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--- |
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language: |
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- tr |
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arXiv: 2403.01308 |
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library_name: transformers |
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pipeline_tag: text2text-generation |
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license: cc-by-nc-sa-4.0 |
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inference: false |
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datasets: |
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- vngrs-ai/vngrs-web-corpus |
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--- |
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# VBART Model Card |
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## Model Description |
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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. |
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The model is capable of conditional text generation tasks such as text summarization, paraphrasing, and title generation when fine-tuned. |
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It outperforms its multilingual counterparts, albeit being much smaller than other implementations. |
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This repository contains pre-trained TensorFlow and Safetensors weights of VBART-Small-Base. |
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- **Developed by:** [VNGRS-AI](https://vngrs.com/ai/) |
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- **Model type:** Transformer encoder-decoder based on mBART architecture |
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- **Language(s) (NLP):** Turkish |
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- **License:** CC BY-NC-SA 4.0 |
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- **Paper:** [arXiv](https://arxiv.org/abs/2403.01308) |
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## Training Details |
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### Training Data |
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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). |
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### Limitations |
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This model is the pre-trained base model and is capable of masked language modeling. |
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Its purpose is to serve as the base model to be fine-tuned for downstream tasks. |
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### Training Procedure |
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Pre-trained for a total of 52B tokens. |
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#### Hardware |
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- **GPUs**: 8 x Nvidia A100-80 GB |
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#### Software |
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- TensorFlow |
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#### Hyperparameters |
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##### Pretraining |
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- **Training regime:** fp16 mixed precision |
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- **Training objective**: Span masking (using mask lengths sampled from Poisson distribution λ=3.5, masking 30% of tokens) |
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- **Optimizer** : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6) |
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- **Scheduler**: Custom scheduler from the original Transformers paper (20,000 warm-up steps) |
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- **Dropout**: 0.1 |
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- **Initial Learning rate**: 5e-6 |
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- **Training tokens**: 52B |
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## Citation |
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``` |
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@article{turker2024vbart, |
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title={VBART: The Turkish LLM}, |
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author={Turker, Meliksah and Ari, Erdi and Han, Aydin}, |
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journal={arXiv preprint arXiv:2403.01308}, |
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year={2024} |
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} |
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``` |
