metadata
language:
- en
tags:
- text2text-generation
- paraphrase-generation
license: apache-2.0
widget:
- text: 'headline: weight loss'
About the model
The model has been trained on a dataset containing 138927 article titles along with their keywords.
The purpose of the model is to generate suggestions of article headlines, given a keyword or multiple keywords.
Generation examples
| Input | Output |
|---|---|
| weight loss | The Last Weight Loss Plan: Lose Weight, Feel Great, and Get in Shape How to Lose Weight Without Giving Up Your Favorite Foods I Lost Weight and Finally Feel Good About My Body |
| property rental, property management | Property rental: The new way to make money We take the hassle out of property rental Is property management your new best friend? |
| diabetic diet plan | A diabetic diet plan that actually works! Lose weight, feel great, and live better with our diabetic diet plan! Diet has never been so tasty: Our diabetic diet plan puts you to the test! |
You can supply multiple keywords by separating them with commas. Higher temperature settings result in more creative headlines; we recommend testing first with the temperature set to 1.5.
The dataset
The dataset was developed by English Voice AI Labs. You can download it from our website: https://www.EnglishVoice.ai/
Sample code
Python code for generating headlines:
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-keywords-to-headline")
tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-keywords-to-headline")
model = model.to(device)
keywords = "weight loss, weight pills"
text = "headline: " + keywords
encoding = tokenizer.encode_plus(text, return_tensors = "pt")
input_ids = encoding["input_ids"].to(device)
attention_masks = encoding["attention_mask"].to(device)
beam_outputs = model.generate(
input_ids = input_ids,
attention_mask = attention_masks,
do_sample = True,
num_return_sequences = 5,
temperature = 0.95,
early_stopping = True,
top_k = 50,
top_p = 0.95,
)
for i in range(len(beam_outputs)):
result = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
print(result)
Sample result:
I Am Losing Weight and I Love It!
New Weight Loss Pill Helps You Get the Body You Want!
I Lost Weight By Taking Pills!
The Truth About Weight Loss Pills!
The Best Weight Loss Pills Money Can Buy!