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Changed dataset to IMDB #5

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2690294
Changed dataset to IMDB
Feb 25, 2022
2a43844
Updated markdown cells for IMDB
Feb 25, 2022
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17 changes: 9 additions & 8 deletions finetune-distilbert.ipynb
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Expand Up @@ -13,7 +13,7 @@
"source": [
"## Introduction\n",
"\n",
"In this demo, you will use the Hugging Faces `transformers` and `datasets` library with Amazon SageMaker to fine-tune a pre-trained transformer on binary text classification. In particular, you will use the pre-trained DistilBERT model with the Amazon Reviews Polarity dataset.\n",
"In this demo, you will use the Hugging Faces `transformers` and `datasets` library with Amazon SageMaker to fine-tune a pre-trained transformer on binary text classification. In particular, you will use the pre-trained DistilBERT model with the IMDB dataset.\n",
"You will then deploy the resulting model for inference using SageMaker Endpoint.\n",
"\n",
"### The model\n",
Expand All @@ -22,7 +22,7 @@
"\n",
"### The data\n",
"\n",
"The [Amazon Reviews Polarity dataset](https://github.com/dsk78/Text-Classification---Amazon-Reviews-Polarity) consists of reviews from Amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. It's avalaible under the [`amazon_polarity`](https://huggingface.co/datasets/amazon_polarity) dataset on [Hugging Face](https://huggingface.co/)."
"The [IMDB dataset](https://github.com/huggingface/datasets/tree/master/datasets/imdb) consists of movie reviews that can be use for binary sentiment classification containing substantially more data than previous benchmark datasets. It provides a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. It's avalaible under the [`imdb`](https://huggingface.co/datasets/imdb) dataset on [Hugging Face](https://huggingface.co/)."
]
},
{
Expand Down Expand Up @@ -148,7 +148,7 @@
"# Data preparation\n",
"\n",
"The data preparation is straightforward as you're using the `datasets` library to download and preprocess the `\n",
"amazon_polarity` dataset directly from Hugging face. After preprocessing, the dataset will be uploaded to our `sagemaker_session_bucket` to be used within our training job."
"imdb` dataset directly from Hugging face. After preprocessing, the dataset will be uploaded to our `sagemaker_session_bucket` to be used within our training job."
]
},
{
Expand All @@ -157,11 +157,11 @@
"metadata": {},
"outputs": [],
"source": [
"dataset_name = 'amazon_polarity'\n",
"dataset_name = 'imdb'\n",
"\n",
"train_dataset, test_dataset = load_dataset(dataset_name, split=['train', 'test'])\n",
"train_dataset = train_dataset.shuffle().select(range(5000)) # limiting the dataset size to speed up the training during the demo\n",
"test_dataset = test_dataset.shuffle().select(range(1000))"
"test_dataset = test_dataset.shuffle().select(range(500))"
]
},
{
Expand Down Expand Up @@ -246,17 +246,18 @@
"source": [
"# Helper function to get the content to tokenize\n",
"def tokenize(batch):\n",
" return tokenizer(batch['content'], padding='max_length', truncation=True)\n",
" return tokenizer(batch['text'], padding='max_length', truncation=True)\n",
"\n",
"# Tokenize\n",
"train_dataset = train_dataset.map(tokenize, batched=True, batch_size=len(train_dataset))\n",
"test_dataset = test_dataset.map(tokenize, batched=True, batch_size=len(test_dataset))\n",
"\n",
"# Set the format to PyTorch\n",
"train_dataset = train_dataset.rename_column(\"label\", \"labels\")\n",
"train_dataset = train_dataset.rename_column(\"text\", \"content\")\n",
"train_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels'])\n",
"test_dataset = test_dataset.rename_column(\"label\", \"labels\")\n",
"test_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels'])"
"test_dataset = test_dataset.rename_column(\"text\", \"content\")"
]
},
{
Expand Down Expand Up @@ -624,7 +625,7 @@
"kernelspec": {
"display_name": "Python 3 (Data Science)",
"language": "python",
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0"
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/datascience-1.0"
},
"language_info": {
"codemirror_mode": {
Expand Down