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" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=400, training_loss=1.9328853976726532, metrics={'train_runtime': 206.7338, 'train_samples_per_second': 28.539, 'train_steps_per_second': 1.935, 'total_flos': 385405747200000.0, 'train_loss': 1.9328853976726532, 'epoch': 50.0})" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Trainer class to handle training process\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=tokenized_dataset,\n", + " tokenizer=tokenizer,\n", + ")" + ], + "metadata": { + "id": "j3E9twuuUrIU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Train the model\n", + "trainer.train()" + ], + "metadata": { + "id": "v1Ljs1KtUso6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Save the model\n", + "trainer.save_model('counsel_model')" + ], + "metadata": { + "id": "hJ8rPSu9R2p5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Function to generate a response based on a user prompt (testing the model)\n", + "def generate_response(prompt):\n", + " inputs = tokenizer.encode(prompt, return_tensors=\"pt\")\n", + " outputs = model.generate(inputs, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)\n", + "\n", + " # Decode the generated output\n", + " response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", + "\n", + " # Remove the prompt from the response\n", + " if response.startswith(prompt):\n", + " response = response[len(prompt):].strip() # Remove the prompt from the response\n", + "\n", + " return response\n" + ], + "metadata": { + "id": "_qzU1wQCAJTn" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Example conversation\n", + "user_input = \"How can I become a Data Scientist?\"\n", + "bot_response = generate_response(user_input)\n", + "print(\"Bot Response:\", bot_response)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ak7qgAqINjr_", + "outputId": "1f84f69a-66e8-4551-fb87-081010dc07aa" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Bot Response: Data scientists are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained to be data scientists. They are trained\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Copying the model to Google Drive (optional)\n", + "import shutil\n", + "\n", + "# Path to the file in Colab\n", + "colab_file_path = '/content/counsel_model/model.safetensors'\n", + "\n", + "# Path to your Google Drive\n", + "drive_file_path = '/content/drive/MyDrive'\n", + "\n", + "# Copy the file\n", + "shutil.copy(colab_file_path, drive_file_path)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "A1kADNirSF70", + "outputId": "f8491430-e191-4c07-9b99-2fe801a6b57f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'/content/drive/MyDrive/model.safetensors'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 13 + } + ] + } + ] +} \ No newline at end of file