## Model Description
Fine-tuning of [uk-mt5-base](https://huggingface.co/kravchenko/uk-mt5-base) model on summarization dataset.
## How to Use

```py
from transformers import AutoTokenizer, T5ForConditionalGeneration, pipeline

tokenizer = AutoTokenizer.from_pretrained('ukr-models/uk-summarizer')
model = T5ForConditionalGeneration.from_pretrained('ukr-models/uk-summarizer')

ppln = pipeline("summarization", model=model, tokenizer=tokenizer, device=0, max_length=128, num_beams=4, no_repeat_ngram_size=2, clean_up_tokenization_spaces=True)

text = "..."
ppln(text)
```
{"language": ["uk"], "license": "mit", "tags": ["ukrainian"]}
transformers pytorch safetensors t5 text2text-generation ukrainian uk license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

## Model Description
Fine-tuning of uk-mt5-base model on summarization dataset.
## How to Use


## Model Description
Fine-tuning of uk-mt5-base model on summarization dataset. ## How to Use
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ## Model Description
Fine-tuning of uk-mt5-base model on summarization dataset. ## How to Use
passage: TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #ukrainian #uk #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
## Model Description
Fine-tuning of uk-mt5-base model on summarization dataset.## How to Use