Overview

Cross-encoder for russian language. Primarily trained for RAG purposes. Take two strings, assess if they are related (question and answer pair).

Usage

import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'

!wget https://huggingface.co/GrigoryT22/cross-encoder-ru/resolve/main/model.pt  # or simply load the file via browser

model = Model()  # copy-past class code (see below) and run it
model.load_state_dict(torch.load('./model.pt'), strict=False)  # path to downloaded file with the model
# missing_keys=['labse.embeddings.position_ids'] - this is [OK](https://github.com/huggingface/transformers/issues/16353) 

string_1 = """
Компания судится с артистом
""".strip()

string_2 = """
По заявлению инвесторов, компания знала о рисках заключения подобного контракта задолго до антисемитских высказываний Уэста, 
которые он озвучил в октябре 2022 года. Однако, несмотря на то, что Adidas прекратил сотрудничество с артистом, 
избежать судебного разбирательства не удалось. После расторжения контракта с рэпером компания потеряет 1,3 миллиарда долларов.
""".strip()

model([
      [string_1, string_2]
      ])
# should be something like this --->>> tensor([[-4.0403,  3.8442]], grad_fn=<AddmmBackward0>)
# model is pretty sure that these two strings are related, second number is bigger (logits for binary classifications, batch size one in this case)

Model class

class Model(nn.Module):
    """
    labse - base bert-like model
    from labse I use pooler layer as input
    then classification head - binary classification to predict if this pair is TRUE question-answer
    """
    def __init__(self):
        super().__init__()
        self.labse_config = AutoConfig.from_pretrained('cointegrated/LaBSE-en-ru')
        self.labse  = AutoModel.from_config(self.labse_config)
        self.tokenizer = AutoTokenizer.from_pretrained('cointegrated/LaBSE-en-ru')
        self.cls = nn.Sequential(OrderedDict(
                                                  [
                                                    ('dropout_in', torch.nn.Dropout(.0)),
                                                    ('layernorm_in' , nn.LayerNorm(768, eps=1e-05)),

                                                    ('fc_1' , nn.Linear(768, 768 * 2)),
                                                    ('act_1' , nn.GELU()),
                                                    ('layernorm_1' , nn.LayerNorm(768 * 2, eps=1e-05)),

                                                    ('fc_2' , nn.Linear(768 * 2, 768 * 2)),
                                                    ('act_2' , nn.GELU()),
                                                    ('layernorm_2' , nn.LayerNorm(768 * 2, eps=1e-05)),

                                                    ('fc_3' , nn.Linear(768 * 2, 768)),
                                                    ('act_3' , nn.GELU()),
                                                    ('layernorm_3' , nn.LayerNorm(768, eps=1e-05)),

                                                    ('fc_4' , nn.Linear(768, 256)),
                                                    ('act_4' , nn.GELU()),
                                                    ('layernorm_4' , nn.LayerNorm(256, eps=1e-05)),

                                                    ('fc_5' , nn.Linear(256, 2, bias=True)),
                                                  ]
                                    ))
    def forward(self, text):
        token = self.tokenizer(text, padding=True, truncation=True, return_tensors='pt').to(device)
        model_output = self.labse(**token)
        result = self.cls(model_output.pooler_output)
        return result