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--- |
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language: |
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- ru |
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license: apache-2.0 |
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--- |
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# FRED-T5 large 800M (Full-scale Russian Enhanced Denoisers T5) |
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Model was trained by [SberDevices](https://sberdevices.ru/). |
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Architecture based on T5. |
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It has 24 layers and 1024 hidden size. More details in config.json. |
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The model trained on a mixture of 7 denoisers like UL2 with several differences (https://arxiv.org/abs/2205.05131). |
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It was trained on Russian language corpus (300GB). The dataset is the same as for ruT5 models. |
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Bbpe tokenizer. 50257 + special tokens 107. Prefix tokens: '\<LM\>', '\<SC1>',.. '\<SC6>' |
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First half of the time model trained on the small part of all dataset (1%,3GB) and without prefixes in each task. |
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For RSG, we trained as described in the T5 paper. First, we trained multitask for all tasks. Then we took the best checkpoint for the task and trained it further. |
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RSG submit here https://russiansuperglue.com/login/submit_info/2060 |
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Total training time was around 35 days on 160 V100 GPUs + 5 days on 80 A100. |
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## Usage (HuggingFace Models Repository) |
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```python |
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import torch |
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from transformers import GPT2Tokenizer, T5ForConditionalGeneration |
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tokenizer = GPT2Tokenizer.from_pretrained('ai-forever/FRED-T5-1.7B',eos_token='</s>') |
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model = T5ForConditionalGeneration.from_pretrained(('ai-forever/FRED-T5-1.7B') |
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device='cuda' |
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model.to(device) |
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#Prefix <LM> |
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lm_text='<LM>Принялся Кутузов рассказывать свою историю как он сюда попал. Началось' |
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input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device) |
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outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True) |
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print(tokenizer.decode(outputs[0][1:])) |
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# print result: с того, что он был в плену у немцев.</s> |
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#Prefix <SC1> |
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lm_text='<SC1>Принялся Кутузов рассказывать свою историю <extra_id_0>. Началось с того, что он был в армии, служил в артиллерии.' |
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input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device) |
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outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True) |
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print(tokenizer.decode(outputs[0][1:])) |
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#print result: '<extra_id_0>, как он жил</s>' |
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# Prefix <SC5> |
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lm_text='<SC5>Принялся Кутузов рассказывать свою историю <extra_id_0>. Началось с того, что он был в армии, служил в артиллерии.' |
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input_ids=torch.tensor([tokenizer.encode(lm_text)]).to(device) |
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outputs=model.generate(input_ids,eos_token_id=tokenizer.eos_token_id,early_stopping=True,max_length=100) |
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print(tokenizer.decode(outputs[0][1:])) |
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#print result: '<extra_id_0>, как он был в армии</s>' |
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``` |
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# Authors |
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+ NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam): |
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+ Dmitry Zmitrovich |
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+ Andrei Kalmykov |
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+ Vitaly Kadulin |
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+ Mikhail Novikov |
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+ Alexey Khoroshilov |
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