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Browse files- README.md +27 -0
- config.json +13 -0
- scaler.pkl +3 -0
- tf_model.h5 +3 -0
README.md
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# data-intelligence-management-raw-material
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Time series forecasting model for raw material prediction.
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## Model details
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- Task: Time series forecasting
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- Architecture: LSTM-based model
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- Input: 5 time steps
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- Output: 5 future predictions
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## Usage with Hugging Face Inference API
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```python
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import requests
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API_URL = "https://api-inference.huggingface.co/models/data-intelligence-management-raw-material"
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headers = {"Authorization": "Bearer YOUR_API_TOKEN"}
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def query(data):
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response = requests.post(API_URL, headers=headers, json={"inputs": data})
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return response.json()
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# Example input: last 5 time steps
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data = [10, 15, 20, 25, 30]
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predictions = query(data)
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print(predictions) # Returns 5 future predictions
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```
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config.json
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{
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"model_type": "time_series_forecast",
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"architectures": [
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"TimeSeriesModel"
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],
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"hidden_size": 128,
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"input_shape": [
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5,
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1
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],
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"output_dim": 5,
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"transformers_version": "4.28.1"
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}
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scaler.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:2495d7c7cc120c7b38198707bfb0b8189845df62005685453e1ddeedd0870c41
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size 473
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:a7832bee282f4b5e61fb9d5bba87cce89ee2706daca7efc2f13e998b201c4564
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size 823584
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