---
language:
- tr
arXiv: 2403.01308
library_name: transformers
license: cc-by-nc-sa-4.0
datasets:
- vngrs-ai/vngrs-web-corpus
---
# VBART Model Card

## Model Description  

VBART is the first sequence-to-sequence LLM pre-trained on Turkish corpora from scratch on a large scale. It was pre-trained by VNGRS in February 2023.  
The model is capable of conditional text generation tasks such as text summarization, paraphrasing, and title generation when fine-tuned.
It outperforms its multilingual counterparts, albeit being much smaller than other implementations.
It comes in two sizes:
- **VBART-Large**: 387M parameters
- **VBART-XLarge**: 740M parameters

VBART-XLarge is created by adding extra Transformer layers between the layers of VBART-Large. Hence it was able to transfer learned weights from the smaller model while doublings its number of layers.
VBART-XLarge improves the results compared to VBART-Large albeit in small margins.


- **Developed by:** [VNGRS-AI](https://vngrs.com/ai/)
- **Model type:**  Transformer encoder-decoder based on mBART architecture
- **Language(s) (NLP):** Turkish
- **License:** CC BY-NC-SA 4.0
- **Paper:** [arXiv](https://arxiv.org/abs/2403.01308)


### Pre-training Data
The base model is pre-trained on [vngrs-web-corpus](https://huggingface.co/datasets/vngrs-ai/vngrs-web-corpus). It is curated by cleaning and filtering Turkish parts of [OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) and [mC4](https://huggingface.co/datasets/mc4) datasets. These datasets consist of documents of unstructured web crawl data. More information about the dataset can be found on their respective pages. Data is filtered using a set of heuristics and certain rules, explained in the appendix of our [paper](https://arxiv.org/abs/2403.01308).
#### Software
- TensorFlow
#### Pre-training Setting
- **Duration**: Pre-trained for 30 days.
- **GPUs**: 8 x Nvidia A100-80 GB
- **Training tokens**: 708B
- **Context Length**: 1024 for both encoder and decoder
- **Training regime:** fp16 mixed precision
- **Training objective**: Sentence permutation and span masking (using mask lengths sampled from Poisson distribution λ=3.5, masking 30% of tokens)
- **Optimizer** : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6)
- **Scheduler**: Custom scheduler from the original Transformers paper (20,000 warm-up steps)
- **Dropout**: 0.1 (dropped to 0.05 and then to 0 in the last 165K and 205k steps, respectively)
- **Initial Learning rate**: 5e-6


## Citation  
```
@article{turker2024vbart,
  title={VBART: The Turkish LLM},
  author={Turker, Meliksah and Ari, Erdi and Han, Aydin},
  journal={arXiv preprint arXiv:2403.01308},
  year={2024}
}
```