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"### Data Preparation"
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"# Import pandas for data analysis\n",
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"\n",
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" Questions \\\n",
"0 What does it mean to have a mental illness? \n",
"1 Who does mental illness affect? \n",
"2 What causes mental illness? \n",
"3 What are some of the warning signs of mental i... \n",
"4 Can people with mental illness recover? \n",
"\n",
" Answers \n",
"0 Mental illnesses are health conditions that di... \n",
"1 It is estimated that mental illness affects 1 ... \n",
"2 It is estimated that mental illness affects 1 ... \n",
"3 Symptoms of mental health disorders vary depen... \n",
"4 When healing from mental illness, early identi... "
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" What does it mean to have a mental illness? | \n",
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\n",
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\n",
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\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
"
\n",
" [400/400 03:25, Epoch 50/50]\n",
"
\n",
" \n",
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" | Step | \n",
" Training Loss | \n",
"
\n",
" \n",
" \n",
" \n",
" | 10 | \n",
" 5.219500 | \n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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" 2.656700 | \n",
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\n",
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\n",
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\n",
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" 2.380500 | \n",
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\n",
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\n",
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\n",
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\n",
" \n",
" | 160 | \n",
" 2.032700 | \n",
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\n",
" \n",
" | 170 | \n",
" 2.000100 | \n",
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\n",
" \n",
" | 180 | \n",
" 1.878100 | \n",
"
\n",
" \n",
" | 190 | \n",
" 1.858100 | \n",
"
\n",
" \n",
" | 200 | \n",
" 1.740900 | \n",
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\n",
" \n",
" | 210 | \n",
" 1.680400 | \n",
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\n",
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\n",
" \n",
" | 230 | \n",
" 1.541100 | \n",
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\n",
" \n",
" | 240 | \n",
" 1.465300 | \n",
"
\n",
" \n",
" | 250 | \n",
" 1.399800 | \n",
"
\n",
" \n",
" | 260 | \n",
" 1.374100 | \n",
"
\n",
" \n",
" | 270 | \n",
" 1.254700 | \n",
"
\n",
" \n",
" | 280 | \n",
" 1.264400 | \n",
"
\n",
" \n",
" | 290 | \n",
" 1.131000 | \n",
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\n",
" \n",
" | 300 | \n",
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\n",
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\n",
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" | 320 | \n",
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\n",
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" | 330 | \n",
" 0.801200 | \n",
"
\n",
" \n",
" | 340 | \n",
" 0.742700 | \n",
"
\n",
" \n",
" | 350 | \n",
" 0.655500 | \n",
"
\n",
" \n",
" | 360 | \n",
" 0.630200 | \n",
"
\n",
" \n",
" | 370 | \n",
" 0.538000 | \n",
"
\n",
" \n",
" | 380 | \n",
" 0.478900 | \n",
"
\n",
" \n",
" | 390 | \n",
" 0.406600 | \n",
"
\n",
" \n",
" | 400 | \n",
" 0.371700 | \n",
"
\n",
" \n",
"
"
]
},
"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
}
]
}
]
}