Pretrained model on Bulgarian language using a causal language modeling (CLM) objective. It was introduced in this paper and first released at this page. The training data is Bulgarian text from OSCAR, Chitanka and Wikipedia. You can use the raw model for: — text generation — auto-complete — spelling correction As the openAI team themselves point out in their model card: > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes.
Модальности:
Генерация текста
Задача: Генерация текста
Автор: rmihaylov
Теги: gpt2, torch, custom_code, bg, text-generation-inference
Лайков: 2 | Загрузок: 102
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
