{ "cells": [ { "cell_type": "code", "id": "initial_id", "metadata": { "ExecuteTime": { "end_time": "2024-12-06T19:54:24.990141Z", "start_time": "2024-12-06T19:53:17.183491Z" }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "initial_id", "outputId": "85dee483-9370-4e16-ecb1-1c2d545ee1fb" }, "source": [ "!pip install geopy > delete.txt\n", "!pip install datasets > delete.txt\n", "!pip install torch torchvision datasets > delete.txt\n", "!pip install huggingface_hub > delete.txt\n", "!pip install pyhocon > delete.txt\n", "!pip install transformers > delete.txt\n", "!rm delete.txt" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ], "execution_count": 1 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-06T19:56:26.136466Z", "start_time": "2024-12-06T19:54:38.679955Z" }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "b0a77c981c32a0c8", "outputId": "fe03df52-1418-4034-8124-3bf9030ed5d7" }, "cell_type": "code", "source": [ "!huggingface-cli login" ], "id": "b0a77c981c32a0c8", "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", "\n", " To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", "Enter your token (input will not be visible): \n", "Add token as git credential? (Y/n) Y\n", "Token is valid (permission: fineGrained).\n", "The token `notebook` has been saved to /root/.cache/huggingface/stored_tokens\n", "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub.\n", "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n", "\n", "git config --global credential.helper store\n", "\n", "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n", "Token has not been saved to git credential helper.\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful.\n", "The current active token is: `notebook`\n" ] } ], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-06T19:57:30.983629Z", "start_time": "2024-12-06T19:57:29.451887Z" }, "colab": { "base_uri": "https://localhost:8080/", "height": 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"c9bc5a6711d24d5ab672800b248eaf72", "2a48402d87114263a9aaf5de42ae16e8", "1c7610b81c2449d5bffa0e54ad4f5f03", "909c1cc8dead486db0f0ffda32685281", "4ac6c48af80e4385b250f89b9853e236", "f9f76cfaaee145d5b69b264957111b23", "b5e54daf47324cd0a9588926b83da4c3", "9c912acc0d3c464481f0cd234f6e13e0", "2d3663112bc3486aaf9e18657fe1ffb3", "81cb2381d92c4020b193ecd926c7ac98", "cab469ab832648338949966f516297a8", "29944ca523bf488693485fcc8c89b2cc", "a4c9cede7717429fa16d1b433d3ed0b6", "e6af92a5af80483389e2d5b2e377d9c1", "d8f6bece0c7240d68047614a3e823358", "cbd99d14c5f044da85785cc7645f7b24", "ec7462379fdc402f8175231e538a2ff1", "fc4be5a694314d8e928d9845b4bf37b1", "ec3f0d1816f24364b402ff2f11bcbaf4", "aa02542defc44fecae211266a03c5511", "d71861e1d2ea4ac5ba59fba8bb3d3684", "024ba77e9c004ed7a362e7d1b3a2ca70", "be2ee566fd4244bdb2d77c614183b265", "e5e266610bb64c358ab67edc75312b24", "52cff177f8994b73a62744cf7b549dcd", "12ade2cda1f24f16870ec43d24025b3c", "62f2f7b12c4848aca74d334db33f1270", "b4353c16501c4c4fbedfdd018a99c899" ] }, "id": "a4aa3b759defc904", "outputId": "b1868c23-e675-41db-aa26-5eed9de60d9f" }, "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "dataset_train = load_dataset(\"CISProject/FOX_NBC\", split=\"train\")\n", "\n", "# MODIFY IT TO THE TEST DATASET PATH\n", "dataset_test = load_dataset(\"CISProject/FOX_NBC\", split=\"test\")" ], "id": "a4aa3b759defc904", "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/24.0 [00:00 1034\u001b[0;31m \u001b[0mconfig_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCONFIG_MAPPING\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconfig_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model_type\"\u001b[0m\u001b[0;34m]\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 1035\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\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/transformers/models/auto/configuration_auto.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapping\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 736\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 737\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'classifier'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mValueError\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 1\u001b[0m \u001b[0;31m# Load model directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAutoConfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CISProject/News-Headline-Classifier-Notebook\"\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 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"CISProject/News-Headline-Classifier-Notebook\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\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/transformers/models/auto/configuration_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0mconfig_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCONFIG_MAPPING\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconfig_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model_type\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1036\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 1037\u001b[0m \u001b[0;34mf\"The checkpoint you are trying to load has model type `{config_dict['model_type']}` \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1038\u001b[0m \u001b[0;34m\"but Transformers does not recognize this architecture. This could be because of an \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: The checkpoint you are trying to load has model type `classifier` but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date." ] } ], "execution_count": 6, "source": [ "# Load model directly\n", "from transformers import AutoModel, AutoConfig\n", "config = AutoConfig.from_pretrained(\"CISProject/News-Headline-Classifier-Notebook\")\n", "model = AutoModel.from_pretrained(\"CISProject/News-Headline-Classifier-Notebook\",config = config)" ], "id": "b20d11caa1d25445" }, { "metadata": { "ExecuteTime": { "end_time": "2024-12-06T19:53:05.824524Z", "start_time": "2024-12-06T19:53:05.550141Z" }, "id": "1d23cedfe1d79660" }, "cell_type": "code", "source": [ "from torch.utils.data import DataLoader\n", "from sklearn.metrics import accuracy_score, classification_report\n", "# Define a collate function to handle the batched data\n", "def collate_fn(batch):\n", " freq_inputs = torch.stack([torch.tensor(item[\"freq_inputs\"]) for item in batch])\n", " seq_inputs = torch.stack([torch.tensor(item[\"seq_inputs\"]) for item in batch])\n", " pos_inputs = torch.stack([torch.tensor(item[\"pos_inputs\"]) for item in batch])\n", " labels = torch.tensor([torch.tensor(item[\"labels\"]) for item in batch])\n", " return {\"freq_inputs\": freq_inputs, \"seq_inputs\": seq_inputs, \"pos_inputs\": pos_inputs}, labels\n", "\n", "train_loader = DataLoader(dataset_train, batch_size=config.train[\"batch_size\"], shuffle=True,collate_fn=collate_fn)\n", "test_loader = DataLoader(dataset_test, batch_size=config.train[\"batch_size\"], shuffle=False,collate_fn=collate_fn)\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model.to(device)\n", "\n", "criterion = torch.nn.BCEWithLogitsLoss()\n", "\n", "def evaluate_model(model, val_loader, criterion, device=\"cuda\"):\n", " model.eval()\n", " val_loss = 0.0\n", " correct = 0\n", " total = 0\n", " all_preds = []\n", " all_labels = []\n", " with torch.no_grad():\n", " for batch_inputs, labels in tqdm(val_loader, desc=\"Testing\", leave=False):\n", " freq_inputs = batch_inputs[\"freq_inputs\"].to(device)\n", " seq_inputs = batch_inputs[\"seq_inputs\"].to(device)\n", " pos_inputs = batch_inputs[\"pos_inputs\"].to(device)\n", " labels = labels[:,None].to(device)\n", "\n", " preds = model({\"freq_inputs\": freq_inputs, \"seq_inputs\": seq_inputs, \"pos_inputs\": pos_inputs})\n", " loss = criterion(preds, labels)\n", "\n", " val_loss += loss.item()\n", " total += labels.size(0)\n", " correct += ((torch.sigmoid(preds) > 0.5).float() == labels).sum().item()\n", "\n", " preds = torch.argmax(logits, dim=1)\n", " all_preds.extend(preds.cpu().numpy())\n", " all_labels.extend(labels.cpu().numpy())\n", "\n", " return accuracy_score(all_labels, all_preds), classification_report(all_labels, all_preds)\n", "\n", "\n", "accuracy, report = evaluate_model(model, test_loader, criterion)\n", "print(f\"Accuracy: {accuracy:.4f}\")\n", "print(report)\n" ], "id": "1d23cedfe1d79660", "outputs": [], "execution_count": null }, { 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