| from dataclasses import dataclass, field |
|
|
| |
|
|
| @dataclass |
| class HPARAMS: |
| |
| seed: int = 42 |
| url: str = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz" |
|
|
| scheduler_hparams: dict = field(default_factory=lambda: { |
| "factor": 0.5, |
| "patience": 2, |
| "mode": "min" |
| }) |
| |
| |
| max_seq_len_gru: int = 256 |
| batch_size_gru: int = 128 |
| vocab_size: int = 10000 |
| glove_txt_path: str = ("/mnt/e/ML_Files/PreTrained_Models/GloVe_Embeddings/glove.2024.wikigiga" |
| ".200d/wiki_giga_2024_200_MFT20_vectors_seed_2024_alpha_0.75_eta_0.05_combined.txt") |
| |
| model_hparams_gru: dict = field(default_factory=lambda: { |
| "embedding_dim": 128, |
| "hidden_size": 128, |
| "dropout": 0.12, |
| "num_gru_layers": 2, |
| "use_dense": False, |
| "dense_dropout_prob": 0.1 |
| }) |
|
|
| optimizer_hparams_gru: dict = field(default_factory=lambda: { |
| "lr": 1e-3, |
| "weight_decay": 5e-4 |
| }) |
|
|
| trainer_hparams_gru: dict = field(default_factory=lambda: { |
| "n_epochs": 20, |
| "use_early_stopping" : True, |
| "early_stopping_patience" : 3, |
| "scheduler_monitor" : "val_loss", |
| "restore_best_model": True, |
| }) |
| |
| |
| max_seq_len_transformer:int = 288 |
| |
| transformer_path: str = "/mnt/d/ML-Files/PreTrained-Models/HuggingFace/Transformer-Encoder/microsoft_deberta-v3-base/" |
| batch_size_transformer: int = 32 |
| transformer_fc_dropout: float = 0.1 |
| |
| optimizer_hparams_transformer: dict = field(default_factory=lambda: { |
| "lr": 3e-5, |
| "weight_decay": 5e-4 |
| }) |
|
|
| trainer_hparams_transformer: dict = field(default_factory=lambda: { |
| "n_epochs": 5, |
| "use_early_stopping" : True, |
| "early_stopping_patience" : 2, |
| "scheduler_monitor" : "val_loss", |
| "restore_best_model": False, |
| }) |
| |
| hp = HPARAMS() |
