metadata
language:
- ru
tags:
- mbart
inference:
parameters:
no_repeat_ngram_size: 4,
num_beams: 5
datasets:
- IlyaGusev/gazeta
- samsum
- samsum_(translated_into_Russian)
widget:
- text: >
Джефф: Могу ли я обучить модель 🤗 Transformers на Amazon SageMaker?
Филипп: Конечно, вы можете использовать новый контейнер для глубокого
обучения HuggingFace.
Джефф: Хорошо.
Джефф: и как я могу начать?
Джефф: где я могу найти документацию?
Филипп: ок, ок, здесь можно найти все:
https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
model-index:
- name: mbart_ruDialogSum
results:
- task:
name: Abstractive Dialogue Summarization
type: abstractive-text-summarization
dataset:
name: SAMSum Corpus (translated to Russian)
type: samsum
metrics:
- name: Validation ROGUE-1
type: rogue-1
value: 34.5
- name: Validation ROGUE-L
type: rogue-l
value: 33
- name: Test ROGUE-1
type: rogue-1
value: 31
- name: Test ROGUE-L
type: rogue-l
value: 28
license: cc
📝 Description
MBart for Russian summarization fine-tuned for dialogues summarization.
This model was firstly fine-tuned by Ilya Gusev on Gazeta dataset. We have fine tuned that model on SamSum dataset translated to Russian using GoogleTranslateAPI
🤗 Moreover! We have implemented a ! telegram bot @summarization_bot ! with the inference of this model. Add it to the chat and get summaries instead of dozens spam messages! 🤗
❓ How to use with code
from transformers import MBartTokenizer, MBartForConditionalGeneration
# Download model and tokenizer
model_name = "Kirili4ik/mbart_ruDialogSum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
model.eval()
article_text = "..."
input_ids = tokenizer(
[article_text],
max_length=600,
padding="max_length",
truncation=True,
return_tensors="pt",
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
top_k=0,
num_beams=3,
no_repeat_ngram_size=3
)[0]
summary = tokenizer.decode(output_ids, skip_special_tokens=True)
print(summary)