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---
datasets:
- ckcl/BTC_USDT_dataset
metrics:
- bertscore
base_model:
- google-bert/bert-base-uncased
pipeline_tag: table-question-answering
tags:
- prediction
---
# 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). |