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FLAN-T5 Small Simplifier

A fine-tuned text simplification and paraphrasing model based on Google's FLAN-T5 Small, designed to enhance text readability while preserving core semantic meaning.

Model Details

Base Model: google/flan-t5-small
Task: Text Simplification and Paraphrasing
Languages: English

Capabilities

The model is specialized in:

Reducing text complexity
Generating more readable paraphrases
Maintaining original semantic content

Intended Use

Primary Use Cases:

Academic writing simplification
Technical document readability enhancement
Content adaptation for diverse audiences

Limitations:

Optimized for English language texts
Best performance on sentence-length inputs
May struggle with highly specialized or mixed-language texts

Usage Example

from transformers import pipeline

simplifier = pipeline(
    "text2text-generation", model="agentlans/flan-t5-small-simplifier"
)

complex_text = "While navigating the labyrinthine corridors of epistemological uncertainty, the precocious philosopher paused to contemplate the intricate interplay between subjective perception and objective reality."

simplified_text = simplifier(complex_text, max_length=128)[0]["generated_text"]
print(simplified_text)

# The precocious philosopher paused to contemplate the complex interplay between subjective perception and objective reality while navigating the labyrinthine corridors of epistemological uncertainty.

Training Details

Dataset: agentlans/sentence-paraphrases

Source: Curated paraphrase collections
Readability assessment using a finetuned DeBERTa v3 XSmall

Training Hyperparameters:

Learning Rate: 5e-05
Batch Size: 8
Optimizer: Adam
Epochs: 2.0

Performance Metrics:

Epoch	Training Loss	Validation Loss
0.22	1.4423	1.2431
0.89	1.3595	1.1787
1.78	1.2952	1.1518

Framework

Transformers 4.43.3
PyTorch 2.3.0+cu121
Datasets 3.2.0

Ethical Considerations

Users should review generated text for accuracy and appropriateness, as the model may inherit biases from training data.

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Model size

77M params

Tensor type

F32

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