需要帮助微调 T5-Base 模型以执行序列到序列任务

Question

我正在尝试微调

t5-base

模型，以针对合规性项目创建适当的问题。合规性项目组是一段文本，我的问题是它们过去的格式。我已经训练了模型，保存并加载回来以供将来使用。

问题是，当我尝试使用该模型针对未知陈述创建新问题时，响应不完整。

代码：

import pandas as pd
import torch
from datasets import Dataset
import transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer, T5Tokenizer

df = pd.read_csv(r'/content/questionsgenerator.csv', encoding='unicode_escape')
df.head()

# Load pre-trained model and tokenizer
model_name = "t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Define the training arguments
training_args = Seq2SeqTrainingArguments(
    output_dir="./output_dir",
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    logging_steps=100,
    save_steps=5000,
    eval_steps=5000,
    num_train_epochs=3,
    learning_rate=1e-4,
    warmup_steps=1000,
    save_total_limit=3,
)

# Define the training dataset
train_dataset = Dataset.from_pandas(df.rename(columns={"Compliance Item": "input_text", "Question": "target_text"}))

# Define the function to preprocess the dataset
def preprocess_function(examples):
    inputs = [f"compliance item: {ci}" for ci in examples["input_text"]]
    targets = [f"{question} </s>" for question in examples["target_text"]]
    model_inputs = tokenizer(inputs, max_length=512, padding="max_length", truncation=True)
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(targets, max_length=32, padding="max_length", truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

# Preprocess the dataset
train_dataset = train_dataset.map(preprocess_function, batched=True)

# Define the trainer
trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Fine-tune the model on the dataset
trainer.train()

model.save_pretrained("./fine_tuned_model_question_generation")

tokenizer = T5Tokenizer.from_pretrained("t5-large")
model = transformers.AutoModelForSeq2SeqLM.from_pretrained("./fine_tuned_model_question_generation")

context = 'When the Installment Due Date falls on a non-business day, the Mortgagee must consider a Borrower’s Notice of Intent to Prepay or the receipt of the prepayment amount for a Mortgage closed before January 21, 2015 timely if received on the next business day.'

encoding = tokenizer.encode_plus(context, return_tensors="pt")

input_ids = encoding["input_ids"]
attention_mask = encoding["attention_mask"]

output = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_length=1000)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)

decoded_output

这里的回应是：

When the Installment Due Date fell on a non-business day, was the Borrower’s Notice of Intent to Prepay or the receipt of the prepayment amount for

这显然是不完整的。

所以我的问题是我需要做什么来增加产量？

1. 我应该增加纪元吗？
2. 或者这个任务有更好的模型吗？

请帮忙。

Answer 1

因为：

labels = tokenizer(targets, max_length=32, padding="max_length", truncation=True)

您的模型很可能已经学会了仅输出/生成约 32 个令牌的输出。

尝试：

labels = tokenizer(targets, max_length=512, padding="max_length", truncation=True)