t5-small-tydiqa-en

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Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: philipp-zettl
• Model type: Seq2Seq
• Language(s) (NLP):
• License: Apache 2.0
• Finetuned from model: google/flan-t5-small

Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("philipp-zettl/t5-small-tydiqa-en")
model = AutoModelForSeq2SeqLM.from_pretrained("philipp-zettl/t5-small-tydiqa-en")

question = "Some question?"
# For instance retrieved using similarity search
context = "A long context ..."

inputs = [f"question: {q} context: {c}" for q, c in [[question, context]]]
model_inputs = tokenizer(inputs, 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=100)
  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", input_text)
print(f"Sample Output", sample_output_text)

Training Details

Training Data

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Trained on the english samples of google-research-datasets/tydiqa using following code

from datasets import load_dataset

# Load SQuAD dataset
squad_dataset = load_dataset('google-research-datasets/tydiqa', 'secondary_task')

# Split the dataset into training and validation
train_dataset = squad_dataset['train'].filter(lambda e: any([e['id'].startswith(lang) for lang in ['english']]))
validation_dataset = squad_dataset['validation'].filter(lambda e: any([e['id'].startswith(lang) for lang in ['english']]))

Training Procedure

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Preprocessing

Code for preprocessing

def preprocess_batch(batch, tokenizer, max_input_length=512, max_output_length=128):
    questions = batch['question']
    contexts = batch['context']
    answers = [answer['text'][0] for answer in batch['answers']]

    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, teacher_tokenizer), batched=True)
validation_dataset = validation_dataset.map(lambda batch: preprocess_batch(batch, teacher_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'])

Training Hyperparameters

Code of training loop:

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()

teacher_model.to(device)

# Training parameters
epochs = 3
learning_rate = 5e-5
temperature = 2.0
batch_size = 2
optimizer = torch.optim.AdamW(teacher_model.parameters(), lr=learning_rate)

# Create a data collator for padding and batching
data_collator = DataCollatorForSeq2Seq(tokenizer=teacher_tokenizer, model=teacher_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('./logs', comment='t5-base')

print("Starting training...")

# Training loop
for epoch in range(epochs):
    teacher_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
        teacher_outputs = teacher_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
        teacher_logits = teacher_outputs.logits

        # Calculate losses
        loss = teacher_outputs.loss  # Cross-entropy loss
        writer.add_scalar("Loss/train", loss, step)

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

        total_loss += loss.item()

        # Verbose logging
        if step % 1 == 0 or step == len(train_dataloader) - 1:
            progress_bar.set_postfix({
                'step': step,
                'loss': loss.item(),
            })

            # Generate a sample output from the student model
            teacher_model.eval()
            with torch.no_grad():
                sample_output = teacher_model.generate(input_ids[:1], max_length=50)
                sample_output_text = teacher_tokenizer.decode(sample_output[0], skip_special_tokens=True)
                input_text = teacher_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)
            teacher_model.train()

    avg_loss = total_loss / len(train_dataloader)
    print(f"Epoch {epoch+1} completed. Average Loss: {avg_loss:.4f}")
    writer.add_scalar("AVG Loss/train", avg_loss, epoch)

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

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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