paraphraser

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paraphraser

This model is a fine-tuned version of t5-base on the cointegrated/ru-paraphrase-NMT-Leipzig dataset. It achieves the following results on the evaluation set:

• Loss: 0.2841

Intended uses & limitations

from transformers import T5TokenizerFast, T5ForConditionalGeneration

encoded = tokenizer.encode('перефразируй: В какой срок необходимо оповестить Вайлдберрис о закрытии?', return_tensors='pt')
output_sequences = model.generate(
    input_ids=encoded,
    max_length=128,
    temperature=0.7,
    top_k=0,
    top_p=0.9,
    repetition_penalty=1,
    do_sample=True,
    num_return_sequences=5,
    pad_token_id=0
)
decoded = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
# Когда нужно будет известить Вайлдберриса о закрытии?

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.35.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.14.1