updated examples for train API (kubeflow#2077)

Signed-off-by: shruti2522 <[email protected]>

Author: shruti2522

examples/pytorch/language-modeling/train_api_hf_dataset.ipynb

@@ -21,7 +21,7 @@
     "from kubeflow.storage_initializer.s3 import S3DatasetParams\n",
     "from kubeflow.storage_initializer.hugging_face import (\n",
     "    HuggingFaceModelParams,\n",
-    "    HuggingFaceTrainParams,\n",
+    "    HuggingFaceTrainerParams,\n",
     "    HfDatasetParams,\n",
     ")\n",
     "from kubeflow.storage_initializer.constants import INIT_CONTAINER_MOUNT_PATH\n",
@@ -71,7 +71,7 @@
     "    # it is assumed for text related tasks, you have 'text' column in the dataset.\n",
     "    # for more info on how dataset is loaded check load_and_preprocess_data function in sdk/python/kubeflow/trainer/hf_llm_training.py\n",
     "    dataset_provider_parameters=HfDatasetParams(repo_id=\"imdatta0/ultrachat_1k\"),\n",
-    "    train_parameters=HuggingFaceTrainParams(\n",
+    "    trainer_parameters=HuggingFaceTrainerParams(\n",
     "        lora_config=LoraConfig(\n",
     "            r=8,\n",
     "            lora_alpha=8,\n",

examples/pytorch/language-modeling/train_api_s3_dataset.ipynb

@@ -20,7 +20,7 @@
     "from kubeflow.training.api.training_client import TrainingClient\n",
     "from kubeflow.storage_initializer.hugging_face import (\n",
     "    HuggingFaceModelParams,\n",
-    "    HuggingFaceTrainParams,\n",
+    "    HuggingFaceTrainerParams,\n",
     "    HfDatasetParams,\n",
     ")\n",
     "from kubeflow.storage_initializer.constants import INIT_CONTAINER_MOUNT_PATH\n",
@@ -90,7 +90,7 @@
     "            \"secret_key\": s3_secret_key,\n",
     "        }\n",
     "    ),\n",
-    "    train_parameters=HuggingFaceTrainParams(\n",
+    "    trainer_parameters=HuggingFaceTrainerParams(\n",
     "        lora_config=LoraConfig(\n",
     "            r=8,\n",
     "            lora_alpha=8,\n",

examples/pytorch/text-classification/Fine-Tune-BERT-LLM.ipynb

@@ -613,7 +613,7 @@
     "from kubeflow.training import TrainingClient\n",
     "from kubeflow.storage_initializer.hugging_face import (\n",
     "    HuggingFaceModelParams,\n",
-    "    HuggingFaceTrainParams,\n",
+    "    HuggingFaceTrainerParams,\n",
     "    HfDatasetParams,\n",
     ")\n",
     "\n",
@@ -651,7 +651,7 @@
     "        split=\"train[:3000]\",\n",
     "    ),\n",
     "    # Specify HuggingFace Trainer parameters. In this example, we will skip evaluation and model checkpoints.\n",
-    "    train_parameters=HuggingFaceTrainParams(\n",
+    "    trainer_parameters=HuggingFaceTrainerParams(\n",
     "        training_parameters=transformers.TrainingArguments(\n",
     "            output_dir=\"test_trainer\",\n",
     "            save_strategy=\"no\",\n",