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Model Description

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

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Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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.

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

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

def eval(inputs, tokenizer, model):
    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=85,
            temperature=0.5,
            do_sample=True
        )
        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}\"")


statement = "The model should change the words but not the meaning of this sentence."
eval([f"paraphrase: {statement} . output: "], tokenizer, model)

Training Details

Training Data

For training we used the grammarly/medit dataset, filtered by german/english paraphrasing samples.

Training Procedure

Preprocessing [optional]

def preprocess_batch(batch, tokenizer, max_input_length=512, max_output_length=128):
    questions = batch['src']
    answers = batch['tgt']

    inputs = [f"paraphrase: {q} output: " for q in questions]
    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.filter(lambda x: x['task'] == 'paraphrasing' and x['lang'] in ['de', 'en']).map(lambda batch: preprocess_batch(batch, tokenizer), batched=True)
validation_dataset = validation_dataset.filter(lambda x: x['task'] == 'paraphrasing' and x['lang'] in ['de', 'en']).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'])

Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

• Hardware Type: [More Information Needed]
• Hours used: 9.83 hr
• Cloud Provider: -
• Compute Region: eu-central-1
• Carbon Emitted:

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