metadata
license: apache-2.0
datasets:
- Ateeqq/news-title-generator
language:
- en
library_name: transformers
tags:
- text-generation-inference
metrics:
- accuracy
widget:
- text: >-
Jumping on the bandwagon in the artificial intelligence race, Meta has
started rolling out AI chatbot feature for its social networking sites
WhatsApp and Instagram across India and Africa, India Today reported. The
AI-powered search bar feature was first introduced in WhatsApp beta for
Android 2.24.7.14 update. It allows users to use the search engine to ask
queries. The feature is mainly being tested out in certain regions such as
India and Africa and is expected to go global soon. Now, the company is
experimenting with putting Meta AI in the Instagram search bar. You can
use it to either chat with AI or to look up content.
example_title: 1st - Meta News
- text: >-
Tesla has slashed the price of its Full Self-Driving (FSD) software
subscription to $99 per month, down from $199 per month, as the electric
vehicle maker aims to boost adoption of its advanced driver assistance
system ahead of first-quarter earnings. The price cut comes a couple of
weeks after Tesla launched a free one-month trial of FSD for every
customer in the U.S. with a compatible Tesla. That trial is still ongoing.
Formerly known as FSD Beta, Tesla is now referring to the software as
“Supervised FSD” to make it clear that the software doesn’t turn Teslas
into autonomous vehicles, and human drivers still need to supervise the
not-so-self-driving software.
example_title: 2nd Tesla News
- text: >-
To assess a community’s risk of extreme weather, policymakers rely first
on global climate models that can be run decades, and even centuries,
forward in time, but only at a coarse resolution. These models might be
used to gauge, for instance, future climate conditions for the
northeastern U.S., but not specifically for Boston. To estimate Boston’s
future risk of extreme weather such as flooding, policymakers can combine
a coarse model’s large-scale predictions with a finer-resolution model,
tuned to estimate how often Boston is likely to experience damaging floods
as the climate warms. But this risk analysis is only as accurate as the
predictions from that first, coarser climate model. “If you get those
wrong for large-scale environments, then you miss everything in terms of
what extreme events will look like at smaller scales, such as over
individual cities,” says Themistoklis Sapsis, the William I. Koch
Professor and director of the Center for Ocean Engineering in MIT’s
Department of Mechanical Engineering.
example_title: 3rd Mit Article
- text: >-
Microsoft is opening a new office in London dedicated to artificial
intelligence (AI) research and development. The tech firm's AI boss
Mustafa Suleyman said it will advertise roles for exceptional individuals
in the coming weeks and months. But he has not said how many jobs will be
created. Microsoft is a major investor in ChatGPT-creator OpenAI, which
itself opened an office in London in 2023. There is an enormous pool of AI
talent and expertise in the UK, said Mr Suleyman in a blog post. Microsoft
AI plans to make a significant, long-term investment in the region as we
begin hiring the best AI scientists and engineers into this new AI hub. Mr
Suleyman co-founded AI research lab DeepMind in the UK, which was bought
by Google in 2014.
example_title: 4th Microsoft
- text: >-
OpenAI, Google, and the French artificial intelligence startup Mistral
have all released new versions of their frontier AI models within 12 hours
of one another, as the industry prepares for a burst of activity over the
summer. The unprecedented flurry of releases come as the sector readies
for the expected launch of the next major version of GPT, the system that
underpins OpenAI’s hit chatbot Chat-GPT. The first came only hours after
Nick Clegg appeared on stage at an event in London, where he confirmed
reports that the third version of Meta’s own AI model, Llama, would be
published in a matter of weeks.
example_title: 5th LLM Release
- text: >-
French AI startup Mistral on Tuesday released Mixtral 8x22B, a new large
language model (LLM) and its latest attempt. Mixtral 8x22B is expected to
outperform Mistral's previous Mixtral 8x7B LLM, which itself showed signs
of outshining OpenAI's GPT-3.5 and Meta's Llama 2, according to Gigazine.
The new Mixtral model boasts a 65,000-token context window, which refers
to the amount of text that an AI model can process and reference at one
time. Further, Mixtral 8x22B has a parameter size of up to 176 billion, a
reference to the number of internal variables that the model uses to make
decisions or predictions.
example_title: 6th Mixtral 8x22B
News Title(Headline) Generator 📰
This document details the development of our innovative News Title Generator, designed to produce compelling and informative titles for your news articles. Check the Live Demo Here.
I've tested several other news headline generators on Hugging Face and across the internet, and I can confidently say that this one is the best. 🤗
Model Architecture:
- Foundation: The T5 base model from the Transformers library is our title generator's foundation. This powerful pre-trained model is adept at various text-to-text tasks, making it an ideal choice for our application.
- Fine-Tuning: To optimize performance specifically for news title generation, we fine-tuned the T5 base model on a curated dataset from Hugging Face https://huggingface.co/datasets/Ateeqq/news-title-generator. This dataset consists of over 78,000 training examples, ensuring the model learns the nuances and structure of effective news titles.
How to use?
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Ateeqq/news-title-generator")
model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/news-title-generator")
def generate_title(input_text):
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids)
decoded_text = tokenizer.decode(output[0], skip_special_tokens=True)
return decoded_text
input_text = "A group of scientists discovered a new planet."
generated_title = generate_title(input_text)
print(f"Generated Title: {generated_title}")
Technical Specifications
- Framework: PyTorch, a popular deep learning framework, provides the foundation for our model's development and execution.
- Dataset Split: The training data is strategically divided into two sets: 78,720 examples for training and 19,681 examples for testing. This split ensures the model is adequately trained while reserving a portion for evaluating its generalizability.
- Model Parameters: The fine-tuned model boasts 223 million trainable parameters, allowing it to capture the intricate relationships between text elements that contribute to strong news titles.
Training Configuration
- Batch Size: 8
- Maximum Epochs: The training process iterates through the entire dataset three times (epochs) to ensure thorough learning.
- Global Seed: A fixed random seed (42) is set to guarantee reproducibility of training results.
- Token Length Limits: The source text (article content) is restricted to a maximum of 128 tokens, while the generated titles are capped at 50 tokens.
Key Takeaways
Our News Title Generator leverages the power of the T5 base model, fine-tuned on a comprehensive news title dataset, to deliver exceptional results. The model's architecture and training configuration are meticulously designed to produce high-quality, informative titles within an appropriate character length. This tool empowers creators and journalists to craft impactful headlines that effectively capture readers' attention.
Contact us (at exnrt.com/contact-us) today to learn more about integrating the News Title Generator into your editorial workflow and unlock the full potential of AI-driven journalism.