---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** catlove
- **Shared by [optional]:** catlove
- **Model type:** bert
- **Language(s) (NLP):** multi-language
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** xlm-roberta-large

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

```[python]
class CFG:
    print_freq = 500
    num_workers = 0
    model = "xlm-roberta-large"
    tokenizer = AutoTokenizer.from_pretrained(model)
    gradient_checkpointing = False
    num_cycles = 0.5
    warmup_ratio = 0.1
    epochs = 3
    encoder_lr = 1e-5
    decoder_lr = 1e-4
    eps = 1e-6
    betas = (0.9, 0.999)
    batch_size = 32
    weight_decay = 0.01
    max_grad_norm = 0.012
    max_len = 512
    n_folds = 5
    seed = 42

class custom_model(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.cfg = cfg
        self.config = AutoConfig.from_pretrained(cfg.model, output_hidden_states = True)
        self.config.hidden_dropout = 0.0
        self.config.hidden_dropout_prob = 0.0
        self.config.attention_dropout = 0.0
        self.config.attention_probs_dropout_prob = 0.0
        self.model = AutoModel.from_pretrained(cfg.model, config = self.config)
        if self.cfg.gradient_checkpointing:
            self.model.gradient_checkpointing_enable()
        self.pool = MeanPooling()
        self.fc = nn.Linear(self.config.hidden_size, 1)
        self._init_weights(self.fc)
    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
    def feature(self, inputs):
        outputs = self.model(**inputs)
        last_hidden_state = outputs.last_hidden_state
        feature = self.pool(last_hidden_state, inputs['attention_mask'])
        return feature
    def forward(self, inputs):
        feature = self.feature(inputs)
        output = self.fc(feature)
        return output

model = custom_model(CFG)
model.load_state_dict(torch.load('./model_saved/custom_model_weights.pth')['model'])
```

## Evaluation

Our CV score is 0.3797 using a threshold of 0.029.