README.md

metadata

language:
  - tr
inference:
  parameters:
    max_new_tokens: 32
arXiv: 2403.01308
library_name: transformers
pipeline_tag: text2text-generation
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 title generation from news spot task.

• Developed by: VNGRS-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

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-XLarge-Title-Generation-from-Spot",
                            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-Title-Generation-from-Spot")#, device_map="auto")

input_text="..."

token_input = tokenizer(input_text, return_tensors="pt")#.to('cuda')
outputs = model.generate(**token_input)
print(tokenizer.decode(outputs[0]))

Training Details

Training Data

The base model is pre-trained on vngrs-web-corpus. It is curated by cleaning and filtering Turkish parts of OSCAR-2201 and 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.

The fine-tuning dataset is the Turkish sections of MLSum, TRNews and XLSum datasets.

Limitations

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.

Training Procedure

Pre-trained for 8 days and for a total of 84B tokens. Finally, finetuned for 15 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 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: 15

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}
}