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"metadata": { + "id": "fDxkomU9ID4-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#cargamos el dataset completo\n", + "dataset = load_dataset(\"FredZhang7/all-scam-spam\")\n", + "print(dataset['train'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 250, + "referenced_widgets": [ + "89b405e70fde4c0294cb19c1e2ee16b3", + "0dc777598a0b430a895a981a019a916e", + "c172346d6c3047af9e7c63292e719738", + "c79d53254d0541789b825628899c976f", + "5c0270bfb640440f9c6de5d3dd8b4319", + "9435e358e58d42059f831f6872d7d2c6", + "cbd0afb46d5b47d1ab4afda408f64f1b", + "72d61429863743399e20ec76cb410163", + "6fec7af1b3de44fa911338bd9db4f449", + "f387ca212b1649a793616a8147db0310", + "1b01a4f3cb8843cc80e7d6b4a4de2613", + "de4ac7e3927e473d8468d2ae98b85007", + "e8697a98accf4ddebb78170dc7ce3086", + "a62a3d849af048dba346db4ed6496220", + "0c29b692f56b431eadcb601eea382343", + "3043ded3f7c54817965c1b723d4282e9", + 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textis_spam
0Re: New Sequences Window\\n\\n Date: W...0
1[zzzzteana] RE: Alexander\\n\\nMartin A posted:\\...0
2[zzzzteana] Moscow bomber\\n\\nMan Threatens Exp...0
3[IRR] Klez: The Virus That Won't Die\\n\\nKlez:...0
4Re: [zzzzteana] Nothing like mama used to make...0
.........
42614Какой у тебя любимый цвет? Фантастический един...1
42615Что ты сегодня ел, Чудо-печенье1
42616Что ты думаешь о спорте? Секретный ключ1
42617Какой твой любимый вид спорта, Волшебный ковер1
42618Ты умеешь петь? Чудо-птица1
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textis_spam
37790No. She's currently in scotland for that.0
12273timesheets\\n\\nhello all :\\ni am afraid i did n...0
15159vp & director count for the research group\\n\\n...0
14437re : mr . sud\\n\\nvince ,\\nit never hurts ( or ...0
7386hpl expense contracts\\n\\nfyi . . . . . . . . c...0
.........
36826Alright, I'll head out in a few minutes, text ...0
28801full time analysts opp .\\n\\nthis candidate is ...0
561Since we were on the subject...\\n\\n\\nGays, gay...0
15536special report ! tivo : now or never ?\\n\\nin t...1
36290increase your penis size\\n\\nyes . . it is poss...1
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tf.data.Dataset.from_tensor_slices((\n", + " dict(train_encodings),\n", + " y_train\n", + "))\n", + "test_dataset = tf.data.Dataset.from_tensor_slices((\n", + " dict(test_encodings),\n", + " y_test\n", + "))\n", + "\n", + "# Load the pre-trained DistilBERT model\n", + "model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')\n", + "\n", + "# Compile the model\n", + "model.compile(\n", + " optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),\n", + " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " metrics=tf.metrics.SparseCategoricalAccuracy()\n", + ")\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_dataset.shuffle(1000).batch(8),\n", + " epochs=2,\n", + " batch_size=8,\n", + " validation_data=test_dataset.batch(16)\n", + ")\n", + "\n", + "# Save the model\n", + "model.save_pretrained('./saved_model/')\n", + "# Save tokenizer\n", + "tokenizer.save_pretrained('./saved_model')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 451, + "referenced_widgets": [ + "01df8ebb4f6d415bb44820443d56a72b", + "42ee8b7b825e45fe9d21c58c0d8560da", + "3430c61e5e9c43e2b1708b17902aeab1", + "a117c261afb1401eb116b402f2dcca60", + "1434712a84924c00a888dc311dec4f8e", + "6be9246533cc4c6e95233f8afb287a11", + "5a48a65de1864a7aa3b6e7bca0960614", + "8ea20c755eaa49e2ae9883e5fa50e309", + "82fff6ddaa5645de96c8fb224de43235", + "f16365856b5845f48dd26af2cf067059", + "19d0e92512094469940d69af8cc08619", + "e8390fac983a40369ff86d98585a124f", + "8f01908e135645289f1c4dc53518ed73", + "091520e89e914b18bcaa92adca682074", + "3683be3647c4445ebc5eaed9f00ccb65", + "8e3af97a942c4c2e8bbd7c7e13caa31e", + "760c8e2971224bf993c50a4fe17a14d0", + "4f93200d961243dea592f81847a98e67", + "d8a2cf1d5650423cbe3dd871c585b624", + "cad9106e56e14014917b0e12b690565a", + "408ede19e5d1405fa2671e2f86c76dc2", + "328482713c9e4826b0274045b5b8cdfc", + "098975b9aeb1482390e0bd3d67b59764", + "7faa476c52e2462e91d423f236f9d0e1", + 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