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  "cells": [
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Pakyy/NLP-Project---MT-en-it/blob/main/seq2seq_pre_trained_t5_mt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "bYFlD7mU0ggx"
      },
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      "source": [
        "%%capture\n",
        "!pip install tqdm\n",
        "!pip install keras\n",
        "!pip install tensorflow\n",
        "!pip install translate-toolkit\n",
        "!pip install evaluate\n",
        "!pip install nltk rouge-score\n",
        "!pip install unbabel-comet\n",
        "!pip install sacrebleu\n",
        "!pip install comet-ml\n",
        "!pip install datasets --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "from google.colab import drive\n",
        "from translate.storage import tmx\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import spacy\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from collections import Counter\n",
        "from wordcloud import WordCloud\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import math, copy, time\n",
        "from tqdm import tqdm\n",
        "import evaluate\n",
        "from evaluate import load\n",
        "import xml.etree.ElementTree as ET\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, TrainingArguments, Trainer\n",
        "import datasets\n",
        "from datasets import Dataset\n",
        "from transformers import MBartForConditionalGeneration, MBartTokenizer\n"
      ],
      "metadata": {
        "id": "dn-Ly7e00jQB"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive to access the file\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "file_path = \"/content/drive/MyDrive/en-it.tmx\"\n",
        "sentence_pairs = []\n",
        "\n",
        "# Open the TMX file in read-binary mode\n",
        "with open(file_path, 'rb') as f:\n",
        "    tmx_file = tmx.tmxfile(f)\n",
        "\n",
        "# Iterate through the translation units and store sentence pairs\n",
        "for unit in tmx_file.units:\n",
        "    source_text = unit.source\n",
        "    target_text = unit.target\n",
        "    if source_text and target_text:\n",
        "        sentence_pairs.append((source_text, target_text))\n",
        "\n",
        "# Create a DataFrame from the sentence pairs\n",
        "df = pd.DataFrame(sentence_pairs, columns=['Source', 'Target'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdPvVgaT0pAk",
        "outputId": "2734d147-2cba-4de5-b355-77c51f1e66aa"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a DataFrame from the sentence pairs\n",
        "df = pd.DataFrame(sentence_pairs, columns=['Source', 'Target'])\n",
        "\n",
        "# Function to clean text by removing special characters and handling accents\n",
        "def clean_text(text):\n",
        "    text = re.sub(r\"http\\S+|www.\\S+\", \"\", text)  # Remove URLs\n",
        "    text = re.sub(r\"[^a-zA-ZàèéìòùÀÈÉÌÒÙçÇ]\", \" \", text)  # Keep letters and specific Italian characters\n",
        "    text = re.sub(r\"\\s+\", \" \", text)  # Replace multiple spaces with single space\n",
        "    return text.strip().lower()  # Convert to lowercase for consistency\n",
        "\n",
        "# Apply cleaning function to Source and Target\n",
        "df['Source_clean'] = df['Source'].apply(clean_text)\n",
        "df['Target_clean'] = df['Target'].apply(clean_text)\n",
        "\n",
        "# Remove rows with empty sentences after cleaning\n",
        "df = df[(df['Source_clean'].str.strip() != '') & (df['Target_clean'].str.strip() != '')]\n",
        "\n",
        "# Remove sentences that are too short (less than 3 words) or too long (more than 96 words)\n",
        "df = df[\n",
        "    df['Source_clean'].apply(lambda x: 3 <= len(x.split()) <= 96) &\n",
        "    df['Target_clean'].apply(lambda x: 3 <= len(x.split()) <= 96)\n",
        "]\n",
        "\n",
        "# Print the length of the dataset after cleaning\n",
        "print(f\"Dataset length after cleaning: {len(df)}\")\n",
        "\n",
        "# Check for any null values\n",
        "print(\"Null values in each column:\")\n",
        "print(df.isnull().sum())\n",
        "\n",
        "# Remove duplicate sentence pairs\n",
        "df.drop_duplicates(subset=['Source_clean', 'Target_clean'], inplace=True)\n",
        "\n",
        "# Display the first 10 cleaned sentence pairs\n",
        "print(\"Sample cleaned data:\")\n",
        "print(df[['Source_clean', 'Target_clean']].head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cakj5eYo04bR",
        "outputId": "6d73a0bd-30c8-4fc6-8abc-c9e32b19a22a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset length after cleaning: 153568\n",
            "Null values in each column:\n",
            "Source          0\n",
            "Target          0\n",
            "Source_clean    0\n",
            "Target_clean    0\n",
            "dtype: int64\n",
            "Sample cleaned data:\n",
            "                                         Source_clean  \\\n",
            "1   there s a tight and surprising link between th...   \n",
            "2             fish health mission blue oceans science   \n",
            "4         stephen palumbi following the mercury trail   \n",
            "5        it can be a very complicated thing the ocean   \n",
            "6   and it can be a very complicated thing what hu...   \n",
            "7   and bringing those two together might seem a v...   \n",
            "8   and those simple themes aren t really themes a...   \n",
            "9   and i m going to start with this one if momma ...   \n",
            "10          we know that right we ve experienced that   \n",
            "11  and if we just take that and we build from the...   \n",
            "\n",
            "                                         Target_clean  \n",
            "1   esiste uno stretto e sorprendente legame tra l...  \n",
            "2             fish health mission blue oceans science  \n",
            "4           stephen palumbi sulle tracce del mercurio  \n",
            "5     può essere una cosa davvero complicata l oceano  \n",
            "6   e può essere una cosa davvero complicata la sa...  \n",
            "7   e mettere insieme queste due cose può sembrare...  \n",
            "8   e questi semplici aspetti non riguardano le co...  \n",
            "9   e vorrei partire da questa mamma infelice tutt...  \n",
            "10    lo sappiamo tutti giusto ci siamo passati tutti  \n",
            "11  e se prendiamo questa idea e partiamo da qui p...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Split into training and testing sets\n",
        "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
        "\n",
        "print(f\"Training set size: {len(train_df)}\")\n",
        "print(f\"Testing set size: {len(test_df)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fEZIggZ2Obm",
        "outputId": "e0546635-5873-448e-feab-30ff517da3f3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 122826\n",
            "Testing set size: 30707\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert DataFrames to Hugging Face Datasets\n",
        "train_dataset = Dataset.from_pandas(train_df[['Source_clean', 'Target_clean']].reset_index(drop=True))\n",
        "test_dataset = Dataset.from_pandas(test_df[['Source_clean', 'Target_clean']].reset_index(drop=True))\n",
        "\n",
        "# Rename columns to fit the expected format\n",
        "train_dataset = train_dataset.rename_column(\"Source_clean\", \"source\")\n",
        "train_dataset = train_dataset.rename_column(\"Target_clean\", \"target\")\n",
        "test_dataset = test_dataset.rename_column(\"Source_clean\", \"source\")\n",
        "test_dataset = test_dataset.rename_column(\"Target_clean\", \"target\")\n",
        "\n",
        "# Set the format for compatibility\n",
        "train_dataset.set_format(type='torch')\n",
        "test_dataset.set_format(type='torch')\n",
        "\n",
        "print(train_dataset)\n",
        "print(test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3PCL13x2cEI",
        "outputId": "0a29cf5d-0f64-4106-a597-a44987236b61"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['source', 'target'],\n",
            "    num_rows: 122826\n",
            "})\n",
            "Dataset({\n",
            "    features: ['source', 'target'],\n",
            "    num_rows: 30707\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
        "\n",
        "# Load the pre-trained T5-small tokenizer and model\n",
        "model_name = \"t5-small\"\n",
        "tokenizer = T5Tokenizer.from_pretrained(model_name)\n",
        "model = T5ForConditionalGeneration.from_pretrained(model_name)\n",
        "\n",
        "# Move the model to GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "# Define the translation task prefix\n",
        "source_prefix = \"translate English to Italian: \"\n",
        "\n",
        "# Function to preprocess the data\n",
        "def preprocess_function(examples):\n",
        "    inputs = [source_prefix + ex for ex in examples['source']]\n",
        "    targets = [ex for ex in examples['target']]\n",
        "    model_inputs = tokenizer(inputs, max_length=96, padding='max_length', truncation=True)\n",
        "\n",
        "    # Tokenize targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(targets, max_length=96, padding='max_length', truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "# Apply the preprocessing\n",
        "tokenized_train = train_dataset.map(preprocess_function, batched=True)\n",
        "tokenized_test = test_dataset.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571,
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        },
        "id": "wG-2GgX72m04",
        "outputId": "15f7d9df-5304-4d7d-db76-685b8ca14df8",
        "collapsed": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "a5c0f2d801da4c32b76270d353b08c82"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e28beb58a64a4b96b5fa1a4c1b07240e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "9e550531ee184ab985e374c75268bf12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/122826 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c618ce019453479287dae1feac2dc214"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:4114: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/30707 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9521061227c644f0a9111dd621c6f32c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['source', 'target', 'input_ids', 'attention_mask', 'labels'],\n",
            "    num_rows: 122826\n",
            "})\n",
            "Dataset({\n",
            "    features: ['source', 'target', 'input_ids', 'attention_mask', 'labels'],\n",
            "    num_rows: 30707\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define training arguments with smaller batch size and gradient accumulation\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./t5_small_en_it\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=3e-4,\n",
        "    per_device_train_batch_size=8,  # Reduced batch size\n",
        "    per_device_eval_batch_size=8,\n",
        "    gradient_accumulation_steps=2,  # Accumulate gradients to simulate larger batch size\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=2,\n",
        "    num_train_epochs=3,  # Reduced number of epochs\n",
        "    fp16=True,  # Use mixed precision\n",
        "    logging_steps=100,\n",
        ")\n",
        "\n",
        "# Define the data collator\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n",
        "\n",
        "# Initialize the Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_train,\n",
        "    eval_dataset=tokenized_test,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        ")\n",
        "\n",
        "# Fine-tune the model\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "kaPyzudQ6ilm",
        "outputId": "6d5cfb23-32a5-4aa7-9409-87513ff98b64"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "<ipython-input-8-05485cfb7941>:20: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
            "  trainer = Trainer(\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
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          "data": {
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            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
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              "    "
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.18.6"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "Run data is saved locally in <code>/content/wandb/run-20241114_132200-ae0fywkm</code>"
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          "output_type": "display_data",
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/p-gravante-universit-degli-studi-di-milano-bicocca/huggingface/runs/ae0fywkm' target=\"_blank\">./t5_small_en_it</a></strong> to <a href='https://wandb.ai/p-gravante-universit-degli-studi-di-milano-bicocca/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br/>"
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              " View project at <a href='https://wandb.ai/p-gravante-universit-degli-studi-di-milano-bicocca/huggingface' target=\"_blank\">https://wandb.ai/p-gravante-universit-degli-studi-di-milano-bicocca/huggingface</a>"
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        {
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          "data": {
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              "<IPython.core.display.HTML object>"
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              " View run at <a href='https://wandb.ai/p-gravante-universit-degli-studi-di-milano-bicocca/huggingface/runs/ae0fywkm' target=\"_blank\">https://wandb.ai/p-gravante-universit-degli-studi-di-milano-bicocca/huggingface/runs/ae0fywkm</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py:657: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:278.)\n",
            "  batch[\"labels\"] = torch.tensor(batch[\"labels\"], dtype=torch.int64)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='23031' max='23031' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [23031/23031 1:18:07, Epoch 3/3]\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>1</td>\n",
              "      <td>0.629500</td>\n",
              "      <td>0.523075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.521300</td>\n",
              "      <td>0.471269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.496100</td>\n",
              "      <td>0.456292</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=23031, training_loss=0.6171066577681644, metrics={'train_runtime': 4704.3231, 'train_samples_per_second': 78.328, 'train_steps_per_second': 4.896, 'total_flos': 9350714310524928.0, 'train_loss': 0.6171066577681644, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prendi alcuni esempi di test per fare delle traduzioni\n",
        "n_examples = 5  # numero di esempi da visualizzare\n",
        "test_samples = tokenized_test.select(range(n_examples))\n",
        "\n",
        "# Genera traduzioni di esempio\n",
        "for i, sample in enumerate(test_samples):\n",
        "    input_ids = torch.tensor(sample['input_ids']).unsqueeze(0).to(device)  # aggiungi batch dimension\n",
        "    attention_mask = torch.tensor(sample['attention_mask']).unsqueeze(0).to(device)\n",
        "\n",
        "    # Genera la traduzione\n",
        "    translated_ids = model.generate(input_ids, attention_mask=attention_mask, max_length=96)\n",
        "    translation = tokenizer.decode(translated_ids[0], skip_special_tokens=True)\n",
        "\n",
        "    # Mostra l'input, la traduzione generata e la traduzione di riferimento\n",
        "    print(f\"\\nEsempio {i+1}:\")\n",
        "    print(\"Input (EN):\", tokenizer.decode(input_ids[0], skip_special_tokens=True).replace(source_prefix, \"\"))\n",
        "    print(\"Traduzione Generata (IT):\", translation)\n",
        "    print(\"Traduzione di Riferimento (IT):\", sample['target'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlZa8d5P0MLs",
        "outputId": "d41cbda9-ac40-4b6e-88e2-83cc8d80f522"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-2d172d68bf41>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.tensor(sample['input_ids']).unsqueeze(0).to(device)  # aggiungi batch dimension\n",
            "<ipython-input-9-2d172d68bf41>:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.tensor(sample['attention_mask']).unsqueeze(0).to(device)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Esempio 1:\n",
            "Input (EN): in an asexual species if you get two different mutations in different creatures a green one and a red one then one has to be better than the other\n",
            "Traduzione Generata (IT): in una specie assue se otteniamo due mutazioni diverse in creature diverse una verde e una rossa allora una deve essere meglio dell altra\n",
            "Traduzione di Riferimento (IT): in una specie asessuata due mutazioni in creature diverse una rossa ed una verde devono essere una migliore dell altra\n",
            "\n",
            "Esempio 2:\n",
            "Input (EN): he s sort of a homer simpson with fins\n",
            "Traduzione Generata (IT): e una specie di homer simpson con polvere\n",
            "Traduzione di Riferimento (IT): e una sorta di homer simpson con le pinne\n",
            "\n",
            "Esempio 3:\n",
            "Input (EN): so if algorithms are going to curate the world for us if they re going to decide what we get to see and what we don t get to see then we need to make sure that they re not just keyed to relevance\n",
            "Traduzione Generata (IT): quindi se gli algoritmi saranno curati il mondo per noi se decideranno cosa vediamo e cosa non vediamo allora dobbiamo assicurarci che non sono solo chiamati alla pertinenza\n",
            "Traduzione di Riferimento (IT): dunque se gli algoritmi dovranno prendersi cura del mondo in nostra vece se ci diranno cosa vedere e cosa non vedere allora dobbiamo assicurarci che non siano guidati unicamente dalla rilevanza\n",
            "\n",
            "Esempio 4:\n",
            "Input (EN): they re not even autonomous\n",
            "Traduzione Generata (IT): non sono nemmeno autonomi\n",
            "Traduzione di Riferimento (IT): non sono nemmeno autonomi\n",
            "\n",
            "Esempio 5:\n",
            "Input (EN): there s a marker line called the trim line above our little red illustration there\n",
            "Traduzione Generata (IT): c è una linea di marcatori chiamata linea di taglio sopra la nostra piccola immagine rossa\n",
            "Traduzione di Riferimento (IT): c è una linea di demarcazione chiamata trim line limite superiore di glaciazione sopra la linea rossa disegnata sull immagine\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Carica BLEU e ROUGE\n",
        "bleu_metric = evaluate.load(\"bleu\")\n",
        "rouge_metric = evaluate.load(\"rouge\")\n",
        "\n",
        "# Importa COMET\n",
        "comet_metric = None\n",
        "try:\n",
        "    from comet import download_model, load_from_checkpoint\n",
        "    comet_path = download_model(\"Unbabel/wmt20-comet-da\")  # Scarica il modello\n",
        "    comet_metric = load_from_checkpoint(comet_path)  # Carica il modello COMET\n",
        "except ImportError:\n",
        "    print(\"Installa `unbabel-comet` per utilizzare la metrica COMET.\")\n",
        "\n",
        "# Funzione di valutazione con barra di progresso\n",
        "def evaluate_translations_in_batches(trainer, test_dataset, batch_size=8):\n",
        "    all_predictions = []\n",
        "    all_references = []\n",
        "\n",
        "    for i in tqdm(range(0, len(test_dataset), batch_size), desc=\"Valutazione in corso\"):\n",
        "        batch = test_dataset.select(range(i, min(i + batch_size, len(test_dataset))))\n",
        "\n",
        "        input_ids = torch.stack([torch.tensor(ex['input_ids']) for ex in batch]).to(trainer.model.device)\n",
        "        attention_mask = torch.stack([torch.tensor(ex['attention_mask']) for ex in batch]).to(trainer.model.device)\n",
        "\n",
        "        translated_ids = trainer.model.generate(input_ids, attention_mask=attention_mask, max_length=96)\n",
        "        translations = [trainer.tokenizer.decode(ids, skip_special_tokens=True) for ids in translated_ids]\n",
        "\n",
        "        references = [[ex['target']] for ex in batch]\n",
        "\n",
        "        all_predictions.extend(translations)\n",
        "        all_references.extend(references)\n",
        "\n",
        "    # Calcola le metriche\n",
        "    bleu_score = bleu_metric.compute(predictions=all_predictions, references=all_references)\n",
        "    rouge_score = rouge_metric.compute(predictions=all_predictions, references=[ref[0] for ref in all_references])\n",
        "\n",
        "    # Calcola COMET se disponibile\n",
        "    comet_score = None\n",
        "    if comet_metric:\n",
        "        # Prepara i dati nel formato corretto per COMET\n",
        "        comet_inputs = [{\"src\": \"\", \"mt\": pred, \"ref\": ref[0]} for pred, ref in zip(all_predictions, all_references)]\n",
        "        comet_score = comet_metric.predict(comet_inputs)\n",
        "\n",
        "    return {\n",
        "        \"BLEU\": bleu_score,\n",
        "        \"ROUGE\": rouge_score,\n",
        "        \"COMET\": comet_score if comet_score else \"COMET non disponibile. Installa `unbabel-comet`.\"\n",
        "    }\n",
        "\n",
        "# Esegui la valutazione\n",
        "results = evaluate_translations_in_batches(trainer, tokenized_test, batch_size=32)\n",
        "print(\"Risultati delle metriche:\")\n",
        "print(\"BLEU:\", results[\"BLEU\"])\n",
        "print(\"ROUGE:\", results[\"ROUGE\"])\n",
        "print(\"COMET:\", results[\"COMET\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329,
          "referenced_widgets": [
            "ba1c0cb8679347f7823dff6bd6fd4021",
            "31a5064630444eec96ce8420c49251d4",
            "54a63f4ed55b451e9c9edbe2d493a34d",
            "7c664f643546483998cba10b268799c2",
            "2478bb81e0f945c4bde6bae14abc1f1c",
            "648c60fdec204153aaa3fe69627adacf",
            "2194ce3695ad47a5b22f6c4adec941d3",
            "17c8e2da55af4f8cae242498c9953e90",
            "791fe8b9363b4d28aefc0777b580bd62",
            "6348679220304fd6b4f7114f8c8ccf81",
            "efa19045b1fc4f07a8f3cf262ff8ee2b"
          ]
        },
        "id": "OBqXU-0J6pwU",
        "outputId": "2b5fb3a7-ba65-422f-e4a0-e8165f9420a6",
        "collapsed": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba1c0cb8679347f7823dff6bd6fd4021"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.3.5 to v2.4.0. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../root/.cache/huggingface/hub/models--Unbabel--wmt20-comet-da/snapshots/4c372befe4d603e6d0363f434248ecad66945607/checkpoints/model.ckpt`\n",
            "Valutazione in corso:   0%|          | 0/960 [00:00<?, ?it/s]<ipython-input-12-6ad5bde38146>:22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  input_ids = torch.stack([torch.tensor(ex['input_ids']) for ex in batch]).to(trainer.model.device)\n",
            "<ipython-input-12-6ad5bde38146>:23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  attention_mask = torch.stack([torch.tensor(ex['attention_mask']) for ex in batch]).to(trainer.model.device)\n",
            "Valutazione in corso: 100%|██████████| 960/960 [28:15<00:00,  1.77s/it]\n",
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "Predicting DataLoader 0: 100%|██████████| 1920/1920 [11:17<00:00,  2.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Risultati delle metriche:\n",
            "BLEU: {'bleu': 0.24445744626873456, 'precisions': [0.5525782613523746, 0.31708660641509095, 0.19561636588796846, 0.12400971851605735], 'brevity_penalty': 0.957403741016096, 'length_ratio': 0.9582857314955175, 'translation_length': 477279, 'reference_length': 498055}\n",
            "ROUGE: {'rouge1': 0.5436858849523856, 'rouge2': 0.3165620202933652, 'rougeL': 0.5245589826143651, 'rougeLsum': 0.5245490580903149}\n",
            "COMET: Prediction([('scores', [0.4218979477882385, -0.8348182439804077, -0.27214768528938293, 1.2973231077194214, -0.40955641865730286, -0.23441454768180847, -0.9453123211860657, -0.3966473639011383, 1.0783299207687378, 0.7264689803123474, 1.1863592863082886, 0.4316979944705963, 0.2704804837703705, -0.33148160576820374, 0.30931714177131653, -1.1440449953079224, 0.5153287053108215, 1.0989596843719482, 0.5858182907104492, 1.2739393711090088, -0.6166977286338806, 0.5208408236503601, 0.5354133248329163, -0.45310842990875244, -0.35072875022888184, 0.37816914916038513, -0.04567456990480423, 0.5184813141822815, 0.710034966468811, -0.7130897045135498, -0.4595680832862854, 0.9943049550056458, -0.18804053962230682, 1.1081064939498901, 1.2430373430252075, -0.20385034382343292, -0.18516488373279572, 0.13669316470623016, 0.2936352491378784, -0.2428632527589798, -0.2875547409057617, -0.4929007887840271, 1.0063295364379883, 0.6710134148597717, 0.8219874501228333, 0.9241557717323303, 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Estrai i punteggi puri dai risultati\n",
        "bleu_score = results[\"BLEU\"][\"bleu\"]  # Solo il BLEU score principale\n",
        "rouge_score = results[\"ROUGE\"][\"rougeL\"]  # ROUGE-L score\n",
        "comet_score = (sum(results[\"COMET\"][\"scores\"]) / len(results[\"COMET\"][\"scores\"]))\n",
        "\n",
        "               #Organizza i risultati in un dizionario e crea una tabella ordinata\n",
        "scores_data = {\n",
        "    \"Metric\": [\"BLEU\", \"ROUGE-L\", \"COMET\"],\n",
        "    \"Score\": [bleu_score, rouge_score, comet_score]\n",
        "}\n",
        "\n",
        "scores_df = pd.DataFrame(scores_data)\n",
        "\n",
        "# Visualizza la tabella con i risultati\n",
        "print(\"\\n--- Risultati delle metriche principali ---\")\n",
        "print(scores_df.to_markdown(index=False))  # Stampa la tabella in formato markdown per una visualizzazione chiara"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "re_eDHfwkjrf",
        "outputId": "672ec966-d818-4964-9a1a-bb1085a3267b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Risultati delle metriche principali ---\n",
            "| Metric   |     Score |\n",
            "|:---------|----------:|\n",
            "| BLEU     | 0.244457  |\n",
            "| ROUGE-L  | 0.524559  |\n",
            "| COMET    | 0.0182936 |\n"
          ]
        }
      ]
    }
  ]
}