5.fine-tune-mixtral-for-sentiment-analysis.ipynb

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   "source": [
    "## Fine-tune Mixtral-7B for sentiment\n"
   ]
  },
  {
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   "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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   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92b8d07c-f73f-4fab-9cf2-24d0a752ddba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "In the following cell there are all the other imports for running the notebook"
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     "name": "stdout",
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     "text": [
      "[2024-02-22 14:12:01,100] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ],
   "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": 5,
   "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": 6,
   "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",
    "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)"
   ]
  },
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   "execution_count": 7,
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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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      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/19 [00:00<?, ?it/s]"
      ]
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   ],
   "source": [
    "model_name = \"/home/jomondal/experiments/mywork/pretrained_models/Mixtral-8x7B-Instruct-v0.1\"\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",
    "\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",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    model_name,\n",
    "    padding_side=\"left\",\n",
    "    add_eos_token=True,\n",
    "    add_bos_token=True,\n",
    ")\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "# tokenizer.pad_token = tokenizer.eos_token\n",
    "# tokenizer.padding_side = \"right\""
   ]
  },
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     "status": "completed"
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   },
   "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,pad_token_id=tokenizer.eos_token_id)\n",
    "        answer = result[0]['generated_text'].split(\"=\")[-1]\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": 10,
   "id": "2cca3dd6",
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    "scrolled": true,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 900/900 [06:31<00:00,  2.30it/s]\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict(test, model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fce6ae97-fbe2-44f5-a41f-aa43be49d881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.502\n",
      "Accuracy for label 0: 0.160\n",
      "Accuracy for label 1: 0.880\n",
      "Accuracy for label 2: 0.467\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.16      0.27       300\n",
      "           1       0.39      0.88      0.54       300\n",
      "           2       0.80      0.47      0.59       300\n",
      "\n",
      "    accuracy                           0.50       900\n",
      "   macro avg       0.72      0.50      0.47       900\n",
      "weighted avg       0.72      0.50      0.47       900\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 48 252   0]\n",
      " [  1 264  35]\n",
      " [  1 159 140]]\n"
     ]
    }
   ],
   "source": [
    "evaluate(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "346dd169",
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   "outputs": [
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       "Map:   0%|          | 0/900 [00:00<?, ? examples/s]"
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       "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=32,\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",
    "    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": 13,
   "id": "9b1f8e31-ec2b-44e6-a05e-2df21a47584c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='336' max='336' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [336/336 38:56, 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>0.750400</td>\n",
       "      <td>0.812775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.569600</td>\n",
       "      <td>0.809627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=336, training_loss=0.7159555227983565, metrics={'train_runtime': 2343.4953, 'train_samples_per_second': 1.152, 'train_steps_per_second': 0.143, 'total_flos': 7.635113166665318e+16, 'train_loss': 0.7159555227983565, 'epoch': 2.99})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "trainer.train()"
   ]
  },
  {
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       "('/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_Mixtral-8x7B-Instruct-v0.1/tokenizer_config.json',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_Mixtral-8x7B-Instruct-v0.1/special_tokens_map.json',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_Mixtral-8x7B-Instruct-v0.1/tokenizer.model',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_Mixtral-8x7B-Instruct-v0.1/added_tokens.json',\n",
       " '/home/jomondal/experiments/mywork/myTasks/mlsp/trained_models/sentiment_fintech_Mixtral-8x7B-Instruct-v0.1/tokenizer.json')"
      ]
     },
     "execution_count": 14,
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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 [07:02<00:00,  2.13it/s]"
     ]
    },
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     "name": "stdout",
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     "text": [
      "Accuracy: 0.851\n",
      "Accuracy for label 0: 0.957\n",
      "Accuracy for label 1: 0.760\n",
      "Accuracy for label 2: 0.837\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.96      0.94       300\n",
      "           1       0.81      0.76      0.78       300\n",
      "           2       0.81      0.84      0.83       300\n",
      "\n",
      "    accuracy                           0.85       900\n",
      "   macro avg       0.85      0.85      0.85       900\n",
      "weighted avg       0.85      0.85      0.85       900\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[287  11   2]\n",
      " [ 17 228  55]\n",
      " [  5  44 251]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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   "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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