i-k-a/my_lenta_model_ru_mt5-base_4_epochs

Model Card for Model ID

This model trained to summarize news post. Trained on data grabbed from russian news site Lenta.ru.

Модель обучена суммаризации новостных статей. Обучение проводилось на данных, полученных с русского новостного сайта Lenta.ru.

Model Details

Model Description

• Developed by: i-k-a
• Shared by [optional]: i-k-a
• Model type: Transformer Text2Text Generation
• Language(s) (NLP): Russian
• Finetuned from model [optional]: mT5-base

Model Sources [optional]

• Repository: link

How to Get Started with the Model

Use code below to infer model.

Используйте код ниже для запуска модели.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
MAX_NEW_TOKENS=400
MODEL_DIR='i-k-a/my_lenta_model_ru_mt5-base_4_epochs'
text = input('Введите текст:')
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_DIR)
inputs = tokenizer(text, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_new_tokens=MAX_NEW_TOKENS, do_sample=False)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f'Резюме от нейросети: "{result}"\n\nИсходный текст: "{text}"')

Training Details

Model trained 4 epochs. Length of input text is cut to 1024 tokens. Output is 400 tokens. Trained using Google Colab resources.

Technical Specifications [optional]

Model Architecture and Objective

google/mt5-base

Compute Infrastructure

Google Colab

Hardware

Google Colab T4 GPU

Software

Python