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\"/content/Stress.csv\",\n", + " \"/content/ds.csv\",\n", + " \"/content/ocd_patient_dataset.csv\",\n", + " \"/content/synthetic_ptsd_patients.csv\",\n", + " \"/content/Mental health Depression disorder Data.csv\",\n", + " \"/content/Copy of ds.csv\"\n", + "]\n", + "\n", + "question_cols = [\n", + " \"question\", \"questions\", \"prompt\", \"input\", \"q\", \"query\", \"text\", \"phrase\"\n", + "]\n", + "answer_cols = [\n", + " \"answer\", \"answers\", \"response\", \"output\", \"a\", \"completion\", \"reply\", \"label\", \"sentiment\"\n", + "]\n", + "\n", + "all_dfs = []\n", + "qa_examples = [] # For mental health depression dataset\n", + "\n", + "for f in files:\n", + " ext = os.path.splitext(f)[1].lower()\n", + " if ext != \".csv\":\n", + " print(f\"⚠️ Skipping non-CSV file: {f}\")\n", + " continue\n", + "\n", + " try:\n", + " try:\n", + " df = pd.read_csv(f, encoding=\"utf-8\")\n", + " except UnicodeDecodeError:\n", + " df = pd.read_csv(f, encoding=\"latin1\")\n", + " except Exception as e:\n", + " print(f\"⚠️ Could not read {f}: {e}\")\n", + " continue\n", + "\n", + " df.columns = [c.strip() for c in df.columns]\n", + "\n", + " # Process Mental health Depression disorder Data.csv using your working snippet\n", + " if os.path.basename(f) == \"Mental health Depression disorder Data.csv\":\n", + " disorder_cols = [\n", + " 'Schizophrenia (%)',\n", + " 'Bipolar disorder (%)',\n", + " 'Eating disorders (%)',\n", + " 'Anxiety disorders (%)',\n", + " 'Drug use disorders (%)',\n", + " 'Depression (%)',\n", + " 'Alcohol use disorders (%)'\n", + " ]\n", + "\n", + " for _, row in df.iterrows():\n", + " entity = row['Entity']\n", + " year = row['Year']\n", + " for disorder in disorder_cols:\n", + " value = row[disorder]\n", + " if pd.isna(value):\n", + " continue\n", + " question = f\"What is the percentage of {disorder.replace(' (%)', '')} in {entity} for year {year}?\"\n", + " answer = f\"{value}%\"\n", + " qa_examples.append({\n", + " \"instruction\": question,\n", + " \"input\": \"\",\n", + " \"output\": answer\n", + " })\n", + " print(f\"✅ Generated {len(qa_examples)} Q&A examples from {f}\")\n", + "\n", + " elif os.path.basename(f) == \"ocd_patient_dataset.csv\":\n", + " if \"Obsession Type\" in df.columns and \"Compulsion Type\" in df.columns:\n", + " df_sub = df[[\"Obsession Type\", \"Compulsion Type\"]].rename(columns={\n", + " \"Obsession Type\": \"Question\",\n", + " \"Compulsion Type\": \"Answer\"\n", + " })\n", + " df_sub = df_sub.dropna(subset=[\"Question\", \"Answer\"])\n", + " all_dfs.append(df_sub)\n", + " print(f\"✅ Loaded {len(df_sub)} rows from {f}\")\n", + " else:\n", + " print(f\"⚠️ Skipping {f}: Missing 'Obsession Type' or 'Compulsion Type' columns\")\n", + "\n", + " elif os.path.basename(f) == \"synthetic_ptsd_patients.csv\":\n", + " if \"trauma_type\" in df.columns and \"has_ptsd\" in df.columns:\n", + " df_sub = df[[\"trauma_type\", \"has_ptsd\"]].rename(columns={\n", + " \"trauma_type\": \"Question\",\n", + " \"has_ptsd\": \"Answer\"\n", + " })\n", + " df_sub = df_sub.dropna(subset=[\"Question\", \"Answer\"])\n", + " all_dfs.append(df_sub)\n", + " print(f\"✅ Loaded {len(df_sub)} rows from {f}\")\n", + " else:\n", + " print(f\"⚠️ Skipping {f}: Missing 'trauma_type' or 'has_ptsd' columns\")\n", + "\n", + " else:\n", + " # Generic Q&A detection for other files\n", + " df.columns = [c.lower() for c in df.columns] # normalize for matching\n", + " q_col = next((c for c in df.columns if c in [qc.lower() for qc in question_cols]), None)\n", + " a_col = next((c for c in df.columns if c in [ac.lower() for ac in answer_cols]), None)\n", + " if not q_col or not a_col:\n", + " print(f\"⚠️ Skipping {f}: Missing 'Question' or 'Answer' column\")\n", + " continue\n", + " df_sub = df[[q_col, a_col]].rename(columns={q_col: \"Question\", a_col: \"Answer\"})\n", + " df_sub = df_sub.dropna(subset=[\"Question\", \"Answer\"])\n", + " all_dfs.append(df_sub)\n", + " print(f\"✅ Loaded {len(df_sub)} rows from {f}\")\n", + "\n", + "# Combine normal Q/A datasets\n", + "if all_dfs:\n", + " df_combined = pd.concat(all_dfs, ignore_index=True)\n", + "else:\n", + " df_combined = pd.DataFrame(columns=[\"Question\", \"Answer\"])\n", + "\n", + "# Write all data to JSONL\n", + "with open(\"training_data.jsonl\", \"w\", encoding=\"utf-8\") as f_out:\n", + " for _, row in df_combined.iterrows():\n", + " q = str(row[\"Question\"]).strip()\n", + " a = str(row[\"Answer\"]).strip()\n", + " if q and a:\n", + " example = {\"instruction\": q, \"input\": \"\", \"output\": a}\n", + " f_out.write(json.dumps(example, ensure_ascii=False) + \"\\n\")\n", + "\n", + " for example in qa_examples:\n", + " f_out.write(json.dumps(example, ensure_ascii=False) + \"\\n\")\n", + "\n", + "total = len(df_combined) + len(qa_examples)\n", + "print(f\"✅ Saved merged dataset with {total} examples to training_data.jsonl\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xysu5AtqZ9mn", + "outputId": "6466af2a-424d-45d4-ab7f-6b9ae379001c" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✅ Loaded 2838 rows from /content/Stress.csv\n", + "✅ Loaded 3479 rows from /content/ds.csv\n", + "✅ Loaded 1500 rows from /content/ocd_patient_dataset.csv\n", + "✅ Loaded 500 rows from /content/synthetic_ptsd_patients.csv\n", + "✅ Generated 45276 Q&A examples from /content/Mental health Depression disorder Data.csv\n", + "✅ Loaded 3479 rows from /content/Copy of ds.csv\n", + "✅ Saved merged dataset with 57072 examples to training_data.jsonl\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Show head of each loaded CSV / processed DF\n", + "for f in files:\n", + " ext = os.path.splitext(f)[1].lower()\n", + " if ext != \".csv\":\n", + " continue\n", + "\n", + " try:\n", + " try:\n", + " df = pd.read_csv(f, encoding=\"utf-8\")\n", + " except UnicodeDecodeError:\n", + " df = pd.read_csv(f, encoding=\"latin1\")\n", + " except Exception as e:\n", + " print(f\"⚠️ Could not read {f}: {e}\")\n", + " continue\n", + "\n", + " print(f\"\\n📄 Head of {os.path.basename(f)}:\")\n", + " display(df.head(5)) # top 5 rows\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "FNvKM7TRZbRJ", + "outputId": "00eb85c6-f5de-418e-dd96-4f6268504a57" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "📄 Head of Stress.csv:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " subreddit post_id sentence_range \\\n", + "0 ptsd 8601tu (15, 20) \n", + "1 assistance 8lbrx9 (0, 5) \n", + "2 ptsd 9ch1zh (15, 20) \n", + "3 relationships 7rorpp [5, 10] \n", + "4 survivorsofabuse 9p2gbc [0, 5] \n", + "\n", + " text label confidence \\\n", + "0 He said he had not felt that way before, sugge... 1 0.8 \n", + "1 Hey there r/assistance, Not sure if this is th... 0 1.0 \n", + "2 My mom then 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Patient IDAgeGenderEthnicityMarital StatusEducation LevelOCD Diagnosis DateDuration of Symptoms (months)Previous DiagnosesFamily History of OCDObsession TypeCompulsion TypeY-BOCS Score (Obsessions)Y-BOCS Score (Compulsions)Depression DiagnosisAnxiety DiagnosisMedications
0101832FemaleAfricanSingleSome College2016-07-15203MDDNoHarm-relatedChecking1710YesYesSNRI
1240669MaleAfricanDivorcedSome College2017-04-28180NaNYesHarm-relatedWashing2125YesYesSSRI
2118857MaleHispanicDivorcedCollege Degree2018-02-02173MDDNoContaminationChecking34NoNoBenzodiazepine
3620027FemaleHispanicMarriedCollege Degree2014-08-25126PTSDYesSymmetryWashing1428YesYesSSRI
4582456FemaleHispanicMarriedHigh School2022-02-20168PTSDYesHoardingOrdering3918NoNoNaN
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patient_idagegendertrauma_typeintrusive_thoughtsnightmaresavoidancenegative_moodhypervigilancepcl5_scorehas_ptsd
0156FemaleAccident23314521
1269MaleCombat-related41214481
2346MaleOther23421481
3432FemaleOther11304270
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0AfghanistanAFG19900.1605600.6977790.1018554.8288301.6770824.0718310.672404
1AfghanistanAFG19910.1603120.6979610.0993134.8297401.6847464.0795310.671768
2AfghanistanAFG19920.1601350.6981070.0966924.8311081.6943344.0883580.670644
3AfghanistanAFG19930.1600370.6982570.0943364.8308641.7053204.0961900.669738
4AfghanistanAFG19940.1600220.6984690.0924394.8294231.7160694.0995820.669260
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phrasesentiment
0\"I love spending time with my family.\"positive
1\"Sunshine always brightens my day.\"positive
2\"Helping others is so rewarding.\"positive
3\"A good book can transport you to another world.\"positive
4\"The smell of freshly baked bread is amazing.\"positive
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No, I ended up reading that this sense of doom can be indicative of various health ailments; one of which I am prone to.. So on top of my \"doom\" to my gloom..I am now f\\'n worried about my heart. I do happen to have a physical in 48 hours.', 'input': '', 'output': '1'}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "torch.cuda.empty_cache()\n" + ], + "metadata": { + "id": "UVYcx_zagCjn" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:128\"\n" + ], + "metadata": { + "id": "2lZ2A97_gEx4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, BitsAndBytesConfig\n", + "from peft import LoraConfig, get_peft_model\n", + "import torch\n", + "\n", + "model_name = \"tanusrich/Mental_Health_Chatbot\"\n", + "data_path = \"training_data.jsonl\"\n", + "\n", + "# Load dataset\n", + "dataset = load_dataset(\"json\", data_files=data_path, split=\"train\")\n", + "\n", + "# Load tokenizer and model with 4-bit quantization using BitsAndBytesConfig (new way)\n", + "bnb_config = BitsAndBytesConfig(load_in_4bit=True)\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " quantization_config=bnb_config,\n", + " device_map=\"auto\",\n", + " torch_dtype=torch.float16, # Added this parameter\n", + ")\n", + "\n", + "# LoRA configuration for LLaMA-based model\n", + "lora_config = LoraConfig(\n", + " r=64,\n", + " lora_alpha=16,\n", + " target_modules=[\"q_proj\", \"v_proj\"], # typical for LLaMA\n", + " lora_dropout=0.1,\n", + " bias=\"none\",\n", + " task_type=\"CAUSAL_LM\"\n", + ")\n", + "\n", + "model = get_peft_model(model, lora_config)\n", + "\n", + "# Tokenization helper (fixed)\n", + "def preprocess(examples):\n", + " inputs = [\n", + " instr + \"\\n\" + inp if inp else instr\n", + " for instr, inp in zip(\n", + " examples[\"instruction\"],\n", + " examples.get(\"input\", [\"\"] * len(examples[\"instruction\"]))\n", + " )\n", + " ]\n", + " outputs = examples[\"output\"]\n", + "\n", + " model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=\"max_length\")\n", + " labels = tokenizer(outputs, max_length=512, truncation=True, padding=\"max_length\").input_ids\n", + "\n", + " model_inputs[\"labels\"] = labels\n", + " return model_inputs\n", + "\n", + "# Apply preprocessing\n", + "tokenized_dataset = dataset.map(\n", + " preprocess,\n", + " batched=True,\n", + " remove_columns=dataset.column_names,\n", + ")\n", + "\n", + "# Training arguments\n", + "training_args = TrainingArguments(\n", + " output_dir=\"./lora_mht_chatbot_finetuned\",\n", + " per_device_train_batch_size=1, # Reduced batch size\n", + " gradient_accumulation_steps=4, # Adjusted accumulation steps\n", + " num_train_epochs=3,\n", + " learning_rate=3e-4,\n", + " fp16=True,\n", + " save_strategy=\"epoch\",\n", + " optim=\"paged_adamw_32bit\",\n", + " logging_steps=10,\n", + " save_total_limit=2,\n", + " report_to=\"none\",\n", + ")\n", + "\n", + "# Trainer\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=tokenized_dataset,\n", + " tokenizer=tokenizer,\n", + ")\n", + "\n", + "trainer.train()\n", + "\n", + "# Save adapters and tokenizer\n", + "model.save_pretrained(\"./lora_mht_chatbot_finetuned\")\n", + "tokenizer.save_pretrained(\"./lora_mht_chatbot_finetuned\")\n", + "\n", + "print(\"✅ Fine-tuning complete and saved!\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "a997f83e7aa64ad1849d5f622eec145d", + "8889742d12364680a5a39470adde2873", + "168e3e4172cc4e36b2ca0544e0fc42b4", + "64f694d338294e5984e5895050d947c3", + "ce354595738b45eeb03386728d316d69", + "d552cb6203ed4718b0504b6d80de581c", + "ae2586c27aa24a0a8c96d88d5d160e70", + "b414fa4834174262a2608f6dfbe894e5", + "ee5c0c7663a64c3c85ea68373f105b42", + "0705592b068e4271bcc30a32791a47ca", + "151b2379bb6849df8dc75848717e9490", + "d7cf2951869343e98322277dd3e52085", + "864b8038624a40e899ddc8ee00d64996", + "6d45c0ee7da34f28932ab54699bd4092", + "8f86dd9f03214308b2bd9505bf249b80", + "c4be6df7bcc34fdc95e5bb6e72eb44ff", + "2ef30bae7c1a48329aa944559c539323", + "55a90b62a4094cf1afbf8fe183751cf9", + "67db80c7406a4ab7806e05451c234fdd", + "b213c00ebe8243a78b0ca91b40261e33", + "3a0bd7815630436982830f05768a52c3", + "2ea41b0b43de4c7db6b690d3c53ee578", + "6952dd69d233420c84f8931cd11d0b94", + "1b90056f7f0a4ad681a025cf5012c607", + "8c06c04a4cf44ec396992ae04f7ad53d", + "0e09e908da8444eca0c9737dd9c93c34", + "f5c7882a83094141b891d5fc8b9cbf17", + "2e4a189f134343fe84dd0ed20939055f", + "d6dfbb37ef7c4447aaa41cc6f6436163", + "46bf9b6c73c34232a129e28176509b9a", + "330a4fb368c142a38a538d78cc70cc76", + "6144b49ad9e64ad2a68b4f85396f41bf", + "936de9b40ae84efc8b5ecb7ed6172ddd" + ] + }, + "id": "beEGorh7eN3I", + "outputId": "0055144c-85fb-422d-dbb2-d49b60a4e49e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating train split: 0 examples [00:00, ? examples/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "a997f83e7aa64ad1849d5f622eec145d" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "`torch_dtype` is deprecated! Use `dtype` instead!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/3 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
1023.653100
2023.332900
3023.227600
4023.208500
5023.424900
6023.403100
7023.305700
8023.368200
9023.283600
10023.233000
11023.211700
12023.203100
13023.178200
14023.331000
15023.031700
16023.388000
17023.361600
18023.361800
19023.193500
20023.318700
21023.331800
22023.256900
23023.349100
24023.115400
25023.023700
26023.321400
27022.990600
28023.115500
29023.204400
30023.366000
31022.765200
32023.341900
33023.242800
34023.098100
35023.304000
36023.417000
37023.369900
38023.214200
39023.324600
40023.313600
41023.023900
42023.100000
43023.174100
44023.301800
45023.268000
46023.215000
47022.954800
48023.131900
49023.396800
50023.423600
51023.040500
52023.399400
53023.193900
54023.395100
55023.196200
56023.387200
57023.189700
58023.188200
59023.192300
60023.410800
61022.736500
62023.095600
63022.848800
64023.120600
65023.184200
66023.107900
67023.187900
68023.264400
69023.334400
70023.149400
71023.125700
72023.182100
73023.260400
74022.861800

" + ] + }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file