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| "execution_count": 1, |
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| "outputs": [], |
| "source": [ |
| "!pip install tf-keras --quiet\n", |
| "import os\n", |
| "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "#step model selection an" |
| ], |
| "metadata": { |
| "id": "4USUQ7J5AA7b" |
| } |
| }, |
| { |
| "cell_type": "code", |
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| "!pip install transformers==4.44.2" |
| ], |
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| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "LMIh2vrL6bNY", |
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| "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", |
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| "Collecting huggingface-hub<1.0,>=0.23.2 (from transformers==4.44.2)\n", |
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| "Collecting tokenizers<0.20,>=0.19 (from transformers==4.44.2)\n", |
| " Downloading tokenizers-0.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n", |
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| "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers==4.44.2) (1.4.3)\n", |
| "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers==4.44.2) (4.15.0)\n", |
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| "\u001b[?25hDownloading tokenizers-0.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n", |
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| "\u001b[?25hInstalling collected packages: 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", |
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| "13ef7295cc9043989bd10d989080e026", |
| "613d45f38ff0443b85c51b0d58409325" |
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| }, |
| "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<?, ? examples/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "0d908a86c3d345cba8058f7a64fcb4e8" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "execute_result", |
| "data": { |
| "text/plain": [ |
| "DatasetDict({\n", |
| " train: Dataset({\n", |
| " features: ['labels', 'Message'],\n", |
| " num_rows: 5573\n", |
| " })\n", |
| "})" |
| ] |
| }, |
| "metadata": {}, |
| "execution_count": 6 |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "dataset = data[\"train\"]\n", |
| "\n", |
| "print(dataset[0])\n", |
| "print(dataset.features) #see structure" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "85uo3uOe8lKl", |
| "outputId": "c63a5efb-97b7-4a9b-d761-f6ccb322b24c" |
| }, |
| "execution_count": 7, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "{'labels': 0, 'Message': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...'}\n", |
| "{'labels': ClassLabel(names=['ham', 'spam', '{\"mode\":\"full\"']), 'Message': Value('string')}\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "split = dataset.train_test_split(test_size=0.2, seed=42)\n", |
| "\n", |
| "\n", |
| "val_test = split[\"test\"].train_test_split(test_size=0.5, seed=42)\n", |
| "\n", |
| "final_data ={\n", |
| " \"train\": split[\"train\"],\n", |
| " \"validation\": val_test[\"train\"],\n", |
| " \"test\": val_test[\"test\"]\n", |
| "}\n", |
| "\n", |
| "\n", |
| "print(\"Train size:\", len(final_data[\"train\"]))\n", |
| "print(\"Val size:\", len(final_data[\"validation\"]))\n", |
| "print(\"Test size:\", len(final_data[\"test\"]))\n" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "FPoJcFE683Yb", |
| "outputId": "c0954f33-d339-44a8-db93-ae3c56bd36ad" |
| }, |
| "execution_count": 8, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Train size: 4458\n", |
| "Val size: 557\n", |
| "Test size: 558\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "| Model | Size | CPU Speed | Best For | When to Use |\n", |
| "| ------------------- | ---------- | ------------- | ---------------------- | ------------------------------ |\n", |
| "| **DistilBERT** | Small | Fast | General classification | Best default choice |\n", |
| "| **MiniLM** | Very Small | Very Fast | Low RAM / old laptop | Best CPU efficiency |\n", |
| "| **RoBERTa-base** | Medium | Moderate | Better accuracy | If CPU can handle slower speed |\n", |
| "| **DeBERTa-v3-base** | Medium | Moderate/Slow | Highest accuracy | Final model for best results |" |
| ], |
| "metadata": { |
| "id": "pkuSnbluCkKm" |
| } |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "| Model Name | Full Form | Best Use Case | Why Use It |\n", |
| "| -------------- | ------------------------------------------------------- | ------------------------------------- | ------------------------------------ |\n", |
| "| **BERT Base** | Bidirectional Encoder Representations from Transformers | General text classification | Standard model, balanced performance |\n", |
| "| **BERT Large** | Same as above | High accuracy tasks | Better performance, but heavy |\n", |
| "| **DistilBERT** | Distilled BERT | Fast inference / low GPU | Smaller + faster than BERT |\n", |
| "| **RoBERTa** | Robustly Optimized BERT | Sentiment, spam, topic classification | Often better than BERT |\n", |
| "| **ALBERT** | A Lite BERT | Low memory training | Fewer parameters |\n", |
| "| **TinyBERT** | Compact BERT | Mobile apps / deployment | Very lightweight |\n", |
| "| **MobileBERT** | Mobile BERT | Edge devices | Optimized for mobile |\n", |
| "| **ELECTRA** | Efficiently Learning Encoder | Fast training + strong results | Better sample efficiency |\n", |
| "| **DeBERTa** | Decoding-enhanced BERT | SOTA classification tasks | Excellent benchmark accuracy |\n", |
| "| **XLNet** | Generalized Autoregressive Pretraining | Complex context tasks | Better context modeling |" |
| ], |
| "metadata": { |
| "id": "aKTQhjjmBKcX" |
| } |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "🎯 Easiest Rule to Remember\n", |
| "\n", |
| "For Small Dataset (few hundred to few thousand rows)\n", |
| "\n", |
| "✅ DeBERTa-v3-base → best accuracy\n", |
| "\n", |
| "✅ RoBERTa-base → very strong alternative\n", |
| "\n", |
| "✅ DistilBERT → faster if CPU weak\n", |
| "\n", |
| "For Large Dataset (10k+ rows)\n", |
| "\n", |
| "✅ DistilBERT → balanced speed + good accuracy\n", |
| "\n", |
| "✅ MiniLM → fastest on CPU\n", |
| "\n", |
| "✅ RoBERTa-base → if you can wait longer" |
| ], |
| "metadata": { |
| "id": "ss143q3KCtZ8" |
| } |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "#step 2 model selection\n", |
| "#choose model and tokenizers\n", |
| "model_name = \"distilbert-base-uncased\" #non case sensitive\n", |
| "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", |
| "\n", |
| "\n" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/", |
| "height": 304, |
| "referenced_widgets": [ |
| "4a9582e4c2684f29a4993eec5f07538d", |
| "b42db50b0604417c90b21bfcf093d81b", |
| "caa673274ffd445da954de6a6c3b3e81", |
| "e98b2c0373804727ad91c37b64c2ac4b", |
| "74877712dd354c08a3732ab5808752dd", |
| "76e5584acf9f435ba70a2cb457e3a253", |
| "36650081328d4360b461b800650522ce", |
| "14c20fda09634bcdba9dadbeaf62c30e", |
| "f500743255df4b20828b85d0dca2d5d4", |
| "c1d31dc15ff54838bcdd44c74a3de889", |
| "2d12d81ed05f4a16a004513f00362f92", |
| "27ebe0ff0af54fa2a0dda96412e876e4", |
| "77a9f6b9451b498280927627c49673af", |
| "4267bf0383ea40119578c4fd7686ed3e", |
| "37353fae0d7d40788868c6925eb13f89", |
| "d641ef36c17c48d0b8431adb7784605a", |
| "3f2fdf0eb54844cdb0ab38ffceb222a0", |
| "2bf1e78d22354e6b908121d88fe467f4", |
| "0fe480ba9f73495ea08d5af0d8f3b42a", |
| "7551f93e281745f0a723cf6c830ce1d1", |
| "0a2a27e0c0e841d1a5597fdd77a8ca87", |
| "4305dc48518f4d6a95ec3d1d3171fd53", |
| "2268db74fcfd4fedbfd272ca35a05629", |
| "9c527383b1104baa96c56c06c34a879b", |
| "f3bf7cf5b94b4c57bffd37676eece256", |
| "40bacb569a35493cbc086cab6a731147", |
| "ffa1f80dbac84539b1f2d264b63005a9", |
| "c5adf8a6f2584e5aa23712c3778eca6a", |
| "1d593803d6a34de79badae2938a947ee", |
| "1dd8d239a03c467aac55f2a417b83fa4", |
| "b7517793da314e879ff9799c22f442f7", |
| "40ee2bca3b914de5ac6608056bc7f298", |
| "8e741246285e4038a21a8cdbe8a3bfe4", |
| "2ab61b63527042aab4235991e97d200a", |
| "3b4d03ad5b8c45549b0f87e6a94d83e8", |
| "96b8e3c6929c4f2ba5b106e5f684020a", |
| "90af2c47e81d4c2ab3bae06f808da080", |
| "d812b0d94b5a447f986cda264ee5f082", |
| "2863e17c18154e05becf3999cfdf1562", |
| "85d61a4b844444cc83744eb349e58811", |
| "5e38ebb83ef4465ab29879630851b486", |
| "82f41e7fcaaf47e59dbb44a15165102c", |
| "ce6295c98f954a62a91f7415a2fa88aa", |
| "f2edea8bd0f347aeaaf29ff585d6d8c9" |
| ] |
| }, |
| "id": "SGIezwZd-Gry", |
| "outputId": "0b795d37-0195-47f1-e368-3fb4e62f00a6" |
| }, |
| "execution_count": 9, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stderr", |
| "text": [ |
| "/usr/local/lib/python3.12/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": [ |
| "tokenizer_config.json: 0%| | 0.00/48.0 [00:00<?, ?B/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "4a9582e4c2684f29a4993eec5f07538d" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "display_data", |
| "data": { |
| "text/plain": [ |
| "config.json: 0%| | 0.00/483 [00:00<?, ?B/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "27ebe0ff0af54fa2a0dda96412e876e4" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "display_data", |
| "data": { |
| "text/plain": [ |
| "vocab.txt: 0.00B [00:00, ?B/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "2268db74fcfd4fedbfd272ca35a05629" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "display_data", |
| "data": { |
| "text/plain": [ |
| "tokenizer.json: 0.00B [00:00, ?B/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "2ab61b63527042aab4235991e97d200a" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "stream", |
| "name": "stderr", |
| "text": [ |
| "/usr/local/lib/python3.12/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": [ |
| "def cut_message(examples): #tokenization function\n", |
| " return tokenizer(\n", |
| " examples[\"Message\"],\n", |
| " truncation=True, #this cut too long message\n", |
| " padding = \"max_length\", #add empty space if short\n", |
| " max_length=128\n", |
| " )\n" |
| ], |
| "metadata": { |
| "id": "2n2nSTycDw9Q" |
| }, |
| "execution_count": 10, |
| "outputs": [] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "model_name = \"distilbert-base-uncased\"\n", |
| "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", |
| "tokenized = {}\n", |
| "\n", |
| "for key in [\"train\", \"validation\", \"test\"]:\n", |
| " tokenized[key] = final_data[key].map(\n", |
| " cut_message,\n", |
| " batched=True,\n", |
| " remove_columns=[\"Message\"]\n", |
| " )" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/", |
| "height": 113, |
| "referenced_widgets": [ |
| "414f357e5cfe4cfe85004fca32cc7ffa", |
| "d75ba19a48864753902c8487d985605e", |
| "6b6b5004b67c4baaab0ac0d21786f709", |
| "b7eb6ebde72244099230c2d23609de3e", |
| "637a70cd8e6d4c5d87c5b728ec6cecd7", |
| "3c2ddf72338d42e781dcd211e301e593", |
| "4a3b8fd4f51046038bc900030b705f53", |
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| "cf0233a9ac004198b145a2dcfda0e590", |
| "68d6ee3f7e4c45549a08b62d86e5fce2", |
| "b484130df4f14eb885de19582e9cfad9", |
| "fb936cebe3bf411f8cc55048236628bb", |
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| "3b1d4e8f853d416794e5f706aa4befe6", |
| "c3b36a45ff4549d994852f1db5059c66" |
| ] |
| }, |
| "id": "rLeyIUWkE8TI", |
| "outputId": "2db2a747-18e2-4c51-adb8-9f17f4d1165a" |
| }, |
| "execution_count": 11, |
| "outputs": [ |
| { |
| "output_type": "display_data", |
| "data": { |
| "text/plain": [ |
| "Map: 0%| | 0/4458 [00:00<?, ? examples/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "414f357e5cfe4cfe85004fca32cc7ffa" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "display_data", |
| "data": { |
| "text/plain": [ |
| "Map: 0%| | 0/557 [00:00<?, ? examples/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "fb936cebe3bf411f8cc55048236628bb" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "display_data", |
| "data": { |
| "text/plain": [ |
| "Map: 0%| | 0/558 [00:00<?, ? examples/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "9f22937e57094b91928762819ba42043" |
| } |
| }, |
| "metadata": {} |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "#creating dataset objects\n", |
| "\n", |
| "train_set = tokenized[\"train\"].to_tf_dataset(\n", |
| " columns=[\"input_ids\", \"attention_mask\"],\n", |
| " label_cols=[\"labels\"],\n", |
| " shuffle=True,\n", |
| " batch_size=16\n", |
| ")\n", |
| "\n", |
| "val_set = tokenized[\"validation\"].to_tf_dataset(\n", |
| " columns=[\"input_ids\", \"attention_mask\"],\n", |
| " label_cols=[\"labels\"],\n", |
| " shuffle=False,\n", |
| " batch_size=32\n", |
| ")\n", |
| "\n", |
| "test_set = tokenized[\"test\"].to_tf_dataset(\n", |
| " columns=[\"input_ids\", \"attention_mask\"],\n", |
| " label_cols=[\"labels\"],\n", |
| " shuffle=False,\n", |
| " batch_size=32\n", |
| ")" |
| ], |
| "metadata": { |
| "id": "7hLs2m8AFapb", |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "outputId": "e0789fa9-0772-47c3-9289-642e38ed3b06" |
| }, |
| "execution_count": 12, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stderr", |
| "text": [ |
| "/usr/local/lib/python3.12/dist-packages/datasets/arrow_dataset.py:403: FutureWarning: The output of `to_tf_dataset` will change when a passing single element list for `labels` or `columns` in the next datasets version. To return a tuple structure rather than dict, pass a single string.\n", |
| "Old behaviour: columns=['a'], labels=['labels'] -> (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<?, ?B/s]" |
| ], |
| "application/vnd.jupyter.widget-view+json": { |
| "version_major": 2, |
| "version_minor": 0, |
| "model_id": "d4662d805b884921a039afecdeb352f7" |
| } |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "stream", |
| "name": "stderr", |
| "text": [ |
| "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_transform.bias']\n", |
| "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n", |
| "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n", |
| "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "import tensorflow as tf\n", |
| "from tensorflow.keras.optimizers import Adam\n", |
| "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n", |
| "\n", |
| "\n", |
| "model.compile(\n", |
| " optimizer=Adam(learning_rate=3e-5),\n", |
| " loss=SparseCategoricalCrossentropy(from_logits=True),\n", |
| " metrics=[\"accuracy\"]\n", |
| ")" |
| ], |
| "metadata": { |
| "id": "LtiDhLZGZOwJ" |
| }, |
| "execution_count": 14, |
| "outputs": [] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "# model fitting\n", |
| "history = model.fit(\n", |
| " train_set,\n", |
| " validation_data=val_set,\n", |
| " epochs= 5 , #try 3-5 usually enough\n", |
| " verbose = 1\n", |
| ")" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "3M9RCOsDZ9Az", |
| "outputId": "eb6c9dda-120d-4bdc-aa64-3666f2ae674b" |
| }, |
| "execution_count": 15, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Epoch 1/5\n", |
| "279/279 [==============================] - 104s 257ms/step - loss: nan - accuracy: 0.9125 - val_loss: nan - val_accuracy: 0.8779\n", |
| "Epoch 2/5\n", |
| "279/279 [==============================] - 60s 215ms/step - loss: nan - accuracy: 0.8632 - val_loss: nan - val_accuracy: 0.8779\n", |
| "Epoch 3/5\n", |
| "279/279 [==============================] - 58s 207ms/step - loss: nan - accuracy: 0.8632 - val_loss: nan - val_accuracy: 0.8779\n", |
| "Epoch 4/5\n", |
| "279/279 [==============================] - 57s 206ms/step - loss: nan - accuracy: 0.8632 - val_loss: nan - val_accuracy: 0.8779\n", |
| "Epoch 5/5\n", |
| "279/279 [==============================] - 57s 205ms/step - loss: nan - accuracy: 0.8632 - val_loss: nan - val_accuracy: 0.8779\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "#testing data\n", |
| "loss, acc = model.evaluate(test_set)\n", |
| "print(f\"Final accuracy on unseen message: {acc:.4f} -> {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": [ |
| " |
| "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": [] |
| } |
| ] |
| } |
