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"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.9)\n", + "Requirement already satisfied: huggingface-hub>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.24.7)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n", + "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.3)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) 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+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n", + "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n", + " Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n", + "Downloading datasets-3.0.1-py3-none-any.whl (471 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m471.6/471.6 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n", + "\u001b[2K 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"cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 373 + }, + "id": "Az9vxNVzR1tI", + "outputId": "07fdbab4-3864-450d-ae76-1ab31e401ba8" + }, + "outputs": [ + { + "output_type": "error", + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/content/llm_dataset.csv'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Load the CSV file into a pandas DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/llm_dataset.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m# Convert the pandas DataFrame to Hugging Face's Dataset format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1024\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 619\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1619\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1620\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1622\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1879\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1880\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 1881\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1882\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 871\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 874\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 875\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/llm_dataset.csv'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from datasets import Dataset\n", + "\n", + "# Load the CSV file into a pandas DataFrame\n", + "df = pd.read_csv('/content/llm_dataset.csv')\n", + "\n", + "# Convert the pandas DataFrame to Hugging Face's Dataset format\n", + "dataset = Dataset.from_pandas(df)\n", + "dataset = dataset.train_test_split(test_size=0.2) # Split the dataset into train and test sets" + ] + }, + { + "cell_type": "code", + "source": [ + "from transformers import T5Tokenizer, T5ForConditionalGeneration\n", + "\n", + "# Load pre-trained T5 tokenizer and model\n", + "model_name = \"t5-small\" # You can choose t5-base or t5-large for better performance\n", + "tokenizer = T5Tokenizer.from_pretrained(model_name)\n", + "model = T5ForConditionalGeneration.from_pretrained(model_name)\n", + "\n", + "# Tokenization function for the dataset\n", + "def preprocess_function(examples):\n", + " inputs = [f\"question: {q}\" for q in examples['Question']]\n", + " model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n", + "\n", + " # Prepare the target (answer) as well\n", + " labels = tokenizer(examples['Answer'], max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"pt\").input_ids\n", + " model_inputs[\"labels\"] = labels\n", + " return model_inputs\n", + "\n", + "# Preprocess the dataset\n", + "tokenized_dataset = dataset.map(preprocess_function, batched=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 414, + "referenced_widgets": [ + "9859b486c04347e28dfec5913018b101", + "86399b590f214ee9aa8d27d1b35b7da6", + 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access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/2.32k [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/1.21k [00:00" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=45, training_loss=7.266212972005208, metrics={'train_runtime': 36.7673, 'train_samples_per_second': 9.383, 'train_steps_per_second': 1.224, 'total_flos': 46692921507840.0, 'train_loss': 7.266212972005208, 'epoch': 3.0})" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "\n", + "def get_answer(question):\n", + " # Preprocess the input question\n", + " inputs = tokenizer(f\"question: {question}\", return_tensors=\"pt\", padding=\"max_length\", truncation=True, max_length=512)\n", + "\n", + " # Move the input tensors to the same device as the model\n", + " # Assuming your model is on CUDA\n", + " inputs = inputs.to(model.device) # Move input tensors to the model's device\n", + "\n", + " # Generate the answer\n", + " outputs = model.generate(inputs.input_ids, max_length=512, num_beams=4, early_stopping=False)\n", + "\n", + " # Decode the generated answer\n", + " answer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", + " return answer\n", + "\n", + "# Example usage\n", + "question = \"What are the symptoms of Diverticulosis?\"\n", + "answer = get_answer(question)\n", + "print(f\"Question: {question}\\nAnswer: {answer}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B3UAWAgTSYhp", + "outputId": "e56c22c6-e43b-4c82-a5a4-5a6f1a6d4d66" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Question: What are the symptoms of Diverticulosis?\n", + "Answer: Diverticulosis\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### GPT 2" + ], + "metadata": { + "id": "gDaAa34WW-rB" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from datasets import Dataset\n", + "\n", + "# Load the CSV file into a pandas DataFrame\n", + "df = pd.read_csv('/content/llm_dataset.csv')\n", + "\n", + "# Concatenate question and answer to form a single text prompt\n", + "df['text'] = df['Question'] + \" Answer: \" + df['Answer']\n", + "\n", + "# Convert the pandas DataFrame to Hugging Face's Dataset format\n", + "dataset = Dataset.from_pandas(df)\n", + "dataset = dataset.train_test_split(test_size=0.2) # Split the dataset into train and test sets\n" + ], + "metadata": { + "id": "X4ii8FCBXT0b" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n", + "\n", + "# Load GPT-2 tokenizer and model\n", + "model_name = \"gpt2\"\n", + "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n", + "\n", + "# GPT-2 does not have padding tokens, so we add one\n", + "tokenizer.pad_token = tokenizer.eos_token\n", + "\n", + "# Load the GPT-2 model with language modeling head\n", + "model = GPT2LMHeadModel.from_pretrained(model_name)\n", + "\n", + "# Tokenization function for the dataset\n", + "def preprocess_function(examples):\n", + " inputs = [q for q in examples['text']]\n", + " model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n", + "\n", + " # Add labels - shifted input_ids\n", + " model_inputs[\"labels\"] = model_inputs[\"input_ids\"].clone()\n", + "\n", + " return model_inputs\n", + "\n", + "# Preprocess the dataset\n", + "tokenized_dataset = dataset.map(preprocess_function, batched=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 464, + "referenced_widgets": [ + "17bd0779af754c938e8fd733e700666f", + "9b35c2fe89674e78bc9b2e2a00ce37c6", + "67905730a0614c019e20c1e92cdf6083", + "008bb028b0cb447a87da202e469cdfa7", + "655e33d8e1c3428db80d8968a8faefc6", + "777b583edf67498e86abee1f64e87ec4", + "20dd861b5898422283d7470298cfff99", + "6edc7f271b324ee6b6b76dc76efa615e", + "3d9852493bed4fa39266bc836d6be17d", + 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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" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" + ], + "text/html": [ + "\n", + "

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EpochTraining LossValidation Loss
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40.4299000.239692
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190.0485000.302693
200.0485000.302897

" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=580, training_loss=0.14133180749827418, metrics={'train_runtime': 390.3092, 'train_samples_per_second': 5.893, 'train_steps_per_second': 1.486, 'total_flos': 600971673600000.0, 'train_loss': 0.14133180749827418, 'epoch': 20.0})" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "source": [ + "def get_answer(question):\n", + " # Preprocess the input question\n", + " input_text = question + \" Answer:\"\n", + " inputs = tokenizer(input_text, return_tensors=\"pt\", max_length=512, truncation=True)\n", + "\n", + " # Move input tensor\n", + "\n", + " inputs = inputs.to(model.device) # Move input tensors to the GPU if model is on the GPU\n", + "\n", + " # Generate the answer from the model\n", + " # Change: Use tokenizer.eos_token_id instead of tokenizer.eos_token\n", + " outputs = model.generate(inputs.input_ids, max_length=512, num_beams=5, early_stopping=True, pad_token_id=tokenizer.eos_token_id)\n", + "\n", + " # Decode the generated answer\n", + " answer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", + "\n", + " # Remove the question part from the answer (if included)\n", + " answer = answer[len(question):].replace(\"Answer:\", \"\").strip()\n", + "\n", + " return answer" + ], + "metadata": { + "id": "bgNcIKjNXK5d" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Example usage\n", + "question = \"What is Diverticulosis?\"\n", + "answer = get_answer(question)\n", + "print(f\"Question: {question}\\nAnswer: {answer}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r5coyxVJYJ6A", + "outputId": "f86b5526-97a5-46aa-a7b3-5048619d967b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Question: What is Diverticulosis?\n", + "Answer: Diverticulosis is a type of diverticulosis that occurs when fluid from the diverticula diverges from the diverticulum. Diverticulosis occurs when fluid from the diverticula diverges from the diverticulum, leading to diverticulosis.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Scratch Model" + ], + "metadata": { + "id": "i42MGcxVlDEN" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from transformers import BertTokenizer\n", + "\n", + "# Load the dataset\n", + "df = pd.read_csv(\"/content/llm_dataset.csv\")\n", + "\n", + "# Initialize the tokenizer\n", + "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", + "\n", + "class QADataset(Dataset):\n", + " def __init__(self, dataframe, tokenizer, max_len):\n", + " self.dataframe = dataframe\n", + " self.tokenizer = tokenizer\n", + " self.max_len = max_len\n", + "\n", + " def __len__(self):\n", + " return len(self.dataframe)\n", + "\n", + " def __getitem__(self, idx):\n", + " question = str(self.dataframe.iloc[idx]['Question'])\n", + " answer = str(self.dataframe.iloc[idx]['Answer'])\n", + "\n", + " # Tokenize question and answer\n", + " question_encodings = self.tokenizer(\n", + " question,\n", + " max_length=self.max_len,\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " return_tensors=\"pt\"\n", + " )\n", + "\n", + " answer_encodings = self.tokenizer(\n", + " answer,\n", + " max_length=self.max_len,\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " return_tensors=\"pt\"\n", + " )\n", + "\n", + " return {\n", + " 'input_ids': question_encodings['input_ids'].squeeze(), # [max_len]\n", + " 'attention_mask': question_encodings['attention_mask'].squeeze(), # [max_len]\n", + " 'labels': answer_encodings['input_ids'].squeeze() # [max_len]\n", + " }\n", + "\n", + "# Parameters\n", + "max_len = 64 # Maximum sequence length\n", + "batch_size = 8\n", + "\n", + "# Create Dataset and DataLoader\n", + "train_dataset = QADataset(df, tokenizer, max_len)\n", + "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ObTcZTzOkN8p", + "outputId": "ff44a741-3243-4511-d465-2a20922f284e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "class TransformerSeq2Seq(nn.Module):\n", + " def __init__(self, vocab_size, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, max_seq_len, dropout=0.1):\n", + " super(TransformerSeq2Seq, self).__init__()\n", + "\n", + " # Embedding layers\n", + " self.encoder_embedding = nn.Embedding(vocab_size, d_model)\n", + " self.decoder_embedding = nn.Embedding(vocab_size, d_model)\n", + "\n", + " # Positional Encoding\n", + " self.positional_encoding = nn.Parameter(torch.zeros(1, max_seq_len, d_model))\n", + "\n", + " # Transformer\n", + " self.transformer = nn.Transformer(\n", + " d_model=d_model,\n", + " nhead=nhead,\n", + " num_encoder_layers=num_encoder_layers,\n", + " num_decoder_layers=num_decoder_layers,\n", + " dim_feedforward=dim_feedforward,\n", + " dropout=dropout\n", + " )\n", + "\n", + " # Output linear layer\n", + " self.fc_out = nn.Linear(d_model, vocab_size)\n", + "\n", + " def forward(self, src, tgt, src_mask=None, tgt_mask=None):\n", + " # Embedding and positional encoding for encoder\n", + " src_emb = self.encoder_embedding(src) + self.positional_encoding[:, :src.size(1), :]\n", + " # Embedding and positional encoding for decoder\n", + " tgt_emb = self.decoder_embedding(tgt) + self.positional_encoding[:, :tgt.size(1), :]\n", + "\n", + " # Transformer expects input of shape (seq_len, batch_size, d_model)\n", + " src_emb = src_emb.transpose(0, 1) # [seq_len, batch_size, d_model]\n", + " tgt_emb = tgt_emb.transpose(0, 1) # [seq_len, batch_size, d_model]\n", + "\n", + " # Pass through Transformer\n", + " transformer_output = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask)\n", + "\n", + " # Transpose back to [batch_size, seq_len, d_model]\n", + " transformer_output = transformer_output.transpose(0, 1)\n", + "\n", + " # Output layer\n", + " output = self.fc_out(transformer_output) # [batch_size, seq_len, vocab_size]\n", + "\n", + " return output\n", + "\n", + "# Model hyperparameters\n", + "vocab_size = tokenizer.vocab_size\n", + "d_model = 256\n", + "nhead = 8\n", + "num_encoder_layers = 4\n", + "num_decoder_layers = 4\n", + "dim_feedforward = 512\n", + "max_seq_len = max_len\n", + "\n", + "# Initialize the model\n", + "model = TransformerSeq2Seq(\n", + " vocab_size=vocab_size,\n", + " d_model=d_model,\n", + " nhead=nhead,\n", + " num_encoder_layers=num_encoder_layers,\n", + " num_decoder_layers=num_decoder_layers,\n", + " dim_feedforward=dim_feedforward,\n", + " max_seq_len=max_seq_len\n", + ")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wL3GaK1ylNYj", + "outputId": "202a7ed4-dfc7-45a4-da9a-cac6b23c41c3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py:307: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n", + " warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Y2ufr8TmlTfv", + "outputId": "6caa1636-5cf3-49c6-c197-bb2d271e668e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TransformerSeq2Seq(\n", + " (embedding): Embedding(30522, 256)\n", + " (transformer): Transformer(\n", + " (encoder): TransformerEncoder(\n", + " (layers): ModuleList(\n", + " (0-3): 4 x TransformerEncoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=256, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=256, bias=True)\n", + " (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (decoder): TransformerDecoder(\n", + " (layers): ModuleList(\n", + " (0-3): 4 x TransformerDecoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (multihead_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=256, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=256, bias=True)\n", + " (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " (dropout3): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (fc_out): Linear(in_features=256, out_features=30522, bias=True)\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch.optim as optim\n", + "\n", + "# Move model to device\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "model = model.to(device)\n", + "\n", + "# Loss function and optimizer\n", + "criterion = nn.CrossEntropyLoss(ignore_index=tokenizer.pad_token_id)\n", + "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n", + "\n", + "# Number of epochs\n", + "epochs = 20\n", + "\n", + "for epoch in range(epochs):\n", + " model.train()\n", + " epoch_loss = 0\n", + "\n", + " for batch in train_loader:\n", + " # Get inputs and move to device\n", + " input_ids = batch['input_ids'].to(device) # [batch_size, max_len]\n", + " attention_mask = batch['attention_mask'].to(device) # [batch_size, max_len]\n", + " labels = batch['labels'].to(device) # [batch_size, max_len]\n", + "\n", + " # Shift labels for decoder input (teacher forcing)\n", + " decoder_input_ids = labels[:, :-1]\n", + " labels = labels[:, 1:].contiguous()\n", + "\n", + " # Zero the gradients\n", + " optimizer.zero_grad()\n", + "\n", + " # Forward pass\n", + " outputs = model(input_ids, decoder_input_ids) # [batch_size, seq_len, vocab_size]\n", + "\n", + " # Reshape for loss computation\n", + " outputs = outputs.view(-1, vocab_size) # [(batch_size * seq_len), vocab_size]\n", + " labels = labels.view(-1) # [(batch_size * seq_len)]\n", + "\n", + " # Compute loss\n", + " loss = criterion(outputs, labels)\n", + " epoch_loss += loss.item()\n", + "\n", + " # Backward pass and optimization\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " avg_loss = epoch_loss / len(train_loader)\n", + " print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HWjFR6lClV0D", + "outputId": "e4b8fe55-cf99-4bd7-8c39-08ea4bcd841c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/20, Loss: 9.7251\n", + "Epoch 2/20, Loss: 8.9657\n", + "Epoch 3/20, Loss: 8.3415\n", + "Epoch 4/20, Loss: 7.7263\n", + "Epoch 5/20, Loss: 7.1735\n", + "Epoch 6/20, Loss: 6.7080\n", + "Epoch 7/20, Loss: 6.3273\n", + "Epoch 8/20, Loss: 6.0067\n", + "Epoch 9/20, Loss: 5.7498\n", + "Epoch 10/20, Loss: 5.5193\n", + "Epoch 11/20, Loss: 5.3198\n", + "Epoch 12/20, Loss: 5.1383\n", + "Epoch 13/20, Loss: 4.9742\n", + "Epoch 14/20, Loss: 4.8258\n", + "Epoch 15/20, Loss: 4.6762\n", + "Epoch 16/20, Loss: 4.5503\n", + "Epoch 17/20, Loss: 4.4374\n", + "Epoch 18/20, Loss: 4.3159\n", + "Epoch 19/20, Loss: 4.2030\n", + "Epoch 20/20, Loss: 4.0945\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def generate_answer(question, max_len=64):\n", + " model.eval()\n", + " with torch.no_grad():\n", + " # Tokenize the question\n", + " tokens = tokenizer(question, return_tensors='pt', padding=True, truncation=True, max_length=max_len)\n", + " input_ids = tokens['input_ids'].to(device) # [1, max_len]\n", + "\n", + " # Initialize decoder input with BOS token\n", + " bos_token_id = tokenizer.bos_token_id if tokenizer.bos_token_id is not None else tokenizer.cls_token_id\n", + " decoder_input = torch.tensor([[bos_token_id]], device=device) # [1, 1]\n", + "\n", + " generated_tokens = []\n", + "\n", + " for _ in range(max_len):\n", + " # Forward pass through the model\n", + " outputs = model(input_ids, decoder_input) # [1, seq_len, vocab_size]\n", + "\n", + " # Get the last token's logits\n", + " next_token_logits = outputs[:, -1, :] # [1, vocab_size]\n", + "\n", + " # Get the token with the highest probability\n", + " next_token_id = torch.argmax(next_token_logits, dim=-1).item()\n", + "\n", + " # If EOS token is generated, stop\n", + " if next_token_id == tokenizer.eos_token_id:\n", + " break\n", + "\n", + " # Append the token to the generated tokens\n", + " generated_tokens.append(next_token_id)\n", + "\n", + " # Update the decoder input\n", + " decoder_input = torch.cat([decoder_input, torch.tensor([[next_token_id]], device=device)], dim=1)\n", + "\n", + " # Decode the generated tokens to text\n", + " answer = tokenizer.decode(generated_tokens, skip_special_tokens=True)\n", + " return answer\n", + "\n", + "# Example usage\n", + "question = \"What are the symptoms of pneumonia?\"\n", + "answer = generate_answer(question)\n", + "print(f\"Question: {question}\\nAnswer: {answer}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6QwvemxClYxo", + "outputId": "6c8d8c56-06b3-43b9-8390-e848d834fa0c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Question: What are the symptoms of pneumonia?\n", + "Answer: pneumonia is a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "7etd9vybpK2S" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Distil GPT 2" + ], + "metadata": { + "id": "ddvOJsAiS9hn" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from datasets import Dataset\n", + "\n", + "# Load dataset\n", + "df = pd.read_csv('/content/llm_dataset.csv')\n", + "\n", + "# Convert DataFrame to Hugging Face Dataset format\n", + "dataset = Dataset.from_pandas(df)" + ], + "metadata": { + "id": "H4kac-3US_2O" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install transformers --upgrade" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 740 + }, + "id": "q4Us1evfT4Ts", + "outputId": "a6dd02b1-79f4-471a-fb60-4750c0407739" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n", + "Collecting transformers\n", + " Downloading transformers-4.45.2-py3-none-any.whl.metadata (44 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n", + "Requirement already satisfied: requests in 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uninstall: tokenizers\n", + " Found existing installation: tokenizers 0.19.1\n", + " Uninstalling tokenizers-0.19.1:\n", + " Successfully uninstalled tokenizers-0.19.1\n", + " Attempting uninstall: transformers\n", + " Found existing installation: transformers 4.44.2\n", + " Uninstalling transformers-4.44.2:\n", + " Successfully uninstalled transformers-4.44.2\n", + "Successfully installed tokenizers-0.20.1 transformers-4.45.2\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "transformers" + ] + }, + "id": "6e9f9ec09e43428a93112653c07427ef" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "8Nzkp3PVWHLh" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer\n", + "\n", + "# Load tokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained('distilgpt2')\n", + "\n", + "# Add a special token separator between the question and answer\n", + "separator_token = tokenizer.eos_token\n", + "\n", + "# Add a padding token to the tokenizer, if it's missing.\n", + "# We are using the 'eos_token' as the 'pad_token' for simplicity\n", + "# but you could add a new pad token like '[PAD]' if you prefer.\n", + "if tokenizer.pad_token is None:\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + "\n", + "def tokenize_function(examples):\n", + " # Create input texts by combining question and answer\n", + " input_texts = [q + separator_token + a for q, a in zip(examples['Question'], examples['Answer'])]\n", + " # Tokenize the input texts with padding and truncation\n", + " tokenized_output = tokenizer(\n", + " input_texts,\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " max_length=128,\n", + " return_tensors='pt'\n", + " )\n", + "\n", + " # Use the input_ids as labels (shifted by one)\n", + " # This is a common practice for language modeling where the model predicts the next token\n", + " tokenized_output[\"labels\"] = tokenized_output[\"input_ids\"].clone()\n", + "\n", + " return tokenized_output\n", + "\n", + "# Apply tokenization to your dataset\n", + "tokenized_dataset = dataset.map(\n", + " tokenize_function,\n", + " batched=True,\n", + " remove_columns=dataset.column_names # Remove original columns to avoid errors\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368, + "referenced_widgets": [ + "64896ff8456643458d7ac64a89d6faa0", + "985ed05bce8e43efaa2e3d9906314a6e", + "3181c30d69364fd59a00714d83154571", + "c79135f34c54468cbc4d85639e02b5d1", + "d7d674b1a1e94706ac981354ed36e296", + "33e474ada72342499c39d171cc3c0403", + "adb57ba559ca4d3fab3ec2133cf547a4", + "2dc1f8f8904344fbbf874bf252d68b2e", + "04a75b2378c14deaa066b069dedcf968", + "8db82bc323cc48dbbf857c1016746dfd", + "7598f8ebcfe140b6b8106071549ff297", + "88d1cd6452084cdf9524db580ac4fce7", + "f86497db1c584360a2534459af8b2c7e", + 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" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=540, training_loss=0.6469527279889142, metrics={'train_runtime': 113.4784, 'train_samples_per_second': 38.069, 'train_steps_per_second': 4.759, 'total_flos': 141100245319680.0, 'train_loss': 0.6469527279889142, 'epoch': 30.0})" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "\n", + "def generate_answer(question, max_length=1024):\n", + " model.eval() # Set the model to evaluation mode\n", + " input_text = question + separator_token\n", + " input_ids = tokenizer.encode(input_text, return_tensors='pt')\n", + "\n", + " # Move input_ids to the same device as the model\n", + " input_ids = input_ids.to(model.device)\n", + "\n", + " output = model.generate(input_ids, max_length=max_length, pad_token_id=tokenizer.eos_token_id)\n", + " answer = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)\n", + " return answer\n", + "\n", + "# Example usage\n", + "question = \"what are the symptoms of pneumonia?\"\n", + "print(generate_answer(question))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nU1NIXATVBHH", + "outputId": "6c3ba493-7c23-4ef3-d665-8a2c27d1eb41" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Symptoms of pneumonia vary depending on the severity of the illness and the underlying cause. Some people may experience milder symptoms, while others may experience a more severe form of pneumonia.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "/# Save the fine-tuned model\n", + "model.save_pretrained('./fine-tuned-distilgpt2')\n", + "tokenizer.save_pretrained('./fine-tuned-distilgpt2')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GPWOHjK7JIvi", + "outputId": "b2aeb21b-66d9-4049-98e6-4fbedc0a8481" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('./fine-tuned-distilgpt2/tokenizer_config.json',\n", + " './fine-tuned-distilgpt2/special_tokens_map.json',\n", + " './fine-tuned-distilgpt2/vocab.json',\n", + " './fine-tuned-distilgpt2/merges.txt',\n", + " './fine-tuned-distilgpt2/added_tokens.json',\n", + " './fine-tuned-distilgpt2/tokenizer.json')" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# copy model to drive\n", + "!cp /content/fine-tuned-distilgpt2/model.safetensors /content/drive/MyDrive/" + ], + "metadata": { + "id": "YfOiLXZrKiJo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "vsbC9b3mLk-A" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file