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README.md

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+---
+library_name: pytorch
+tags:
+- biosignals
+- testmodel
+metrics:
+- validation_loss
+---
+# Model Card for Testmodel
+
+    
+- Collection: NeuralLib: Deep Learning Models for Biosignals Processing
+
+- Description: This is a Test
+
+
+```json
+{
+    "architecture": "GRUseq2seq",
+    "model_name": "ECGPeakDetector",
+    "train_dataset": "private_gib01",
+    "biosignal": "ECG",
+    "sampling_frequency": 360,
+    "task": "peak detection",
+    "gpu_model": "NVIDIA GeForce GTX 1080 Ti",
+    "epochs": 80,
+    "optimizer": "Adam (\nParameter Group 0\n    amsgrad: False\n    betas: (0.9, 0.999)\n    capturable: False\n    differentiable: False\n    eps: 1e-08\n    foreach: None\n    fused: None\n    initial_lr: 0.001\n    lr: 0.001\n    maximize: False\n    weight_decay: 1e-05\n)",
+    "learning_rate": 0.001,
+    "validation_loss": 0.14879398047924042,
+    "training_time": 11375.492486476898,
+    "retraining": false,
+    "efficiency_flops": 0,
+    "efficiency_params": 0
+}
+
+
+## Hyperparameters
+
+bidirectional: true
+dropout: 0
+hid_dim:
+- 32
+- 64
+- 64
+learning_rate: 0.001
+model_name: ECGPeakDetector
+multi_label: true
+n_features: 1
+n_layers: 3
+num_classes: 1
+task: classification
+
+
+# Example
+
+import torch
+
+from production_models import Testmodel
+
+model = Testmodel()
+
+signal = torch.rand(1, 100, 1)  # Example input signal
+
+predictions = model.predict(signal)
+
+print(predictions)
+

hparams.yaml

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+bidirectional: true
+dropout: 0
+hid_dim:
+- 32
+- 64
+- 64
+learning_rate: 0.001
+model_name: ECGPeakDetector
+multi_label: true
+n_features: 1
+n_layers: 3
+num_classes: 1
+task: classification

model_weights.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:67c5326c142ff01ec3e911f2459b42826392bf06dcc53ca2d26b051daaa785df
+size 533628

training_info.json

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+{
+    "architecture": "GRUseq2seq",
+    "model_name": "ECGPeakDetector",
+    "train_dataset": "private_gib01",
+    "biosignal": "ECG",
+    "sampling_frequency": 360,
+    "task": "peak detection",
+    "gpu_model": "NVIDIA GeForce GTX 1080 Ti",
+    "epochs": 80,
+    "optimizer": "Adam (\nParameter Group 0\n    amsgrad: False\n    betas: (0.9, 0.999)\n    capturable: False\n    differentiable: False\n    eps: 1e-08\n    foreach: None\n    fused: None\n    initial_lr: 0.001\n    lr: 0.001\n    maximize: False\n    weight_decay: 1e-05\n)",
+    "learning_rate": 0.001,
+    "validation_loss": 0.14879398047924042,
+    "training_time": 11375.492486476898,
+    "retraining": false,
+    "efficiency_flops": 0,
+    "efficiency_params": 0
+}