Create hparams.yaml
Browse files- hparams.yaml +38 -0
hparams.yaml
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activations: Tanh
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batch_size: 4
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class_identifier: unified_metric
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cross_entropy_weights: null
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dropout: 0.1
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encoder_learning_rate: 1.0e-06
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encoder_model: XLM-RoBERTa
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error_labels:
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- minor
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- major
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final_activation: null
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hidden_sizes:
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- 3072
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- 1024
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input_segments:
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- mt
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- src
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- ref
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keep_embeddings_frozen: true
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layer: mix
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layer_norm: false
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layer_transformation: sparsemax
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layerwise_decay: 0.95
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learning_rate: 1.5e-05
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load_pretrained_weights: true
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loss: mse
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loss_lambda: 0.65
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nr_frozen_epochs: 0.3
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optimizer: AdamW
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pool: avg
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pretrained_model: xlm-roberta-large
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sent_layer: mix
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train_data:
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- data/wmt-human-eval/metric_dataset.csv
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validation_data: 0.1
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warmup_steps: 0
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word_layer: 24
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word_level_training: false
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