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patch_tst_pretrain

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README.md ADDED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: pretraining
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # pretraining
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9934
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 0.9973 | 1.0 | 13 | 0.9918 |
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+ | 0.9964 | 2.0 | 26 | 0.9961 |
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+ | 0.9966 | 3.0 | 39 | 0.9974 |
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+ | 0.996 | 4.0 | 52 | 0.9960 |
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+ | 0.996 | 5.0 | 65 | 1.0023 |
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+ | 0.9966 | 6.0 | 78 | 0.9973 |
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+ | 0.9953 | 7.0 | 91 | 0.9934 |
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+ | 0.9972 | 8.0 | 104 | 0.9954 |
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+ | 1.0003 | 9.0 | 117 | 0.9965 |
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+ | 0.9975 | 10.0 | 130 | 0.9989 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.1.0+cu121
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+ - Tokenizers 0.15.2
config.json ADDED
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+ {
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+ "activation_function": "gelu",
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+ "architectures": [
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+ "PatchTSTForPretraining"
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+ ],
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+ "attention_dropout": 0.0,
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+ "bias": true,
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+ "channel_attention": false,
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+ "channel_consistent_masking": false,
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+ "context_length": 512,
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+ "d_model": 128,
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+ "distribution_output": "student_t",
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+ "do_mask_input": true,
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+ "dropout": 0.0,
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+ "ff_dropout": 0.0,
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+ "ffn_dim": 512,
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+ "head_dropout": 0.0,
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+ "init_std": 0.02,
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+ "loss": "mse",
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+ "mask_type": "random",
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+ "mask_value": 0,
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+ "model_type": "patchtst",
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+ "norm_eps": 1e-05,
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+ "norm_type": "batchnorm",
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+ "num_attention_heads": 4,
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+ "num_forecast_mask_patches": [
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+ 2
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+ ],
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+ "num_hidden_layers": 3,
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+ "num_input_channels": 3,
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+ "num_parallel_samples": 100,
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+ "num_targets": 1,
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+ "output_range": null,
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+ "patch_length": 12,
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+ "patch_stride": 12,
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+ "path_dropout": 0.0,
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+ "pooling_type": "mean",
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+ "positional_dropout": 0.0,
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+ "positional_encoding_type": "sincos",
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+ "pre_norm": true,
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+ "prediction_length": 24,
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+ "random_mask_ratio": 0.4,
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+ "scaling": "std",
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+ "share_embedding": true,
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+ "share_projection": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.37.2",
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+ "unmasked_channel_indices": null,
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+ "use_cls_token": true
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+ }
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+ "dropout": 0.0,
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+ "init_std": 0.02,
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+ "loss": "mse",
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+ "model_type": "patchtst",
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+ "norm_eps": 1e-05,
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+ "norm_type": "batchnorm",
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+ "num_attention_heads": 4,
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+ "num_forecast_mask_patches": [
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+ "num_hidden_layers": 3,
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+ "num_input_channels": 3,
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+ "num_parallel_samples": 100,
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+ "num_targets": 1,
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+ "output_range": null,
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+ "patch_length": 12,
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+ "path_dropout": 0.0,
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+ "pooling_type": "mean",
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+ "positional_dropout": 0.0,
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+ "pre_norm": true,
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+ "prediction_length": 24,
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+ "random_mask_ratio": 0.4,
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+ "scaling": "std",
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+ "share_embedding": true,
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+ "transformers_version": "4.37.2",
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+ "unmasked_channel_indices": null,
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+ "use_cls_token": true
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