diff --git a/experiments/cs433/README.md b/experiments/cs433/README.md new file mode 100644 index 0000000..5047347 --- /dev/null +++ b/experiments/cs433/README.md @@ -0,0 +1,16 @@ +# Experiments + +Luka Secilmis, Thomas Ecabert, Yanis De Busschere + +## Abstract + +This folder contains all the notebooks used for experimenting with the differents models during the project. + +## Summary of experiments + +| Experiment | Folder | Training Time | Prediction Time | Accuracy | F1-Score | +|---------------------|----------------------------------------------------|---------------|-----------------|----------|----------| +| BERT (english only) | [./en-spam-classifier](./en-spam-classifier) | 1h02 | 0.005s | 98.759 | 98.600 | +| BERT (multilingual) | [./multi-spam-classifier](./multi-spam-classifier) | 1h09 | 0.005 | 98.814 | 98.779 | + +All computation and time measurement were made using an NVIDIA RTX A5000. diff --git a/experiments/cs433/en-spam-classifier/README.md b/experiments/cs433/en-spam-classifier/README.md new file mode 100644 index 0000000..8c1d980 --- /dev/null +++ b/experiments/cs433/en-spam-classifier/README.md @@ -0,0 +1,13 @@ +### BERT (English only) + +## Abstract + +We developed an NLP-based spam classifier through a transfer learning approach, by fine-tuning a pre-trained English DistilBERT model on the Zenodo dataset for text classification. + +## Results + +| Training Time | Prediction Time | Accuracy | F1-Score | +|---------------|-----------------|----------|----------| +| 1h02 | 0.005s | 98.759 | 98.600 | + +All computation and time measurement were made using an NVIDIA RTX A5000. diff --git a/experiments/cs433/en-spam-classifier/training_eng_sc.ipynb b/experiments/cs433/en-spam-classifier/training_eng_sc.ipynb new file mode 100644 index 0000000..3e8e90f --- /dev/null +++ b/experiments/cs433/en-spam-classifier/training_eng_sc.ipynb @@ -0,0 +1,5616 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "3ILVeQZ5Gfew" + }, + "source": [ + "### EPFL CS-433 - Machine Learning Project 2\n", + "#### CERN - Zenodo: Adaptable Spam Filter Modelling for Digital Scientific Research Repository \n", + "Training a DistilBERT model for the task of english spam detection.\n", + "\n", + "Authors: Luka Secilmis, Yanis De Busschere, Thomas Ecabert" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "731Nc_OQF3bl" + }, + "outputs": [], + "source": [ + "# Install required packages\n", + "!pip install transformers\n", + "!pip install pandas\n", + "!pip install numpy\n", + "!pip install datasets\n", + "!pip install sklearn\n", + "!pip install torch" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "4gGUsIXLF_TI" + }, + "outputs": [], + "source": [ + "# Import required packages\n", + "import transformers\n", + "import pandas as pd\n", + "import numpy as np\n", + "import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5xpevkPtGCoZ", + "outputId": "9d35a50a-b3ec-46a2-d2b7-fa08ab5c9bd2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU: NVIDIA RTX A5000\n" + ] + } + ], + "source": [ + "# Set Hardware accelerator to GPU in Edit: Notebook Settings (in Google Colab)\n", + "# Check if GPU is available, this will significantly speed up fine-tuning\n", + "if torch.cuda.is_available(): \n", + " device = torch.device(\"cuda\") \n", + " print('GPU:', torch.cuda.get_device_name(0))\n", + "else:\n", + " print('No GPU available, do not train')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dBlaE9efGbfW" + }, + "source": [ + "## Import pre-processed data\n", + "Run script *feat-eng-esc.py* to generate the pre-processed data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "4z1ZyD0uGbyJ" + }, + "outputs": [], + "source": [ + "# Load the processed dataset\n", + "df = pd.read_csv('dataset-esc.csv')\n", + "df = df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WobbJ86FQMVP", + "outputId": "dcf6efbd-cae4-4e3c-e20d-6685e37a5fbc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(83778, 2)\n" + ] + } + ], + "source": [ + "print(df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "nHN7dKsdQT7s", + "outputId": "ce8aafbb-34cc-4922-861b-c755f358cebd" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " description label\n", + "0 FonePaw iPhone Data Recovery features in recov... 1\n", + "1 FonePaw iOS Transfer is mainly designed to tra... 1\n", + "2 This is my first upload 1\n", + "3 Lost photos from iPhone can be recovered with ... 1\n", + "4 I can’t play WLMP file directly, what player d... 1" + ], + "text/html": [ + "\n", + "
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test_trainer/checkpoint-13500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-12000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-14000\n", + "Configuration saved in test_trainer/checkpoint-14000/config.json\n", + "Model weights saved in test_trainer/checkpoint-14000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-12500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-14500\n", + "Configuration saved in test_trainer/checkpoint-14500/config.json\n", + "Model weights saved in test_trainer/checkpoint-14500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-13000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-15000\n", + "Configuration saved in test_trainer/checkpoint-15000/config.json\n", + "Model weights saved in test_trainer/checkpoint-15000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-13500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-15500\n", + "Configuration saved in test_trainer/checkpoint-15500/config.json\n", + "Model weights saved in test_trainer/checkpoint-15500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-14000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-16000\n", + "Configuration saved in test_trainer/checkpoint-16000/config.json\n", + "Model weights saved in test_trainer/checkpoint-16000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-14500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-16500\n", + "Configuration saved in test_trainer/checkpoint-16500/config.json\n", + "Model weights saved in test_trainer/checkpoint-16500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-15000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-17000\n", + "Configuration saved in test_trainer/checkpoint-17000/config.json\n", + "Model weights saved in test_trainer/checkpoint-17000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-15500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-17500\n", + "Configuration saved in test_trainer/checkpoint-17500/config.json\n", + "Model weights saved in test_trainer/checkpoint-17500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-16000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-18000\n", + "Configuration saved in test_trainer/checkpoint-18000/config.json\n", + "Model weights saved in test_trainer/checkpoint-18000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-16500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-18500\n", + "Configuration saved in test_trainer/checkpoint-18500/config.json\n", + "Model weights saved in test_trainer/checkpoint-18500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-17000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-19000\n", + "Configuration saved in test_trainer/checkpoint-19000/config.json\n", + "Model weights saved in test_trainer/checkpoint-19000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-17500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-19500\n", + "Configuration saved in test_trainer/checkpoint-19500/config.json\n", + "Model weights saved in test_trainer/checkpoint-19500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-18000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-20000\n", + "Configuration saved in test_trainer/checkpoint-20000/config.json\n", + "Model weights saved in test_trainer/checkpoint-20000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-18500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-20500\n", + "Configuration saved in test_trainer/checkpoint-20500/config.json\n", + "Model weights saved in test_trainer/checkpoint-20500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-19000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-21000\n", + "Configuration saved in test_trainer/checkpoint-21000/config.json\n", + "Model weights saved in test_trainer/checkpoint-21000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-19500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-21500\n", + "Configuration saved in test_trainer/checkpoint-21500/config.json\n", + "Model weights saved in test_trainer/checkpoint-21500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-20000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-22000\n", + "Configuration saved in test_trainer/checkpoint-22000/config.json\n", + "Model weights saved in test_trainer/checkpoint-22000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-20500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-22500\n", + "Configuration saved in test_trainer/checkpoint-22500/config.json\n", + "Model weights saved in test_trainer/checkpoint-22500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-21000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-23000\n", + "Configuration saved in test_trainer/checkpoint-23000/config.json\n", + "Model weights saved in test_trainer/checkpoint-23000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-21500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-23500\n", + "Configuration saved in test_trainer/checkpoint-23500/config.json\n", + "Model weights saved in test_trainer/checkpoint-23500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-22000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-24000\n", + "Configuration saved in test_trainer/checkpoint-24000/config.json\n", + "Model weights saved in test_trainer/checkpoint-24000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-22500] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-24500\n", + "Configuration saved in test_trainer/checkpoint-24500/config.json\n", + "Model weights saved in test_trainer/checkpoint-24500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-23000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-25000\n", + "Configuration saved in test_trainer/checkpoint-25000/config.json\n", + "Model weights saved in test_trainer/checkpoint-25000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-23500] due to args.save_total_limit\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=25134, training_loss=0.052604342072317574, metrics={'train_runtime': 3743.4451, 'train_samples_per_second': 53.711, 'train_steps_per_second': 6.714, 'total_flos': 2.6634689978167296e+16, 'train_loss': 0.052604342072317574, 'epoch': 3.0})" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Finally, we fine-tune our model\n", + "trainer.train() # Train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 236 + }, + "id": "gmYZ24ZTQqj2", + "outputId": "21940fd4-9e3b-4f3a-ddf2-2d43ac0a1cf6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "***** Running Evaluation *****\n", + " Num examples = 16756\n", + " Batch size = 8\n", + "The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "

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descriptionlabel
0Masih bingung dalam hal makanan penambah berat...1
1FonePaw iPhone Data Recovery features in recov...1
2FonePaw iOS Transfer is mainly designed to tra...1
3This is my first upload1
4Doyantoto merupakan sebuah website Agen Togel ...1
\n", + "" + ], + "text/plain": [ + " description label\n", + "0 Masih bingung dalam hal makanan penambah berat... 1\n", + "1 FonePaw iPhone Data Recovery features in recov... 1\n", + "2 FonePaw iOS Transfer is mainly designed to tra... 1\n", + "3 This is my first upload 1\n", + "4 Doyantoto merupakan sebuah website Agen Togel ... 1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YZPyhMFbQXiq", + "outputId": "c23765ac-aaf1-4ccd-eaae-fb84d0643295" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 52834\n", + "1 37784\n", + "Name: label, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['label'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eN38xG9dQZhz" + }, + "source": [ + "## Training Set-up" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "K1SyRzDPQcld" + }, + "outputs": [], + "source": [ + "# Split data into train and test sets\n", + "# Note: stratify on 'label' to preserve the same proportions of labels in each set as observed in the original dataset\n", + "train, test = train_test_split(df, test_size=0.2, stratify=df[['label']], random_state=42)\n", + "test_multi = test.copy()\n", + "test_no_en = test.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "1e6d4ba2b7b94f4dac9284a4f093d321", + "0cf595effd0e4cdcae790f2aec3fc176", + "80b55e96450648a880766c0492299029", + "7cd75a5a8b9e4365bb7adb7ce339b533", + "9dc7769a72244901883af1e209c02ab0", + "6e500e22b9ac47f29a2b60695eaeee1b", + "499d54d5f9d64ec5a7297780d01d7542", + "ccad7e6b754643d1872cbafef7158603", + 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StepTraining Loss
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75000.081400
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100000.047400
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145000.042600
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165000.045900
170000.037800
175000.046500
180000.036300
185000.029500
190000.029300
195000.027500
200000.029400
205000.024800
210000.013800
215000.027000
220000.020900
225000.039700
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test_trainer/checkpoint-26500/config.json\n", + "Model weights saved in test_trainer/checkpoint-26500/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-25000] due to args.save_total_limit\n", + "Saving model checkpoint to test_trainer/checkpoint-27000\n", + "Configuration saved in test_trainer/checkpoint-27000/config.json\n", + "Model weights saved in test_trainer/checkpoint-27000/pytorch_model.bin\n", + "Deleting older checkpoint [test_trainer/checkpoint-25500] due to args.save_total_limit\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=27186, training_loss=0.05157783799180919, metrics={'train_runtime': 4167.7462, 'train_samples_per_second': 52.182, 'train_steps_per_second': 6.523, 'total_flos': 2.880927479450419e+16, 'train_loss': 0.05157783799180919, 'epoch': 3.0})" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Finally, we fine-tune our model\n", + "trainer.train() # Training took about 1h15 with GPU: NVIDIA RTX A5000" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "gmYZ24ZTQqj2" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`, you can safely ignore this message.\n", + "***** Running Evaluation *****\n", + " Num examples = 18124\n", + " Batch size = 8\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "

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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'eval_loss': 0.05935591459274292,\n", + " 'eval_accuracy': 0.9881372765393953,\n", + " 'eval_runtime': 98.1932,\n", + " 'eval_samples_per_second': 184.575,\n", + " 'eval_steps_per_second': 23.077,\n", + " 'epoch': 3.0}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate model on test set\n", + "trainer.evaluate()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "nDHgPCeiQs23" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving model checkpoint to msc-model\n", + "Configuration saved in msc-model/config.json\n", + "Model weights saved in msc-model/pytorch_model.bin\n" + ] + } + ], + "source": [ + "# Save model\n", + "trainer.save_model(\"msc-model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance on Test Set: Multilingual Spam Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading configuration file msc-model/config.json\n", + "Model config DistilBertConfig {\n", + " \"_name_or_path\": \"msc-model\",\n", + " \"activation\": \"gelu\",\n", + " \"architectures\": [\n", + " \"DistilBertForSequenceClassification\"\n", + " ],\n", + " \"attention_dropout\": 0.1,\n", + " \"dim\": 768,\n", + " \"dropout\": 0.1,\n", + " \"hidden_dim\": 3072,\n", + " \"initializer_range\": 0.02,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_type\": \"distilbert\",\n", + " \"n_heads\": 12,\n", + " \"n_layers\": 6,\n", + " \"output_past\": true,\n", + " \"pad_token_id\": 0,\n", + " \"problem_type\": \"single_label_classification\",\n", + " \"qa_dropout\": 0.1,\n", + " \"seq_classif_dropout\": 0.2,\n", + " \"sinusoidal_pos_embds\": false,\n", + " \"tie_weights_\": true,\n", + " \"torch_dtype\": \"float32\",\n", + " \"transformers_version\": \"4.25.1\",\n", + " \"vocab_size\": 119547\n", + "}\n", + "\n", + "loading weights file msc-model/pytorch_model.bin\n", + "All model checkpoint weights were used when initializing DistilBertForSequenceClassification.\n", + "\n", + "All the weights of DistilBertForSequenceClassification were initialized from the model checkpoint at msc-model.\n", + "If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForSequenceClassification for predictions without further training.\n", + "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/fb240273126596a03b35c85793d2e82a5b13ac79/config.json\n", + "Model config DistilBertConfig {\n", + " \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n", + " \"activation\": \"gelu\",\n", + " \"architectures\": [\n", + " \"DistilBertForMaskedLM\"\n", + " ],\n", + " \"attention_dropout\": 0.1,\n", + " \"dim\": 768,\n", + " \"dropout\": 0.1,\n", + " \"hidden_dim\": 3072,\n", + " \"initializer_range\": 0.02,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_type\": \"distilbert\",\n", + " \"n_heads\": 12,\n", + " \"n_layers\": 6,\n", + " \"output_past\": true,\n", + " \"pad_token_id\": 0,\n", + " \"qa_dropout\": 0.1,\n", + " \"seq_classif_dropout\": 0.2,\n", + " \"sinusoidal_pos_embds\": false,\n", + " \"tie_weights_\": true,\n", + " \"transformers_version\": \"4.25.1\",\n", + " \"vocab_size\": 119547\n", + "}\n", + "\n", + "loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/fb240273126596a03b35c85793d2e82a5b13ac79/vocab.txt\n", + "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/fb240273126596a03b35c85793d2e82a5b13ac79/tokenizer.json\n", + "loading file added_tokens.json from cache at None\n", + "loading file special_tokens_map.json from cache at None\n", + "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/fb240273126596a03b35c85793d2e82a5b13ac79/tokenizer_config.json\n", + "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/fb240273126596a03b35c85793d2e82a5b13ac79/config.json\n", + "Model config DistilBertConfig {\n", + " \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n", + " \"activation\": \"gelu\",\n", + " \"architectures\": [\n", + " \"DistilBertForMaskedLM\"\n", + " ],\n", + " \"attention_dropout\": 0.1,\n", + " \"dim\": 768,\n", + " \"dropout\": 0.1,\n", + " \"hidden_dim\": 3072,\n", + " \"initializer_range\": 0.02,\n", + " \"max_position_embeddings\": 512,\n", + " \"model_type\": \"distilbert\",\n", + " \"n_heads\": 12,\n", + " \"n_layers\": 6,\n", + " \"output_past\": true,\n", + " \"pad_token_id\": 0,\n", + " \"qa_dropout\": 0.1,\n", + " \"seq_classif_dropout\": 0.2,\n", + " \"sinusoidal_pos_embds\": false,\n", + " \"tie_weights_\": true,\n", + " \"transformers_version\": \"4.25.1\",\n", + " \"vocab_size\": 119547\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "from transformers import pipeline\n", + "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n", + "# Load both models\n", + "model = AutoModelForSequenceClassification.from_pretrained('msc-model')\n", + "model_tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-multilingual-cased\")\n", + "\n", + "# Create a pipeline to facilitate the use of the model for classification\n", + "classifier = pipeline(\"text-classification\", model=model, tokenizer=model_tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Disabling tokenizer parallelism, we're using DataLoader multithreading already\n" + ] + } + ], + "source": [ + "y_true = test_multi['label'].tolist()\n", + "y_predict = classifier(test_multi['description'].map(lambda x: str(x)).tolist(), padding=True, truncation=True)\n", + "y_predict = [1 if pred['label'] == 'LABEL_1' else 0 for pred in y_predict]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 98.81372765393954 %\n", + "\n", + "F1 Score: 98.77898987594016 %\n", + "\n", + "True positives: 98.26650787349477 %\n", + "True negatives: 99.20507239519259 %\n" + ] + } + ], + "source": [ + "# compute accuracy\n", + "from sklearn.metrics import accuracy_score\n", + "accuracy_score(y_true, y_predict)\n", + "print(f'Accuracy: {100 * accuracy_score(y_true, y_predict)} %')\n", + "print()\n", + "# compute f1 score\n", + "from sklearn.metrics import f1_score\n", + "f1_score(y_true, y_predict, average='macro')\n", + "print(f'F1 Score: {100 * f1_score(y_true, y_predict, average=\"macro\")} %')\n", + "print()\n", + "# compute confusion matrix\n", + "from sklearn.metrics import confusion_matrix\n", + "confusion_matrix = confusion_matrix(y_true, y_predict)\n", + "\n", + "print('True positives: ', 100 * confusion_matrix[1][1]/(confusion_matrix[1][1] + confusion_matrix[1][0]), '%')\n", + "print('True negatives: ', 100 * confusion_matrix[0][0]/(confusion_matrix[0][0] + confusion_matrix[0][1]), '%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance on Test Set: all non-english languages Spam Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n" + ] + } + ], + "source": [ + "# Filter out all the english texts\n", + "import re\n", + "from ftlangdetect import detect\n", + "CLEANING_REGEX = re.compile(r'[^a-zA-Z0-9\\s]', re.MULTILINE)\n", + "def detect_lang(descr):\n", + " d = CLEANING_REGEX.sub('', str(descr))\n", + " d = d.replace('\\r', ' ').replace('\\n', ' ')\n", + " lang = detect(d)['lang']\n", + " return lang\n", + "\n", + "test_no_en['lang'] = test_no_en['description'].map(lambda x: detect_lang(x))\n", + "test_no_en = test_no_en[test_no_en['lang'] != 'en']\n", + "test_no_en = test_no_en.drop(columns=['lang'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "y_true = test_no_en['label'].tolist()\n", + "y_predict = classifier(test_no_en['description'].map(lambda x: str(x)).tolist(), padding=True, truncation=True)\n", + "y_predict = [1 if pred['label'] == 'LABEL_1' else 0 for pred in y_predict]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 98.31041257367387 %\n", + "\n", + "F1 Score: 97.48498193971817 %\n", + "\n", + "True positives: 98.50746268656717 %\n", + "True negatives: 97.57009345794393 %\n" + ] + } + ], + "source": [ + "# compute accuracy\n", + "from sklearn.metrics import accuracy_score\n", + "accuracy_score(y_true, y_predict)\n", + "print(f'Accuracy: {100 * accuracy_score(y_true, y_predict)} %')\n", + "print()\n", + "# compute f1 score\n", + "from sklearn.metrics import f1_score\n", + "f1_score(y_true, y_predict, average='macro')\n", + "print(f'F1 Score: {100 * f1_score(y_true, y_predict, average=\"macro\")} %')\n", + "print()\n", + "# compute confusion matrix\n", + "from sklearn.metrics import confusion_matrix\n", + "confusion_matrix = confusion_matrix(y_true, y_predict)\n", + "print('True positives: 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