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 text summarization 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 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, XLSum and Wikilingua datasets. This model is fine-tuned…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: vngrs
Теги: tf, mbart, text2text-generation, tr, endpoints_compatible
Лайков: 3 | Загрузок: 57
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
