| This is a version of paraphrase detector by DeepPavlov ([details in the documentation](http://docs.deeppavlov.ai/en/master/features/overview.html#ranking-model-docs)) ported to the `Transformers` format. | |
| All credit goes to the authors of DeepPavlov. | |
| The model has been trained on the dataset from http://paraphraser.ru/. | |
| It classifies texts as paraphrases (class 1) or non-paraphrases (class 0). | |
| ```python | |
| import torch | |
| from transformers import AutoModelForSequenceClassification, BertTokenizer | |
| model_name = 'cointegrated/rubert-base-cased-dp-paraphrase-detection' | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name).cuda() | |
| tokenizer = BertTokenizer.from_pretrained(model_name) | |
| def compare_texts(text1, text2): | |
| batch = tokenizer(text1, text2, return_tensors='pt').to(model.device) | |
| with torch.inference_mode(): | |
| proba = torch.softmax(model(**batch).logits, -1).cpu().numpy() | |
| return proba[0] # p(non-paraphrase), p(paraphrase) | |
| print(compare_texts('Сегодня на улице хорошая погода', 'Сегодня на улице отвратительная погода')) | |
| # [0.7056226 0.2943774] | |
| print(compare_texts('Сегодня на улице хорошая погода', 'Отличная погодка сегодня выдалась')) | |
| # [0.16524374 0.8347562 ] | |
| ``` | |
| P.S. In the DeepPavlov repository, the tokenizer uses `max_seq_length=64`. This model, however, uses `model_max_length=512`. Therefore, results on long texts might be inadequate. |