t5-small-paraphraser

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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.

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Direct Use

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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

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

Training Procedure

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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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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Results

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Summary

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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).

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

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