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---
language:
- ru
- ru-RU
tags:
- summarization
- t5
datasets:
- IlyaGusev/gazeta
license: apache-2.0
---
# RuT5SumGazeta
## Model description
This is the model for abstractive summarization for Russian based on [rut5-base](https://huggingface.co/cointegrated/rut5-base).
## Intended uses & limitations
#### How to use
Colab: [link](https://colab.research.google.com/drive/1re5E26ZIDUpAx1gOCZkbF3hcwjozmgG0)
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
model_name = "IlyaGusev/rut5_base_sum_gazeta"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
article_text = "..."
input_ids = tokenizer(
[article_text],
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=3,
early_stopping=True
)[0]
summary = tokenizer.decode(output_ids, skip_special_tokens=True)
print(summary)
```
## Training data
- Dataset: https://github.com/IlyaGusev/gazeta
## Training procedure
- Training script: https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py
## Eval results
Gazeta v1 train -> Gazeta v1 test
| Model | R-1-f | R-2-f | R-L-f | chrF | METEOR | BLEU |
|:--------------------------|:------|:------|:------|:-------|:-------|:-----|
| mbart_ru_sum_gazeta | 32.1 | 14.3 | **27.9** | **39.7** | **25.7** | **12.4** |
| rut5_base_sum_gazeta | **32.3** | **14.5** | 27.9 | 39.6 | 25.1 | 11.5 |
Gazeta v1 train -> Gazeta v2 test
| Model | R-1-f | R-2-f | R-L-f | chrF | METEOR | BLEU |
|:--------------------------|:------|:------|:------|:-------|:-------|:-----|
| mbart_ru_sum_gazeta | **28.8** | **11.1** | **24.5** | **37.4** | **22.7** | **9.5** |
| rut5_base_sum_gazeta | 28.5 | 11.0 | 24.2 | 36.8 | 21.2 | 8.6 |
Predicting all summaries:
```python
import json
import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration
from datasets import load_dataset
def gen_batch(inputs, batch_size):
batch_start = 0
while batch_start < len(inputs):
yield inputs[batch_start: batch_start + batch_size]
batch_start += batch_size
def predict(
model_name,
input_records,
output_file,
max_source_tokens_count=400,
max_target_tokens_count=200,
batch_size=16
):
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
predictions = []
for batch in gen_batch(input_records, batch_size):
texts = [r["text"] for r in batch]
input_ids = tokenizer(
texts,
add_special_tokens=True,
max_length=max_source_tokens_count,
padding="max_length",
truncation=True,
return_tensors="pt"
)["input_ids"].to(device)
output_ids = model.generate(
input_ids=input_ids,
max_length=max_target_tokens_count,
no_repeat_ngram_size=3,
early_stopping=True
)
summaries = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
for s in summaries:
print(s)
predictions.extend(summaries)
with open(output_file, "w") as w:
for p in predictions:
w.write(p.strip().replace("\n", " ") + "\n")
gazeta_test = load_dataset('IlyaGusev/gazeta', script_version="v1.0")["test"]
predict("IlyaGusev/rut5_base_sum_gazeta", list(gazeta_test), "t5_predictions.txt")
```
Evaluation: https://github.com/IlyaGusev/summarus/blob/master/evaluate.py
Flags: --language ru --tokenize-after --lower
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