rut5-base-summ

Model

Finetuned ai-forever/ruT5-base for text and dialogue summarization.

Data

All 'train' subsets was concatenated and shuffled with seed 1000 - 7.

Train subset = 155678 rows.

Metrics

Evaluation on 10% of concatenated 'validation' subsets = 1458 rows.

See WandB logs.

See report at REPORT WIP.

Notes

Scheduler, optimizer and trainer states are saved into this repo, so you can use that to continue finetune with your own data with existing gradients.

Usage

Summarization pipeline

from transformers import pipeline


pipe = pipeline('summarization', model='d0rj/rut5-base-summ')
pipe(text)

Text-to-text generation

from transformers import T5Tokenizer, T5ForConditionalGeneration


tokenizer = T5Tokenizer.from_pretrained('d0rj/rut5-base-summ')
model = T5ForConditionalGeneration.from_pretrained('d0rj/rut5-base-summ').eval()

input_ids = tokenizer(text, return_tensors='pt').input_ids
outputs = model.generate(input_ids)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)