YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
bart-finetuned-text-summarization - bnb 8bits
- Model creator: https://huggingface.co/suriya7/
- Original model: https://huggingface.co/suriya7/bart-finetuned-text-summarization/
Original model description:
license: mit pipeline_tag: summarization widget: - text: >- Now, there is no doubt that one of the most important aspects of any Pixel phone is its camera. And there might be good news for all camera lovers. Rumours have suggested that the Pixel 9 could come with a telephoto lens, improving its photography capabilities even further. Google will likely continue to focus on using AI to enhance its camera performance, in order to make sure that Pixel phones remain top contenders in the world of mobile photography. - text: >- The Samastha Kerala Sunni Students Federation (SKSSF) has also expressed concern over holding the election on Friday. In a statement issued in Kozhikode on Saturday, SKSSF state secretariat asked the EC to postpone the election to another day. It said conducting elections on Friday will cause inconvenience to people from the Muslim community deputed on poll duty or as booth agents of political parties to participate in Friday juma prayers. Meanwhile, the Wisdom Islamic Organisation has asked the state government to officially demand the EC to hold the elections in Kerala and Tamil Nadu on some other day, citing inconvenience of believers. State president P N Abdul Latheef Madani said all secular forces should put pressure on the poll panel to change the date of elections. datasets: - EdinburghNLP/xsum language: - en
BART Large CNN Text Summarization Model
This model is based on the Facebook BART (Bidirectional and Auto-Regressive Transformers) architecture, specifically the large variant fine-tuned for text summarization tasks. BART is a sequence-to-sequence model introduced by Facebook AI, capable of handling various natural language processing tasks, including summarization.
Model Details:
- Architecture: BART Large CNN
- Pre-trained model: BART Large
- Fine-tuned for: Text Summarization
- Fine-tuning dataset: xsum
Usage:
Installation:
You can install the necessary libraries using pip:
pip install transformers
Inferecnce
provided a simple snippet of how to use this model for the task of paragraph summarization in PyTorch.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")
def generate_summary(text):
inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
text_to_summarize = """Now, there is no doubt that one of the most important aspects of any Pixel phone is its camera.
And there might be good news for all camera lovers. Rumours have suggested that the Pixel 9 could come with a telephoto lens,
improving its photography capabilities even further. Google will likely continue to focus on using AI to enhance its camera performance,
in order to make sure that Pixel phones remain top contenders in the world of mobile photography."""
summary = generate_summary(text_to_summarize)
print(summary)
Google is rumoured to be about to unveil its next-generation Pixel smartphone,
the Google Pixel 9,which is expected to come with a telephoto lens and an artificial intelligence (AI)
system to improve its camera capabilities, as well as improve the quality of its images.
Training Parameters
num_train_epochs=1,
warmup_steps = 500,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
weight_decay = 0.01,
gradient_accumulation_steps=16
- Downloads last month
- 4