---
library_name: transformers
datasets:
  - google-research-datasets/tydiqa
license: apache-2.0
pipeline_tag: text2text-generation
base_model: google/flan-t5-small
widget:
  - text: "question: What is the huggingface hub? context: The Hugging Face Hub is a
      platform with over 350k models, 75k datasets, and 150k demo apps (Spaces),
      all open source and publicly available, in an online platform where people
      can easily collaborate and build ML together. The Hub works as a central
      place where anyone can explore, experiment, collaborate, and build
      technology with Machine Learning. Are you ready to join the path towards
      open source Machine Learning? 🤗"
    example_title: 🤗 Hub
  - text: "question: What is huggingface datasets? context: 🤗 Datasets is a library
      for easily accessing and sharing datasets for Audio, Computer Vision, and
      Natural Language Processing (NLP) tasks. Load a dataset in a single line
      of code, and use our powerful data processing methods to quickly get your
      dataset ready for training in a deep learning model. Backed by the Apache
      Arrow format, process large datasets with zero-copy reads without any
      memory constraints for optimal speed and efficiency. We also feature a
      deep integration with the Hugging Face Hub, allowing you to easily load
      and share a dataset with the wider machine learning community. Find your
      dataset today on the Hugging Face Hub, and take an in-depth look inside of
      it with the live viewer."
    example_title: 🤗 datasets

---

# Model Card for Model ID

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



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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](https://huggingface.co/philipp-zettl)
- **Model type:** Seq2Seq
- **Language(s) (NLP):** 
- **License:** Apache 2.0
- **Finetuned from model:** [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. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

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

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

```python
# 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

<!-- 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. -->
Trained on the english samples of [google-research-datasets/tydiqa](https://huggingface.co/datasets/google-research-datasets/tydiqa) using following code
```python
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

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing
Code for preprocessing
```python
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:
```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()

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

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]