README.md

metadata

language:
  - ru
tags:
  - summarization
license: apache-2.0

RuT5TelegramHeadlines

Model description

Based on rut5-base model

Intended uses & limitations

How to use

from transformers import AutoTokenizer, T5ForConditionalGeneration

model_name = "IlyaGusev/rut5_telegram_headlines"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

article_text = "..."

input_ids = tokenizer(
    [article_text],
    max_length=600,
    add_special_tokens=True,
    padding="max_length",
    truncation=True,
    return_tensors="pt"
)["input_ids"]

output_ids = model.generate(
    input_ids=input_ids,
    no_repeat_ngram_size=4
)[0]

headline = tokenizer.decode(output_ids, skip_special_tokens=True)
print(headline)

Training data

• Dataset: ru_all_split.tar.gz

Training procedure

• Training script: train.py