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agentlans/
flan-t5-small-title

Name: flan-t5-small-title
Availability: InStock

agentlans/wikipedia-paragraph-titles

google/flan-t5-small

finetune

apache-2.0

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Flan-T5 Small Title Generator

Model Description

This model is a fine-tuned version of the Flan-T5 small model, specifically adapted for generating attention-grabbing titles based on given text. Flan-T5 is an improved version of the T5 (Text-To-Text Transfer Transformer) model developed by Google, which has been instruction-tuned on a diverse set of tasks.

Architecture: Flan-T5 small
Purpose: Generate engaging titles from input text
Base Model: google/flan-t5-small

Intended Uses & Limitations

Intended Uses

Generating catchy titles for articles, blog posts, or news stories
Summarizing key points of a text passage into a concise headline
Assisting content creators in brainstorming title ideas

Limitations

Requires clear context from the input paragraph to generate relevant titles
May produce exaggerated or off-topic titles if the context is ambiguous
Outputs should always be reviewed by a human before use
Not suitable for generating titles for sensitive or critical content without human oversight

Training Details

Training Data

The model was fine-tuned on the "Wikipedia Paragraphs and AI-Generated Titles Dataset" (agentlans/wikipedia-paragraph-titles), which contains:

Pairs of Wikipedia paragraphs and corresponding AI-generated titles
A mix of human-written content and machine-generated titles
Diverse topics from Wikipedia articles

Training details

Training Procedure

Base Model: google/flan-t5-small
Fine-tuning Approach: Further trained on the title generation task
Input Format: topic || text
Output Format: Attention-grabbing title based on the input text

Training Hyperparameters

Learning rate: 5e-05
Train batch size: 8
Eval batch size: 8
Seed: 42
Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
LR scheduler type: linear
Number of epochs: 10.0

The model was trained using the following framework versions:

Transformers 4.45.1
PyTorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0

Ethical Considerations & Biases

The model may inherit biases present in the Wikipedia content used for training
There's a risk of generating sensationalized or misleading titles, especially for ambiguous content
Users should be aware of potential biases in title generation, particularly for sensitive topics
The model should not be used as the sole source for generating titles in professional or journalistic contexts without human review

Usage

To use the model, follow these steps:

Input format: topic||text
The model will generate an attention-grabbing title based on the input text
Always review the output for relevance and appropriateness

Example Usage

Here's a code example demonstrating how to use the Flan-T5 small model for title generation:

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "agentlans/flan-t5-small-title"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
topic = "The Serenity of Nature" # a cue to establish context (not necessary but recommended)
text = "As dawn breaks, the world awakens to a symphony of colors and sounds. The golden rays of sunlight filter through the leaves, casting playful shadows on the forest floor. Birds chirp melodiously, their songs weaving through the crisp morning air, while a gentle breeze rustles the branches overhead. Dew-kissed flowers bloom in vibrant hues, their fragrant scents mingling with the earthy aroma of damp soil. In this tranquil setting, one can’t help but feel a profound sense of peace and connection to the natural world, reminding us of the simple joys that life has to offer."

input_text = f"{topic}||{text}"

# Tokenize the input
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)

# Generate the title
outputs = model.generate(**inputs, max_length=30, num_return_sequences=1)

# Decode and print the generated title
generated_title = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_title) # The Serenity of Nature: A Symbol of Peace and Harmony

License

This model is released under the Apache 2.0 license.