metadata

license: openrail
inference:
  parameters:
    temperature: 0.7
    max_length: 24
datasets:
  - Ateeqq/Title-Keywords-SEO
language:
  - en
library_name: transformers
pipeline_tag: text2text-generation
tags:
  - text-generation-inference
widget:
  - text: 'generate title: Importance, Dataset, AI'
    example_title: Example 1
  - text: 'generate title: Amazon, Product, Business'
    example_title: Example 2
  - text: 'generate title: History, Computer, Software'
    example_title: Example 3

Generate Title using Keywords

Title Generator is an online tool that helps you create great titles for your content. By entering specific keywords or information about content, you receive topic suggestions that increase content appeal.

Developed by https://exnrt.com

Fine Tuned: T5-Base
Parameters: 223M
Train Dataset Length: 10,000
Validation Dataset Length: 2000
Batch Size: 1
Epochs: 2
Train Loss: 1.6578
Validation Loss: 1.8115

You can also use t5-small (77M params) available in mini folder.

How to use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("Ateeqq/keywords-title-generator", token='your_token')
model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/keywords-title-generator", token='your_token').to(device)

def generate_title(keywords):
    input_ids = tokenizer(keywords, return_tensors="pt", padding="longest", truncation=True, max_length=24).input_ids.to(device)
    outputs = model.generate(
        input_ids,
        num_beams=5,
        num_beam_groups=5,
        num_return_sequences=5,
        repetition_penalty=10.0,
        diversity_penalty=3.0,
        no_repeat_ngram_size=2,
        temperature=0.7,
        max_length=24
    )
    return tokenizer.batch_decode(outputs, skip_special_tokens=True)

keywords = 'model, Fine-tuning, Machine Learning'
generate_title(keywords)

Output:

['How to Fine-tune Your Machine Learning Model for Better Performance',
 'Fine-tuning your Machine Learning model with a simple technique',
 'Using fine tuning to fine-tune your machine learning model',
 'Machine Learning: Fine-tuning your model to fit the needs of machine learning',
 'The Art of Fine-Tuning Your Machine Learning Model']

Disclaimer:

It grants a non-exclusive, non-transferable license to use the this model. This means you can't freely share it with others or sell the model itself. However you can use the model for commercial purposes.