6.fine-tune-gemma-2-for-sentiment-analysis.ipynb

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    "tags": []
   },
   "source": [
    "## Fine-tune gemma-7B for sentiment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11117c49",
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   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92b8d07c-f73f-4fab-9cf2-24d0a752ddba",
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   "source": [
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
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   "source": [
    "In the following cell there are all the other imports for running the notebook"
   ]
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import bitsandbytes as bnb\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import transformers\n",
    "from datasets import Dataset\n",
    "from peft import LoraConfig, PeftConfig\n",
    "from trl import SFTTrainer\n",
    "from transformers import (AutoModelForCausalLM, \n",
    "                          AutoTokenizer, \n",
    "                          BitsAndBytesConfig, \n",
    "                          TrainingArguments, \n",
    "                          pipeline, \n",
    "                          logging)\n",
    "from sklearn.metrics import (accuracy_score, \n",
    "                             classification_report, \n",
    "                             confusion_matrix)\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "424480a1",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pytorch version 2.1.2+cu121\n"
     ]
    }
   ],
   "source": [
    "print(f\"pytorch version {torch.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "461114ef",
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   "source": [
    "filename = \"/home/jomondal/experiments/mywork/llama_exp/dataset/fin_sentiment/all-data.csv\"\n",
    "\n",
    "df = pd.read_csv(filename, \n",
    "                 names=[\"sentiment\", \"text\"],\n",
    "                 encoding=\"utf-8\", encoding_errors=\"replace\")\n",
    "\n",
    "X_train = list()\n",
    "X_test = list()\n",
    "for sentiment in [\"positive\", \"neutral\", \"negative\"]:\n",
    "    train, test  = train_test_split(df[df.sentiment==sentiment], \n",
    "                                    train_size=300,\n",
    "                                    test_size=300, \n",
    "                                    random_state=42)\n",
    "    X_train.append(train)\n",
    "    X_test.append(test)\n",
    "\n",
    "X_train = pd.concat(X_train).sample(frac=1, random_state=10)\n",
    "X_test = pd.concat(X_test)\n",
    "\n",
    "eval_idx = [idx for idx in df.index if idx not in list(train.index) + list(test.index)]\n",
    "X_eval = df[df.index.isin(eval_idx)]\n",
    "X_eval = (X_eval\n",
    "          .groupby('sentiment', group_keys=False)\n",
    "          .apply(lambda x: x.sample(n=50, random_state=10, replace=True)))\n",
    "X_train = X_train.reset_index(drop=True)\n",
    "\n",
    "\n",
    "def generate_prompt(data_point):\n",
    "    return f\"\"\"\n",
    "            Analyze the sentiment of the news headline enclosed in square brackets, \n",
    "            determine if it is positive, neutral, or negative, and return the answer as \n",
    "            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\".\n",
    "\n",
    "            [{data_point[\"text\"]}] = {data_point[\"sentiment\"]}\n",
    "            \"\"\".strip()\n",
    "\n",
    "def generate_test_prompt(data_point):\n",
    "    return f\"\"\"\n",
    "            Analyze the sentiment of the news headline enclosed in square brackets, \n",
    "            determine if it is positive, neutral, or negative, and return the answer as \n",
    "            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\".\n",
    "\n",
    "            [{data_point[\"text\"]}] = \"\"\".strip()\n",
    "\n",
    "X_train = pd.DataFrame(X_train.apply(generate_prompt, axis=1), \n",
    "                       columns=[\"text\"])\n",
    "X_eval = pd.DataFrame(X_eval.apply(generate_prompt, axis=1), \n",
    "                      columns=[\"text\"])\n",
    "\n",
    "y_true = X_test.sentiment\n",
    "X_test = pd.DataFrame(X_test.apply(generate_test_prompt, axis=1), columns=[\"text\"])\n",
    "\n",
    "train_data = Dataset.from_pandas(X_train)\n",
    "eval_data = Dataset.from_pandas(X_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c3ba256",
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   "source": [
    "def evaluate(y_true, y_pred):\n",
    "    labels = ['positive', 'neutral', 'negative']\n",
    "    mapping = {'positive': 2, 'neutral': 1, 'none':1, 'negative': 0}\n",
    "    def map_func(x):\n",
    "        return mapping.get(x, 1)\n",
    "    \n",
    "    y_true = np.vectorize(map_func)(y_true)\n",
    "    y_pred = np.vectorize(map_func)(y_pred)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
    "    print(f'Accuracy: {accuracy:.3f}')\n",
    "    \n",
    "    # Generate accuracy report\n",
    "    unique_labels = set(y_true)  # Get unique labels\n",
    "    \n",
    "    for label in unique_labels:\n",
    "        label_indices = [i for i in range(len(y_true)) \n",
    "                         if y_true[i] == label]\n",
    "        label_y_true = [y_true[i] for i in label_indices]\n",
    "        label_y_pred = [y_pred[i] for i in label_indices]\n",
    "        accuracy = accuracy_score(label_y_true, label_y_pred)\n",
    "        print(f'Accuracy for label {label}: {accuracy:.3f}')\n",
    "        \n",
    "    # Generate classification report\n",
    "    class_report = classification_report(y_true=y_true, y_pred=y_pred)\n",
    "    print('\\nClassification Report:')\n",
    "    print(class_report)\n",
    "    \n",
    "    # Generate confusion matrix\n",
    "    conf_matrix = confusion_matrix(y_true=y_true, y_pred=y_pred, labels=[0, 1, 2])\n",
    "    print('\\nConfusion Matrix:')\n",
    "    print(conf_matrix)"
   ]
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       "model_id": "9e78d071393c42dfbda47027a022f736",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = \"/home/jomondal/experiments/mywork/pretrained_models/gemma-7b-it\"\n",
    "trained_model_name = f\"/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_{model_name.split('/')[-1]}\"\n",
    "\n",
    "compute_dtype = getattr(torch, \"float16\")\n",
    "\n",
    "bnb_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",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    device_map=\"auto\",\n",
    "    quantization_config=bnb_config, \n",
    ")\n",
    "\n",
    "model.config.use_cache = False\n",
    "model.config.pretraining_tp = 1\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, \n",
    "                                          trust_remote_code=True,\n",
    "                                         )\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\""
   ]
  },
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   "execution_count": 20,
   "id": "f27f53d4",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "def predict(test, model, tokenizer):\n",
    "    y_pred = []\n",
    "    for i in tqdm(range(len(X_test))):\n",
    "        prompt = X_test.iloc[i][\"text\"]\n",
    "        # pipe = pipeline(task=\"text-generation\", \n",
    "        #                 model=model, \n",
    "        #                 tokenizer=tokenizer, \n",
    "        #                 max_new_tokens = 1, \n",
    "        #                 temperature = 0.001,\n",
    "        #                )\n",
    "        # result = pipe(prompt)\n",
    "        # answer = result[0]['generated_text'].split(\"=\")[-1]\n",
    "        \n",
    "        input_ids = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
    "        outputs = model.generate(**input_ids, max_new_tokens=1, temperature=0.0)\n",
    "        result = tokenizer.decode(outputs[0])\n",
    "        \n",
    "        answer = result.split(\"=\")[-1].lower()\n",
    "        if \"positive\" in answer:\n",
    "            y_pred.append(\"positive\")\n",
    "        elif \"negative\" in answer:\n",
    "            y_pred.append(\"negative\")\n",
    "        elif \"neutral\" in answer:\n",
    "            y_pred.append(\"neutral\")\n",
    "        else:\n",
    "            y_pred.append(\"none\")\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "134dfdf8-6061-48a6-8339-38ae9b6c079a",
   "metadata": {},
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   "source": []
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   "cell_type": "code",
   "execution_count": 21,
   "id": "2cca3dd6",
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     "status": "completed"
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    "scrolled": true,
    "tags": []
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 900/900 [00:56<00:00, 15.82it/s]\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict(test, model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "582c4689",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.642\n",
      "Accuracy for label 0: 0.890\n",
      "Accuracy for label 1: 0.070\n",
      "Accuracy for label 2: 0.967\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.89      0.90       300\n",
      "           1       0.51      0.07      0.12       300\n",
      "           2       0.51      0.97      0.67       300\n",
      "\n",
      "    accuracy                           0.64       900\n",
      "   macro avg       0.64      0.64      0.56       900\n",
      "weighted avg       0.64      0.64      0.56       900\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[267  15  18]\n",
      " [ 23  21 256]\n",
      " [  5   5 290]]\n"
     ]
    }
   ],
   "source": [
    "evaluate(y_true, y_pred)"
   ]
  },
  {
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   "outputs": [
    {
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      "text/plain": [
       "Map:   0%|          | 0/900 [00:00<?, ? examples/s]"
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      "text/plain": [
       "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detected kernel version 5.4.17, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    }
   ],
   "source": [
    "peft_config = LoraConfig(\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0.1,\n",
    "    r=64,\n",
    "    bias=\"none\",\n",
    "    target_modules=[\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir=\"logs\",\n",
    "    num_train_epochs=3,\n",
    "    per_device_train_batch_size=1,\n",
    "    gradient_accumulation_steps=8, # 4\n",
    "    optim=\"paged_adamw_32bit\",\n",
    "    save_steps=0,\n",
    "    logging_steps=25,\n",
    "    learning_rate=2e-4,\n",
    "    weight_decay=0.001,\n",
    "    fp16=True,\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=\"cosine\",\n",
    "    report_to=\"tensorboard\",\n",
    "    evaluation_strategy=\"epoch\"\n",
    ")\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=train_data,\n",
    "    eval_dataset=eval_data,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"text\",\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing=False,\n",
    "    max_seq_length=1024,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b1f8e31-ec2b-44e6-a05e-2df21a47584c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='336' max='336' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [336/336 11:53, Epoch 2/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.986400</td>\n",
       "      <td>0.835397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.383700</td>\n",
       "      <td>0.889137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=336, training_loss=0.989723516362054, metrics={'train_runtime': 715.8244, 'train_samples_per_second': 3.772, 'train_steps_per_second': 0.469, 'total_flos': 1.1242627208595456e+16, 'train_loss': 0.989723516362054, 'epoch': 2.99})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "trainer.train()"
   ]
  },
  {
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   "id": "4cb372cc",
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       "('/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_gemma-7b-it/tokenizer_config.json',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_gemma-7b-it/special_tokens_map.json',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_gemma-7b-it/tokenizer.model',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_gemma-7b-it/added_tokens.json',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_gemma-7b-it/tokenizer.json')"
      ]
     },
     "execution_count": 29,
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   "source": [
    "# Save trained model\n",
    "trainer.model.save_pretrained(trained_model_name)\n",
    "trainer.tokenizer.save_pretrained(trained_model_name)"
   ]
  },
  {
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    {
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     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 900/900 [03:01<00:00,  4.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.884\n",
      "Accuracy for label 0: 0.957\n",
      "Accuracy for label 1: 0.877\n",
      "Accuracy for label 2: 0.820\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.96       300\n",
      "           1       0.80      0.88      0.84       300\n",
      "           2       0.89      0.82      0.86       300\n",
      "\n",
      "    accuracy                           0.88       900\n",
      "   macro avg       0.89      0.88      0.88       900\n",
      "weighted avg       0.89      0.88      0.88       900\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[287  12   1]\n",
      " [  9 263  28]\n",
      " [  1  53 246]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict(test, model, tokenizer)\n",
    "evaluate(y_true, y_pred)"
   ]
  },
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    "evaluation = pd.DataFrame({'text': X_test[\"text\"], \n",
    "                           'y_true':y_true, \n",
    "                           'y_pred': y_pred},\n",
    "                         )\n",
    "evaluation.to_csv(\"results/fintech_sentiment/test_predictions.csv\", index=False)"
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