{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "QeLkrw3YcwXk"
      },
      "outputs": [],
      "source": [
        "%pip install transformers\n",
        "%pip install torch\n",
        "%pip install pandas\n",
        "%pip install scikit-learn\n",
        "%pip install datasets\n",
        "%pip install evaluate\n",
        "%pip install tqdm\n",
        "%pip install openpyxl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PelpmuZhT5E1",
        "outputId": "581a84b2-c2fa-41bd-90b9-267715404a15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cEpWVgUyd0Xw"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForMaskedLM, DataCollatorWithPadding, AutoModelForSequenceClassification, TrainingArguments, Trainer, DataCollator\n",
        "from datasets import Dataset, DatasetDict\n",
        "import torch\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import re"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "2GbEdK5dd5Ty",
        "outputId": "77d3eb43-c40e-4f4a-b9df-b1e1ca9d55b8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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"
          ]
        },
        {
          "data": {
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              "model_id": "b463fdb9f6e84e16b6ab8669ed2184c8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        {
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            },
            "text/plain": [
              "config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f23cd374731144f1ac0d14a65ad33fc7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7ef2ad9ed0cc4fb1a032af05f6b6db7f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.10M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "local_directory = \"xlm-roberta-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "VYWEBZbNeWi6"
      },
      "outputs": [],
      "source": [
        "data = pd.read_excel('/content/drive/MyDrive/data/sentiment_analysis_data_km.xlsx')\n",
        "test_df = pd.read_excel('/content/drive/MyDrive/data/test.xlsx')\n",
        "data['text_prep'] = data['text_prep'].apply(lambda x: ' '.join(re.sub(r'[០១២៣៤៥៦៧៨៩។៕៖ៗ៘៙៚]+', ' ', x).strip().split()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wukBGdTSejVp"
      },
      "outputs": [],
      "source": [
        "X = data['text_prep']\n",
        "y = data['sentiment']\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=1234, shuffle=True, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "1QZvwUl147In"
      },
      "outputs": [],
      "source": [
        "data.drop_duplicates(subset=['text_prep'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1-gLtW_25Owu"
      },
      "outputs": [],
      "source": [
        "train = pd.DataFrame({'text': X_train, 'labels': y_train})\n",
        "val = pd.DataFrame({'text': X_val, 'labels': y_val})\n",
        "test = pd.DataFrame({'text': test_df['X_test'] , 'labels': test_df['y_test']})\n",
        "train.reset_index(inplace=True)\n",
        "val.reset_index( inplace=True)\n",
        "test.reset_index( inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ExzXpnTp5rXw"
      },
      "outputs": [],
      "source": [
        "train_dataset = Dataset.from_pandas(train)\n",
        "val_dataset = Dataset.from_pandas(val)\n",
        "test_dataset = Dataset.from_pandas(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzSI8s0fellp",
        "outputId": "355487b7-4a4b-4a27-a2f1-dad8f64eda2f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['index', 'text', 'labels'],\n",
              "        num_rows: 8583\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['index', 'text', 'labels'],\n",
              "        num_rows: 2146\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['index', 'text', 'labels'],\n",
              "        num_rows: 400\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a DatasetDict\n",
        "data_dict = DatasetDict({\n",
        "    'train': train_dataset,\n",
        "    'validation': val_dataset,\n",
        "    'test': test_dataset\n",
        "})\n",
        "data_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XfQ-UMewer7H"
      },
      "outputs": [],
      "source": [
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"text\"], truncation=True, padding=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "OiLN4Q1setcD",
        "outputId": "2482f784-97c7-4312-f93b-962f560a8c5e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e9d62dbfc82846fc8af6d1aabb2f4313",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/8583 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ad3b5a29453248e5a9f21681a15d8dd9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/2146 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8649afe69a54f3b974a3b4a818acc08",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/400 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['index', 'text', 'labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 8583\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['index', 'text', 'labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 2146\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['index', 'text', 'labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 400\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#pass the tokenize function to tokenize each sentence in the example\n",
        "tokenized_datasets = data_dict.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer)\n",
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lOTLbv16e1WD"
      },
      "outputs": [],
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns(['index','text'])\n",
        "tokenized_datasets = tokenized_datasets.with_format('torch')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "twWqpF36KZVA",
        "outputId": "29c05bda-e3e3-4508-f1bd-9a0cecf1d603"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 8583\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 2146\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 400\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7mNjMHIq8jC0"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "train_dataloader = DataLoader(tokenized_datasets['train'], batch_size=8, shuffle=True, collate_fn=data_collator  )\n",
        "eval_dataloader = DataLoader(tokenized_datasets['validation'], batch_size=8, collate_fn=data_collator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTGtLhGmGEvm",
        "outputId": "a075f03d-a7f6-45f5-a1c1-f3c015fb6f5e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'labels': torch.Size([8]),\n",
              " 'input_ids': torch.Size([8, 512]),\n",
              " 'attention_mask': torch.Size([8, 512])}"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for batch in train_dataloader:\n",
        "    break\n",
        "{k: v.shape for k, v in batch.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "2e7c4cd845fb4e369d28a0e62180598f",
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          ]
        },
        "id": "-qgl8RL8CNxX",
        "outputId": "574dd446-5a77-49e5-f7e9-ae75eb05618f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e7c4cd845fb4e369d28a0e62180598f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(local_directory, num_labels=2,  ignore_mismatched_sizes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0EPHILxCXll",
        "outputId": "fd4a9a05-d29d-4c7a-9bf2-c9731e3fd98e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zODDlmvAChgS",
        "outputId": "19b28847-3373-4e69-ff7c-291e48f9a457"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2146\n"
          ]
        }
      ],
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_epochs = 2\n",
        "num_training_steps = num_epochs * len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")\n",
        "print(num_training_steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrlz-SBxOnv7",
        "outputId": "13b21252-8d52-424c-a456-10e36e196b52"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "XLMRobertaForSequenceClassification(\n",
              "  (roberta): XLMRobertaModel(\n",
              "    (embeddings): XLMRobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(250002, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): XLMRobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x XLMRobertaLayer(\n",
              "          (attention): XLMRobertaAttention(\n",
              "            (self): XLMRobertaSdpaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): XLMRobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): XLMRobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): XLMRobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): XLMRobertaClassificationHead(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "    (out_proj): Linear(in_features=768, out_features=2, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)  # Check the output\n",
        "model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "a05905ace1934dc7b4b0857a022e647f"
          ]
        },
        "id": "tiFbBPcsDXw-",
        "outputId": "a449dd6f-7d68-4acc-fcba-92823fbe5cf4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4117f6a8bab343c39b23912935823cc3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2146 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from tqdm.notebook import tqdm  # Use tqdm.notebook\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch in train_dataloader:\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174,
          "referenced_widgets": [
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            "70d53790b8de451cbcaffee5dd3c6dfe"
          ]
        },
        "id": "EKSWN7HGWTT7",
        "outputId": "0071ec28-a30b-40bb-e049-46e6fe055d0a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "838e6c4060324591a3bd6ea6ef514f64",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Evaluating:   0%|          | 0/269 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evaluation Accuracy: 0.8541472506989748\n",
            "Evaluation Precision: 0.8553107984468175\n",
            "Evaluation Recall: 0.8541472506989748\n",
            "Evaluation F1 Score: 0.85403705456117\n",
            "Confusion Matrix:\n",
            "[[945 126]\n",
            " [187 888]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from evaluate import load # Import load instead of load_metric\n",
        "\n",
        "# Load multiple metrics\n",
        "accuracy_metric = load(\"accuracy\")\n",
        "precision_metric = load(\"precision\")\n",
        "recall_metric = load(\"recall\")\n",
        "f1_metric = load(\"f1\")\n",
        "\n",
        "\n",
        "model.eval()\n",
        "\n",
        "all_predictions = []\n",
        "all_labels = []\n",
        "\n",
        "for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):  # Use tqdm for progress bar\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**batch)\n",
        "\n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "\n",
        "    # Add batch to each metric\n",
        "    accuracy_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    precision_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    recall_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    f1_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "\n",
        "    # Store predictions and labels for further analysis (e.g., confusion matrix)\n",
        "    all_predictions.extend(predictions.cpu().numpy())\n",
        "    all_labels.extend(batch[\"labels\"].cpu().numpy())\n",
        "\n",
        "# Compute and print metrics\n",
        "accuracy = accuracy_metric.compute()\n",
        "precision = precision_metric.compute(average=\"weighted\")  # Use weighted average for multi-class\n",
        "recall = recall_metric.compute(average=\"weighted\")      # Use weighted average for multi-class\n",
        "f1 = f1_metric.compute(average=\"weighted\")            # Use weighted average for multi-class\n",
        "\n",
        "print(f\"Evaluation Accuracy: {accuracy['accuracy']}\")\n",
        "print(f\"Evaluation Precision: {precision['precision']}\")\n",
        "print(f\"Evaluation Recall: {recall['recall']}\")\n",
        "print(f\"Evaluation F1 Score: {f1['f1']}\")\n",
        "\n",
        "\n",
        "# Further analysis (optional):\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(all_labels, all_predictions)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)"
      ]
    },
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        "id": "3DCCUukWch3d",
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "daa5c3073bde46a69ad7c4721ab7b49c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0aba686c7c1c4968990671dc71e1377a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.55k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "d0dac945240746708aaab5ab35ac4958",
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              "version_minor": 0
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            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.36k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fe3f2c808527405db4a9d78d4e9f5cb6",
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              "version_minor": 0
            },
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              "Downloading builder script:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fd17b7d078bc4561af15158dcde3fc70",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Evaluating on Test Set:   0%|          | 0/50 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Set Accuracy: 0.8325\n",
            "Test Set Precision: 0.8355478766025641\n",
            "Test Set Recall: 0.8325\n",
            "Test Set F1 Score: 0.8325177969862311\n",
            "Confusion Matrix:\n",
            "[[167  42]\n",
            " [ 25 166]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from evaluate import load\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Load metrics\n",
        "accuracy_metric = load(\"accuracy\")\n",
        "precision_metric = load(\"precision\")\n",
        "recall_metric = load(\"recall\")\n",
        "f1_metric = load(\"f1\")\n",
        "\n",
        "# Assuming you have already created and tokenized your test_dataset from test_df\n",
        "# If not, you can create it using:\n",
        "# test_dataset = Dataset.from_pandas(test_df)\n",
        "# tokenized_test_dataset = test_dataset.map(tokenize_function, batched=True)\n",
        "# tokenized_test_dataset = tokenized_test_dataset.remove_columns(['index','text'])\n",
        "# tokenized_test_dataset = tokenized_test_dataset.with_format('torch')\n",
        "\n",
        "# Create a DataLoader for the test dataset\n",
        "test_dataloader = DataLoader(tokenized_datasets['test'], batch_size=8, collate_fn=data_collator)\n",
        "\n",
        "\n",
        "model.eval()\n",
        "\n",
        "all_predictions = []\n",
        "all_labels = []\n",
        "\n",
        "for batch in tqdm(test_dataloader, desc=\"Evaluating on Test Set\"):\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**batch)\n",
        "\n",
        "    logits = outputs.logits\n",
        "    predictions = torch.argmax(logits, dim=-1)\n",
        "\n",
        "    accuracy_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    precision_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    recall_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "    f1_metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
        "\n",
        "    all_predictions.extend(predictions.cpu().numpy())\n",
        "    all_labels.extend(batch[\"labels\"].cpu().numpy())\n",
        "\n",
        "# Compute and print metrics\n",
        "accuracy = accuracy_metric.compute()\n",
        "precision = precision_metric.compute(average=\"weighted\")\n",
        "recall = recall_metric.compute(average=\"weighted\")\n",
        "f1 = f1_metric.compute(average=\"weighted\")\n",
        "\n",
        "print(f\"Test Set Accuracy: {accuracy['accuracy']}\")\n",
        "print(f\"Test Set Precision: {precision['precision']}\")\n",
        "print(f\"Test Set Recall: {recall['recall']}\")\n",
        "print(f\"Test Set F1 Score: {f1['f1']}\")\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(all_labels, all_predictions)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F1DA1A_YcvDt"
      },
      "outputs": [],
      "source": [
        "# model.save_pretrained('/content/drive/MyDrive/models/my_trained_model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uwT2NnxXeg6C"
      },
      "outputs": [],
      "source": [
        "loaded_model = AutoModelForSequenceClassification.from_pretrained('/content/drive/MyDrive/models/my_trained_model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOOWvyPne5dB",
        "outputId": "55ecf7dc-c8e4-4754-ece2-230459b2fb56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted Class: positive\n"
          ]
        }
      ],
      "source": [
        "text = \"ខ្ញុំកំពុងរៀនពីកំហុសរបស់ខ្ញុំ និងមានអារម្មណ៍ថាវាជាពិធីបន្ថែមតម្លៃ។\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n",
        "inputs\n",
        "\n",
        "labels_mapping = {0: 'negative', 1: 'positive'}\n",
        "\n",
        "outputs = loaded_model(**inputs)\n",
        "logits = outputs.logits\n",
        "predicted_class = torch.argmax(logits, dim=1).item()\n",
        "\n",
        "print(f\"Predicted Class: {labels_mapping[predicted_class]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3pRbKl09fEzb"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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