vngrs-ai
/

VBART-Large-Base

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
+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
+- **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).
 #### Hardware
+- **GPUs**: 8 x Nvidia A100-80 GB
 #### Software
+- TensorFlow
+#### Pre-training Setting
+- **Duration**: Pre-trained for 30 days.
+- **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}
+}
+```