'+example[\"sentence\"]+'', max_length=512, truncation=True)\n",
+ " output_dict['labels'] = ClassLabels.str2int(example['tag'])\n",
+ " return output_dict\n",
+ "#tokenized_datasets = dataset.map(preprocess_function, batched=False, remove_columns=dataset[\"train\"].column_names)\n",
+ "tokenized_datasets = dataset.map(preprocess_function, batched=False)\n",
+ "tokenized_datasets = tokenized_datasets.cast_column(\"labels\", ClassLabel(names=labels))\n",
+ "\n",
+ "#데이터셋 토크나이징 확인\n",
+ "random_id = 1\n",
+ "print(\"Input IDS:\", tokenized_datasets[\"train\"][random_id][\"input_ids\"])\n",
+ "print(\"Labels:\", tokenized_datasets[\"train\"][random_id][\"labels\"])\n",
+ "tokenized_datasets.save_to_disk('data/tok')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 161,
+ "referenced_widgets": [
+ "84390e707f044ae59592dffd9ef147f1",
+ "af92d70cbf7a457b892d30dc5d1b18e7",
+ "db7d8503dfc448fbb3b324c03bbc78e9",
+ "726f414a945f4c9eae690c4aea644a1c",
+ "964264a84b98481b90439e206d684d24",
+ "f425494e4dc044bb8245fb5b46bcf704",
+ "923b81c76f3941bf93bfe695f1ca8f13",
+ "6eb7ff7f30b44c65959faecb2d048832",
+ "46c05da7be3a4cd8b7f715f10ace9d33",
+ "9bd9f465b22b4b79a59dd372c16623f1",
+ "e10f21a958ee4262abc6b0a58289aa8c"
+ ]
+ },
+ "id": "SH9q78NfXyDe",
+ "outputId": "59410fa9-d38f-4d52-fcc0-86c8f98746fc"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at kkmkorea/checkpoint25000 and are newly initialized: ['pooler.dense.bias', 'classifier.bias', 'pooler.dense.weight', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#KorSciDeBERTa 모델 로딩\n",
+ "from transformers import AutoModelForSequenceClassification\n",
+ "\n",
+ "num_labels = len(labels)\n",
+ "def model_init():\n",
+ " #return AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=6)\n",
+ " #return AutoModelForSequenceClassification.from_pretrained(model_repository, num_labels=num_labels, hidden_dropout_prob=0.3, attention_probs_dropout_prob=0.25)\n",
+ " return AutoModelForSequenceClassification.from_pretrained(model_repository, num_labels=num_labels, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1)\n",
+ "model = model_init()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gS-NPpVQw60q"
+ },
+ "source": [
+ "Next, we load a data collator. A [data collator](https://huggingface.co/docs/transformers/main_classes/data_collator) is responsible for making sure each batch is correctly padded during training, which should happen dynamically since training samples are reshuffled before each epoch."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "-qKPqqN5z-Og"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test: Counter({4: 221, 1: 123, 5: 111, 0: 93, 3: 76, 6: 48, 2: 1})\n"
+ ]
+ }
+ ],
+ "source": [
+ "#DataCollator 확인\n",
+ "from transformers import DataCollatorWithPadding\n",
+ "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
+ "from collections import Counter\n",
+ "print(\"Test:\", Counter(tokenized_datasets[\"test\"][\"labels\"]))\n",
+ "\n",
+ "#정확도 척도\n",
+ "from datasets import load_metric\n",
+ "accuracy = load_metric(\"accuracy\")\n",
+ "\n",
+ "import numpy as np\n",
+ "def compute_metrics(pred):\n",
+ " pred_logits = pred.predictions\n",
+ " pred_classes = np.argmax(pred_logits, axis=-1)\n",
+ " labels = np.asarray(pred.label_ids)\n",
+ " acc = accuracy.compute(predictions=pred_classes, references=labels)\n",
+ " return {\"accuracy\": acc[\"accuracy\"]}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nAuDv6KFrT6r"
+ },
+ "source": [
+ "The validation and test data sets are as balanced as they can be, so we can safely use accuracy here!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mwDmOVsbouee"
+ },
+ "source": [
+ " Let's load the [accuracy metric](https://huggingface.co/metrics/accuracy) via the datasets library."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "znHf9L5pzhJI"
+ },
+ "source": [
+ "Next, we define the `compute_metrics` which will be applied to the predicted outputs of the model which is of type [`EvalPrediction`](https://huggingface.co/docs/transformers/main/en/internal/trainer_utils#transformers.EvalPrediction) and therefore exposes the model's predictions and the gold labels.\n",
+ "We compute the predicted label class by taking the `argmax` of the model's prediction before passing it alongside the gold labels to the accuracy metric."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "efyAC3zU0S0T"
+ },
+ "source": [
+ "Great, now all components required for training are ready and all that's left to do is to define the hyper-parameters of the `Trainer`. We need to make sure that the model checkpoints are uploaded to the Hugging Face Hub during training. By setting `push_to_hub=True`, this is done automatically at every `save_steps` via the convenient [`push_to_hub`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.Trainer.push_to_hub) method.\n",
+ "\n",
+ "Besides, we define some standard hyper-parameters such as learning rate, warm-up steps and training epochs. We will log the loss every 500 steps and run evaluation every 5000 steps."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#training_args 설정\n",
+ "#다음 에러 발생시 output_dir을 변경하여 다시 시도\n",
+ "#MlflowException: Changing param values is not allowed. Param with key=\n",
+ "\n",
+ "import gc\n",
+ "gc.collect()\n",
+ "from transformers import TrainingArguments\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"korscideberta_abs1\",\n",
+ " num_train_epochs=4, \n",
+ " #learning_rate=5e-5,\n",
+ " learning_rate=1.5e-5,\n",
+ " per_device_train_batch_size=16,\n",
+ " per_device_eval_batch_size=8,\n",
+ " weight_decay=0.01, \n",
+ " fp16=True, # Use mixed precision\n",
+ " fp16_opt_level=\"01\", # mixed precision mode\n",
+ " warmup_steps=500,\n",
+ " logging_steps=200,\n",
+ " save_steps=2000,\n",
+ " eval_steps=500,\n",
+ " push_to_hub=True,\n",
+ " evaluation_strategy=\"steps\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "id": "Js3ZFkEDXyDc"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Cloning https://huggingface.co/kkmkorea/deberta_sent4455 into local empty directory.\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+ " warnings.warn(\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py:30: UserWarning: \n",
+ " There is an imbalance between your GPUs. You may want to exclude GPU 1 which\n",
+ " has less than 75% of the memory or cores of GPU 0. You can do so by setting\n",
+ " the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES\n",
+ " environment variable.\n",
+ " warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [1516/1516 07:09, Epoch 4/4]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Accuracy | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 300 | \n",
+ " 1.469400 | \n",
+ " 0.872505 | \n",
+ " 0.702823 | \n",
+ "
\n",
+ " \n",
+ " 600 | \n",
+ " 0.620200 | \n",
+ " 0.629578 | \n",
+ " 0.781575 | \n",
+ "
\n",
+ " \n",
+ " 900 | \n",
+ " 0.451200 | \n",
+ " 0.645206 | \n",
+ " 0.781575 | \n",
+ "
\n",
+ " \n",
+ " 1200 | \n",
+ " 0.356700 | \n",
+ " 0.622896 | \n",
+ " 0.803863 | \n",
+ "
\n",
+ " \n",
+ " 1500 | \n",
+ " 0.286400 | \n",
+ " 0.608902 | \n",
+ " 0.812779 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py:30: UserWarning: \n",
+ " There is an imbalance between your GPUs. You may want to exclude GPU 1 which\n",
+ " has less than 75% of the memory or cores of GPU 0. You can do so by setting\n",
+ " the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES\n",
+ " environment variable.\n",
+ " warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py:30: UserWarning: \n",
+ " There is an imbalance between your GPUs. You may want to exclude GPU 1 which\n",
+ " has less than 75% of the memory or cores of GPU 0. You can do so by setting\n",
+ " the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES\n",
+ " environment variable.\n",
+ " warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py:30: UserWarning: \n",
+ " There is an imbalance between your GPUs. You may want to exclude GPU 1 which\n",
+ " has less than 75% of the memory or cores of GPU 0. You can do so by setting\n",
+ " the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES\n",
+ " environment variable.\n",
+ " warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py:30: UserWarning: \n",
+ " There is an imbalance between your GPUs. You may want to exclude GPU 1 which\n",
+ " has less than 75% of the memory or cores of GPU 0. You can do so by setting\n",
+ " the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES\n",
+ " environment variable.\n",
+ " warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2b4ded57c5984466b966776b22356ffe",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Upload file pytorch_model.bin: 0%| | 1.00/704M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b2e31bd4766647d8b4e83bb9e9f36620",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Upload file runs/Aug09_15-49-35_deeplatform/events.out.tfevents.1691563784.deeplatform.3790494.1: 0%| …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "To https://huggingface.co/kkmkorea/deberta_sent4455\n",
+ " 08a3042..30123bb main -> main\n",
+ "\n",
+ "To https://huggingface.co/kkmkorea/deberta_sent4455\n",
+ " 30123bb..231b8b6 main -> main\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'https://huggingface.co/kkmkorea/deberta_sent4455/commit/30123bb98433c0d175707d169d1ea7a2a93ecdd9'"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Trainer 설정 후 학습 시작\n",
+ "import gc\n",
+ "gc.collect()\n",
+ "\n",
+ "from transformers import Trainer\n",
+ "trainer = Trainer(\n",
+ " args=training_args,\n",
+ " compute_metrics=compute_metrics,\n",
+ " model=model,\n",
+ " #tokenizer=tokenizer, #에러 유발: TypeError: save_vocabulary() got an unexpected keyword argument 'filename_prefix'\n",
+ " data_collator=data_collator,\n",
+ " train_dataset=tokenized_datasets[\"train\"],\n",
+ " eval_dataset=tokenized_datasets[\"test\"]\n",
+ ")\n",
+ "train_metrics = trainer.train().metrics\n",
+ "trainer.save_metrics(\"train\", train_metrics)\n",
+ "trainer.push_to_hub()\n",
+ "#### 파인튜닝 및 모델 업로드 완료"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K1SWtSpJ1WpZ"
+ },
+ "source": [
+ "Putting it all together, we can finally instantiate the Trainer by passing all required components. We'll use the `\"validation\"` split as the held-out dataset during training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#하이퍼파라미터 최적화 시험 학습(현재 동작하지 않음)\n",
+ "'''\n",
+ "def wandb_hp_space(trial):\n",
+ " return {\n",
+ " \"method\": \"random\",\n",
+ " \"metric\": {\"name\": \"objective\", \"goal\": \"minimize\"},\n",
+ " \"parameters\": {\n",
+ " \"learning_rate\": {\"distribution\": \"uniform\", \"min\": 1e-6, \"max\": 1e-4},\n",
+ " \"per_device_train_batch_size\": {\"values\": [16, 32, 64, 128]},\n",
+ " },\n",
+ " }\n",
+ "'''\n",
+ "def my_hp_space_optuna(trial):\n",
+ " return {\n",
+ " \"learning_rate\": trial.suggest_float(\"learning_rate\", 2e-6, 2e-4, log=True),\n",
+ " \"warmup_steps\": trial.suggest_float(\"warmup_steps\", 0., 0.9, step=0.3),\n",
+ " \"weight_decay\": trial.suggest_float(\"weight_decay\", 1e-6, 1e-1)\n",
+ " }\n",
+ "\n",
+ "def my_hp_space_ray(trial):\n",
+ " from ray import tune\n",
+ " return {\n",
+ " \"learning_rate\": tune.loguniform(2e-6, 1e-2),\n",
+ " \"num_train_epochs\": tune.choice(range(1, 1)),\n",
+ " \"seed\": tune.choice(range(1, 42)),\n",
+ " \"per_device_train_batch_size\": tune.choice([8]),\n",
+ " \"weight_decay\": tune.loguniform(1e-6,1e-1)\n",
+ " }\n",
+ "\n",
+ "def my_objective(metrics):\n",
+ " #return metrics[\"eval_f1\"]\n",
+ " return metrics[\"accuracy\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4yWtH0xvXyDd",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "trainer_hps = Trainer(\n",
+ " model_init = model_init,\n",
+ " args=training_args,\n",
+ " compute_metrics=compute_metrics,\n",
+ " #tokenizer=tokenizer, #에러 유발: TypeError: save_vocabulary() got an unexpected keyword argument 'filename_prefix'\n",
+ " data_collator=data_collator,\n",
+ " #train_dataset=tokenized_datasets[\"train\"],\n",
+ " train_dataset=tokenized_datasets[\"train\"].select(range(2000)),\n",
+ " eval_dataset=tokenized_datasets[\"dev\"]\n",
+ ")\n",
+ "\n",
+ "import wandb\n",
+ "import os\n",
+ "wandb.finish()\n",
+ "os.environ[\"WANDB__SERVICE_WAIT\"] = \"10\"\n",
+ "os.environ[\"WANDB_MODE\"] = \"offline\"\n",
+ "#wandb.init(settings=wandb.Settings(_service_wait=10))\n",
+ "wandb.init(settings=wandb.Settings(_service_wait=10), reinit=True, group='test')\n",
+ "#wandb.init()\n",
+ "\n",
+ "from transformers.integrations import MLflowCallback\n",
+ "#trainer_hps.callback_handler.remove_callback(MLflowCallback)\n",
+ "\n",
+ "sa = trainer_hps.hyperparameter_search(\n",
+ " direction=\"maximize\", \n",
+ " n_trials=5,\n",
+ " #hp_space=my_hp_space_ray, \n",
+ " hp_space=my_hp_space_optuna, \n",
+ " #compute_objective=my_objective,\n",
+ " #compute_metrics=compute_metrics #error\n",
+ ")\n",
+ "print(str(sa))\n",
+ "trainer.push_to_hub()\n",
+ "#하이퍼파라미터 최적화 시험 학습 완료(현재 동작하지 않음)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3LOkqNzi1tJ6"
+ },
+ "source": [
+ "The Trainer is ready to go 🚀 You can start training by calling `trainer.train()`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CaLVY2nfmcgS"
+ },
+ "source": [
+ "Cool, we see that the model seems to learn something! Training loss and validation loss is going down and the accuracy also ends up being well over random chance (20%). Interestingly, we see accuracy of around **58.6 %** already after 5000 steps which doesn't improve that much anymore afterward. Choosing a bigger model or training for longer would have probably given better results here, but that's good enough for our hypothetical use case!\n",
+ "\n",
+ "Alright, finally let's upload the model checkpoint to the Hub."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FDg4g1vxvv4t"
+ },
+ "source": [
+ "## **Evaluate / Analyse the model**\n",
+ "\n",
+ "Now that we have fine-tuned the model we need to be very careful about analyzing its performance. It's usually not enough to just look at basic metrics defining the quality of a model purely on a metric, such as *accuracy*.\n",
+ "The better approach is to find a metric that best describes the actual use case of the model.\n",
+ "\n",
+ "Let's dive into evaluating the model 🤿."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wvefwWDdwkl4"
+ },
+ "source": [
+ "The model has been uploaded to the Hub under [`deberta_v3_amazon_reviews`](https://huggingface.co/patrickvonplaten/deberta_v3_amazon_reviews) after training, so in a first step, let's download it from there again. If this notebook is run all at once the following cell will simply load the model from the cache."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
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+ ]
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+ ]
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+ "text": [
+ "/media/hdd1/kkm/deberta/HF/deberta_sent4455 is already a clone of https://huggingface.co/kkmkorea/deberta_sent4455. Make sure you pull the latest changes with `repo.git_pull()`.\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/data_parallel.py:30: UserWarning: \n",
+ " There is an imbalance between your GPUs. You may want to exclude GPU 1 which\n",
+ " has less than 75% of the memory or cores of GPU 0. You can do so by setting\n",
+ " the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES\n",
+ " environment variable.\n",
+ " warnings.warn(imbalance_warn.format(device_ids[min_pos], device_ids[max_pos]))\n",
+ "/home/kgm86/anaconda3/envs/deberta/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
+ " warnings.warn('Was asked to gather along dimension 0, but all '\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
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+ {
+ "data": {
+ "text/plain": [
+ "{'test_loss': 0.6081676483154297,\n",
+ " 'test_accuracy': 0.812778603268945,\n",
+ " 'test_runtime': 4.4393,\n",
+ " 'test_samples_per_second': 151.601,\n",
+ " 'test_steps_per_second': 9.686}"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Huggingface에 업로드한 파인튜닝 모델 및 성능 확인\n",
+ "from transformers import AutoModelForSequenceClassification\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(\"kkmkorea/deberta_sent4455\")\n",
+ "#model = AutoModelForSequenceClassification.from_pretrained(\"patrickvonplaten/deberta_v3_amazon_reviews\")\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " args=training_args,\n",
+ " compute_metrics=compute_metrics,\n",
+ " model=model,\n",
+ " #tokenizer=tokenizer,\n",
+ " data_collator=data_collator,\n",
+ ")\n",
+ "\n",
+ "prediction_metrics = trainer.predict(tokenized_datasets[\"test\"]).metrics\n",
+ "#prediction_metrics = trainer.predict(tokenized_datasets[\"validation\"]).metrics\n",
+ "prediction_metrics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "r8HWjIBIxMlr"
+ },
+ "source": [
+ "The Trainer is not only an excellent class to train a model, but also to evaluate a model on a dataset. Let's instantiate the trainer with the same instances and functions as before, but this time there is no need to pass a training dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tj2jh-1uyOG0"
+ },
+ "source": [
+ "We use the Trainer's [`predict`]( ) function to evaluate the model on the test dataset on the same metric "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tG5ejdE8LbSx"
+ },
+ "source": [
+ "And let's run the evaluation again with our new metric computation which is better suited for our use case."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lDmOHbYVPIKe"
+ },
+ "source": [
+ "It does seem to generalize quite well to real-world data 🔥"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JAmfpV6GRzHk",
+ "tags": []
+ },
+ "source": [
+ "## Optimization\n",
+ "\n",
+ "As soon as you think the model's performance is good enough for production it's all about making the model as memory efficient and fast as possible.\n",
+ "\n",
+ "There are some obvious solutions to this like choosing the best suited accelerated hardware, *e.g.* better GPUs, making sure no gradients are computed during the forward pass, or lowering the precision, *e.g.* to float16. \n",
+ "\n",
+ "More advanced optimization methods include using open-source accelerator libraries such as [ONNX Runtime](https://onnxruntime.ai/index.html), [quantization](https://pytorch.org/docs/stable/quantization.html), and inference servers like [Triton](https://developer.nvidia.com/nvidia-triton-inference-server).\n",
+ "\n",
+ "At Hugging Face, we have been working a lot to facilitate the optimization of models, especially with our open-source [Optimum library](https://huggingface.co/hardware). Optimum makes it extremely simple to optimize most 🤗 Transformers models.\n",
+ "\n",
+ "If you're looking for **highly optimized** solutions which don't require any technical knowledge, you might be interested in one of Hugging Face's paid inference services:\n",
+ "\n",
+ "- [Inference API](https://huggingface.co/inference-api)\n",
+ "- [Infinity](https://huggingface.co/infinity)"
+ ]
+ }
+ ],
+ "metadata": {
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+ "name": "ODSC 2022 - 🤗 Transformers Workshop",
+ "provenance": [],
+ "toc_visible": true
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