Rubert Ext Sum Gazeta

🚀 RuBERTExtSumGazeta

A model for extractive summarization based on rubert-base-cased

🚀 Quick Start

✨ Features

• This is a model for extractive summarization, leveraging the rubert-base-cased architecture.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

You can use this model in Google Colab. Link

import razdel
from transformers import AutoTokenizer, BertForTokenClassification

model_name = "IlyaGusev/rubert_ext_sum_gazeta"

tokenizer = AutoTokenizer.from_pretrained(model_name)
sep_token = tokenizer.sep_token
sep_token_id = tokenizer.sep_token_id

model = BertForTokenClassification.from_pretrained(model_name)

article_text = "..."
sentences = [s.text for s in razdel.sentenize(article_text)]
article_text = sep_token.join(sentences)

inputs = tokenizer(
    [article_text],
    max_length=500,
    padding="max_length",
    truncation=True,
    return_tensors="pt",
)
sep_mask = inputs["input_ids"][0] == sep_token_id

# Fix token_type_ids
current_token_type_id = 0 
for pos, input_id in enumerate(inputs["input_ids"][0]):
    inputs["token_type_ids"][0][pos] = current_token_type_id
    if input_id == sep_token_id:
        current_token_type_id = 1 - current_token_type_id

# Infer model
with torch.no_grad(): 
    outputs = model(**inputs) 
logits = outputs.logits[0, :, 1]

# Choose sentences 
logits = logits[sep_mask]
logits, indices = logits.sort(descending=True)
logits, indices = logits.cpu().tolist(), indices.cpu().tolist()
pairs = list(zip(logits, indices))
pairs = pairs[:3]
indices = list(sorted([idx for _, idx in pairs]))
summary = " ".join([sentences[idx] for idx in indices])
print(summary)

📚 Documentation

Intended uses

This model is designed for extractive summarization, especially suitable for Gazeta.ru articles.

Limitations and bias

• The model should work well with Gazeta.ru articles, but for any other agencies it can suffer from domain shift

🔧 Technical Details

No technical details are provided in the original document, so this section is skipped.

📄 License

This model is licensed under the apache-2.0 license.

Information Table