metadata

license: apache-2.0
tags:
  - summarization
  - generated_from_trainer
metrics:
  - rouge
widget:
  - text: >
      As the demands of climate change grow, businesses are realizing the
      imperative of embracing sustainability. Driven by ecological necessity and
      evolving consumer expectations, this shift necessitates a complete
      overhaul of traditional business models towards a circular economy,
      emphasizing resource efficiency and waste reduction.
       Adopting sustainable practices offers businesses multiple benefits: reduced operating costs, enhanced brand reputation, and increased customer loyalty.  As such, sustainability is a strategic tool for businesses looking to future-proof themselves.
       Companies like Unilever and Tesla serve as models of this transformation. Unilever's sustainable living brands have outperformed the rest of their portfolio,  while Tesla's entire business model centres around sustainability, proving that environmental consciousness and profitability can coexist.
       In our interconnected world, the impacts of businesses extend to society and the environment, necessitating alignment with the global push for sustainability.  With sustainability no longer being a choice but an imperative, businesses adopting it will be the leaders in the new business paradigm.  In a nutshell, to thrive in the evolving market, embracing sustainability is the new business imperative. The future of business is unquestionably green.
model-index:
  - name: t5-base-news_headlines
    results: []
language:
  - en
datasets:
  - valurank/News_headlines

t5-base-news_headlines

This model is a fine-tuned version of t5-base on an valurank/News_headlines dataset. It achieves the following results on the evaluation set:

Loss: 0.9975
Rouge1: 53.7064
Rouge2: 34.6278
Rougel: 50.5129
Rougelsum: 50.5108

Model description

More information needed

Intended uses & limitations

More information needed

Usage

from transformers import pipeline
summarizer = pipeline("summarization", model="antonkurylo/t5-base-news_headlines")

text = "As the demands of climate change grow, businesses are realizing the imperative of embracing sustainability." \
       "Driven by ecological necessity and evolving consumer expectations, this shift necessitates a complete " \
       "overhaul of traditional business models towards a circular economy, emphasizing resource efficiency and " \
       "waste reduction.\nAdopting sustainable practices offers businesses multiple benefits: reduced operating " \
       "costs, enhanced brand reputation, and increased customer loyalty. As such, sustainability is a strategic " \
       "tool for businesses looking to future-proof themselves.\nCompanies like Unilever and Tesla serve as " \
       "models of this transformation. Unilever's sustainable living brands have outperformed the rest of their " \
       "portfolio,  while Tesla's entire business model centres around sustainability, proving that environmental " \
       "consciousness and profitability can coexist.\nIn our interconnected world, the impacts of businesses " \
       "extend to society and the environment, necessitating alignment with the global push for sustainability. " \
       "With sustainability no longer being a choice but an imperative, businesses adopting it will be the " \
       "leaders in the new business paradigm. In a nutshell, to thrive in the evolving market, embracing " \
       "sustainability is the new business imperative. The future of business is unquestionably green."

summarizer(text)

Expected Output

[{'summary_text': "The future of business is unquestionably green. Here's how it works . Unilever and Tesla are examples of the transformation"}]

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5.6e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
weight_decay: 0.01
lr_scheduler_type: linear
num_epochs: 7
max_text_length: 512
max_target_length: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.9933	1.0	1531	1.4942	44.2439	22.1239	40.5281	40.5525
1.6029	2.0	3062	1.2824	46.5726	25.1122	43.131	43.151
1.409	3.0	4593	1.2358	48.3188	27.7403	44.9576	45.0009
1.2699	4.0	6124	1.1600	50.9858	30.6655	47.775	47.8414
1.1696	5.0	7655	1.0607	52.2212	32.6952	49.0023	49.0812
1.0934	6.0	9186	1.0173	53.1629	33.9552	49.9629	50.0118
1.049	7.0	10717	0.9975	53.7064	34.6278	50.5129	50.5108

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3