Datasets
Models
Downloads: 1
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"# !pip install transformers\n",
"# !pip install accelerate -U\n",
"# !pip install transformers==4.30\n",
"# !pip install accelerate==0.21.0\n",
"# !pip install datasets\n",
"# !pip install torch\n",
"# !pip install accelerate"
],
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"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.2)\n",
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"Collecting accelerate\n",
" Downloading accelerate-0.28.0-py3-none-any.whl (290 kB)\n",
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"Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n",
"Successfully installed accelerate-0.28.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105\n",
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"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.30)\n",
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"Installing collected packages: tokenizers, transformers\n",
" Attempting uninstall: tokenizers\n",
" Found existing installation: tokenizers 0.15.2\n",
" Uninstalling tokenizers-0.15.2:\n",
" Successfully uninstalled tokenizers-0.15.2\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.38.2\n",
" Uninstalling transformers-4.38.2:\n",
" Successfully uninstalled transformers-4.38.2\n",
"Successfully installed tokenizers-0.13.3 transformers-4.30.0\n",
"Collecting accelerate==0.21.0\n",
" Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
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"Installing collected packages: accelerate\n",
" Attempting uninstall: accelerate\n",
" Found existing installation: accelerate 0.28.0\n",
" Uninstalling accelerate-0.28.0:\n",
" Successfully uninstalled accelerate-0.28.0\n",
"Successfully installed accelerate-0.21.0\n",
"Collecting datasets\n",
" Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
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"Installing collected packages: xxhash, dill, multiprocess, datasets\n",
"Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n",
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]
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoTokenizer\n",
"from datasets import load_dataset\n",
"import torch\n",
"\n",
"# Load the IMDb dataset\n",
"dataset = load_dataset(\"imdb\")\n",
"\n",
"# Display dataset information\n",
"print(dataset)\n",
"\n",
"# Initialize tokenizer and model\n",
"tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2) # Binary classification (positive/negative)\n",
"\n",
"# Tokenize the dataset\n",
"def tokenize_function(examples):\n",
" return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n",
"\n",
"tokenized_dataset = dataset.map(tokenize_function, batched=True)\n",
"\n",
"# Split dataset into training and test sets\n",
"train_dataset = tokenized_dataset[\"train\"]\n",
"eval_dataset = tokenized_dataset[\"test\"]\n",
"\n",
"# Define your training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./imdb_results\",\n",
" num_train_epochs=3,\n",
" per_device_train_batch_size=20,\n",
" per_device_eval_batch_size=20,\n",
" logging_dir=\"./imdb_blogs\",\n",
")\n",
"\n",
"# Define Trainer\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=eval_dataset,\n",
")\n",
"\n",
"# Train the model\n",
"trainer.train()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1333,
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]
},
"id": "p0PqCJMjEDPK",
"outputId": "59011824-2eb0-4f47-9d86-b3670bdba84a"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
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"DatasetDict({\n",
" train: Dataset({\n",
" features: ['text', 'label'],\n",
" num_rows: 25000\n",
" })\n",
" test: Dataset({\n",
" features: ['text', 'label'],\n",
" num_rows: 25000\n",
" })\n",
" unsupervised: Dataset({\n",
" features: ['text', 'label'],\n",
" num_rows: 50000\n",
" })\n",
"})\n"
]
},
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{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight']\n",
"- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/25000 [00:00<?, ? examples/s]"
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-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"
]
},
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"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='3750' max='3750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [3750/3750 28:36, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.296200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.238100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>0.184000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2000</td>\n",
" <td>0.124100</td>\n",
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" <tr>\n",
" <td>2500</td>\n",
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" <tr>\n",
" <td>3000</td>\n",
" <td>0.053800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3500</td>\n",
" <td>0.041500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=3750, training_loss=0.1437871671040853, metrics={'train_runtime': 1718.246, 'train_samples_per_second': 43.649, 'train_steps_per_second': 2.182, 'total_flos': 1.9733329152e+16, 'train_loss': 0.1437871671040853, 'epoch': 3.0})"
]
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoTokenizer\n",
"from datasets import load_dataset\n",
"import torch\n",
"\n",
"# Load the CoLA dataset\n",
"dataset = load_dataset(\"glue\", \"cola\")\n",
"\n",
"# Display dataset information\n",
"print(dataset)\n",
"\n",
"# Tokenize the dataset\n",
"tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2) # Binary classification (positive/negative)\n",
"\n",
"def tokenize_function(examples):\n",
" return tokenizer(examples[\"sentence\"], padding=\"max_length\", truncation=True)\n",
"\n",
"tokenized_dataset = dataset.map(tokenize_function, batched=True)\n",
"\n",
"# Split dataset into training and test sets\n",
"train_dataset = tokenized_dataset[\"train\"]\n",
"eval_dataset = tokenized_dataset[\"validation\"]\n",
"\n",
"# Define your training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./gluecola_results\",\n",
" num_train_epochs=3,\n",
" per_device_train_batch_size=20,\n",
" per_device_eval_batch_size=20,\n",
" logging_dir=\"./gluecola_logs\",\n",
")\n",
"\n",
"# Define Trainer\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=eval_dataset,\n",
")\n",
"\n",
"# Train the model\n",
"trainer.train()\n"
],
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"id": "yKtr-oJRAx2v",
"outputId": "152f6c49-a95e-4a0f-fc40-368bb3c7d028"
},
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"outputs": [
{
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"data": {
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],
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{
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"text": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['sentence', 'label', 'idx'],\n",
" num_rows: 8551\n",
" })\n",
" validation: Dataset({\n",
" features: ['sentence', 'label', 'idx'],\n",
" num_rows: 1043\n",
" })\n",
" test: Dataset({\n",
" features: ['sentence', 'label', 'idx'],\n",
" num_rows: 1063\n",
" })\n",
"})\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight']\n",
"- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
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"text": [
"/usr/local/lib/python3.10/dist-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"
]
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"\n",
" <div>\n",
" \n",
" <progress value='1284' max='1284' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1284/1284 09:46, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.466900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.215300</td>\n",
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" </tbody>\n",
"</table><p>"
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{
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"TrainOutput(global_step=1284, training_loss=0.29287985923505644, metrics={'train_runtime': 586.8007, 'train_samples_per_second': 43.717, 'train_steps_per_second': 2.188, 'total_flos': 6749587903150080.0, 'train_loss': 0.29287985923505644, 'epoch': 3.0})"
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