metadata
language:
- ru
license: apache-2.0
library_name: transformers
pipeline_tag: text2text-generation
tags:
- keyphrase-generation
- russian
- t5
base_model:
- sberbank-ai/ruT5-base
datasets:
- aglazkova/keyphrase_extraction_russian
model-index:
- name: ruT5 keyphrase generator
results:
- task:
type: text2text-generation
name: Keyphrase Generation
dataset:
name: aglazkova/keyphrase_extraction_russian
type: aglazkova/keyphrase_extraction_russian
metrics:
- type: rougeL
value: 0.XX
- type: exact_match
value: 0.XX
ruT5 Keyphrase Generator (Russian)
Base model: sberbank-ai/ruT5-base
Dataset: aglazkova/keyphrase_extraction_russian
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tok = AutoTokenizer.from_pretrained("denis-gordeev/rut5-keyphrase-ru", use_fast=False)
mdl = AutoModelForSeq2SeqLM.from_pretrained("denis-gordeev/rut5-keyphrase-ru")
text = "keyphrase: Новая модель обнаруживает аномалии в банковских транзакциях..."
inp = tok(text, return_tensors="pt")
out = mdl.generate(**inp, max_new_tokens=64, num_beams=4, no_repeat_ngram_size=3)
print(tok.decode(out[0], skip_special_tokens=True))