README.md

---
license: mit
datasets:
- philipp-zettl/long-qa
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
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---

# Model Card for t5-small-long-qa

<!-- Provide a quick summary of what the model is/does. -->

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model was trained to generate answers for questions out of a given context.


- **Developed by:** [philipp-zettl](https://huggingface.co/philipp-zettl)
- **Model type:** Transformer (T5)
- **Language(s) (NLP):** English
- **License:** M.I.T
- **Finetuned from model [optional]:** [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)

### Model Sources [optional]

<!-- Provide the basic links for the model. -->
Fine-tune of the amazing [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
It's intended to use the model to answers for questions from given context.
The context should not exceed the model's _context_ length.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

No bias evaluation was performed on this model.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
context = "This is a long text based of multiple concatenated paragraphs."
question = "My question about something mentioned inside the context."

model_inputs = tokenizer([f"question: {question} context: {context}"], max_length=512, padding=True, truncation=True)
input_ids = torch.tensor(model_inputs['input_ids']).to(device)
attention_mask = torch.tensor(model_inputs['attention_mask']).to(device)
with torch.no_grad():
    sample_output = model.generate(input_ids[:1], max_length=85)
    sample_output_text = tokenizer.decode(sample_output[0], skip_special_tokens=True)
    input_text = tokenizer.decode(input_ids[0], skip_special_tokens=True)
    print(f"Sample Input:\n \"{input_text}\"\n\n")
    print(f"Model Output: \"{sample_output_text}\"")
```

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

This model was trained on [philipp-zettl/long-qa](https://huggingface.co/datasets/philipp-zettl/long-qa).

A synthetic data set created from [philipp-zettl/qg-tidyqa_squad2](https://huggingface.co/datasets/philipp-zettl/qg-tydiqa_squad2) using [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct).

The data set was created by prompting Phi-3 using the prompt template
```python
msg = f"""
Answer the following question using the content provided in the context.
Do not answer questions where the answer isn't inside the context.


Question: {sample['question']}
Context: {sample['context']}
"""
```

After generating synthetic answers, the data set was manually corrected and validated to ensure high quality as well as consistent longer answers than the original data sets.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Below you can find the full training pipeline used to achieve this fine-tune.

```python
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Base model (e.g., T5-large)
# https://huggingface.co/collections/google/flan-t5-release-65005c39e3201fff885e22fb
model_name = 'google/flan-t5-small'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Move only the student model to GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
```

Load dataset
```python
from datasets import load_dataset

# Load dataset
ds = load_dataset('philipp-zettl/long-qa')

# Split the dataset into training and validation
train_dataset = ds['train']
validation_dataset = ds['test']
```

Preprocessing: tokenize inputs and labels for faster training cycles, i.e. no need for tokenization during training anymore
```python
def preprocess_batch(batch, tokenizer, max_input_length=512, max_output_length=128):
    questions = batch['question']
    contexts = batch['context']
    answers = batch['answer']

    inputs = [f"question: {q} context: {c}" for q, c in zip(questions, contexts)]
    model_inputs = tokenizer(inputs, max_length=max_input_length, padding=True, truncation=True)

    labels = tokenizer(answers, max_length=max_output_length, padding=True, truncation=True)
    model_inputs['labels'] = labels['input_ids']

    return model_inputs

# Tokenize the dataset
train_dataset = train_dataset.map(lambda batch: preprocess_batch(batch, tokenizer), batched=True)
validation_dataset = validation_dataset.map(lambda batch: preprocess_batch(batch, tokenizer), batched=True)

# Set format for PyTorch
train_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
validation_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
```

The train loop
```python
from tqdm import tqdm
from transformers import AdamW, DataCollatorForSeq2Seq
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

torch.cuda.empty_cache()

model_name = 'google/flan-t5-small'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Move only the student model to GPU if available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# Training parameters
epochs = 50
learning_rate = 3e-5
temperature = 2.0
batch_size = 8
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

# Create a data collator for padding and batching
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)

# Create DataLoaders with the data collator
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=data_collator)
validation_dataloader = DataLoader(validation_dataset, batch_size=batch_size, collate_fn=data_collator)

writer = SummaryWriter(comment='t5-small-long-qa')

# Store losses and learning rates
train_losses = []
val_losses = []
learning_rates = []

print("Starting training...")

# Training loop
for epoch in range(epochs):
    model.train()
    total_loss = 0
    print(f"Epoch {epoch+1}/{epochs}")

    progress_bar = tqdm(train_dataloader, desc="Training", leave=False)

    for step, batch in enumerate(progress_bar):
        # Move student inputs to GPU
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)

        # Teacher forward pass on CPU
        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
        logits = outputs.logits

        # Calculate losses
        loss = outputs.loss  # Cross-entropy loss
        writer.add_scalar("Loss/train", loss, epoch * len(train_dataloader) + step)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_loss += loss.item()

        # Verbose logging
        if step % len(train_dataloader)//10 == 1 or step == len(train_dataloader) - 1:
            progress_bar.set_postfix({
                'step': step,
                'loss': loss.item(),
            })

            # Generate a sample output from the student model
            model.eval()
            with torch.no_grad():
                sample_output = model.generate(input_ids[:1], max_length=50)
                sample_output_text = tokenizer.decode(sample_output[0], skip_special_tokens=True)
                input_text = tokenizer.decode(input_ids[0], skip_special_tokens=True)
                writer.add_text(f"Sample Input", input_text, step)
                writer.add_text(f"Sample Output", sample_output_text, step)
            model.train()

    
    avg_train_loss = total_loss / len(train_dataloader)
    train_losses.append(avg_train_loss)
    learning_rates.append(optimizer.param_groups[0]['lr'])

    # Validation step
    model.eval()
    total_val_loss = 0
    with torch.no_grad():
        for batch in validation_dataloader:
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            labels = batch['labels'].to(device)

            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
            val_loss = outputs.loss
            total_val_loss += val_loss.item()

    avg_val_loss = total_val_loss / len(validation_dataloader)
    val_losses.append(avg_val_loss)

    writer.add_scalar("AVG Loss/train", avg_train_loss, epoch)
    writer.add_scalar("AVG Loss/val", avg_val_loss, epoch)

    print(f"Epoch {epoch+1} completed. Avg Train Loss: {avg_train_loss:.4f}, Avg Val Loss: {avg_val_loss:.4f}")


print("Training complete.")
writer.close()
```