Fix model name in notebooks

boun-tabi-LMG · Feb 2, 2024 · c71f310 · c71f310
1 parent e454a04
commit c71f310
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Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/docs/tutorials/finetuning.ipynb b/docs/tutorials/finetuning.ipynb
@@ -53,7 +53,7 @@
     "task = \"summarization\"\n",
     "task_mode = '' # either '', '[NLU]', '[NLG]', '[S2S]'\n",
     "task_format=\"conditional_generation\"\n",
-    "model_name = \"boun-tabi-lmt/TURNA\"\n",
+    "model_name = \"boun-tabi-LMG/TURNA\"\n",
     "max_input_length = 764\n",
     "max_target_length = 128\n",
     "\n",
@@ -121,8 +121,8 @@
     "\n",
     "model_trainer = TrainerForConditionalGeneration(\n",
     "    model_name=model_name, task=task,\n",
-    "    optimizer_params=optimizer_params,\n",
     "    training_params=training_params,\n",
+    "    optimizer_params=optimizer_params,\n",
     "    model_save_path=\"turna_summarization_tr_news\",\n",
     "    max_input_length=max_input_length,\n",
     "    max_target_length=max_target_length, \n",

diff --git a/docs/tutorials/getting-started.ipynb b/docs/tutorials/getting-started.ipynb
@@ -42,7 +42,7 @@
     "dataset_name = \"tr_news\"\n",
     "task = \"summarization\"\n",
     "task_format = \"conditional_generation\"\n",
-    "model_name = \"boun-tabi-lmt/TURNA\"\n",
+    "model_name = \"boun-tabi-LMG/TURNA\"\n",
     "max_input_length = 764\n",
     "max_target_length = 128\n",
     "\n",
@@ -76,13 +76,13 @@
     "    'optimizer_type': 'adafactor',\n",
     "    'scheduler': False\n",
     "}\n",
+    "model_save_path = \"turna_summarization_tr_news\"\n",
     "\n",
     "# Finetuning the model\n",
-    "model_trainer = TrainerForConditionalGeneration(model_name, task, optimizer_params, training_params, \"turna_summarization_tr_news\", max_input_length, max_target_length, dataset_processor.dataset.postprocess_data)\n",
+    "model_trainer = TrainerForConditionalGeneration(model_name, task, training_params, optimizer_params, model_save_path, max_input_length, max_target_length, dataset_processor.dataset.postprocess_data)\n",
     "trainer, model = model_trainer.train_and_evaluate(train_dataset, eval_dataset, None)\n",
     "\n",
     "# Save the model\n",
-    "model_save_path = \"turna_summarization_tr_news\"\n",
     "model.save_pretrained(model_save_path)\n",
     "dataset_processor.tokenizer.save_pretrained(model_save_path)\n",
     "```\n"