bert.ipynb

{
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        "<a href=\"https://colab.research.google.com/github/Urdatorn/automatisk-metrisk-analys/blob/master/Untitled8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
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          "output_type": "stream",
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          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at cabrooks/LOGION-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
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        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-6068e936c3c1>\u001b[0m in \u001b[0;36m<cell line: 34>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtokenized_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0mtrain_tokenized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencode_examples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0mvalid_tokenized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencode_examples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-1-6068e936c3c1>\u001b[0m in \u001b[0;36mencode_examples\u001b[0;34m(text_file, tokenizer)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'], ['\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/iliad_train.txt'"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "\n",
        "from transformers import BertTokenizer, BertForTokenClassification, Trainer, TrainingArguments\n",
        "import torch\n",
        "\n",
        "# Load the pre-trained model and tokenizer\n",
        "model_name = \"cabrooks/LOGION-base\"\n",
        "model = BertForTokenClassification.from_pretrained(model_name, num_labels=3)\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Prepare your datasets\n",
        "train_path = \"/content/iliad_train.txt\"\n",
        "valid_path = \"/content/iliad_valid.txt\"\n",
        "\n",
        "# This function will tokenize the text and create the labels accordingly\n",
        "def encode_examples(text_file, tokenizer):\n",
        "    tokenized_inputs = []\n",
        "    labels = []\n",
        "\n",
        "    with open(text_file, \"r\", encoding=\"utf-8\") as f:\n",
        "        for line in f:\n",
        "            text, label_str = line.strip().split('], [')\n",
        "            text = text.replace('[', '').replace(']', '').strip()\n",
        "            label_str = label_str.replace(']', '').strip()\n",
        "            inputs = tokenizer(text, max_length=128, truncation=True, padding='max_length', return_tensors=\"pt\")\n",
        "            label_mapping = {'-': 0, 'u': 1, ' ': 2}  # Define your label mapping\n",
        "            label_ids = [label_mapping[label] for label in label_str if label in label_mapping]\n",
        "            # Use the real 'labels' length, rest is padded\n",
        "            labels.append(label_ids + [label_mapping[' ']] * (128 - len(label_ids)))\n",
        "            tokenized_inputs.append(inputs)\n",
        "\n",
        "    return tokenized_inputs, labels\n",
        "\n",
        "train_tokenized, train_labels = encode_examples(train_path, tokenizer)\n",
        "valid_tokenized, valid_labels = encode_examples(valid_path, tokenizer)\n",
        "\n",
        "# Convert to torch dataset\n",
        "class GreekMeterDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "train_dataset = GreekMeterDataset(train_tokenized, train_labels)\n",
        "valid_dataset = GreekMeterDataset(valid_tokenized, valid_labels)\n",
        "\n",
        "# Define the training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',          # output directory\n",
        "    num_train_epochs=3,              # number of training epochs\n",
        "    per_device_train_batch_size=16,  # batch size per device during training\n",
        "    per_device_eval_batch_size=64,   # batch size for evaluation\n",
        "    warmup_steps=500,                # number of warmup steps for learning rate scheduler\n",
        "    weight_decay=0.01,               # strength of weight decay\n",
        "    logging_dir='./logs',            # directory for storing logs\n",
        "    logging_steps=10,\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=valid_dataset\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IQzSLPlfrze1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b88e65af-ff87-4b61-e8b3-73ddeb29b45e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " -- uu -u u-uu \n"
          ]
        }
      ],
      "source": [
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "import torch\n",
        "\n",
        "# Load the tokenizer from the original pre-trained model\n",
        "model_name = \"cabrooks/LOGION-base\"  # Make sure this is the model you trained with\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Specify the checkpoint directory\n",
        "checkpoint = \"/content/results/checkpoint-2526\"  # Replace with your checkpoint directory\n",
        "\n",
        "# Load the model from the checkpoint\n",
        "model = BertForTokenClassification.from_pretrained(checkpoint)\n",
        "\n",
        "# Prepare the text you want to scan\n",
        "text_to_scan = \"ὣς εἰπὼν σάκος εἷλε τετυγμένον υἷος ἑοῖο\"  # Replace with your text\n",
        "\n",
        "# Tokenize the text\n",
        "inputs = tokenizer(text_to_scan, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "\n",
        "# Get predictions\n",
        "with torch.no_grad():\n",
        "    outputs = model(**inputs)\n",
        "\n",
        "# Get the predicted token class IDs\n",
        "predictions = torch.argmax(outputs.logits, dim=-1)\n",
        "\n",
        "# Convert IDs to labels\n",
        "label_mapping = {0: '-', 1: 'u', 2: ' '}  # The same mapping used during training\n",
        "predicted_labels = [label_mapping[label_id] for label_id in predictions[0].numpy()]\n",
        "\n",
        "# Now we have to align the predicted labels back to our original text tokens\n",
        "tokens = tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"][0])\n",
        "\n",
        "# We should skip special tokens like [CLS], [SEP], [PAD]\n",
        "predictions_aligned = [(token, label) for token, label in zip(tokens, predicted_labels) if token not in tokenizer.all_special_tokens]\n",
        "\n",
        "# Join the labels to form the scanned meter\n",
        "scanned_meter = ''.join(label for token, label in predictions_aligned)\n",
        "print(scanned_meter)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print tokens and their corresponding predicted labels\n",
        "for token, label in zip(tokens, predicted_labels):\n",
        "    if token not in tokenizer.all_special_tokens:\n",
        "        print(f\"{token}: {label}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJkrl-R-3Ec3",
        "outputId": "db2c4b5d-92ff-41df-e157-7a90cdcc0569"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ως:  \n",
            "ειπω: -\n",
            "##ν: -\n",
            "σακο:  \n",
            "##ς: u\n",
            "ει: u\n",
            "##λε:  \n",
            "τετ: -\n",
            "##υ: u\n",
            "##γμενο:  \n",
            "##ν: u\n",
            "υιος: -\n",
            "ε: u\n",
            "##οι: u\n",
            "##ο:  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization to access your Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Copy the folder to Google Drive\n",
        "!cp -r \"/content/results/checkpoint-2526\" \"/content/drive/MyDrive/\"\n",
        "\n",
        "# Optionally, you can print the contents of the directory to confirm the copy\n",
        "!echo \"Copied Files:\"\n",
        "!ls \"/content/drive/MyDrive/checkpoint-2526\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aqq3Ex0k40xd",
        "outputId": "a2ac4955-0151-451b-9016-752af6fe387d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Copied Files:\n",
            "config.json\t   optimizer.pt   scheduler.pt\t      training_args.bin\n",
            "model.safetensors  rng_state.pth  trainer_state.json\n"
          ]
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