---
datasets:
- ckcl/BTC_USDT_dataset
metrics:
- bertscore
base_model:
- google-bert/bert-base-uncased
- meta-llama/Llama-3.1-8B-Instruct
tags:
- prediction
license: mit
library_name: adapter-transformers
new_version: meta-llama/Llama-3.1-8B-Instruct
---
# Custom Transformer Model for MEXC Price Prediction

## [中文版](CN_README.md)
 https://huggingface.co/ckcl/mexc_price_model/blob/main/CN_README.md

## Model Description

This model is a custom Transformer model designed to predict MEXC contract prices. It consists of an embedding layer followed by multiple Transformer encoder layers, and a fully connected layer at the end to produce the output.

## Model Architecture

- **Input Dimension:** 13
- **Model Dimension:** 64
- **Number of Heads:** 8
- **Number of Layers:** 2
- **Output Dimension:** 1

## Training Data

The model was trained on historical MEXC contract transaction data. The features include open, close, high, low prices, volume, amount, real open, real close, real high, real low prices, and moving averages.

## Training Details

- **Optimizer:** Adam
- **Learning Rate:** 0.001
- **Loss Function:** Mean Squared Error (MSE)
- **Batch Size:** 32
- **Number of Epochs:** 50

## Usage

To use this model for prediction, follow these steps:

1. Load the model and configuration:

    ```python
    import torch
    import torch.nn as nn
    from transformers import AutoConfig

    class CustomTransformerModel(nn.Module):
        def __init__(self, config):
            super(CustomTransformerModel, self).__init__()
            self.embedding = nn.Linear(config.input_dim, config.model_dim)
            self.encoder_layer = nn.TransformerEncoderLayer(d_model=config.model_dim, nhead=config.num_heads, batch_first=True)
            self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=config.num_layers)
            self.fc = nn.Linear(config.model_dim, config.output_dim)
        
        def forward(self, src):
            src = self.embedding(src)
            output = self.transformer_encoder(src)
            output = self.fc(output[:, -1, :])
            return output

    config = AutoConfig.from_pretrained("your-username/mexc_price_model", config_file_name="BTC_USDT.json")
    model = CustomTransformerModel(config)
    model.load_state_dict(torch.load("model_repo/mexc_price.pth"))
    model.eval()
    ```

2. Prepare input data and make predictions:

    ```python
    import numpy as np
    from sklearn.preprocessing import StandardScaler

    new_data = np.array([
        [1.727087e+09, 63483.9, 63426.2, 63483.9, 63411.6, 1193897.0, 7.575486e+06, 63483.8, 63426.2, 63483.9, 63411.6, 0.00, 0.0, 0.0]
    ])

    scaler = StandardScaler()
    new_data_scaled = scaler.fit_transform(new_data)
    input_tensor = torch.tensor(new_data_scaled, dtype=torch.float32).unsqueeze(1)

    with torch.no_grad():
        prediction = model(input_tensor)

    predicted_value = prediction.squeeze().item()
    print(f"Predicted Value: {predicted_value}")
    ```

## License

This model is licensed under the [MIT License](LICENSE).