YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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.

This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for sentence-level text paraphrasing 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-Large
  • Paper: arXiv

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing",
                            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-Large-Paraphrasing")#, 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 a mixture of OpenSubtitles, TED Talks (2013) and Tatoeba datasets.

Limitations

This model is fine-tuned for paraphrasing tasks and finetuned in sentence level only. 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 30 days and for a total of 708B tokens. Finetuned for 20 epoch.

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)
  • Dropout: 0.1 (dropped to 0.05 and then to 0 in the last 165k and 205k steps, respectively)
  • Initial Learning rate: 5e-6
  • Training tokens: 708B
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: 1e-5
  • Fine-tune epochs: 20

Metrics

image/png

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}
}
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