From 1038c71200cd06f1d9ec179f2a4c7bc3b9d47c31 Mon Sep 17 00:00:00 2001 From: "Peter Droogmans (attiks)" Date: Wed, 3 May 2023 12:08:14 +0200 Subject: [PATCH] chore: Typos in readme --- README.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 92a0118..742f139 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ trainer = ClassifierTrainer() trained_model = trainer.train_classification_model( - train_df: pd.DataFrame: Training DataFrame with 2 columns: ["excerpt", "target_classification"], - val_df: pd.DataFrame: Validation DataFrame with 2 columns: ["excerpt", "target_classification"], - - architecture_setup: str: one of ["base_architecture", "multiabel_architecture"], default='multiabel_architecture', + - architecture_setup: str: one of ["base_architecture", "multilabel_architecture"], default='multilabel_architecture', - backbone_name: str: Backbone Name in HuggingFace, default='nlp-thedeep/humbert', - results_dir: str: Results directory, default='results', - enable_checkpointing: bool: Whether or not to save model checkpoints while training, default=True, @@ -68,8 +68,8 @@ and treats all the categories with the same encoded embedding. - **Our proposed Architecture**: our approach shares N-1 Transformer layers of the LLM across all the categories, while the Nth layer is replicated K times, K being the number of classification tasks. A linear classification head is then defined on top of each Transformer -sub-layer, predicting only the labels belonging to the corresponding task. -The resulting architecture is a combination of the shared parameters and specific components and follows +sub-layer, predicting only the labels belonging to the corresponding task. +The resulting architecture is a combination of the shared parameters and specific components and follows the relations and hierarchy of the analytical framework's label space.

@@ -85,7 +85,7 @@ import pandas as pd trainer = ClassifierTrainer() train_df = pd.read_csv(TRAINING DATAFRAME PATH) -val_df = pd.red_csv(VALIDATION DATAFRAME PATH) +val_df = pd.read_csv(VALIDATION DATAFRAME PATH) trained_model = trainer.train_classification_model(train_df, val_df) ``` @@ -105,4 +105,9 @@ test_set_predictions = trainer.generate_test_predictions(test_df.excerpt.tolist( **Generate test set results** ``` test_set_results = trainer.generate_test_results(test_df) -``` \ No newline at end of file +``` + +**multilabel_architecture** + +When using the `multilabel_architecture` make sure you have a nested hierarchy +of exactly 3 levels separated with `->`