
### Model Description

<!-- Provide a longer summary of what this model is. -->
The dataset contains around 40K articles about politics, science, technology, social life collected until June 2021 from Hromadske.ua.

##### Load the model and mt tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("google/mt5-large")

model = AutoModelForSeq2SeqLM.from_pretrained("SGaleshchuk/t5-large-ua-news")

summarizer = pipeline("summarization", model=model, tokenizer=tokenizer, framework="pt")
##### Try on your example

summary = summarizer("російські війська захопили запорізьку аес на початку повномасштабного вторгнення 4 березня 2022 року . попри оголошену окупантами «анексію» запорізької аес, на станції продовжують працювати українські фахівці . але для роботи окупанти змушують отримувати російські паспорти й підписувати договір з «росатомом» . за даними «енергоатому», зараз усі шість енергоблоків заес зупинені, а окупанти блокують їхнє ввімкнення . окупована станція продовжує постійно споживати електроенергію на власні потреби з енергосистеми україни . «російські окупанти продовжують перетворювати запорізьку аес на військову базу, мінуючи периметр довкола станції . і ці дії не можуть не мати наслідків», - зазначили там .", min_length=3, max_length = 128)
print(summary)
[{'summary_text': 'окупаційна влада рф продовжує перетворювати запорізьку атомну електростанцію на військову базу . '}]
```



- **Model type:** sequence-to-sequence, summarization
- **Language(s) (NLP):** Ukrainian
- **Finetuned from model :** mT5-large

### Model Sources 

- **Dataset:** [UberText](https://lang.org.ua/en/corpora/)
- **Paper:** Svitlana Galeshchuk, Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset. Proceedings of UNLP Workshop at EACL 2023.
- **Demo:** to be provided


#### Preprocessing

It is recommended to lowercase an input text.


#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

The benchmark metric for abstractive summarization tasks adopted by the research community is the ROUGE score. The metric compares a generated summary against a reference. We employ three sub-categories of the ROUGE score:

•	ROUGE-1: unigram overlap

•	ROUGE-2: bigram overlap

•	ROUGE-L: Longest Common Subsequence

### Results

•	ROUGE-1: 22.09

•	ROUGE-2: 7.04

•	ROUGE-L: 22.12


{"language": ["uk"], "datasets": ["UberText"], "metrics": ["rouge"], "max_length": [128], "pipeline_tag": "summarization", "widget": [{"text": "\u0440\u043e\u0441\u0456\u0439\u0441\u044c\u043a\u0456 \u0432\u0456\u0439\u0441\u044c\u043a\u0430 \u0437\u0430\u0445\u043e\u043f\u0438\u043b\u0438 \u0437\u0430\u043f\u043e\u0440\u0456\u0437\u044c\u043a\u0443 \u0430\u0435\u0441 \u043d\u0430 \u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u043f\u043e\u0432\u043d\u043e\u043c\u0430\u0441\u0448\u0442\u0430\u0431\u043d\u043e\u0433\u043e \u0432\u0442\u043e\u0440\u0433\u043d\u0435\u043d\u043d\u044f 4 \u0431\u0435\u0440\u0435\u0437\u043d\u044f 2022 \u0440\u043e\u043a\u0443 .  \u043f\u043e\u043f\u0440\u0438 \u043e\u0433\u043e\u043b\u043e\u0448\u0435\u043d\u0443 \u043e\u043a\u0443\u043f\u0430\u043d\u0442\u0430\u043c\u0438 \u00ab\u0430\u043d\u0435\u043a\u0441\u0456\u044e\u00bb \u0437\u0430\u043f\u043e\u0440\u0456\u0437\u044c\u043a\u043e\u0457 \u0430\u0435\u0441, \u043d\u0430 \u0441\u0442\u0430\u043d\u0446\u0456\u0457 \u043f\u0440\u043e\u0434\u043e\u0432\u0436\u0443\u044e\u0442\u044c \u043f\u0440\u0430\u0446\u044e\u0432\u0430\u0442\u0438 \u0443\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0456 \u0444\u0430\u0445\u0456\u0432\u0446\u0456 . \u0430\u043b\u0435 \u0434\u043b\u044f \u0440\u043e\u0431\u043e\u0442\u0438 \u043e\u043a\u0443\u043f\u0430\u043d\u0442\u0438 \u0437\u043c\u0443\u0448\u0443\u044e\u0442\u044c \u043e\u0442\u0440\u0438\u043c\u0443\u0432\u0430\u0442\u0438 \u0440\u043e\u0441\u0456\u0439\u0441\u044c\u043a\u0456 \u043f\u0430\u0441\u043f\u043e\u0440\u0442\u0438 \u0439 \u043f\u0456\u0434\u043f\u0438\u0441\u0443\u0432\u0430\u0442\u0438 \u0434\u043e\u0433\u043e\u0432\u0456\u0440 \u0437 \u00ab\u0440\u043e\u0441\u0430\u0442\u043e\u043c\u043e\u043c\u00bb . \u0437\u0430 \u0434\u0430\u043d\u0438\u043c\u0438 \u00ab\u0435\u043d\u0435\u0440\u0433\u043e\u0430\u0442\u043e\u043c\u0443\u00bb, \u0437\u0430\u0440\u0430\u0437 \u0443\u0441\u0456 \u0448\u0456\u0441\u0442\u044c \u0435\u043d\u0435\u0440\u0433\u043e\u0431\u043b\u043e\u043a\u0456\u0432 \u0437\u0430\u0435\u0441 \u0437\u0443\u043f\u0438\u043d\u0435\u043d\u0456, \u0430 \u043e\u043a\u0443\u043f\u0430\u043d\u0442\u0438 \u0431\u043b\u043e\u043a\u0443\u044e\u0442\u044c \u0457\u0445\u043d\u0454 \u0432\u0432\u0456\u043c\u043a\u043d\u0435\u043d\u043d\u044f . \u043e\u043a\u0443\u043f\u043e\u0432\u0430\u043d\u0430 \u0441\u0442\u0430\u043d\u0446\u0456\u044f \u043f\u0440\u043e\u0434\u043e\u0432\u0436\u0443\u0454 \u043f\u043e\u0441\u0442\u0456\u0439\u043d\u043e \u0441\u043f\u043e\u0436\u0438\u0432\u0430\u0442\u0438 \u0435\u043b\u0435\u043a\u0442\u0440\u043e\u0435\u043d\u0435\u0440\u0433\u0456\u044e \u043d\u0430 \u0432\u043b\u0430\u0441\u043d\u0456 \u043f\u043e\u0442\u0440\u0435\u0431\u0438 \u0437 \u0435\u043d\u0435\u0440\u0433\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u0438 \u0443\u043a\u0440\u0430\u0457\u043d\u0438 . \u00ab\u0440\u043e\u0441\u0456\u0439\u0441\u044c\u043a\u0456 \u043e\u043a\u0443\u043f\u0430\u043d\u0442\u0438 \u043f\u0440\u043e\u0434\u043e\u0432\u0436\u0443\u044e\u0442\u044c \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u044e\u0432\u0430\u0442\u0438 \u0437\u0430\u043f\u043e\u0440\u0456\u0437\u044c\u043a\u0443 \u0430\u0435\u0441 \u043d\u0430 \u0432\u0456\u0439\u0441\u044c\u043a\u043e\u0432\u0443 \u0431\u0430\u0437\u0443, \u043c\u0456\u043d\u0443\u044e\u0447\u0438 \u043f\u0435\u0440\u0438\u043c\u0435\u0442\u0440 \u0434\u043e\u0432\u043a\u043e\u043b\u0430 \u0441\u0442\u0430\u043d\u0446\u0456\u0457 .  \u0456 \u0446\u0456 \u0434\u0456\u0457 \u043d\u0435 \u043c\u043e\u0436\u0443\u0442\u044c \u043d\u0435 \u043c\u0430\u0442\u0438 \u043d\u0430\u0441\u043b\u0456\u0434\u043a\u0456\u0432\u00bb, - \u0437\u0430\u0437\u043d\u0430\u0447\u0438\u043b\u0438 \u0442\u0430\u043c ."}]}
transformers pytorch mt5 text2text-generation summarization uk dataset:UberText autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #uk #dataset-UberText #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


### Model Description


The dataset contains around 40K articles about politics, science, technology, social life collected until June 2021 from URL.

##### Load the model and mt tokenizer :




- Model type: sequence-to-sequence, summarization
- Language(s) (NLP): Ukrainian
- Finetuned from model : mT5-large

### Model Sources 

- Dataset: UberText
- Paper: Svitlana Galeshchuk, Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with URL News Dataset. Proceedings of UNLP Workshop at EACL 2023.
- Demo: to be provided


#### Preprocessing

It is recommended to lowercase an input text.


#### Metrics



The benchmark metric for abstractive summarization tasks adopted by the research community is the ROUGE score. The metric compares a generated summary against a reference. We employ three sub-categories of the ROUGE score:

•	ROUGE-1: unigram overlap

•	ROUGE-2: bigram overlap

•	ROUGE-L: Longest Common Subsequence

### Results

•	ROUGE-1: 22.09

•	ROUGE-2: 7.04

•	ROUGE-L: 22.12


### Model Description


The dataset contains around 40K articles about politics, science, technology, social life collected until June 2021 from URL. ##### Load the model and mt tokenizer :




- Model type: sequence-to-sequence, summarization
- Language(s) (NLP): Ukrainian
- Finetuned from model : mT5-large ### Model Sources 

- Dataset: UberText
- Paper: Svitlana Galeshchuk, Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with URL News Dataset. Proceedings of UNLP Workshop at EACL 2023.
- Demo: to be provided #### Preprocessing

It is recommended to lowercase an input text. #### Metrics



The benchmark metric for abstractive summarization tasks adopted by the research community is the ROUGE score. The metric compares a generated summary against a reference. We employ three sub-categories of the ROUGE score:

•	ROUGE-1: unigram overlap

•	ROUGE-2: bigram overlap

•	ROUGE-L: Longest Common Subsequence ### Results

•	ROUGE-1: 22.09

•	ROUGE-2: 7.04

•	ROUGE-L: 22.12
TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #uk #dataset-UberText #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Model Description


The dataset contains around 40K articles about politics, science, technology, social life collected until June 2021 from URL. ##### Load the model and mt tokenizer :




- Model type: sequence-to-sequence, summarization
- Language(s) (NLP): Ukrainian
- Finetuned from model : mT5-large ### Model Sources 

- Dataset: UberText
- Paper: Svitlana Galeshchuk, Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with URL News Dataset. Proceedings of UNLP Workshop at EACL 2023.
- Demo: to be provided #### Preprocessing

It is recommended to lowercase an input text. #### Metrics



The benchmark metric for abstractive summarization tasks adopted by the research community is the ROUGE score. The metric compares a generated summary against a reference. We employ three sub-categories of the ROUGE score:

•	ROUGE-1: unigram overlap

•	ROUGE-2: bigram overlap

•	ROUGE-L: Longest Common Subsequence ### Results

•	ROUGE-1: 22.09

•	ROUGE-2: 7.04

•	ROUGE-L: 22.12
TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #uk #dataset-UberText #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Model Description


The dataset contains around 40K articles about politics, science, technology, social life collected until June 2021 from URL.##### Load the model and mt tokenizer :




- Model type: sequence-to-sequence, summarization
- Language(s) (NLP): Ukrainian
- Finetuned from model : mT5-large### Model Sources 

- Dataset: UberText
- Paper: Svitlana Galeshchuk, Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with URL News Dataset. Proceedings of UNLP Workshop at EACL 2023.
- Demo: to be provided#### Preprocessing

It is recommended to lowercase an input text.#### Metrics



The benchmark metric for abstractive summarization tasks adopted by the research community is the ROUGE score. The metric compares a generated summary against a reference. We employ three sub-categories of the ROUGE score:

•	ROUGE-1: unigram overlap

•	ROUGE-2: bigram overlap

•	ROUGE-L: Longest Common Subsequence### Results

•	ROUGE-1: 22.09

•	ROUGE-2: 7.04

•	ROUGE-L: 22.12