{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Notebook for Toxic Comment Classification Challenge\n\n### State of the Art Method of Attention model is used.\n### XLM-Roberta Model which is capable of cross-lingual machine translation and language modelling is used."},{"metadata":{},"cell_type":"markdown","source":"## Importing necessary libraries"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport gc\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Dense, Input, Dropout\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nfrom kaggle_datasets import KaggleDatasets\nimport transformers\nfrom transformers import TFAutoModel, AutoTokenizer\nfrom tqdm.notebook import tqdm\nfrom tokenizers import Tokenizer, models, pre_tokenizers, decoders, processors","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Supporting Functions"},{"metadata":{"trusted":true},"cell_type":"code","source":"def regular_encode(texts, tokenizer, maxlen=512):\n    enc_di = tokenizer.batch_encode_plus(\n        texts, \n        return_attention_masks=False, \n        return_token_type_ids=False,\n        pad_to_max_length=True,\n        max_length=maxlen\n    )\n    \n    return np.array(enc_di['input_ids'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def build_model(transformer, max_len=512):\n   \n    input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n    sequence_output = transformer(input_word_ids)[0]\n    cls_token = sequence_output[:, 0, :]\n    out = Dense(1, activation='sigmoid')(cls_token)\n    \n    model = Model(inputs=input_word_ids, outputs=out)\n    model.compile(Adam(lr=1e-5), loss='binary_crossentropy', metrics=['accuracy'])\n    \n    return model","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Configuring TPU"},{"metadata":{"trusted":true},"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nelse:\n    strategy = tf.distribute.get_strategy()\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"AUTO = tf.data.experimental.AUTOTUNE\n\nGCS_DS_PATH = KaggleDatasets().get_gcs_path('jigsaw-multilingual-toxic-comment-classification')\n\nEPOCHS = 3\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\nMAX_LEN = 192\nMODEL = 'jplu/tf-xlm-roberta-large'","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Creating tokenizer"},{"metadata":{"trusted":true},"cell_type":"code","source":"# First load the real tokenizer\ntokenizer = AutoTokenizer.from_pretrained(MODEL)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Load model into the TPU"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nwith strategy.scope():\n    transformer_layer = TFAutoModel.from_pretrained(MODEL)\n    model = build_model(transformer_layer, max_len=MAX_LEN)\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# **Training ** "},{"metadata":{"trusted":true},"cell_type":"code","source":"train1 = pd.read_csv(\"/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-toxic-comment-train.csv\")\ntrain2 = pd.read_csv(\"/kaggle/input/jigsaw-multilingual-toxic-comment-classification/jigsaw-unintended-bias-train.csv\")\nvalid = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/validation.csv')\ntest = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/test.csv')\nsub = pd.read_csv('/kaggle/input/jigsaw-multilingual-toxic-comment-classification/sample_submission.csv')\ntrain2.toxic = train2.toxic.round().astype(int)\ntrain = pd.concat([train1[['comment_text', 'toxic']],\n    train2[['comment_text', 'toxic']].query('toxic==1'),\n    train2[['comment_text', 'toxic']].query('toxic==0').sample(n=100000)\n    ])\ndel train1,train2\nx_train = regular_encode(train.comment_text.values, tokenizer, maxlen=MAX_LEN)\nx_valid = regular_encode(valid.comment_text.values, tokenizer, maxlen=MAX_LEN)\nx_test = regular_encode(test.content.values, tokenizer, maxlen=MAX_LEN)\ny_train = train.toxic.values\ny_valid = valid.toxic.values\ntrain_dataset = (\n    tf.data.Dataset\n    .from_tensor_slices((x_train, y_train))\n    .repeat()\n    .shuffle(2048)\n    .batch(BATCH_SIZE)\n    .prefetch(AUTO)\n)\n\nvalid_dataset = (\n    tf.data.Dataset\n    .from_tensor_slices((x_valid, y_valid))\n    .batch(BATCH_SIZE)\n    .cache()\n    .prefetch(AUTO)\n)\n\ntest_dataset = (\n    tf.data.Dataset\n    .from_tensor_slices(x_test)\n    .batch(BATCH_SIZE)\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"n_steps = x_train.shape[0] // BATCH_SIZE\ntrain_history = model.fit(\n    train_dataset,\n    steps_per_epoch=n_steps,\n    validation_data=valid_dataset,\n    epochs=EPOCHS\n)\n\nEPOCHS_V=10\nn_steps = x_valid.shape[0] // BATCH_SIZE\ntrain_history_2 = model.fit(\n    valid_dataset.repeat(),\n    steps_per_epoch=n_steps,\n    epochs=EPOCHS_V\n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Submission"},{"metadata":{"trusted":true},"cell_type":"code","source":"sub['toxic'] = model.predict(test_dataset, verbose=1)\nsub.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}