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--- |
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language: |
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- tr |
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inference: |
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parameters: |
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max_new_tokens: 32 |
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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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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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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. |
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VBART-XLarge improves the results compared to VBART-Large albeit in small margins. |
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This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for title generation from news body task. |
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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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- **Finetuned from:** VBART-XLarge |
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- **Paper:** [arXiv](https://arxiv.org/abs/2403.01308) |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-XLarge-Title-Generation-from-News", |
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model_input_names=['input_ids', 'attention_mask']) |
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# Uncomment the device_map kwarg and delete the closing bracket to use model for inference on GPU |
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model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-XLarge-Title-Generation-from-News")#, device_map="auto") |
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input_text="..." |
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token_input = tokenizer(input_text, return_tensors="pt")#.to('cuda') |
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outputs = model.generate(**token_input) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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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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The fine-tuning dataset is the Turkish sections of [MLSum](https://huggingface.co/datasets/mlsum), [TRNews](https://huggingface.co/datasets/batubayk/TR-News) and [XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum) datasets. |
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### Limitations |
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This model is fine-tuned for title generation tasks. It is not intended to be used in any other case and can not be fine-tuned to any other task with full performance of the base model. It is also not guaranteed that this model will work without specified prompts. |
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### Training Procedure |
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Pre-trained for 8 days and for a total of 84B tokens. Finally, finetuned for 15 epochs. |
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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**: Sentence permutation and 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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- **Weight Initialization**: Model Enlargement from VBART-Large. See the related section in the [paper](https://arxiv.org/abs/2403.01308) for the details. |
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- **Dropout**: 0.1 (dropped to 0.05 and then to 0 in the last 80K and 80k steps, respectively) |
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- **Initial Learning rate**: 5e-6 |
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- **Training tokens**: 84B |
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##### Fine-tuning |
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- **Training regime:** fp16 mixed precision |
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- **Optimizer** : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6) |
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- **Scheduler**: Linear decay scheduler |
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- **Dropout**: 0.1 |
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- **Learning rate**: 5e-6 |
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- **Fine-tune epochs**: 15 |
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#### Metrics |
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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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``` |
