RuGPT3MediumSumGazeta
Model description
This is the model for abstractive summarization for Russian based on rugpt3medium_based_on_gpt2.
Intended uses & limitations
How to use
Colab: link
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "IlyaGusev/rugpt3medium_sum_gazeta"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
article_text = "..."
text_tokens = tokenizer(
article_text,
max_length=600,
add_special_tokens=False,
padding=False,
truncation=True
)["input_ids"]
input_ids = text_tokens + [tokenizer.sep_token_id]
input_ids = torch.LongTensor([input_ids])
output_ids = model.generate(
input_ids=input_ids,
no_repeat_ngram_size=4
)
summary = tokenizer.decode(output_ids[0], skip_special_tokens=False)
summary = summary.split(tokenizer.sep_token)[1]
summary = summary.split(tokenizer.eos_token)[0]
print(summary)
Training data
- Dataset: Gazeta
Training procedure
- Training script: train.py
- Config: gpt_training_config.json
Eval results
- Train dataset: Gazeta v1 train
- Test dataset: Gazeta v1 test
- Source max_length: 600
- Target max_length: 200
- no_repeat_ngram_size: 4
- num_beams: 5
Model | R-1-f | R-2-f | R-L-f | chrF | METEOR | BLEU | Avg char length |
---|---|---|---|---|---|---|---|
mbart_ru_sum_gazeta | 32.4 | 14.3 | 28.0 | 39.7 | 26.4 | 12.1 | 371 |
rut5_base_sum_gazeta | 32.2 | 14.4 | 28.1 | 39.8 | 25.7 | 12.3 | 330 |
rugpt3medium_sum_gazeta | 26.2 | 7.7 | 21.7 | 33.8 | 18.2 | 4.3 | 244 |
- Train dataset: Gazeta v1 train
- Test dataset: Gazeta v2 test
- Source max_length: 600
- Target max_length: 200
- no_repeat_ngram_size: 4
- num_beams: 5
Model | R-1-f | R-2-f | R-L-f | chrF | METEOR | BLEU | Avg char length |
---|---|---|---|---|---|---|---|
mbart_ru_sum_gazeta | 28.7 | 11.1 | 24.4 | 37.3 | 22.7 | 9.4 | 373 |
rut5_base_sum_gazeta | 28.6 | 11.1 | 24.5 | 37.2 | 22.0 | 9.4 | 331 |
rugpt3medium_sum_gazeta | 24.1 | 6.5 | 19.8 | 32.1 | 16.3 | 3.6 | 242 |
Evaluation script: evaluate.py
Flags: --language ru --tokenize-after --lower
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