Russian GPT-3 models
ruGPT3XL, ruGPT3Large, ruGPT3Medium, ruGPT3Small and ruGPT2Large
This repository contains bunch of autoregressive transformer language models trained on a huge dataset of russian language.
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Russian GPT-3 models (ruGPT3XL, ruGPT3Large, ruGPT3Medium, ruGPT3Small) trained with 2048 sequence length with sparse and dense attention blocks. We also provide Russian GPT-2 large model (ruGPT2Large) trained with 1024 sequence length.
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Try Model Generation In Colab! ruGPT-3 XL:
or ruGPT-3 smaller models: -
Usage examples are described in detail here. See how fine-tuning works:
Table of contents
- ruGPT3XL
- Setup
- Usage
- Finetune
- Pretraining details ruGPT3XL
- ruGPT3Large, ruGPT3Medium, ruGPT3Small, ruGPT2Large
- Setup
- Usage
- Pretraining details
- Papers mentioning ruGPT3
- OpenSource Solutions with ruGPT3
ruGPT3XL
Setup
For colab we recommend use the following installation instructions:
export LD_LIBRARY_PATH=/usr/lib/
apt-get install clang-9 llvm-9 llvm-9-dev llvm-9-tools
git clone https://github.com/qywu/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
pip install triton
DS_BUILD_CPU_ADAM=1 DS_BUILD_SPARSE_ATTN=1 pip install deepspeed
pip install transformers
pip install huggingface_hub
pip install timm==0.3.2
git clone https://github.com/sberbank-ai/ru-gpts
cp ru-gpts/src_utils/trainer_pt_utils.py /usr/local/lib/python3.8/dist-packages/transformers/trainer_pt_utils.py
cp ru-gpts/src_utils/_amp_state.py /usr/local/lib/python3.8/dist-packages/apex/amp/_amp_state.py
After installation env please restart colab. For checking is all ok, run the following commands:
!ds_report
# Output:
...
sparse_attn ............ [YES] ...... [OKAY]
...
import deepspeed.ops.sparse_attention.sparse_attn_op
Usage
Here is a simple example of usage. For more see this example or .
import sys
from src.xl_wrapper import RuGPT3XL
import os
# If run to from content root.
sys.path.append("ru-gpts/")
os.environ["USE_DEEPSPEED"] = "1"
# We can change address and port
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "5000"
gpt = RuGPT3XL.from_pretrained("sberbank-ai/rugpt3xl", seq_len=512)
gpt.generate(
"Кто был президентом США в 2020? ",
max_length=50,
no_repeat_ngram_size=3,
repetition_penalty=2.,
)
Finetuning
Example of finetune, load finetuned model and generate is here.
Our example of finetuning script here
Pretraining details ruGPT3XL
Model was trained with 512 sequence length using Deepspeed and Megatron code by Devices team, on 80B tokens dataset for 4 epochs. After that model was finetuned 1 epoch with sequence length 2048.
Note! Model has sparse attention blocks.
Total training time was around 10 days on 256 GPUs.
Final perplexity on test set is 12.05.
🤗HuggingFace model card link.
ruGPT3Large, ruGPT3Medium, ruGPT3Small, ruGPT2Large
Setup
For using ruGPT3Large, ruGPT3Medium, ruGPT3Small, ruGPT2Large just install 🤗HuggingFace transformers.
pip install transformers==4.24.0
Usage
Here we can obtain examples of finetuning or generation.
Also this examples is adapted for google colab:
- finetuning: .
- generation:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model_name_or_path = "sberbank-ai/rugpt3large_based_on_gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path)
model = GPT2LMHeadModel.from_pretrained(model_name_or_path).cuda()
text = "Александр Сергеевич Пушкин родился в "
input_ids = tokenizer.encode(text, return_tensors="pt").cuda()
out = model.generate(input_ids.cuda())
generated_text = list(map(tokenizer.decode, out))[0]
print(generated_text)
# Output should be like this:
# Александр Сергеевич Пушкин родился в \n1799 году. Его отец был крепостным крестьянином, а мать – крепостной крестьянкой. Детство и юность Пушкина прошли в деревне Михайловское под Петербургом. В 1820-х годах семья переехала
Pretraining details
All pretraining was done on Nvidia Tesla V100-SXM3 32 Gb GPUs on a Christofari Cluster. Following are the details of pretraining for each model.
Pretraining details ruGPT3Large
Model was trained with sequence length 1024 using transformers lib by Devices team on 80B tokens for 3 epochs. After that model was finetuned 1 epoch with sequence length 2048.
Total training time was around 14 days on 128 GPUs for 1024 context and few days on 16 GPUs for 2048 context.
Final perplexity on test set is 13.6.
You can obtain this model by using transformers with model name sberbank-ai/rugpt3large_based_on_gpt2.
🤗HuggingFace model card link
Our pretraining script here
Pretraining details ruGPT3Medium
Model was trained with sequence length 1024 using transformers lib by Devices team on 80B tokens for 3 epoch. After that model was finetuned on 2048 context.
Total training time was around 16 days on 64 GPUs.
Final perplexity on test set is 17.4.
You can obtain this model by using transformers with model name sberbank-ai/rugpt3medium_based_on_gpt2.
🤗HuggingFace model card link
Our pretraining script here
Pretraining details ruGPT3Small
Model was trained with sequence length 1024 using transformers by Devices team on 80B tokens around 3 epoch. After that model was finetuned on 2048 context.
Total training time took around one week on 32 GPUs.
You can obtain this model by using transformers with model name sberbank-ai/rugpt3small_based_on_gpt2.
🤗HuggingFace model card link
Our pretraining script here
Pretraining details ruGPT2Large
Model was trained with sequence length 1024 using transformers by Devices team on 170Gb data on 64 GPUs 3 weeks.
You can obtain this model by using transformers with model name sberbank-ai/rugpt2large.
🤗HuggingFace model card link
OpenSource Solutions with ruGPT3
- ruCLIP Github
- Simplification with ruGPT-3 XL Github
- Word normalization (RuNormAS shared task) Github
- AI CopyWriter Github
- ЕГЭ Generation Github
- NeuroZhirinovsky Github
- PseudoKant Github
- DostoevskyDoesntWriteIt Github
Papers mentioning ruGPT3
According to google scholar search - feel free to add links to this list
Text Simplification
@article{shatilovsentence,
title={Sentence simplification with ruGPT3},
author={Shatilov, AA and Rey, AI},
url={http://www.dialog-21.ru/media/5281/shatilovaaplusreyai142.pdf}
}
@article{fenogenovatext,
title={Text Simplification with Autoregressive Models},
author={Fenogenova, Alena},
url={http://www.dialog-21.ru/media/5250/fenogenovaa141.pdf}}
Text Detoxification
@article{dementieva2021methods,
title={Methods for Detoxification of Texts for the Russian Language},
author={Dementieva, Daryna and Moskovskiy, Daniil and Logacheva, Varvara and Dale, David and Kozlova, Olga and Semenov, Nikita and Panchenko, Alexander},
journal={arXiv preprint arXiv:2105.09052},
year={2021},
url={https://arxiv.org/abs/2105.09052}
}
Paraphrasing and Data Augmentation
@inproceedings{fenogenova2021russian,
title={Russian Paraphrasers: Paraphrase with Transformers},
author={Fenogenova, Alena},
booktitle={Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing},
pages={11--19},
year={2021},
url={https://www.aclweb.org/anthology/2021.bsnlp-1.2.pdf}
}
Model Evaluation
@article{malykh2021morocco,
title={MOROCCO: Model Resource Comparison Framework},
author={Malykh, Valentin and Kukushkin, Alexander and Artemova, Ekaterina and Mikhailov, Vladislav and Tikhonova, Maria and Shavrina, Tatiana},
journal={arXiv preprint arXiv:2104.14314},
year={2021},
url={https://arxiv.org/abs/2104.14314}}