Need help in fine-tuning T5-Base Model for a sequence task
Hello,I am trying to fine-tune at5-basemodel for creating appropriate question against a compliance item. Compliance iteams are paragraph of texts and my question are in the past format of them. I have trained the model, saved it and loaded it back for future usecases.The problem is when I am trying to use the model for creating new questions on unknown statements the response is coming as incomplete.Code: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_outputHere the response isWhen 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 forwhich is incomplete.So my question is what do i need to do increase the output?Should I increase the epochs?Or is there a better model for this task?Please help in this.