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"code", "source": [ "!pip install transformers==4.44.2" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LMIh2vrL6bNY", "outputId": "cf1ea14b-a921-4718-eb49-871fe8c5fb78" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting transformers==4.44.2\n", " Downloading transformers-4.44.2-py3-none-any.whl.metadata (43 kB)\n", "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/43.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers==4.44.2) (3.25.2)\n", "Collecting huggingface-hub<1.0,>=0.23.2 (from transformers==4.44.2)\n", " Downloading huggingface_hub-0.36.2-py3-none-any.whl.metadata (15 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huggingface-hub, tokenizers, transformers\n", " Attempting uninstall: huggingface-hub\n", " Found existing installation: huggingface_hub 1.10.1\n", " Uninstalling huggingface_hub-1.10.1:\n", " Successfully uninstalled huggingface_hub-1.10.1\n", " Attempting uninstall: tokenizers\n", " Found existing installation: tokenizers 0.22.2\n", " Uninstalling tokenizers-0.22.2:\n", " Successfully uninstalled tokenizers-0.22.2\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 5.0.0\n", " Uninstalling transformers-5.0.0:\n", " Successfully uninstalled transformers-5.0.0\n", "Successfully installed huggingface-hub-0.36.2 tokenizers-0.19.1 transformers-4.44.2\n" ] } ] }, { "cell_type": "code", "source": [ "import tensorflow as tf\n", "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n", "from datasets import load_dataset\n", "import numpy as np" ], "metadata": { "id": "xMYVJJj36lsG" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "#loading data\n", "data = load_dataset(\"csv\", data_files=\"/content/email.csv\")\n", "data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 153, "referenced_widgets": [ "00246b55702b41e9a87a16b3f83f95eb", "580da037542843e3927d56b59ea841cb", "3e1f27bb59c446acb8b0f67cfc0aefca", "620ed1cecf324aa49396ff0750ebecfc", "daac39c1ae44444b812cc0bf430d35b5", "9823517201af409faa12e65cb116fb2b", "3247f76b9f3043aa91e41cf2e440b1e3", "860447aa13e94d2b95ef054b019929f6", "ea778ac1c19d48129784fd5a5a4f849a", "13ef7295cc9043989bd10d989080e026", "613d45f38ff0443b85c51b0d58409325" ] }, "id": "wNjVG1-w7xeu", "outputId": "4c7550f1-f1be-43ec-b973-d3379dde1990" }, "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "00246b55702b41e9a87a16b3f83f95eb" } }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['Category', 'Message'],\n", " num_rows: 5573\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "data = data.rename_column(\"Category\", \"labels\")\n", "data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9_BedzXr8BUS", "outputId": "71cc1590-8599-4555-b831-465e2d95d6cd" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['labels', 'Message'],\n", " num_rows: 5573\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "data = data.class_encode_column(\"labels\")\n", "data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 153, "referenced_widgets": [ "0d908a86c3d345cba8058f7a64fcb4e8", "9e2266c7044e470b8ad1fa6e1bc204ac", "bed34f3623a54ff199aa96aa6ae304b4", "40dd7a3540994dc18bdc6b49b7289417", "4c231840de1c4f28894654c7b2f824d5", "01486dd5dcac436088494bc0946cd366", "5426f42b50e74beaa8f976eddc3bc777", "f4dd5c91b7774776a56ad1e1b42557d2", "cbb15be573694e62b781f94ea49dfee0", "44c5740bc0d24e78965621434f13133d", "0784b837f5534f48ab46050992e58a07" ] }, "id": "6Icl7i0q8SGa", "outputId": "ed974214-6a01-44f8-914c-12183f732c4c" }, "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Casting to class labels: 0%| | 0/5573 [00:00 (tf.Tensor, tf.Tensor) \n", " : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) \n", "New behaviour: columns=['a'],labels=['labels'] -> ({'a': tf.Tensor}, {'labels': tf.Tensor}) \n", " : columns='a', labels='labels' -> (tf.Tensor, tf.Tensor) \n", " warnings.warn(\n" ] } ] }, { "cell_type": "code", "source": [ "#load brain and decision part\n", "model = TFAutoModelForSequenceClassification.from_pretrained(\n", " model_name,\n", " num_labels=2\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 156, "referenced_widgets": [ "d4662d805b884921a039afecdeb352f7", "43b0ab76563f499baa995949599839d5", "3ede539125c741f7a89b4222411bae2d", "b18af81b5a154f01b27f2a78110322a8", "e914821c468b4aca95b90d6bb5c6e33e", "b739257e31af471790e6bdface84822f", "0b9ce59bad1348d499348b708635f8ce", "3b8cf5fd83104546a4e536a0a085c9d0", "a12763a2e7e440e7829bc14d1725bbf0", "6d8f4d770ca840d6ad17dff22d3cbdc9", "2eeb1d53292c4ac58f35ddebf60a4ec6" ] }, "id": "0r5F2buqV6_E", "outputId": "b28777ca-15d9-4d65-f875-57ca20433e5f" }, "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/268M [00:00 {acc*100:.2f}%\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pUGdzRYma3AW", "outputId": "5ef27767-ff87-4eea-b6c3-05feda3500d1" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "18/18 [==============================] - 3s 134ms/step - loss: nan - accuracy: 0.8746\n", "Final accuracy on unseen message: 0.8746 -> 87.46%\n" ] } ] }, { "cell_type": "code", "source": [ "# predictive model\n", "def is_spam(text):\n", " inputs = tokenizer(text, return_tensors=\"tf\", truncation=True, padding=True, max_length=128)\n", " output = model(inputs)\n", " probs = tf.nn.softmax(output.logits, axis=-1)\n", " spam_score = probs[0][1].numpy() # probability of spam\n", " return \"SPAM!\" if spam_score > 0.5 else \"ham\", spam_score\n", "\n", "# Test examples\n", "print(is_spam(\"Hey, are you free tonight? Let's meet!\"))\n", "print(is_spam(\"WINNER!! You won 1 crore! Call 9876543210 now!\"))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R3Au4vAycASP", "outputId": "7e1f506a-19d7-44eb-cc3a-316dd8f2c733" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "('ham', np.float32(nan))\n", "('ham', np.float32(nan))\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "ByYQdPDteGfw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "d0RYA72reEXj" }, "execution_count": null, "outputs": [] } ] }