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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
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
article_text = "..."
model_name = "IlyaGusev/rut5-base-sum-gazeta"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
input_ids = tokenizer(
[article_text],
add_special_tokens=True,
padding="max_length",
truncation=True,
max_length=400,
return_tensors="pt"
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=200,
no_repeat_ngram_size=3,
num_beams=5,
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: [TBA]
## Eval results
| Model | R-1-f | R-2-f | R-L-f | chrF | BLEU |
|:--------------------------|:------|:------|:------|:-----|:-----|
| rut5-base-sum-gazeta | 32.3 | 14.5 | 27.9 | 39.6 | 11.5 |
Predicting all summaries:
```python
import json
import torch
from transformers import T5Tokenizer, 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 = MBartTokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.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,
num_beams=5,
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/mbart_ru_sum_gazeta", gazeta_test["test"], "t5_predictions.txt")
```
Evaluation: https://github.com/IlyaGusev/summarus/blob/master/evaluate.py
Flags: --language ru --tokenize-after --lower
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