From 3887ae5cf6153cf44b543b20965968a3a8505882 Mon Sep 17 00:00:00 2001
From: Emmanuel Dellandrea <emmanuel.dellandrea@ec-lyon.fr>
Date: Tue, 25 Feb 2025 06:44:50 +0100
Subject: [PATCH] Update Subject_7_LLM.ipynb
---
Practical_sessions/Session_7/Subject_7_LLM.ipynb | 15 ++++-----------
1 file changed, 4 insertions(+), 11 deletions(-)
diff --git a/Practical_sessions/Session_7/Subject_7_LLM.ipynb b/Practical_sessions/Session_7/Subject_7_LLM.ipynb
index 7101e1e..ac18981 100644
--- a/Practical_sessions/Session_7/Subject_7_LLM.ipynb
+++ b/Practical_sessions/Session_7/Subject_7_LLM.ipynb
@@ -703,7 +703,7 @@
"source": [
"model_save_path = \"custom_bert_model.pth\"\n",
"\n",
- "torch.save(model.state_dict(), model_save_path)\n"
+ "torch.save(model.state_dict(), model_save_path)"
]
},
{
@@ -718,8 +718,7 @@
"\n",
"loaded_model.load_state_dict(torch.load(model_save_path))\n",
"\n",
- "loaded_model.to(device)\n",
- "\n"
+ "loaded_model.to(device)\n"
]
},
{
@@ -777,11 +776,9 @@
" nn.Linear(config.hidden_size, 128),\n",
" nn.ReLU(),\n",
" nn.Dropout(0.1),\n",
- " nn.Linear(128, config.num_labels) # Binary classification\n",
+ " nn.Linear(128, config.num_labels) \n",
" )\n",
- " self.init_weights()\n",
"\n",
- " # Freeze DistilBERT backbone if specified\n",
" if freeze_backbone:\n",
" for param in self.distilbert.parameters():\n",
" param.requires_grad = False\n",
@@ -789,8 +786,7 @@
" def forward(self, input_ids, attention_mask=None, labels=None):\n",
" outputs = self.distilbert(input_ids=input_ids, attention_mask=attention_mask)\n",
" logits = self.classifier(outputs.last_hidden_state[:, 0, :]) # Use [CLS] token output\n",
- " return logits\n",
- "\n"
+ " return logits\n"
]
},
{
@@ -812,15 +808,12 @@
"source": [
"from transformers import DistilBertTokenizer\n",
"\n",
- "# Initialize the configuration with custom attributes\n",
"config = AutoConfig.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n",
"config.architectures = [\"CustomDistilBERTModel\"]\n",
"\n",
- "# Initialize the model and tokenizer\n",
"model = CustomDistilBERTModel(config)\n",
"tokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
"\n",
- "# Save locally\n",
"model.save_pretrained(\"custom_distilbert_model\")\n",
"tokenizer.save_pretrained(\"custom_distilbert_model\")\n",
"\n",
--
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