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README.md

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+---
+library_name: pytorch
+tags:
+- biosignals
+- ecgdenoisernl
+metrics:
+- validation_loss
+---
+# Model Card for ECGDenoiserNL
+
+    
+- Collection: NeuralLib: Deep Learning Models for Biosignals Processing
+
+- Description: GRU-based model for ECG denoising
+
+
+```json
+{
+    "architecture": "GRUseq2seq",
+    "model_name": "ECGDenoiserNL",
+    "train_dataset": "ptb-xl+mit-bih-noise",
+    "task": "ecg denoising",
+    "gpu_model": "NVIDIA RTX 6000 Ada Generation",
+    "epochs": 70,
+    "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: 0.005\n    lr: 0.005\n    maximize: False\n    weight_decay: 1e-05\n)",
+    "learning_rate": 0.005,
+    "validation_loss": 0.014517052099108696,
+    "training_time": 5837.353876829147,
+    "retraining": false
+}
+
+
+## Hyperparameters
+
+bidirectional: true
+dropout: 0
+hid_dim: 64
+learning_rate: 0.005
+model_name: ECGDenoiserNL
+multi_label: false
+n_features: 1
+n_layers: 2
+num_classes: NA
+task: regression
+
+
+# Example
+
+import torch
+
+from production_models import ECGDenoiserNL
+
+model = ECGDenoiserNL()
+
+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: 64
+learning_rate: 0.005
+model_name: ECGDenoiserNL
+multi_label: false
+n_features: 1
+n_layers: 2
+num_classes: NA
+task: regression

model_weights.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61caad3ae86cbd65eb537b8e487bcb5162ec04ec7a96576c488ac40347cbf791
+size 407652

training_info.json

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+{
+    "architecture": "GRUseq2seq",
+    "model_name": "ECGDenoiserNL",
+    "train_dataset": "ptb-xl+mit-bih-noise",
+    "task": "ecg denoising",
+    "gpu_model": "NVIDIA RTX 6000 Ada Generation",
+    "epochs": 70,
+    "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: 0.005\n    lr: 0.005\n    maximize: False\n    weight_decay: 1e-05\n)",
+    "learning_rate": 0.005,
+    "validation_loss": 0.014517052099108696,
+    "training_time": 5837.353876829147,
+    "retraining": false
+}