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# t5-base-news_headlines

This model is a fine-tuned version of [t5-base](https://huggingface.co/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
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="antonkurylo/t5-base-news_headlines_7")

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
{"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["valurank/News_headlines"], "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.\n 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.\n 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.\n 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.\n"}], "model-index": [{"name": "t5-base-news_headlines", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation summarization generated_from_trainer en dataset:valurank/News_headlines license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #en #dataset-valurank/News_headlines #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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
-----


### Expected Output


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



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Expected Output


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 ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #en #dataset-valurank/News_headlines #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Expected Output


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 ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3