ruT5-large (737M)
Создан 12.01.2024 11:21Общая оценка: 0.19
Таблица скроллится влево
| Задача | Результат | Метрика |
|---|---|---|
| BPS | 0.402 | Accuracy |
| LCS | 0.11 | Accuracy |
| RCB | 0.326 / 0.296 | Avg. F1 / Accuracy |
| USE | 0 | Grade Norm |
| RWSD | 0.485 | Accuracy |
| PARus | 0.498 | Accuracy |
| ruTiE | 0.505 | Accuracy |
| MultiQ | 0.01 / 0 | F1-score/EM |
| ruMMLU | 0.24 | Accuracy |
| CheGeKa | 0 / 0 | F1 / EM |
| ruModAr | 0.0 | Accuracy |
| SimpleAr | 0.0 | Accuracy |
| ruMultiAr | 0.0 | Accuracy |
| MathLogicQA | 0.254 | Accuracy |
| ruHumanEval | 0 / 0 / 0 | pass@k |
| ruWorldTree | 0.259 / 0.159 | Avg. F1 / Accuracy |
| ruOpenBookQA | 0.263 / 0.158 | Avg. F1 / Accuracy |
Оценка на диагностических датасетах:
Не учитывается в общем рейтингеТаблица скроллится влево
| Задача | Результат | Метрика | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ruHHH |
0.534
|
Accuracy | ||||||||||||||||||||||||
| ruHateSpeech |
0.46
|
Accuracy | ||||||||||||||||||||||||
| ruDetox |
|
Общая средняя оценка (J) Оценка сохранения смысла (SIM) Оценка натуральности (FL) Точность переноса стиля (STA) |
||||||||||||||||||||||||
| ruEthics |
Результаты таблицы:
[[0.047, 0.029
, 0.02, 0.051
, 0.034], |
5 MCC |
Информация о сабмите:
Команда:
MERA
Название ML-модели:
ruT5-large (737M)
Ссылка на ML-модель:
https://huggingface.co/ai-forever/ruT5-largeДополнительные ссылки:
https://arxiv.org/abs/2309.10931Описание архитектуры:
ruT5 is one of the first encoder-decoder LMs pretrained only on Russian textual data. The ruT5 model is designed analogically to the T5 model.
Описание обучения:
The models are pretrained on a masked language modeling “span corruption” objective, where consecutive spans of the input tokens are masked, and the model is trained to reconstruct the masked tokens. The authors use the SentencePiece tokenizer with the vocabulary size of 32 tokens.
Данные претрейна:
300GB of texts. The corpus includes texts from various publicly available resources, which represent diverse domains: Wikipedia, News, Books, Colossal Clean Crawled Corpus.
Детали обучения:
The ruT5 models is pretrained using a linear scheduler with the learning rate of 1e−4 and the Adam optimizer with β1 = 0.9, β2 = 0.99, and ϵ = 1e−8. The sequence length is set to 512/512 for inputs and targets.
Лицензия:
MIT
Стратегия, генерация и параметры:
Code version v.1.1.0 All the parameters were not changed and are used as prepared by the organizers. Details: - 1 x NVIDIA A100 - dtype auto - Pytorch 2.1.2 + CUDA 12.1 - Transformers 4.36.2 - Context length 512
