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---
language:
- ru
tags:
- summarization
license: apache-2.0
inference:
parameters:
no_repeat_ngram_size: 4
---
# RuBertTelegramHeadlines
## Model description
Example model for [Headline generation competition](https://competitions.codalab.org/competitions/29905)
Based on [RuBERT](http://docs.deeppavlov.ai/en/master/features/models/bert.html) model
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, EncoderDecoderModel
model_name = "IlyaGusev/rubert_telegram_headlines"
tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False, do_basic_tokenize=False, strip_accents=False)
model = EncoderDecoderModel.from_pretrained(model_name)
article_text = "..."
input_ids = tokenizer(
[article_text],
add_special_tokens=True,
max_length=256,
padding="max_length",
truncation=True,
return_tensors="pt",
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=64,
no_repeat_ngram_size=3,
num_beams=10,
top_p=0.95
)[0]
headline = tokenizer.decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(headline)
```
## Training data
- Dataset: [ru_all_split.tar.gz](https://www.dropbox.com/s/ykqk49a8avlmnaf/ru_all_split.tar.gz)
## Training procedure
```python
import random
import torch
from torch.utils.data import Dataset
from tqdm.notebook import tqdm
from transformers import BertTokenizer, EncoderDecoderModel, Trainer, TrainingArguments, logging
def convert_to_tensors(
tokenizer,
text,
max_text_tokens_count,
max_title_tokens_count = None,
title = None
):
inputs = tokenizer(
text,
add_special_tokens=True,
max_length=max_text_tokens_count,
padding="max_length",
truncation=True
)
result = {
"input_ids": torch.tensor(inputs["input_ids"]),
"attention_mask": torch.tensor(inputs["attention_mask"]),
}
if title is not None:
outputs = tokenizer(
title,
add_special_tokens=True,
max_length=max_title_tokens_count,
padding="max_length",
truncation=True
)
decoder_input_ids = torch.tensor(outputs["input_ids"])
decoder_attention_mask = torch.tensor(outputs["attention_mask"])
labels = decoder_input_ids.clone()
labels[decoder_attention_mask == 0] = -100
result.update({
"labels": labels,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask
})
return result
class GetTitleDataset(Dataset):
def __init__(
self,
original_records,
sample_rate,
tokenizer,
max_text_tokens_count,
max_title_tokens_count
):
self.original_records = original_records
self.sample_rate = sample_rate
self.tokenizer = tokenizer
self.max_text_tokens_count = max_text_tokens_count
self.max_title_tokens_count = max_title_tokens_count
self.records = []
for record in tqdm(original_records):
if random.random() > self.sample_rate:
continue
tensors = convert_to_tensors(
tokenizer=tokenizer,
title=record["title"],
text=record["text"],
max_title_tokens_count=self.max_title_tokens_count,
max_text_tokens_count=self.max_text_tokens_count
)
self.records.append(tensors)
def __len__(self):
return len(self.records)
def __getitem__(self, index):
return self.records[index]
def train(
train_records,
val_records,
pretrained_model_path,
train_sample_rate=1.0,
val_sample_rate=1.0,
output_model_path="models",
checkpoint=None,
max_text_tokens_count=256,
max_title_tokens_count=64,
batch_size=8,
logging_steps=1000,
eval_steps=10000,
save_steps=10000,
learning_rate=0.00003,
warmup_steps=2000,
num_train_epochs=3
):
logging.set_verbosity_info()
tokenizer = BertTokenizer.from_pretrained(
pretrained_model_path,
do_lower_case=False,
do_basic_tokenize=False,
strip_accents=False
)
train_dataset = GetTitleDataset(
train_records,
train_sample_rate,
tokenizer,
max_text_tokens_count=max_text_tokens_count,
max_title_tokens_count=max_title_tokens_count
)
val_dataset = GetTitleDataset(
val_records,
val_sample_rate,
tokenizer,
max_text_tokens_count=max_text_tokens_count,
max_title_tokens_count=max_title_tokens_count
)
model = EncoderDecoderModel.from_encoder_decoder_pretrained(pretrained_model_path, pretrained_model_path)
training_args = TrainingArguments(
output_dir=output_model_path,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
do_train=True,
do_eval=True,
overwrite_output_dir=False,
logging_steps=logging_steps,
eval_steps=eval_steps,
evaluation_strategy="steps",
save_steps=save_steps,
learning_rate=learning_rate,
warmup_steps=warmup_steps,
num_train_epochs=num_train_epochs,
max_steps=-1,
save_total_limit=1,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset
)
trainer.train(checkpoint)
model.save_pretrained(output_model_path)
``` |