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0What does it mean to have a mental illness?Mental illnesses are health conditions that di...
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2What causes mental illness?It is estimated that mental illness affects 1 ...
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Addictions may not be included in PPMB applications. \\n PWD benefits are for people who have a disability that prevents them from working or going about their daily activities. Find information from the Ministry and read a fact sheet from the Disability Alliance of BC with information about the application process. \\n PPMB benefits are for people who experience multiple barriers to work. Find information from the Ministry and read a fact sheet from the Disability Alliance of BC with information about the application process. \\n The Disability Alliance of BC (formerly the BC Coalition of People with Disabilities) and help you navigate PWD and PPMB programs as well as Canada Pension Plan benefits. They have in-depth information on preparing your application, working through the application process, and handling appeals. PovNet can also connect you with an advocate to help you through the application process. \\n For more on provincial PWD benefits and advocacy tips, see Getting By on Social Assistance: Navigating the \\u2018welfare\\u2019 system or read other articles in our Income issue of Visions Journal.\",\n \"Prodrome is a medical term for early signs or symptoms of an illness or health problem that appear before the major signs or symptoms start. \\n Psychosis, a group of symptoms found in disorders like schizophrenia, is one illness with a specific prodrome. With psychosis, prodrome signs and symptoms start before full symptoms like hallucinations and delusions appear. \\n Common symptoms in the prodrome of psychosis include: \\n These symptoms are found in many other mental illnesses, so it isn\\u2019t always clear at the time if this is a prodrome of psychosis or schizophrenia or something else. It\\u2019s best to seek help whenever you are concerned about your mental health or a loved one\\u2019s mental health. Psychosis and schizophrenia are much easier to manage and treat when treatment starts early.\",\n \"When healing from mental illness, early identification and treatment are of vital importance. Based on the nature of the illness, there are a range of effective treatments available. For any type of treatment, it is essential that the person affected is proactive and fully engaged in their own recovery process.\\nMany people with mental illnesses who are diagnosed and treated respond well, although some might experience a return of symptoms. Even in such cases, with careful monitoring and management of the disorder, it is still quite possible to live a fulfilled and productive life.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } - }, - "metadata": {}, - "execution_count": 3 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df.shape # 118 rows | 2 cols" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0F-EwjZC_myX", - "outputId": "7714af60-b8f0-42ef-ee8d-2926f4b0909c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(118, 2)" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ] - }, - { - "cell_type": "code", - "source": [ - "len(df.Questions)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "f8l_XZNnFhOH", - "outputId": "39478932-5974-469a-aa00-c4e702c52068" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "118" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ] - }, - { - "cell_type": "code", - "source": [ - "len(df.Answers)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pwuyeIGIHaWz", - "outputId": "71155c72-4910-4adc-aa8f-78165aeb2ed7" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "118" - ] - }, - "metadata": {}, - "execution_count": 6 - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install cleantext" - ], - "metadata": { - "id": "Vd5PZvq2XbC7" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Function to clean text data by removing unwanted characters and formatting\n", - "import cleantext\n", - "\n", - "def clean(textdata):\n", - " '''Use the cleantext library to remove extra spaces, lowercase the text,\n", - " and remove numbers and punctuation'''\n", - " cleaned_text = []\n", - " for i in textdata:\n", - " cleaned_text.append(cleantext.clean(str(i), extra_spaces=True, lowercase=True, stopwords=False, stemming=False, numbers=True, punct=True, clean_all = True))\n", - "\n", - " return cleaned_text" - ], - "metadata": { - "id": "alJin81bDm1Q" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Apply the clean function to the questions and answers columns\n", - "\n", - "df.Questions = list(clean(df.Questions))\n", - "df.Answers = list(clean(df.Answers))" - ], - "metadata": { - "id": "tCEayZSpEfhD" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Save the cleaned data into a new CSV file & save\n", - "df.to_csv(\"cleaned_QA_data.csv\", index=False)" - ], - "metadata": { - "id": "w5advveuHzY-" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### GPT-2 Model" - ], - "metadata": { - "id": "LhWVNHn7Kqan" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install datasets" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "collapsed": true, - "id": "HjD5OqLpIAUf", - "outputId": "8576a25f-f6fa-423d-ffb0-94c51ba48389" - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting datasets\n", - " Downloading datasets-3.0.0-py3-none-any.whl.metadata (19 kB)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.0)\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n", - "Collecting pyarrow>=15.0.0 (from datasets)\n", - " Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n", - "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n", - " Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n", - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n", - 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This behaviour is the source of the following dependency conflicts.\n", - "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n", - "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed datasets-3.0.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "pyarrow" - ] - }, - "id": "899b2eba72594abda4346d7c62d93887" - } - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments\n", - "import torch\n", - "\n", - "# Load the GPT-2 model and tokenizer\n", - "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n", - "model = GPT2LMHeadModel.from_pretrained('gpt2')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 400, - "referenced_widgets": [ - "40b4587992164793aaf5740ffbf0a5bb", - "cff31a7e1fce48669dec507fc7b52ab9", - "e2b477900a3946acaa3e928f7f1d07b6", - "a93a7ef111e243e0b208964d878861fe", - "b99bec09d8954316b4c8ce73edf8e474", - "de5aabaca2984a8f86979bb2fd0ee3da", - "66c1e5bb11b4460cb077cb8a1cb5af14", - "4b04e43fd59c46c8b483be843b1349e3", - "b17417bb999149919285d03d65bb1713", - "6dc62bf6e5444201a73e826ee14f6eb8", - "b42a476a50204983804e751ec6dccd0a", - "47f2e6e330344429971ac1ffbb63b2ff", - "a9c9c1d1ccdf44c8b65427f1b8c79726", - "38be19ee911640708150b78c5d4de8a0", - "edc0b04da0de44e39478bea0093d6e41", - "b08a0f2f74de4ce0aba7d8b2f83a5581", - "59f22444aa38492089482033f5bd1b6d", - "5f65cb7da0f446678aa2fd72e3ca1bb4", - "5dbf051fb2e94383b531879098346ef5", - "5509f96991cc42db941f0a3d35fd349b", - "ac453183cea047e58621bad6bbb86592", - "bb0e0e8f299e412fb312e4751903552f", - "f4ea4d3dade14cc08280b08415cf7742", - "a1b41c27f62446b99ef735da468f658c", - "0144bdae95c44db88d10bfb58ee8f26c", - "8b148375290a4305b0f9598eca8648b5", - "4b68791552904f17bb709f42cead99c5", - "b71dac04d5964197a82e4641d30fba7a", - "2d2ca51e88c74c5f912340a732ed6b0c", - "ca722054ccd8445ba4b5e8b97e3af00a", - "8d636f922e6c48a3b2bea810c84c2519", - "45e679b480c24d878b2bd3eccc13a203", - "e01a84e787e74273915648b2ab6f4eed", - "fae89fd63ed242419178498c91deee8c", - "4836cc0558c2468dadadd0ba736d909e", - "327af1af07ac4003b97c57aa5d6016ec", - "7c39af708dc046ed80f6e0a8648fe6f3", - "8e860acaa26e447fb0f236b9cd3d7ea5", - "d3f7b0c6501548cc894107cc9750f764", - "cb1b84f808904f6899f2c59eef044c3c", - "35a3f7ee9d8749d38c24a8a9cb7ed48f", - "05a8a1fa819346d68ab00c35c3eb9644", - "0887348b100a41fd92e3026741f4d7cf", - "955370903824422e99059b7a5a613dc9", - "9973207a907f4fa6879823bf5a6047a1", - "540ca23113964efc887caaab284545fd", - "a8f3253e827f42ce89aba112f0d14a06", - "f74dcf950c30446ca94ef7141f51103f", - "4ed90e9340744294a1881a8bb4fd635e", - "a0911541ceaa42d18c8586f95e19e23a", - "2f52fee77555497387c0f221fa43e851", - "73e301449f0442049671051159745c37", - "6ff49c5e5d954fc9b25973e5c4b52ec9", - "6f9fa08c05ea40aba251dde4d7fa0bf9", - "96433463fbfe44488ef82b130f591ca4", - "ef4efa7d522846aab53771e9f9958e29", - "91499ab7ef444d73b148ce9f0c4b6a1c", - "e7f04e5d43eb410687817da22302259f", - "034dce949af343ad81fcdce057d065e8", - "f941ec7bfab64edeb2af83d240769bd3", - "2ca5f89c52384ddfb184800e4ab180e5", - "eb6fcde708774825a9ace087c96330f1", - "582afc020a5a4010a6066146a83b75bc", - "07abe0f4f6e0407fa40c6b69f262bb4d", - "a10cf8bf9b36465fbebd187d3b8916d1", - "66d68667cc8246b3b252d369dc1c92cc", - "fdbbfe2612f74d78a0700c609277db94", - "3064465d76964517baa70e56788ab567", - "18d04ca8a3a94e928748cb986f063cbe", - "4072fa1dd8b547b3814e08fd22053504", - "cc2f89e25f4d4d969ad576937bbe3ea6", - "bfe981dda9ca449fb6ea4bf98dffcf22", - "53b066ac21ac436bbcab3278b66a15b3", - "6f643998364e4d998ffcb803cc3f2926", - "c6ee311a3b7948cdb2561375f5ea4d50", - "fc5d28a15d5f4bd59d0f966f62d15f3c", - "05de7959c4dc422f8a4788fdc64873ac" - ] - }, - "id": "xuK2p20fGJkO", - "outputId": "68f10647-ab4d-45ba-a8b2-1a67ce5a2c8d" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" - ], - "text/html": [ - "\n", - "
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StepTraining Loss
105.219500
204.857000
304.465500
403.937700
503.427000
603.187500
702.869200
802.853000
902.656700
1002.581300
1102.454600
1202.380500
1302.259000
1402.269800
1502.059000
1602.032700
1702.000100
1801.878100
1901.858100
2001.740900
2101.680400
2201.682900
2301.541100
2401.465300
2501.399800
2601.374100
2701.254700
2801.264400
2901.131000
3001.049100
3100.971700
3200.889100
3300.801200
3400.742700
3500.655500
3600.630200
3700.538000
3800.478900
3900.406600
4000.371700

" - ] - }, - "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