README.md

metadata

language:
  - en
license: apache-2.0
base_model: google/flan-t5-small
tags:
  - text-simplification
  - paraphrase
  - natural-language-processing
datasets:
  - agentlans/sentence-paraphrases
pipeline_tag: text2text-generation

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.