diff --git a/.gitignore b/.gitignore index fc9826b612048b0f6f0d31ab177aa1a95c20c7e2..ac5aa4b67363e07a02541935c18acf2a302b4141 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ Utils/gold Linker/__pycache__ Configuration/__pycache__ __pycache__ +TensorBoard diff --git a/Linker/Linker.py b/Linker/Linker.py index 195f8e084e7ab2cbfdd33bed459cd1911bba1cdc..c4069215ed55374c4b035e9ad69553a13a7510a8 100644 --- a/Linker/Linker.py +++ b/Linker/Linker.py @@ -81,7 +81,7 @@ class Linker(Module): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - def __preprocess_data(self, batch_size, df_axiom_links, validation_rate=0.0): + def __preprocess_data(self, batch_size, df_axiom_links, validation_rate=0.1): r""" Args: batch_size : int @@ -106,7 +106,7 @@ class Linker(Module): dataset = TensorDataset(atoms_batch_tokenized, atoms_polarity_batch, truth_links_batch, sentences_tokens, sentences_mask) - if validation_rate > 0: + if validation_rate > 0.0: train_size = int(0.9 * len(dataset)) val_size = len(dataset) - train_size train_dataset, val_dataset = random_split(dataset, [train_size, val_size]) diff --git a/train.py b/train.py index abf0b8072e4cbe9e4ff9aab037bcbb472cd95076..77e89b21a82e4ffd8483ed8295c03b387bdedd4b 100644 --- a/train.py +++ b/train.py @@ -6,12 +6,9 @@ from utils import read_csv_pgbar torch.cuda.empty_cache() batch_size = int(Configuration.modelTrainingConfig['batch_size']) -nb_sentences = batch_size * 20 +nb_sentences = batch_size * 400 epochs = int(Configuration.modelTrainingConfig['epoch']) -file_path_axiom_links = 'Datasets/goldANDsilver_dataset_links.csv' -nb_sentences = batch_size * 20 -epochs = int(Configuration.modelTrainingConfig['epoch']) file_path_axiom_links = 'Datasets/gold_dataset_links.csv' df_axiom_links = read_csv_pgbar(file_path_axiom_links, nb_sentences) @@ -21,10 +18,8 @@ supertagger = SuperTagger() supertagger.load_weights("models/flaubert_super_98%_V2_50e.pt") -sents_tokenized, sents_mask = supertagger.sent_tokenizer.fit_transform_tensors(sentences_batch) - print("Linker") linker = Linker(supertagger) linker = linker.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) print("Linker Training") -linker.train_linker(df_axiom_links, sents_tokenized, sents_mask, validation_rate=0.1, epochs=epochs, batch_size=batch_size, checkpoint=False, validate=True) +linker.train_linker(df_axiom_links, batch_size=batch_size, checkpoint=False, tensorboard=True)