| --- |
| language: |
| - tr |
| arXiv: 2403.01308 |
| library_name: transformers |
| pipeline_tag: text2text-generation |
| inference: |
| parameters: |
| max_new_tokens: 32 |
| num_beams: 8 |
| do_sample: false |
| widget: |
| - text: >- |
| Soru yarat: Alan Mathison Turing İngiliz matematikçi, bilgisayar bilimcisi |
| ve kriptolog. II. Dünya Savaşı sırasında Alman şifrelerinin kırılmasında çok |
| önemli bir rol oynadığı için savaş kahramanı sayılmıştır. Ayrıca Manchester |
| Üniversitesi'nde çalıştığı yıllarda, Turing makinesi denilen algoritma |
| tanımı ile modern bilgisayarların kavramsal temelini atmıştır. |
| example_title: Question generation |
| - text: >- |
| Soru cevapla: Turing makinesi denilen algoritma tanımı ile modern |
| bilgisayarların kavramsal temelini atan bilim insanı kimdir? kaynak: Alan |
| Mathison Turing İngiliz matematikçi, bilgisayar bilimcisi ve kriptolog. II. |
| Dünya Savaşı sırasında Alman şifrelerinin kırılmasında çok önemli bir rol |
| oynadığı için savaş kahramanı sayılmıştır. Ayrıca Manchester |
| Üniversitesi'nde çalıştığı yıllarda, Turing makinesi denilen algoritma |
| tanımı ile modern bilgisayarların kavramsal temelini atmıştır. |
| example_title: Question answering |
| - text: >- |
| yanıtları çıkar: Alan Mathison Turing İngiliz matematikçi, bilgisayar |
| bilimcisi ve kriptolog. II. Dünya Savaşı sırasında Alman şifrelerinin |
| kırılmasında çok önemli bir rol oynadığı için savaş kahramanı sayılmıştır. |
| <hl> Ayrıca Manchester Üniversitesi'nde çalıştığı yıllarda, Turing makinesi |
| denilen algoritma tanımı ile modern bilgisayarların kavramsal temelini |
| atmıştır <hl> . |
| example_title: Answer Extraction |
| 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. |
|
|
| 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. |
|
|
| This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for question-answering and generation tasks described in the [paper](https://doi.org/10.55730/1300-0632.3914). |
|
|
| - **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 |
| - **Finetuned from:** VBART-XLarge |
| - **Paper:** [arXiv](https://arxiv.org/abs/2403.01308) |
| ## How to Get Started with the Model |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| |
| tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-XLarge-QAQG", |
| model_input_names=['input_ids', 'attention_mask']) |
| # Uncomment the device_map kwarg and delete the closing bracket to use model for inference on GPU |
| model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-XLarge-QAQG")#, device_map="auto") |
| |
| context="..." |
| question="..." |
| highlighted_context="..." |
| |
| # Prompt for question generation |
| qg_prompt = f'Soru yarat: cevap: {context}' |
| # Prompt for question answering |
| qa_prompt = f'Soru cevapla: {question} kaynak: {context}' |
| # Prompt for answer extraction |
| ae_prompt = f'yanıtları çıkar: {highlighted_context}' |
| |
| |
| token_input = tokenizer(ae_prompt, return_tensors="pt")#.to('cuda') |
| outputs = model.generate(**token_input) |
| print(tokenizer.decode(outputs[0])) |
| ``` |
| |
| ## Training Details |
| ### Fine-tuning prompt |
| This model is fine-tuned on three tasks: |
| - question answering: Answer a question in a given context. Prompted with |
| ```Soru cevapla: <question> kaynak: <context>``` |
| - question generation: Generate a question from a given context. Will accept a highlight token (`<hl>`, without spaces) to specify the answer to the question generated. Prompted with |
| ```Soru yarat: <context>``` |
| - answer extraction: Will extract possible answers from a highlighted range (using the same highlight token). Prompted with |
| ``` yanıtları çıkar: <context with highlighted parts>``` |
| |
| ### 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). |
|
|
| The fine-tuning dataset is [TQuAD](https://github.com/obss/turkish-question-generation), which has two versions. We have concatenated them and dropped duplicate samples. More information about this process can be found in Appendix B of our [paper](https://arxiv.org/abs/2403.01308). |
|
|
| ### Limitations |
| This model is fine-tuned for question-answering and question-generation tasks with specific prompts. 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. |
|
|
| ### Training Procedure |
| Pre-trained for 8 days and for a total of 84B tokens. Finally, finetuned for 55 epochs. |
| #### Hardware |
| - **GPUs**: 8 x Nvidia A100-80 GB |
| #### Software |
| - TensorFlow |
| #### Hyperparameters |
| ##### Pretraining |
| - **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) |
| - **Weight Initialization**: Model Enlargement from VBART-Large. See the related section in the [paper](https://arxiv.org/abs/2403.01308) for the details. |
| - **Dropout**: 0.1 (dropped to 0.05 and then to 0 in the last 80K and 80k steps, respectively) |
| - **Initial Learning rate**: 5e-6 |
| - **Training tokens**: 84B |
|
|
| ##### Fine-tuning |
| - **Training regime:** fp16 mixed precision |
| - **Optimizer** : Adam optimizer (β1 = 0.9, β2 = 0.98, Ɛ = 1e-6) |
| - **Scheduler**: Linear decay scheduler |
| - **Dropout**: 0.1 |
| - **Learning rate**: 5e-6 |
| - **Fine-tune epochs**: 55 |
|
|
| #### Metrics |
|  |
|
|
| ## 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} |
| } |
| ``` |
