From d019c4c8815cb782f0079957808bdfb0055e4901 Mon Sep 17 00:00:00 2001
From: Eleven Liu <jiejyunliu@gmail.com>
Date: Fri, 26 Apr 2024 11:36:47 +0800
Subject: [PATCH 1/2] Remove embed_vecs from Model and PLTModel. Ignore
 word_dict and network from save_parameters

---
 libmultilabel/nn/attentionxml.py | 3 ---
 libmultilabel/nn/model.py        | 5 +----
 libmultilabel/nn/nn_utils.py     | 1 -
 3 files changed, 1 insertion(+), 8 deletions(-)

diff --git a/libmultilabel/nn/attentionxml.py b/libmultilabel/nn/attentionxml.py
index c327cbea1..a5166baa6 100644
--- a/libmultilabel/nn/attentionxml.py
+++ b/libmultilabel/nn/attentionxml.py
@@ -381,7 +381,6 @@ def fit(self, datasets):
         model_1 = PLTModel(
             classes=self.classes,
             word_dict=self.word_dict,
-            embed_vecs=self.embed_vecs,
             network=network,
             log_path=self.log_path,
             learning_rate=self.learning_rate,
@@ -521,7 +520,6 @@ def __init__(
         self,
         classes,
         word_dict,
-        embed_vecs,
         network,
         loss_function="binary_cross_entropy_with_logits",
         log_path=None,
@@ -530,7 +528,6 @@ def __init__(
         super().__init__(
             classes=classes,
             word_dict=word_dict,
-            embed_vecs=embed_vecs,
             network=network,
             loss_function=loss_function,
             log_path=log_path,
diff --git a/libmultilabel/nn/model.py b/libmultilabel/nn/model.py
index 040d35fb8..bfe3344c3 100644
--- a/libmultilabel/nn/model.py
+++ b/libmultilabel/nn/model.py
@@ -182,7 +182,6 @@ class Model(MultiLabelModel):
     Args:
         classes (list): List of class names.
         word_dict (torchtext.vocab.Vocab): A vocab object which maps tokens to indices.
-        embed_vecs (torch.Tensor): The pre-trained word vectors of shape (vocab_size, embed_dim).
         network (nn.Module): Network (i.e., CAML, KimCNN, or XMLCNN).
         loss_function (str, optional): Loss function name (i.e., binary_cross_entropy_with_logits,
             cross_entropy). Defaults to 'binary_cross_entropy_with_logits'.
@@ -193,7 +192,6 @@ def __init__(
         self,
         classes,
         word_dict,
-        embed_vecs,
         network,
         loss_function="binary_cross_entropy_with_logits",
         log_path=None,
@@ -201,10 +199,9 @@ def __init__(
     ):
         super().__init__(num_classes=len(classes), log_path=log_path, **kwargs)
         self.save_hyperparameters(
-            ignore=["log_path"]
+            ignore=["log_path", "word_dict", "network"]
         )  # If log_path is saved, loading the checkpoint will cause an error since each experiment has unique log_path (result_dir).
         self.word_dict = word_dict
-        self.embed_vecs = embed_vecs
         self.classes = classes
         self.network = network
         self.configure_loss_function(loss_function)
diff --git a/libmultilabel/nn/nn_utils.py b/libmultilabel/nn/nn_utils.py
index b75ecb4f7..a4ac82c22 100644
--- a/libmultilabel/nn/nn_utils.py
+++ b/libmultilabel/nn/nn_utils.py
@@ -100,7 +100,6 @@ def init_model(
     model = Model(
         classes=classes,
         word_dict=word_dict,
-        embed_vecs=embed_vecs,
         network=network,
         log_path=log_path,
         learning_rate=learning_rate,

From 5e7148b3157aa2f67377d2acba7363f94a092d0d Mon Sep 17 00:00:00 2001
From: Eleven Liu <jiejyunliu@gmail.com>
Date: Fri, 26 Apr 2024 12:04:00 +0800
Subject: [PATCH 2/2] Cannot ignore word_dict and network for now.

---
 libmultilabel/nn/model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libmultilabel/nn/model.py b/libmultilabel/nn/model.py
index bfe3344c3..1f0ab95f8 100644
--- a/libmultilabel/nn/model.py
+++ b/libmultilabel/nn/model.py
@@ -199,7 +199,7 @@ def __init__(
     ):
         super().__init__(num_classes=len(classes), log_path=log_path, **kwargs)
         self.save_hyperparameters(
-            ignore=["log_path", "word_dict", "network"]
+            ignore=["log_path"]
         )  # If log_path is saved, loading the checkpoint will cause an error since each experiment has unique log_path (result_dir).
         self.word_dict = word_dict
         self.classes = classes