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huggingface_text_classification_quickstart.ipynb
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{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/andrewm4894/ce57c09848580c9deb46eb244bb16cea/huggingface_text_classification_quickstart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"# install what we need if not already installed\n",
"#!pip install datasets transformers"
],
"metadata": {
"id": "0USd3nerpZ96"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8abgt_d7owDr"
},
"outputs": [],
"source": [
"# minimal example here should work for either 'pytorch' or 'tensorflow'\n",
"framework = 'tensorflow'"
]
},
{
"cell_type": "markdown",
"source": [
"# Imports"
],
"metadata": {
"id": "Gx7igJNVo-g5"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"from datasets import load_dataset, Dataset, DatasetDict, Value, ClassLabel, Features\n",
"from transformers import DataCollatorWithPadding, AutoTokenizer, pipeline\n",
"\n",
"if framework == 'tensorflow':\n",
" from transformers import TFAutoModelForSequenceClassification, create_optimizer\n",
" import tensorflow as tf\n",
"else:\n",
" from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
"\n",
"\n",
"def preprocess_function(examples):\n",
" return tokenizer(examples[\"text\"], truncation=True)\n"
],
"metadata": {
"id": "immGFdDxo6UC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Params"
],
"metadata": {
"id": "PqMIFVy3qlag"
}
},
{
"cell_type": "code",
"source": [
"# downsample data for speed in this example\n",
"n_train = 1000\n",
"n_test = 1000\n",
"\n",
"# ml inputs\n",
"batch_size = 16\n",
"learning_rate = 2e-5\n",
"num_epochs = 2\n",
"weight_decay = 0.01"
],
"metadata": {
"id": "656s4baDqm_i"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Get Data"
],
"metadata": {
"id": "CJ85wluppAg6"
}
},
{
"cell_type": "code",
"source": [
"# load imdb data\n",
"data = load_dataset(\"imdb\")\n",
"\n",
"# pull data into pandas dataframes to downsample\n",
"df_train = pd.DataFrame.from_dict(data['train']).sample(n_train)\n",
"df_test = pd.DataFrame.from_dict(data['test']).sample(n_test)\n",
"\n",
"# now build back up a DatasetDict based on the downsampled data\n",
"\n",
"# define the features\n",
"features = Features({\n",
" \"text\": Value(\"string\"), \n",
" \"label\": ClassLabel(num_classes=2, names=['neg','pos']),\n",
" \"__index_level_0__\": Value(\"string\") \n",
" })\n",
"\n",
"# recreate the data object using the smaller df's\n",
"data = DatasetDict({\n",
" 'train': Dataset.from_pandas(df_train, features=features),\n",
" 'test': Dataset.from_pandas(df_test, features=features),\n",
" })\n",
"\n",
"# remove index col (seems to be coming in from pandas for some reason)\n",
"data = data.remove_columns([\"__index_level_0__\"])\n",
"\n",
"# look at data\n",
"print(data['train'].features)\n",
"print(data['test'].features)\n",
"print(data)"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 295,
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{
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"text": [
"WARNING:datasets.builder:Reusing dataset imdb (/root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\n"
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"text": [
"{'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None)}\n",
"{'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None)}\n",
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['text', 'label'],\n",
" num_rows: 1000\n",
" })\n",
" test: Dataset({\n",
" features: ['text', 'label'],\n",
" num_rows: 1000\n",
" })\n",
"})\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Tokenize"
],
"metadata": {
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},
{
"cell_type": "code",
"source": [
"# create tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
"\n",
"# tokenize the data\n",
"tokenized_data = data.map(preprocess_function, batched=True)"
],
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},
"id": "Pv--vigxpOYa",
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"execution_count": null,
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" 0%| | 0/1 [00:00<?, ?ba/s]"
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},
{
"cell_type": "markdown",
"source": [
"# Train"
],
"metadata": {
"id": "Rinpt0BOpH9K"
}
},
{
"cell_type": "code",
"source": [
"# train mode based on framework\n",
"if framework == 'pytorch':\n",
" \n",
" data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
" \n",
" model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n",
" \n",
" training_args = TrainingArguments(\n",
" output_dir=\"./results\",\n",
" learning_rate=learning_rate,\n",
" per_device_train_batch_size=batch_size,\n",
" per_device_eval_batch_size=batch_size,\n",
" num_train_epochs=num_epochs,\n",
" weight_decay=weight_decay,\n",
" )\n",
" \n",
" trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=tokenized_data[\"train\"],\n",
" eval_dataset=tokenized_data[\"test\"],\n",
" tokenizer=tokenizer,\n",
" data_collator=data_collator,\n",
" ) \n",
" \n",
" trainer.train()\n",
" \n",
"elif framework == 'tensorflow':\n",
" \n",
" data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
" \n",
" tf_train_set = tokenized_data[\"train\"].to_tf_dataset(\n",
" columns=[\"attention_mask\", \"input_ids\", \"label\"],\n",
" shuffle=True,\n",
" batch_size=batch_size,\n",
" collate_fn=data_collator,\n",
" )\n",
"\n",
" tf_validation_set = tokenized_data[\"test\"].to_tf_dataset(\n",
" columns=[\"attention_mask\", \"input_ids\", \"label\"],\n",
" shuffle=False,\n",
" batch_size=batch_size,\n",
" collate_fn=data_collator,\n",
" )\n",
" \n",
" batches_per_epoch = len(tokenized_data[\"train\"]) // batch_size\n",
" total_train_steps = int(batches_per_epoch * num_epochs)\n",
" optimizer, schedule = create_optimizer(init_lr=learning_rate, num_warmup_steps=0, num_train_steps=total_train_steps)\n",
" \n",
" model = TFAutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n",
" \n",
" model.compile(optimizer=optimizer)\n",
" \n",
" model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=num_epochs)\n",
"\n",
"else: \n",
" \n",
" raise ValueError('unsupported framework')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m6X5UBpGpHMa",
"outputId": "693b69fd-8e68-4e22-94ab-6bcab53a1fa5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_transform', 'vocab_projector', 'activation_13', 'vocab_layer_norm']\n",
"- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['dropout_19', 'classifier', 'pre_classifier']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/2\n",
"63/63 [==============================] - 56s 726ms/step - loss: 0.5812 - val_loss: 0.3526\n",
"Epoch 2/2\n",
"63/63 [==============================] - 44s 702ms/step - loss: 0.2713 - val_loss: 0.2748\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Inference Pipeline"
],
"metadata": {
"id": "a_6YFAMPpQqa"
}
},
{
"cell_type": "code",
"source": [
"# create pipeline for inference\n",
"classifier = pipeline(\n",
" task=\"text-classification\", \n",
" model=model, \n",
" tokenizer=tokenizer, \n",
" device=0\n",
" )"
],
"metadata": {
"id": "4AkV11XJpTbB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"classifier(\"this is a great movie\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5vwRTe1OpUsS",
"outputId": "524ec120-9877-41f1-9ca3-5b0ff3d3a789"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'label': 'LABEL_1', 'score': 0.9083077311515808}]"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"classifier(\"this is a terrible movie\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lb2OEWvXpVP7",
"outputId": "a343b03a-649e-456b-d7ab-60c2a2f96330"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'label': 'LABEL_0', 'score': 0.7608168125152588}]"
]
},
"metadata": {},
"execution_count": 10
}
]
}
]
}
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