which began with the eponymous 1977 film and quickly became a worldwide pop-culture phenomenon", "Who created the Star Wars franchise?"],
    ["generate question", "Anakin was Luke's father" , "Who was Luke's father?"],
]

到

train_data = [
    ["binary classification", "Anakin was Luke's father" , '1'],
    ["binary classification", "Luke was a Sith Lord" , '0'],
    ["generate question", "Star Wars is an American epic space-opera media franchise created by George Lucas, which began with the eponymous 1977 film and quickly became a worldwide pop-culture phenomenon", "Who created the Star Wars franchise?"],
    ["generate question", "Anakin was Luke's father" , "Who was Luke's father?"],
]

该错误不再发生 - 所以这是由于标签不是 type str。

于 2021-05-30T17:54:12.710 回答