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| label = conll04_eval | |
| model_type = spert | |
| model_path = data/models/ade | |
| tokenizer_path = data/models/ade | |
| dataset_path = data/datasets/ade/ade_split_0_test.json | |
| types_path = data/datasets/ade/ade_types.json | |
| eval_batch_size = 1 | |
| rel_filter_threshold = 0.4 | |
| size_embedding = 25 | |
| prop_drop = 0.1 |
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| _column_definition = [ | |
| ('id', DataTypes.REAL_VALUED, InputTypes.ID), | |
| ('hours_from_start', DataTypes.REAL_VALUED, InputTypes.TIME), | |
| ('power_usage', DataTypes.REAL_VALUED, InputTypes.TARGET), | |
| ('hour', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT), | |
| ('day_of_week', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT), | |
| ('hours_from_start', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT), | |
| ('categorical_id', DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT), | |
| ] |
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| model = model_class.from_pretrained(self.args.model_path, | |
| config=config, | |
| # SpERT model parameters | |
| cls_token=self._tokenizer.convert_tokens_to_ids('[CLS]'), | |
| relation_types=input_reader.relation_type_count - 1, | |
| entity_types=input_reader.entity_type_count, | |
| max_pairs=self.args.max_pairs, | |
| prop_drop=self.args.prop_drop, | |
| size_embedding=self.args.size_embedding, | |
| freeze_transformer=self.args.freeze_transformer) |
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| import torch | |
| class TransformerXCBasic(torch.nn.Module): | |
| """ Transformer model """ | |
| def __init__(self, n_time_series, out_seq_len, device, d_model=128, dropout=.5, n_head=8): | |
| super(TransformerXCBasic, self).__init__() | |
| self.input_dim = n_time_series | |
| self.n_head = n_head |
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| wandb_version: 1 | |
| GCS: | |
| desc: null | |
| value: true | |
| _wandb: | |
| desc: null | |
| value: | |
| cli_version: 0.10.17 | |
| framework: torch |
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| { | |
| "model_name": "Crossformer", | |
| "use_decoder": true, | |
| "model_type": "PyTorch", | |
| "model_params": { | |
| "n_time_series": 4, | |
| "forecast_history":6, | |
| "forecast_length": 4, | |
| "seg_len": 6 | |
| }, |
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