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href=\"https://colab.research.google.com/github/sAndreotti/MedicalMeadow/blob/main/few-shot\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>","metadata":{"id":"view-in-github","colab_type":"text"}},{"cell_type":"code","source":"MODEL = \"meta-llama/Llama-3.2-1B-Instruct\"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install datasets accelerate peft bitsandbytes transformers trl==0.12.0 plotly\n!pip install --upgrade smart_open\n!pip install --upgrade gensim","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SAho3HGib9-U","outputId":"13493781-c8b7-4fd0-974e-e2ea9481144b","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\nimport torch\nfrom transformers import (\n    BitsAndBytesConfig,\n)\nfrom transformers import TrainingArguments\nfrom trl import SFTTrainer\n\nfrom datasets import load_dataset\nimport numpy as np\nfrom collections import Counter\nfrom peft import LoraConfig","metadata":{"id":"rIaZ4Ni_XSsk","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Investigate Dataset","metadata":{"id":"XKL46PcIc21t"}},{"cell_type":"code","source":"ds = load_dataset(\"medalpaca/medical_meadow_medical_flashcards\")\nds = ds['train']\nds","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":307,"referenced_widgets":["2fe9bda844374f5fb17c95655691488f","1084e0d562164f818b2ff84e389016dc","74f129a4917d45d6b36731f6b2d96102","8ed587cf1bd54c119f9cdc25d222d155","72b5c6e70ea44798b5b7c8b2a7b2b502","80fd2f41a7e54a92add73432c5c6b525","af68082e06da486297331aba5beb5082","bec67473dda34344b0e6b1f31152eb2b","85ce584cb482482ab9d8f962cbdd3ee7","62f570e69cac4680a631ce53608fe034","101a57b49bc2466b805d27d98d728cf7","63c1a05a693f4fc9828086972f712eb2","f4b93fe309c543d8a84e5c476ad6aced","098bac2b3b5040eaba5bef4628e2f0ad","f5076c9e27674fd0a5758353bfd3f11e","7c466df91fdd427bac52d9be94652247","4b4d8b7a9f484cb78790140ee25785d5","9a51be445a4a4c03942608184ada5ada","ee0fe0f5636940639571d5cb50861c58","c35e96728ce34d169a82c4a199e1b2cd","332f97cac7a94b069e870e85de7c6e23","e93f60dc60ee4791a8cbcdc848efe4dd","d1604a02e20e4198969edfe10b72da5e","8b901ccfdfc04512b2a6820167aec344","fc6c378002d14c4ab14cf91a76e8bbd4","2dbd7a15d1564586829ee8b16271949d","656d047d4a22499a9e85cc594d81c25c","e8911f6909914420913c3e46e77b0a49","69301ac80e91422e892ffac70329bb59","bd8024a268ae425b89140680b4eaa83a","212e0f3d975d4f2faaa500b49d1adb41","735b1056b53a4b76a51be2d2251b7141","c5e6e362322e4702b4b03039ad3592fd"]},"id":"YltBm7i4b0IM","outputId":"0f3d8ccb-6a2a-46d1-e4e2-3cb02be02334","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### Create dataset","metadata":{"id":"VFt5KLR-8w3p"}},{"cell_type":"code","source":"from torch.utils.data import Dataset\n\nclass MedDataset(Dataset):\n  def __init__(self, instruction, input, output):\n    self.instruction = instruction\n    self.input = input\n    self.output = output\n\n  def __len__(self):\n    return len(self.instruction)\n\n  def __getitem__(self, idx):\n    sentence = \"<s>[INST] \"+self.instruction[idx]+\". \"+self.input[idx]+\" [/INST] \"+self.output[idx]+\" </s>\"\n    return sentence","metadata":{"id":"ND0M0R633VbB","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install huggingface_hub\n\n# Load model directly\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nfrom huggingface_hub import login\n\nlogin(token=\"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\")\n\n#tokenizer = AutoTokenizer.from_pretrained(\"TinyLlama/TinyLlama-1.1B-Chat-v0.1\")\ntokenizer = AutoTokenizer.from_pretrained(MODEL)\ntokenizer.pad_token = tokenizer.eos_token\ntokenizer.padding_side = \"right\"","metadata":{"id":"Ak2dRuLhdExx","trusted":true,"outputId":"54da07df-1824-4f43-ea6e-f243094fea96","colab":{"base_uri":"https://localhost:8080/","height":385,"referenced_widgets":["c188276722bf4f71b6e98e90bdf1dc35","3b8c7eb4ff514cfba87c26d2900d9ba8","7e5afa46908541c89f443aa1a8c00072","741515ae32a14be59d4e049d79342bf9","84059a1e81e44d1aad70948936ed7e27","3af681e589cc47e5a6462316188ea166","3c94af7af07b4974a15e4f53d1907d1a","544853468ac74c21b0534bcc93de543e","dc7cc41ccd4e4288b14809d7344ca6d1","6d6694c1142549319172402c4aeac150","a8603688dc4140aabebb624cfc4bfbd2","24fe3f9ca70b4a0c8e8c5aa3a6e753bb","6081b450ea564a94b528c3942550c549","24033c1fa57c45c198a4cb506e5a9751","f0f4bbb4a9b14f1998a8f62a1618a136","c75561bbd3f449af86956f8588e06476","75a9f5ecc18640a798133404160132a9","375a99abe55847b2a9ad47fb20c1a419","00931ffd06be4e398cd6aba5c644074b","74649b76a3404bcbb4bdddf2fe02abca","3739a0d4aaab4b558c18687fe5fae54a","fc6bd2ff497642d28b3b0e584df05cea","99250953bb26432989bea57995e8fde9","ea713ec47d9d49ffbe0905546bba5d0a","0ee2a4aef117471691b097e8685ba16e","4787d583192a49b7baefa76967af5ad5","d2dc34ab4d724135bf306ab12604e435","4d2cec4bc12141249567a8bb9a569854","00d13032ea5d43268ffac83431e5504a","87e9467332854a32bae147a686351a6a","e9962f3994d647f785727f9a6b7b5942","e0c6ad6b24314c1b811f713ff5da437d","f4524841a77a4de6b95f7616b9016192","a94092b77f994627a94c4ee375fc7533","992620ff4e804da2a4a0bb1de45e62a7","9a9f3bcd53bf4469914bb0e4d3db1ef6","d893869f37574ff3957b8e28f8d58712","3394d79061e9488a845dec6c8d12d1e2","362ae1a71a6d4c98bc0a6bcf5de45eef","4a334757596347fcbd8f262a7a063cf7","deb33b0b163e427e95dcd1633fbaf3fd","91ec778dcc754dbaa10dab6ea720b809","dd3c9c3b445a4756ac93371c8f0e7447","1a44ac47c76840a287704186ef86a113","77aa68ba10aa4a64a7bd3cb16cc39314","5f5e06a3ae4d45ca9655845af93a31ef","ce7fb353233845b48994dcb236690820","d5c4cd481d1044c6851388c43092e594","c97273eff4b244e68b2c6ea6595eaf03","e4fa47f325ce416b917febb6c53be58f","adc83232e35d489786bb8662a1238215","25b60dff3a794981adfab1b1b4a13976","a7cf0c7b10a8450ca7527f67c8eb2190","1470a3c7f6714cff916c75e648998fe3","72fd9865c33d4140a8cde9f61bc11170"]}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from datasets import Dataset as HFDataset\n\ndef convert_to_hf_dataset(med_dataset):\n    # Create lists to store all formatted text\n    formatted_texts = []\n\n    # Iterate through all items in the original dataset\n    for idx in range(len(med_dataset.instruction)):\n        # Get the formatted text directly using the dataset's __getitem__\n        formatted_text = med_dataset[idx]\n        formatted_texts.append(formatted_text)\n\n    # Create a dictionary with the required format\n    dataset_dict = {\n        'text': formatted_texts\n    }\n\n    # Convert to HuggingFace Dataset\n    hf_dataset = HFDataset.from_dict(dataset_dict)\n\n    return hf_dataset","metadata":{"id":"UXLnghrqDWIL","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def generate_response(model, tokenizer, question):\n    input_text = question\n    input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids.to(model.device)\n    with torch.no_grad():\n        outputs = model.generate(\n            inputs=input_ids,\n            max_new_tokens=100,\n            temperature=0.7,\n            top_p=0.9,\n            do_sample=True,\n            pad_token_id=tokenizer.eos_token_id\n        )\n    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)\n    return decoded_output","metadata":{"id":"hi634hGJaOL3","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 0-shot","metadata":{"id":"RztMOivjVkwt"}},{"cell_type":"code","source":"compute_dtype = getattr(torch, \"float16\")\n\nquant_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_quant_type=\"nf4\",\n    bnb_4bit_compute_dtype=compute_dtype,\n    bnb_4bit_use_double_quant=False,\n    bnb_4bit_representation=\"nested\"\n)\n\n# Define the model\nmodel0 = AutoModelForCausalLM.from_pretrained(\n    #\"TinyLlama/TinyLlama-1.1B-Chat-v0.1\",\n    MODEL,\n    quantization_config=quant_config,\n    device_map={\"\": 0},\n    torch_dtype=torch.float16,\n    trust_remote_code=True\n)\nmodel0.config.use_cache = False\nmodel0.config.pretraining_tp = 1","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Few-shot","metadata":{"id":"tp_Tl-LWVm4q"}},{"cell_type":"code","source":"few_dataset = MedDataset(ds['instruction'][:5], ds['input'][:5], ds['output'][:5])\nprint(few_dataset)","metadata":{"id":"4w166TXSYjQb","outputId":"b5ca54ef-466c-4b23-d383-f6c19767ee5b","colab":{"base_uri":"https://localhost:8080/"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"few_hf_dataset = convert_to_hf_dataset(few_dataset)","metadata":{"id":"_1CTwHCGZcW9","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"compute_dtype = getattr(torch, \"float16\")\n\nquant_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_quant_type=\"nf4\",\n    bnb_4bit_compute_dtype=compute_dtype,\n    bnb_4bit_use_double_quant=False,\n    bnb_4bit_representation=\"nested\"\n)\n\n# Define the model\nmodelFEW = AutoModelForCausalLM.from_pretrained(\n    #\"TinyLlama/TinyLlama-1.1B-Chat-v0.1\",\n    MODEL,\n    quantization_config=quant_config,\n    device_map={\"\": 0},\n    torch_dtype=torch.float16,\n    trust_remote_code=True\n)\nmodelFEW.config.use_cache = False\nmodelFEW.config.pretraining_tp = 1\n\ntraining_params = TrainingArguments(\n    output_dir=\"./results\",\n    num_train_epochs=1,\n    per_device_train_batch_size=1,\n    gradient_accumulation_steps=4,\n    optim=\"paged_adamw_32bit\",\n    save_steps=25,\n    logging_steps=25,\n    learning_rate=2e-4,\n    weight_decay=0.001,\n    fp16=False,\n    bf16=False,\n    max_grad_norm=0.3,\n    max_steps=-1,\n    warmup_ratio=0.03,\n    group_by_length=True,\n    lr_scheduler_type=\"constant\",\n    report_to=\"tensorboard\",\n    gradient_checkpointing=True\n)\n\npeft_params = LoraConfig(\n    lora_alpha=16,\n    lora_dropout=0.1,\n    r=64,\n    bias=\"none\",\n    task_type=\"CAUSAL_LM\",\n)\n\ntrainer = SFTTrainer(\n    model=modelFEW,\n    train_dataset=few_hf_dataset,\n    peft_config=peft_params,\n    dataset_text_field=\"text\",\n    max_seq_length=256,\n    tokenizer=tokenizer,\n    args=training_params,\n    packing=False,\n)","metadata":{"id":"LXFeictlWgOk","outputId":"80026554-8d1b-407f-d270-5a6561a4b7d6","colab":{"base_uri":"https://localhost:8080/"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Few shot training\ntrainer.train()","metadata":{"id":"PLxLVgeRW0vq","outputId":"51ee44e7-0abb-45ac-f347-cf664b6ad764","colab":{"base_uri":"https://localhost:8080/","height":165},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# question = \"What type of cells are found in the body of the stomach, where are they located, and what substances do they secrete?\"\n#question = 'What conditions are suggested by high ESR/CK and bilateral proximal muscle weakness, and high ESR/CRP with stiffness/pain in the shoulders, hip, and neck?'\nquestion = 'What does dyspepsia refer to?'\nresponseFEW = generate_response(modelFEW, tokenizer, question)\nprint(\"Response FEW shot: \", responseFEW)\n\nresponse0 = generate_response(model0, tokenizer, question)\nprint(\"\\nResponse 0 shot: \", response0)","metadata":{"id":"4x39HCcajZhV","outputId":"b250b230-14d3-4b31-8e2a-5308e71e3244","colab":{"base_uri":"https://localhost:8080/"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Evaluate answer","metadata":{"id":"WALd3_Hvc9f1"}},{"cell_type":"markdown","source":"scibert_scivocab_uncased is a pretrained model on scientific text","metadata":{}},{"cell_type":"code","source":" from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline\n\n# Load the tokenizer and model\n#tokenizer = AutoTokenizer.from_pretrained(\"allenai/scibert_scivocab_uncased\")\nbenchmark = AutoModelForQuestionAnswering.from_pretrained(\"allenai/scibert_scivocab_uncased\")\n\n# Create a QA pipeline\nqa_pipeline = pipeline(\"question-answering\", model=benchmark, tokenizer=tokenizer)\n\n# Get the model's answer\nresult = qa_pipeline(question=question, context=responseFEW)\n\n# Print the answer\nprint(\"Extracted Answer:\", result[\"answer\"])\nprint(\"Score:\", result[\"score\"])","metadata":{"id":"EIodhbikdF16","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"second method for evaluation: BLEU (Bilingual Evaluation Understudy) measures n-gram overlap between the generated and reference answers. It's less suitable for medical QA due to its sensitivity to word order and inability to capture semantic meaning.\nrequire couples question - answer","metadata":{}},{"cell_type":"code","source":"!pip install nltk","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import nltk\nnltk.download('punkt')\n\nfrom nltk.translate.bleu_score import sentence_bleu\n\ndef calculate_bleu(reference, candidate):\n    \"\"\"\n    Comput BLUE score between the candiate (generate answer) and the reference answer\n    \"\"\"\n    try:\n      score = sentence_bleu(reference, candidate)\n      return score\n    except Exception as e:\n      print(f\"Error during BLUE computing: {e}\")\n      return None\n\nreference = 'Dyspepsia refers to a spectrum of epigastric symptoms, including heartburn, \"indigestion,\" bloating, and epigastric pain/discomfort.'.split()\ncandidate = responseFEW.split()\n\nbleu_score = calculate_bleu(reference, candidate)\n\nif bleu_score is not None:\n    print(f\"Punteggio BLEU: {bleu_score}\")","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}
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