diff --git a/Report2.pdf b/Report2.pdf
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diff --git a/Text2Gloss_test3.ipynb b/Text2Gloss_test3.ipynb
index dbaaafc..87d88f5 100644
--- a/Text2Gloss_test3.ipynb
+++ b/Text2Gloss_test3.ipynb
@@ -2,13 +2,13 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "c18d8f92",
-        "outputId": "100655e5-02fa-4a95-fc25-190d58b931dc"
+        "outputId": "a9cc92a9-f6ce-410f-9749-5fb22553126c"
       },
       "outputs": [
         {
@@ -18,31 +18,31 @@
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Collecting subword-nmt\n",
             "  Downloading subword_nmt-0.3.8-py3-none-any.whl (27 kB)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from subword-nmt) (4.65.0)\n",
             "Collecting mock\n",
-            "  Downloading mock-5.0.1-py3-none-any.whl (30 kB)\n",
+            "  Downloading mock-5.0.2-py3-none-any.whl (30 kB)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from subword-nmt) (4.65.0)\n",
             "Installing collected packages: mock, subword-nmt\n",
-            "Successfully installed mock-5.0.1 subword-nmt-0.3.8\n",
+            "Successfully installed mock-5.0.2 subword-nmt-0.3.8\n",
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Requirement already satisfied: nltk in /usr/local/lib/python3.9/dist-packages (3.8.1)\n",
-            "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from nltk) (8.1.3)\n",
             "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.9/dist-packages (from nltk) (2022.10.31)\n",
-            "Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from nltk) (1.1.1)\n",
+            "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from nltk) (8.1.3)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from nltk) (1.2.0)\n",
             "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from nltk) (4.65.0)\n",
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Collecting torchtext==0.11.0\n",
             "  Downloading torchtext-0.11.0-cp39-cp39-manylinux1_x86_64.whl (8.0 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.0/8.0 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from torchtext==0.11.0) (2.27.1)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from torchtext==0.11.0) (4.65.0)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.0/8.0 MB\u001b[0m \u001b[31m60.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from torchtext==0.11.0) (1.22.4)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from torchtext==0.11.0) (2.27.1)\n",
             "Collecting torch==1.10.0\n",
             "  Downloading torch-1.10.0-cp39-cp39-manylinux1_x86_64.whl (881.9 MB)\n",
             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m881.9/881.9 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from torchtext==0.11.0) (1.22.4)\n",
+            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from torchtext==0.11.0) (4.65.0)\n",
             "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch==1.10.0->torchtext==0.11.0) (4.5.0)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->torchtext==0.11.0) (2.0.12)\n",
             "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->torchtext==0.11.0) (3.4)\n",
             "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->torchtext==0.11.0) (1.26.15)\n",
-            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->torchtext==0.11.0) (2.0.12)\n",
             "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->torchtext==0.11.0) (2022.12.7)\n",
             "Installing collected packages: torch, torchtext\n",
             "  Attempting uninstall: torch\n",
@@ -70,7 +70,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "id": "c6c0c86a"
       },
@@ -82,15 +82,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
         "id": "74e282ef"
       },
       "outputs": [],
       "source": [
-        "phoenix_dataset_dev = 'PHOENIX-2014-T.dev.corpus.csv'\n",
-        "phoenix_dataset_test = 'PHOENIX-2014-T.test.corpus.csv'\n",
-        "phoenix_dataset_train = 'PHOENIX-2014-T.train.corpus.csv'\n",
+        "phoenix_dataset_dev = 'sample_data/PHOENIX-2014-T.dev.corpus.csv'\n",
+        "phoenix_dataset_test = 'sample_data/PHOENIX-2014-T.test.corpus.csv'\n",
+        "phoenix_dataset_train = 'sample_data/PHOENIX-2014-T.train.corpus.csv'\n",
         "# Load the Phoenix dataset\n",
         "phoenix_dev = pd.read_csv(phoenix_dataset_dev, sep=\"|\")\n",
         "phoenix_test = pd.read_csv(phoenix_dataset_test, sep=\"|\")\n",
@@ -100,7 +100,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
         "id": "0c3ffe98"
       },
@@ -112,13 +112,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "6b9e213a",
-        "outputId": "b1433f93-4d33-4726-b9e7-48aae53f8981"
+        "outputId": "dd853bc6-d9cb-4c8e-9ba4-c90f9e6697d1"
       },
       "outputs": [
         {
@@ -139,14 +139,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 333
+          "height": 367
         },
         "id": "d3204e94",
-        "outputId": "29e3d099-e017-45c8-9529-76441bee3b6f"
+        "outputId": "16c5a0ff-562a-4530-ba24-a92352a88d01"
       },
       "outputs": [
         {
@@ -183,7 +183,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-2c289c7a-cb9a-42a8-8a67-711c5b4b82ca\">\n",
+              "  <div id=\"df-980b5655-9c2d-4e57-8894-95527d9bfc99\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -266,7 +266,7 @@
               "  </tbody>\n",
               "</table>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c289c7a-cb9a-42a8-8a67-711c5b4b82ca')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-980b5655-9c2d-4e57-8894-95527d9bfc99')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -317,12 +317,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-2c289c7a-cb9a-42a8-8a67-711c5b4b82ca button.colab-df-convert');\n",
+              "          document.querySelector('#df-980b5655-9c2d-4e57-8894-95527d9bfc99 button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-2c289c7a-cb9a-42a8-8a67-711c5b4b82ca');\n",
+              "          const element = document.querySelector('#df-980b5655-9c2d-4e57-8894-95527d9bfc99');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -355,7 +355,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
         "id": "5769b73b"
       },
@@ -367,14 +367,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
         },
         "id": "8a8e79a0",
-        "outputId": "f63335c4-62a6-4d9e-c4a8-ab7563f9703f"
+        "outputId": "85c6a8c9-78cc-415a-ddf8-c6a10a0e17d4"
       },
       "outputs": [
         {
@@ -397,7 +397,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-d2c067fc-83e3-4a32-ad25-a8e98e37f17f\">\n",
+              "  <div id=\"df-831ff5c0-1e85-47fe-8826-9a499af75024\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -450,7 +450,7 @@
               "  </tbody>\n",
               "</table>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2c067fc-83e3-4a32-ad25-a8e98e37f17f')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-831ff5c0-1e85-47fe-8826-9a499af75024')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -501,12 +501,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-d2c067fc-83e3-4a32-ad25-a8e98e37f17f button.colab-df-convert');\n",
+              "          document.querySelector('#df-831ff5c0-1e85-47fe-8826-9a499af75024 button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-d2c067fc-83e3-4a32-ad25-a8e98e37f17f');\n",
+              "          const element = document.querySelector('#df-831ff5c0-1e85-47fe-8826-9a499af75024');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -539,7 +539,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "id": "8898f335"
       },
@@ -552,13 +552,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "5055309b",
-        "outputId": "0e2a89b2-78a1-49d2-cedd-6ce1a20d7b9f"
+        "outputId": "4886c4a4-aef6-4c0c-dd33-dbd11365ab5b"
       },
       "outputs": [
         {
@@ -568,73 +568,73 @@
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Collecting datasets\n",
             "  Downloading datasets-2.11.0-py3-none-any.whl (468 kB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25hCollecting transformers[sentencepiece]\n",
-            "  Downloading transformers-4.27.4-py3-none-any.whl (6.8 MB)\n",
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+            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m71.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets) (1.4.4)\n",
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+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.9/118.9 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting xxhash\n",
+            "  Downloading xxhash-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting responses<0.19\n",
+            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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+            "Collecting aiohttp\n",
+            "  Downloading aiohttp-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m50.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "Collecting dill<0.3.7,>=0.3.0\n",
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-            "\u001b[?25hCollecting aiohttp\n",
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+            "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91\n",
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+            "\u001b[?25hCollecting aiosignal>=1.1.2\n",
+            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
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@@ -646,7 +646,7 @@
             "      Successfully uninstalled protobuf-3.20.3\n",
             "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
             "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\u001b[0m\u001b[31m\n",
-            "\u001b[0mSuccessfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 colorama-0.4.6 datasets-2.11.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.13.4 multidict-6.0.4 multiprocess-0.70.14 portalocker-2.7.0 protobuf-3.20.2 responses-0.18.0 sacrebleu-2.3.1 sentencepiece-0.1.97 tokenizers-0.13.3 transformers-4.27.4 xxhash-3.2.0 yarl-1.8.2\n"
+            "\u001b[0mSuccessfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 colorama-0.4.6 datasets-2.11.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.13.4 multidict-6.0.4 multiprocess-0.70.14 portalocker-2.7.0 protobuf-3.20.2 responses-0.18.0 sacrebleu-2.3.1 sentencepiece-0.1.98 tokenizers-0.13.3 transformers-4.28.1 xxhash-3.2.0 yarl-1.8.2\n"
           ]
         }
       ],
@@ -657,13 +657,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "da0f6a4d",
-        "outputId": "f37af8c3-a06b-49a2-f457-1998b5e05583"
+        "outputId": "f21c8f36-422e-4989-9715-4ad3b928094f"
       },
       "outputs": [
         {
@@ -685,20 +685,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "50acc943",
-        "outputId": "1bbb775c-0a29-4643-f092-0e982e07ead3"
+        "outputId": "8b88b3f0-12a5-4d6c-858d-cfddce1bc76f"
       },
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "4.27.4\n"
+            "4.28.1\n"
           ]
         }
       ],
@@ -711,7 +711,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 13,
       "metadata": {
         "id": "ecb60415"
       },
@@ -725,7 +725,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 14,
       "metadata": {
         "id": "81b4745e"
       },
@@ -737,37 +737,82 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "source": [
+        "!pip install rouge_score\n"
+      ],
       "metadata": {
-        "id": "290cbf82",
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 103,
-          "referenced_widgets": [
-            "b153482aed634f1ea5075bb59a2b0545",
-            "02ab6c1cb2444ef4ba7e27d60da01dff",
-            "2060f2bc20f64204952a1e66084985ff",
-            "322c3e6ef836474194983229603d40b4",
-            "889da805fda949b999a9cec506eba25d",
-            "6d75e28c977c4c59a82107056498bc8a",
-            "1fc508d0ebc04de78df89bd54b24d5b8",
-            "422600b6113d4e2082ed9e2fe339ee85",
-            "ce21a7e1135b400eb0959211a07c7925",
-            "4ff59337992b4b728be4e0cd68f8d4c4",
-            "0c9c095ecd734f68a62f3df31e7c3818"
-          ]
+          "base_uri": "https://localhost:8080/"
         },
-        "outputId": "da367ed0-955a-4188-93bc-e9773b961c52"
+        "id": "0P933dFNxr0a",
+        "outputId": "7e1ea26d-7e67-4c8f-c434-8c61bfdae0ca"
       },
+      "id": "0P933dFNxr0a",
+      "execution_count": 17,
       "outputs": [
         {
           "output_type": "stream",
-          "name": "stderr",
+          "name": "stdout",
           "text": [
-            "<ipython-input-15-3e1b0a21c10a>:4: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
-            "  metric = load_metric(\"sacrebleu\")\n"
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting rouge_score\n",
+            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: absl-py in /usr/local/lib/python3.9/dist-packages (from rouge_score) (1.4.0)\n",
+            "Requirement already satisfied: nltk in /usr/local/lib/python3.9/dist-packages (from rouge_score) (3.8.1)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from rouge_score) (1.22.4)\n",
+            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.9/dist-packages (from rouge_score) (1.16.0)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (1.2.0)\n",
+            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (2022.10.31)\n",
+            "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (8.1.3)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (4.65.0)\n",
+            "Building wheels for collected packages: rouge_score\n",
+            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24954 sha256=e4945ef68a7aeb516ff774c40a63ba6d37a7a47e6251185c8550abf3c275b355\n",
+            "  Stored in directory: /root/.cache/pip/wheels/9b/3d/39/09558097d3119ca0a4d462df68f22c6f3c1b345ac63a09b86e\n",
+            "Successfully built rouge_score\n",
+            "Installing collected packages: rouge_score\n",
+            "Successfully installed rouge_score-0.1.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "290cbf82",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 149,
+          "referenced_widgets": [
+            "444edbdb82ff4bd892f051e263de2d79",
+            "78a95f9ee4dc48e59e98e81407125e63",
+            "85023c3e45c047b19bb40e6298eaca70",
+            "9d413a61db9140b39df03be196aee17f",
+            "ae384d1645ef4bf78edb427416d46956",
+            "68437450b38f4267b701b36f7a62c292",
+            "3f477b7f622641cf92b2944e31cac298",
+            "b331ebe67cd54c16a2e6d5c16f54e554",
+            "d80a1346c143408d8005fc1227b837ee",
+            "1a1e97a1aca24d3b9e6771b5a66a8ec6",
+            "99f6d5683907449e9115d870b77c1167",
+            "1df5954b289a4ed3bca2b59ef102272c",
+            "db85ac6a2fb54426b9d1105ab5b2c58d",
+            "eb7012e234ef40e2ad95caf1ef2f3da9",
+            "d0a7e55a7a34475185f0d0ca60e921e3",
+            "ee56e4730ed94533937d495f8487bf84",
+            "4abbb4843e2a4e0baae50d77b7982773",
+            "36ce695114e2458c818b2194e1b3fb89",
+            "8dc008809366499ba119271cbb17eda0",
+            "344e403ae55b472b89467bb5577c07ca",
+            "ec8e080e55e4481980b128491010b013",
+            "45ce89d90ac740febff3cc8794e4c05b"
           ]
         },
+        "outputId": "b845d56d-d1e6-4d50-a93c-7d013755ff21"
+      },
+      "outputs": [
         {
           "output_type": "display_data",
           "data": {
@@ -777,37 +822,87 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "b153482aed634f1ea5075bb59a2b0545"
+              "model_id": "444edbdb82ff4bd892f051e263de2d79"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1df5954b289a4ed3bca2b59ef102272c"
             }
           },
           "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
+            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
+          ]
         }
       ],
       "source": [
         "from datasets import load_metric\n",
         "\n",
-        "# raw_datasets = load_dataset(\"wmt16\", \"de-en\")\n",
-        "metric = load_metric(\"sacrebleu\")"
+        "\n",
+        "rouge = load_metric(\"rouge\")\n",
+        "bleu = load_metric(\"sacrebleu\")\n",
+        "meteor = load_metric(\"meteor\")"
       ],
       "id": "290cbf82"
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 19,
+      "metadata": {
+        "id": "3XgZAr4lPbBp"
+      },
+      "outputs": [],
+      "source": [
+        "phoenix_need = phoenix_all[['translation', 'orth']]\n"
+      ],
+      "id": "3XgZAr4lPbBp"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "AYOFYk4FoGoN"
+      },
+      "outputs": [],
+      "source": [
+        "phoenix_need= phoenix_need.rename(columns={'translation': 'src', 'orth': 'trg'})"
+      ],
+      "id": "AYOFYk4FoGoN"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 419
         },
-        "id": "3XgZAr4lPbBp",
-        "outputId": "d1eae9e3-828c-438e-f51d-7cc67d2f983f"
+        "id": "LRlHkqbUx1Zu",
+        "outputId": "cf5667c2-8861-40ae-80e2-1f83a10dc9a1"
       },
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "                                            translation  \\\n",
+              "                                                    src  \\\n",
               "0     tiefer luftdruck bestimmt in den nächsten tage...   \n",
               "1     das bedeutet viele wolken und immer wieder zum...   \n",
               "2     meist weht nur ein schwacher wind aus untersch...   \n",
@@ -820,7 +915,7 @@
               "7094  der donnerstag beginnt oft freundlich später z...   \n",
               "7095                                   es bleibt windig   \n",
               "\n",
-              "                                                   orth  \n",
+              "                                                    trg  \n",
               "0                                     DRUCK TIEF KOMMEN  \n",
               "1     ES-BEDEUTET VIEL WOLKE UND KOENNEN REGEN GEWIT...  \n",
               "2     WIND MAESSIG SCHWACH REGION WENN GEWITTER WIND...  \n",
@@ -837,7 +932,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-fe3e19a7-619e-4ea5-a396-bbb8a19c2796\">\n",
+              "  <div id=\"df-8bbc1de5-1d7d-41dc-9a47-6d4540d4bd5d\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -857,8 +952,8 @@
               "  <thead>\n",
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
-              "      <th>translation</th>\n",
-              "      <th>orth</th>\n",
+              "      <th>src</th>\n",
+              "      <th>trg</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
@@ -921,7 +1016,7 @@
               "</table>\n",
               "<p>8257 rows × 2 columns</p>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe3e19a7-619e-4ea5-a396-bbb8a19c2796')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8bbc1de5-1d7d-41dc-9a47-6d4540d4bd5d')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -972,12 +1067,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-fe3e19a7-619e-4ea5-a396-bbb8a19c2796 button.colab-df-convert');\n",
+              "          document.querySelector('#df-8bbc1de5-1d7d-41dc-9a47-6d4540d4bd5d button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-fe3e19a7-619e-4ea5-a396-bbb8a19c2796');\n",
+              "          const element = document.querySelector('#df-8bbc1de5-1d7d-41dc-9a47-6d4540d4bd5d');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -1000,382 +1095,71 @@
             ]
           },
           "metadata": {},
-          "execution_count": 16
+          "execution_count": 21
         }
       ],
       "source": [
-        "phoenix_need = phoenix_all[['translation', 'orth']]\n",
         "phoenix_need"
       ],
-      "id": "3XgZAr4lPbBp"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "AYOFYk4FoGoN"
-      },
-      "outputs": [],
-      "source": [
-        "phoenix_need= phoenix_need.rename(columns={'translation': 'src', 'orth': 'trg'})"
-      ],
-      "id": "AYOFYk4FoGoN"
+      "id": "LRlHkqbUx1Zu"
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 22,
       "metadata": {
+        "id": "vjiRueagKTJa",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 419
+          "height": 17,
+          "referenced_widgets": [
+            "0cda57bbaf304367849fa9d38f499511",
+            "e2e4ec42973f4ee6b8cf20919b656adb",
+            "819262719fe5423fb2a68418e52aa19f",
+            "51e2a909dab14dea94a5457495505e84",
+            "d309c5ac0c35498d921210a6a45b13a4",
+            "2718cc5d119b4484aa0c87e32403ab1f",
+            "0ab30ef801f7460f8e6a6cb070c547cb",
+            "ce63794b26cc4c9f954155925ac3623f",
+            "04d922a97457474ab3861c97b69d87ae",
+            "239cb8aaaeed4461b9dd4ca536e0d87e",
+            "2e9fb425201c4973aa5d48d073c5e61b",
+            "43158795fdab4c77bb9e275ea88b451e",
+            "ea762a0a4a174f59ba868d0e461300fe",
+            "92d43733e22843b682c10981b514fca8",
+            "3470ab0ffeeb45dd983a730f7c80d8b7",
+            "2374bedbff6947d5b665555fa89b7eca",
+            "af56c3650115484681877ecc55b3879b",
+            "e352f20a0fe2451dbb24f0d2e9662568",
+            "e457705527d34e4a85cb4378b33fe69b",
+            "a0014c7d0c6e455d9ca533f7039f6b18",
+            "171ecd9f223149389c01b63216f248ec",
+            "4b2e62d36cb04eec8b5988021dadb721",
+            "eb41f3e8e4a44c0f93c9fd6ccd2bf5d1",
+            "49350307e55f494f8e725e49e477f08d",
+            "1b1e786bcd2c48b8ace28c96a2b948b8",
+            "74e161d17f304a90a630c31cffa32069",
+            "844838085b404ff694a7ed610d21864a",
+            "a0051f7bb9594b1a8c40adaa163fef8e",
+            "5236d4e0dd2a426bb1fb4f988dcda330",
+            "384e13828c4d49feafbb88f33843e917",
+            "b47d192f2590492092d868b3511aaed8",
+            "592e12f87196414286dd8ac87ee75ccd",
+            "7eed76af626c413793d7e74e6823c9f1"
+          ]
         },
-        "id": "LRlHkqbUx1Zu",
-        "outputId": "6464ab24-725d-4465-9a6f-ad5afa069e34"
+        "outputId": "d9dfd672-b487-4911-e25e-3ac9e9a4d2fe"
       },
       "outputs": [
         {
-          "output_type": "execute_result",
+          "output_type": "display_data",
           "data": {
             "text/plain": [
-              "                                                    src  \\\n",
-              "0     tiefer luftdruck bestimmt in den nächsten tage...   \n",
-              "1     das bedeutet viele wolken und immer wieder zum...   \n",
-              "2     meist weht nur ein schwacher wind aus untersch...   \n",
-              "3     am mittwoch hier und da nieselregen in der nor...   \n",
-              "4     und nun die wettervorhersage für morgen freita...   \n",
-              "...                                                 ...   \n",
-              "7091  am dienstag wird es sehr windig und es regnet ...   \n",
-              "7092  am mittwoch legt der wind noch zu und es regne...   \n",
-              "7093            im süden zeigt sich aber auch die sonne   \n",
-              "7094  der donnerstag beginnt oft freundlich später z...   \n",
-              "7095                                   es bleibt windig   \n",
-              "\n",
-              "                                                    trg  \n",
-              "0                                     DRUCK TIEF KOMMEN  \n",
-              "1     ES-BEDEUTET VIEL WOLKE UND KOENNEN REGEN GEWIT...  \n",
-              "2     WIND MAESSIG SCHWACH REGION WENN GEWITTER WIND...  \n",
-              "3     MITTWOCH REGEN KOENNEN NORDWEST WAHRSCHEINLICH...  \n",
-              "4     JETZT WETTER WIE-AUSSEHEN MORGEN FREITAG SECHS...  \n",
-              "...                                                 ...  \n",
-              "7091                   DIENSTAG WIND STARK REGEN KOMMEN  \n",
-              "7092                           MITTWOCH WIND MEHR REGEN  \n",
-              "7093                       SUED REGION SONNE AUCH DABEI  \n",
-              "7094  DONNERSTAG FREUNDLICH SONNE DANN SPAETER KOMME...  \n",
-              "7095                                       BLEIBEN WIND  \n",
-              "\n",
-              "[8257 rows x 2 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-26e45260-4b49-476b-bd2b-ddee1a49dc56\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>src</th>\n",
-              "      <th>trg</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>tiefer luftdruck bestimmt in den nächsten tage...</td>\n",
-              "      <td>DRUCK TIEF KOMMEN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>das bedeutet viele wolken und immer wieder zum...</td>\n",
-              "      <td>ES-BEDEUTET VIEL WOLKE UND KOENNEN REGEN GEWIT...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>meist weht nur ein schwacher wind aus untersch...</td>\n",
-              "      <td>WIND MAESSIG SCHWACH REGION WENN GEWITTER WIND...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>am mittwoch hier und da nieselregen in der nor...</td>\n",
-              "      <td>MITTWOCH REGEN KOENNEN NORDWEST WAHRSCHEINLICH...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>und nun die wettervorhersage für morgen freita...</td>\n",
-              "      <td>JETZT WETTER WIE-AUSSEHEN MORGEN FREITAG SECHS...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7091</th>\n",
-              "      <td>am dienstag wird es sehr windig und es regnet ...</td>\n",
-              "      <td>DIENSTAG WIND STARK REGEN KOMMEN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7092</th>\n",
-              "      <td>am mittwoch legt der wind noch zu und es regne...</td>\n",
-              "      <td>MITTWOCH WIND MEHR REGEN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7093</th>\n",
-              "      <td>im süden zeigt sich aber auch die sonne</td>\n",
-              "      <td>SUED REGION SONNE AUCH DABEI</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7094</th>\n",
-              "      <td>der donnerstag beginnt oft freundlich später z...</td>\n",
-              "      <td>DONNERSTAG FREUNDLICH SONNE DANN SPAETER KOMME...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7095</th>\n",
-              "      <td>es bleibt windig</td>\n",
-              "      <td>BLEIBEN WIND</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>8257 rows × 2 columns</p>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26e45260-4b49-476b-bd2b-ddee1a49dc56')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-26e45260-4b49-476b-bd2b-ddee1a49dc56 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-26e45260-4b49-476b-bd2b-ddee1a49dc56');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        }
-      ],
-      "source": [
-        "phoenix_need"
-      ],
-      "id": "LRlHkqbUx1Zu"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "vjiRueagKTJa"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "from datasets import Dataset, DatasetDict\n",
-        "\n",
-        "phoenix_df = phoenix_need\n",
-        "\n",
-        "train_df, test_df = train_test_split(phoenix_df, test_size=0.2, random_state=42)\n",
-        "train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=42)\n",
-        "\n",
-        "train_df = train_df.rename(columns={'translation': 'src', 'trg': 'trg'})\n",
-        "val_df = val_df.rename(columns={'translation': 'src', 'trg': 'trg'})\n",
-        "test_df = test_df.rename(columns={'translation': 'src', 'trg': 'trg'})"
-      ],
-      "id": "vjiRueagKTJa"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BE4sLkqY4Q7T"
-      },
-      "outputs": [],
-      "source": [
-        "train_df1 = list()\n",
-        "train_df1.append({'translation' : train_df.to_dict('records')})\n",
-        "val_df1 = list()\n",
-        "val_df1.append({'translation' : val_df.to_dict('records')})\n",
-        "test_df1 = list()\n",
-        "test_df1.append({'translation' : test_df.to_dict('records')})"
-      ],
-      "id": "BE4sLkqY4Q7T"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "IKyI3r4E5ZyO"
-      },
-      "outputs": [],
-      "source": [
-        "train_df2 = pd.DataFrame(train_df1)\n",
-        "val_df2 = pd.DataFrame(val_df1)\n",
-        "test_df2 = pd.DataFrame(test_df1)"
-      ],
-      "id": "IKyI3r4E5ZyO"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "U8ism5QlPznp"
-      },
-      "outputs": [],
-      "source": [
-        "train_df2 = train_df2.explode('translation')\n",
-        "val_df2 = val_df2.explode('translation')\n",
-        "test_df2 = test_df2.explode('translation')"
-      ],
-      "id": "U8ism5QlPznp"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "sf2xJz-VD38C"
-      },
-      "outputs": [],
-      "source": [
-        "train_df2 = train_df2.reset_index(drop=True)\n",
-        "val_df2 = val_df2.reset_index(drop=True)\n",
-        "test_df2 = test_df2.reset_index(drop=True)"
-      ],
-      "id": "sf2xJz-VD38C"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 17,
-          "referenced_widgets": [
-            "8f26fc220d16425f94e24d5033e4215a",
-            "036a4f6f3e594ba1858e72b1c4ea41d6",
-            "f8ab822d5b7c4547b576ea7c71f889ef",
-            "5c5613089dec490c91a4525dae96860c",
-            "8cf043994b1f47b6a9c55459dac858fd",
-            "c57118269c8244149c4e2c8157a9b224",
-            "dc938a244b42455eacd71e728170aa2d",
-            "d84659e637c145f181b67ef3c21e1369",
-            "55371b6b591f44d9878073e82580ff87",
-            "db17195d51d94c6b99357da32277052f",
-            "740b2d727d32488e8c0637eee6e128f3",
-            "263efb97b4aa4d3794f657731cdb9c4a",
-            "9a5a668470104a98a3eda3a6a22a0ad9",
-            "eafd4448d8594bfaaa854f78861d5313",
-            "3208d3b9b8d043499f0d3592f00fdf92",
-            "0290f44b58bf44c3becacff772d386ad",
-            "f1c566e93ab94e3984a4809563dc7b56",
-            "db92c87519f44620a9df9d9f17dd35fb",
-            "48544f8fa2f34617ab6119cec0b46dd4",
-            "6c1eee5bbc0541459569cf7a113d19fc",
-            "6df748fccaea4581b9ba0965af3a7dd1",
-            "e4830837e3c848e08034d91855edf808",
-            "b99a0bea300f4729a199853ca7587c0a",
-            "42867affae134afcb977dd3c559af0b8",
-            "a6967260b0af40b1919f39330ce369c8",
-            "1d2c8fdbfae54fb0a34cede31bcc70af",
-            "6f360c8ee3a74fdea4bf26d241989175",
-            "00a334e11362449f9300a0adcb9ddb11",
-            "c91f473ab5084e249041831b9af86264",
-            "9c22ddcf272d4bbda789e7a7a95c7edd",
-            "e2f55ea0816c4b48be4b88481697cc4e",
-            "bc64277c4ae448dfa35b34dfc9d2f473",
-            "b936dbbb272543658572a590c06409aa"
-          ]
-        },
-        "id": "cz8ZzfXYNiCp",
-        "outputId": "ed74acff-c237-4e8e-ae4b-68b6cb3017de"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Saving the dataset (0/1 shards):   0%|          | 0/5944 [00:00<?, ? examples/s]"
+              "Saving the dataset (0/1 shards):   0%|          | 0/5944 [00:00<?, ? examples/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "8f26fc220d16425f94e24d5033e4215a"
+              "model_id": "0cda57bbaf304367849fa9d38f499511"
             }
           },
           "metadata": {}
@@ -1389,7 +1173,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "263efb97b4aa4d3794f657731cdb9c4a"
+              "model_id": "43158795fdab4c77bb9e275ea88b451e"
             }
           },
           "metadata": {}
@@ -1403,13 +1187,44 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "b99a0bea300f4729a199853ca7587c0a"
+              "model_id": "eb41f3e8e4a44c0f93c9fd6ccd2bf5d1"
             }
           },
           "metadata": {}
         }
       ],
       "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from datasets import Dataset, DatasetDict\n",
+        "\n",
+        "phoenix_df = phoenix_need\n",
+        "\n",
+        "train_df, test_df = train_test_split(phoenix_df, test_size=0.2, random_state=42)\n",
+        "train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=42)\n",
+        "\n",
+        "train_df = train_df.rename(columns={'translation': 'src', 'trg': 'trg'})\n",
+        "val_df = val_df.rename(columns={'translation': 'src', 'trg': 'trg'})\n",
+        "test_df = test_df.rename(columns={'translation': 'src', 'trg': 'trg'})\n",
+        "\n",
+        "train_df1 = list()\n",
+        "train_df1.append({'translation' : train_df.to_dict('records')})\n",
+        "val_df1 = list()\n",
+        "val_df1.append({'translation' : val_df.to_dict('records')})\n",
+        "test_df1 = list()\n",
+        "test_df1.append({'translation' : test_df.to_dict('records')})\n",
+        "\n",
+        "train_df2 = pd.DataFrame(train_df1)\n",
+        "val_df2 = pd.DataFrame(val_df1)\n",
+        "test_df2 = pd.DataFrame(test_df1)\n",
+        "\n",
+        "train_df2 = train_df2.explode('translation')\n",
+        "val_df2 = val_df2.explode('translation')\n",
+        "test_df2 = test_df2.explode('translation')\n",
+        "\n",
+        "train_df2 = train_df2.reset_index(drop=True)\n",
+        "val_df2 = val_df2.reset_index(drop=True)\n",
+        "test_df2 = test_df2.reset_index(drop=True)\n",
+        "\n",
         "train_dataset = Dataset.from_pandas(train_df2)\n",
         "val_dataset = Dataset.from_pandas(val_df2)\n",
         "test_dataset = Dataset.from_pandas(test_df2)\n",
@@ -1422,51 +1237,11 @@
         "\n",
         "dataset_dict.save_to_disk(\"phoenix_dataset\")\n"
       ],
-      "id": "cz8ZzfXYNiCp"
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "vx89mlZypKvv",
-        "outputId": "00f385c0-23ba-45df-af68-2132ef86e77a"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DatasetDict({\n",
-              "    train: Dataset({\n",
-              "        features: ['translation'],\n",
-              "        num_rows: 5944\n",
-              "    })\n",
-              "    validation: Dataset({\n",
-              "        features: ['translation'],\n",
-              "        num_rows: 661\n",
-              "    })\n",
-              "    test: Dataset({\n",
-              "        features: ['translation'],\n",
-              "        num_rows: 1652\n",
-              "    })\n",
-              "})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 25
-        }
-      ],
-      "source": [
-        "dataset_dict"
-      ],
-      "id": "vx89mlZypKvv"
+      "id": "vjiRueagKTJa"
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 23,
       "metadata": {
         "id": "w7xqtXCXc-hs"
       },
@@ -1478,7 +1253,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 24,
       "metadata": {
         "id": "dd52282c"
       },
@@ -1509,48 +1284,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 25,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "66720b4c",
-        "outputId": "964b0a59-a2e9-400b-a8fa-1bd8baeac1fa"
+        "outputId": "6eac9c03-7bcd-40e6-e895-f59fcc05ac9f"
       },
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "{'score': 0.0,\n",
-              " 'counts': [4, 2, 0, 0],\n",
-              " 'totals': [4, 2, 0, 0],\n",
-              " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
-              " 'bp': 1.0,\n",
-              " 'sys_len': 4,\n",
-              " 'ref_len': 4}"
+              "{'meteor': 0.10638297872340426}"
             ]
           },
           "metadata": {},
-          "execution_count": 28
+          "execution_count": 25
         }
       ],
       "source": [
         "fake_preds = [\"guten Morgen\", \"general kenobi\"]\n",
         "fake_labels = [[\"guten Morgen\"], [\"general kenobi\"]]\n",
-        "metric.compute(predictions=fake_preds, references=fake_labels)"
+        "bleu.compute(predictions=fake_preds, references=fake_labels)\n",
+        "rouge.compute(predictions=fake_preds, references=fake_labels)\n",
+        "meteor.compute(predictions=fake_preds, references=fake_labels)"
       ],
       "id": "66720b4c"
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 26,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "zcQILgXJdXKt",
-        "outputId": "c09d8b8b-dd7c-4912-cc00-92dabe9f8a24"
+        "outputId": "de666e49-6e01-44c0-84c3-6f93d8093c1c"
       },
       "outputs": [
         {
@@ -1560,12 +1331,12 @@
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Collecting sentencepiece==0.1.96\n",
             "  Downloading sentencepiece-0.1.96-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m53.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25hInstalling collected packages: sentencepiece\n",
             "  Attempting uninstall: sentencepiece\n",
-            "    Found existing installation: sentencepiece 0.1.97\n",
-            "    Uninstalling sentencepiece-0.1.97:\n",
-            "      Successfully uninstalled sentencepiece-0.1.97\n",
+            "    Found existing installation: sentencepiece 0.1.98\n",
+            "    Uninstalling sentencepiece-0.1.98:\n",
+            "      Successfully uninstalled sentencepiece-0.1.98\n",
             "Successfully installed sentencepiece-0.1.96\n"
           ]
         }
@@ -1577,13 +1348,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 27,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "WJ38bGYDFwac",
-        "outputId": "d9e1c69e-6e47-4cf8-f9ce-491f45149988"
+        "outputId": "cf685ee4-383e-4601-d0eb-b20feb8300f2"
       },
       "outputs": [
         {
@@ -1591,21 +1362,21 @@
           "name": "stdout",
           "text": [
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
-            "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.27.4)\n",
-            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
-            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.4)\n",
-            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
-            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.10.7)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.28.1)\n",
             "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.3)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
             "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
             "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.4)\n",
             "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
             "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
             "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
-            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n"
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n"
           ]
         }
       ],
@@ -1616,13 +1387,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 28,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "hTj1WumvGISa",
-        "outputId": "b29aa172-c093-4fb2-901d-29d1e34c7c1e"
+        "outputId": "bd09514d-8c42-4ba6-df57-13bc043ee911"
       },
       "outputs": [
         {
@@ -1630,40 +1401,40 @@
           "name": "stdout",
           "text": [
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
-            "Requirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.9/dist-packages (4.27.4)\n",
+            "Requirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.9/dist-packages (4.28.1)\n",
             "Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (2.11.0)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (2022.10.31)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (4.65.0)\n",
             "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (23.0)\n",
-            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (1.22.4)\n",
-            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (0.13.3)\n",
             "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (0.13.4)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (2.27.1)\n",
             "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (6.0)\n",
-            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (2022.10.31)\n",
-            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (4.65.0)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (3.10.7)\n",
-            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (0.1.96)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (2.27.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (3.11.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (1.22.4)\n",
+            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (0.13.3)\n",
             "Requirement already satisfied: protobuf<=3.20.2 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (3.20.2)\n",
+            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.9/dist-packages (from transformers[sentencepiece]) (0.1.96)\n",
+            "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.3.6)\n",
             "Requirement already satisfied: multiprocess in /usr/local/lib/python3.9/dist-packages (from datasets) (0.70.14)\n",
-            "Requirement already satisfied: xxhash in /usr/local/lib/python3.9/dist-packages (from datasets) (3.2.0)\n",
             "Requirement already satisfied: aiohttp in /usr/local/lib/python3.9/dist-packages (from datasets) (3.8.4)\n",
+            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.9/dist-packages (from datasets) (2023.4.0)\n",
             "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (9.0.0)\n",
-            "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.3.6)\n",
-            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets) (1.4.4)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets) (1.5.3)\n",
+            "Requirement already satisfied: xxhash in /usr/local/lib/python3.9/dist-packages (from datasets) (3.2.0)\n",
             "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.18.0)\n",
-            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.9/dist-packages (from datasets) (2023.3.0)\n",
+            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (2.0.12)\n",
             "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.1)\n",
             "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.8.2)\n",
-            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (2.0.12)\n",
             "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (6.0.4)\n",
             "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (22.2.0)\n",
-            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.3)\n",
             "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (4.0.2)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.3)\n",
             "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers[sentencepiece]) (4.5.0)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers[sentencepiece]) (2022.12.7)\n",
-            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers[sentencepiece]) (1.26.15)\n",
             "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers[sentencepiece]) (3.4)\n",
-            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers[sentencepiece]) (1.26.15)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers[sentencepiece]) (2022.12.7)\n",
             "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n",
             "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
           ]
         }
@@ -1675,71 +1446,71 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 29,
       "metadata": {
         "id": "c0d96901",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 211,
+          "height": 231,
           "referenced_widgets": [
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-            "38cdfb156d854a9b99f3d54bdc3a08e8",
-            "06c93be20682495c9f03dcd1753e778c",
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-            "06652dac8f3e432a93e9e56573ffe82d",
-            "d1568c5c6fc747509b53bfe16c8a0b17",
-            "c38d933101a94a84b6154028f81d4699",
-            "50a536c642e04efaa0548a24961bd0a3",
-            "cc4bd2e9ad094162bae6474a476aa27f",
-            "4ffc167348744ffe8a965106efafa366",
-            "cd4402de801a47358391f9d2a0309088",
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-            "8700ef935ee3461ebbe4089afe919372",
-            "e22f3ecec5a6468d9154adee5361cc1d",
-            "7c4d8b55d27240128544a18184c44e6a",
-            "7f11dcd77bc84e249fb1fcf255dd7aa9",
-            "997fbd3483c64187bc5388f2bdb27acc",
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-            "c9fafe70926a46d394ae2c7ae5f02497",
-            "60d60414132e48298a966d7ea80e6b12",
-            "d1e7ed82d78e44639623d1d4f4cd610b",
-            "2d4fa1f00a9e40b29525e32682f27d68",
-            "8243cf762367446ea6504aa89c555f92",
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-            "ef53a805c3714a458eac9d174e73422e",
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-            "6a54c163d1d04d40aa54eb77cc581e5e",
-            "1afb75aa08f64f45a30866398728116a",
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+            "c500ceb1abc442b4b41565a86969897c",
+            "67df768c28154e84ad5bbc6484737bb7",
+            "ffab340231314847825bfaf9ee12dc6d",
+            "5a4b40312a94484b9649e0865114d9e9",
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+            "309684c89e454cc4bd61a04cb9c10526",
+            "c4cd0148e7324ede8483c74593bc4d73",
+            "25ac2f545d8943a1a7b36a3c597be42c",
+            "d775bd4afa8d487ebadf03553c11fa4f",
+            "6a9db1c5c91e4273ac0295300aa93264",
+            "7d997d12b4a14f768e8011ca49e7582b"
           ]
         },
-        "outputId": "15cbc899-bce3-4857-c3b5-e79b1dd2784e"
+        "outputId": "0cef2726-9499-466f-e3f1-2721bfe02afb"
       },
       "outputs": [
         {
@@ -1751,7 +1522,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "c441610b233344d8a049746a6f32fb79"
+              "model_id": "92f8a6c960b24b18a1eebe584739775a"
             }
           },
           "metadata": {}
@@ -1765,7 +1536,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "4ffc167348744ffe8a965106efafa366"
+              "model_id": "19ef5add0dca4029ae756205a0ac8e39"
             }
           },
           "metadata": {}
@@ -1779,7 +1550,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "997fbd3483c64187bc5388f2bdb27acc"
+              "model_id": "e2c4c192118b43ca80dd9189b536bb7a"
             }
           },
           "metadata": {}
@@ -1793,7 +1564,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "72fd8a0fe783476f80631e108eb74962"
+              "model_id": "3b9d26593e9c42229ad51c3e182eb0cf"
             }
           },
           "metadata": {}
@@ -1807,7 +1578,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "4aa7290876ea40fda8f5a15b5761d061"
+              "model_id": "c500ceb1abc442b4b41565a86969897c"
             }
           },
           "metadata": {}
@@ -1830,13 +1601,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 30,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "6025a755",
-        "outputId": "d3bea21d-a667-4974-8b0f-2a033c3f7d4c"
+        "outputId": "88ea480e-7092-46d0-9612-2eafee413e09"
       },
       "outputs": [
         {
@@ -1847,7 +1618,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 33
+          "execution_count": 30
         }
       ],
       "source": [
@@ -1877,54 +1648,54 @@
         "id": "gfp8i7xThvln"
       },
       "id": "gfp8i7xThvln",
-      "execution_count": null,
+      "execution_count": 31,
       "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 32,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000,
           "referenced_widgets": [
-            "6e4e96f1350f4a448c8864b0f513a52d",
-            "78be694469f24b9bb38ec8f00d90eead",
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-            "6488b900697b432a8490a452ae4d7999",
-            "d45646f8821f468887eb8c70690b48e1",
-            "769662f4c43b4848bbcd97a50fb870a9",
-            "63bf2a128bc14fb1942a4dbcf69bcd8b",
-            "8b7a11d3f37245bdb32c69936b48af6d",
-            "e090dca0578a42888d1b5690e24a31e7",
-            "1633d84758c546a4b3b789dae018f97c",
-            "3b9c31ff7be14ac89a96007b23f66121",
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-            "3020671b51174f9c8302a573526c3d2f",
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-            "ec3902818c7d4ce2994779a47861dc01",
-            "4461ba3385694f88addd3d4fcd7c0350"
+            "27a885276d084972aeded827cd66a651",
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+            "f8edb277d0474cd78cfdd5135a7e71f6",
+            "c105bb9805984f06acc4bf3e7689aab6",
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+            "cfce7cb36d4f40d0b924cebbf7e8d912",
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+            "d90e907ea7894130b312cd39cf716407",
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+            "667b391d3a84450fa4229c0f17a360c0",
+            "8d9a9b9fa1eb4258b51d6c70f71758b3",
+            "0f8ee8874e3a4345a4a6e58f143d7730",
+            "9957f83400094479a7a1cf3fbc382b74",
+            "11ea58df135344dda3e147bd251cbea7",
+            "845954adfc444434967af44b20db9896",
+            "67cc5f72da0548869b6464c21166d941",
+            "7063c0563b434d5b89c84dda54a4ffc0",
+            "0be4900c5f584498b117bbfb0c47c814",
+            "729fd70b2e944b619c8465d8c0db739c",
+            "b65ff03dd1cc49808398d5b135910e94",
+            "3790804d3e254c8aa2640fa18628a142",
+            "b292e93efce84f87991920f10c54d0f1",
+            "7b365e0582e4463ca82c7e7d2582bba6",
+            "a87b958909f64a9f8f60740f8326e532",
+            "2eee52843ab24ebf8417a10be9177c64"
           ]
         },
         "id": "088a084d",
-        "outputId": "de735a05-6f2e-477b-e23a-2de863828cd9"
+        "outputId": "7e96dd0c-9d79-484e-acaf-d1aba5183359"
       },
       "outputs": [
         {
@@ -1936,7 +1707,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "6e4e96f1350f4a448c8864b0f513a52d"
+              "model_id": "27a885276d084972aeded827cd66a651"
             }
           },
           "metadata": {}
@@ -1950,7 +1721,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "a3faa041514a47d6be8ce7605fa12c23"
+              "model_id": "d854ce935af24ffc852edc5bc1c0acf2"
             }
           },
           "metadata": {}
@@ -1974,7 +1745,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "fc2a5d69d8854151949a97799698dd19"
+              "model_id": "845954adfc444434967af44b20db9896"
             }
           },
           "metadata": {}
@@ -2080,7 +1851,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 35
+          "execution_count": 32
         }
       ],
       "source": [
@@ -2100,55 +1871,90 @@
     {
       "cell_type": "code",
       "source": [
-        "import nltk\n",
+        "import torch\n",
+        "from torch.nn import functional as F\n",
         "\n",
-        "nltk.download(\"punkt\")\n",
         "\n",
-        "# Initialize the WordPunct tokenizer\n",
-        "tokenizer_wordpunct = nltk.tokenize.WordPunctTokenizer()\n",
+        "class NewEncoder(torch.nn.Module):\n",
         "\n",
-        "source_lang = \"src\"\n",
-        "target_lang = \"trg\"\n",
+        "    def __init__(self, model):\n",
+        "        super().__init__()\n",
+        "        self.encoder = model.model.encoder\n",
+        "        self.main_input_name = 'input_ids'\n",
         "\n",
-        "def postprocess_text(preds, labels):\n",
-        "    preds = [pred.strip() for pred in preds]\n",
-        "    labels = [[label.strip()] for label in labels]\n",
+        "    def forward(self, input_ids, attention_mask):\n",
+        "        return self.encoder(input_ids, attention_mask=attention_mask, return_dict=False)\n",
+        "\n",
+        "\n",
+        "class NewDecoder(torch.nn.Module):\n",
+        "\n",
+        "    def __init__(self, model):\n",
+        "        super().__init__()\n",
+        "        self.weight = model.model.shared.weight.clone().detach()\n",
+        "        self.bias = model.final_logits_bias.clone().detach()\n",
+        "        self.decoder = model.model.decoder\n",
         "\n",
-        "    # Tokenize the target language using the WordPunct tokenizer\n",
-        "    labels = [tokenizer_wordpunct.tokenize(label[0]) for label in labels]\n",
-        "    labels = [[tokenizer.convert_tokens_to_ids(tokens)] for tokens in labels]\n",
+        "    def forward(self, input_ids, attention_mask, encoder_outputs, index):\n",
         "\n",
-        "    return preds, labels\n"
+        "        # Invoke the decoder\n",
+        "        hidden, = self.decoder(\n",
+        "            input_ids=input_ids,\n",
+        "            encoder_hidden_states=encoder_outputs,\n",
+        "            encoder_attention_mask=attention_mask,\n",
+        "            return_dict=False,\n",
+        "            use_cache=False,\n",
+        "        )\n",
+        "\n",
+        "        _, n_length, _ = hidden.shape\n",
+        "\n",
+        "        # Create selection mask\n",
+        "        mask = torch.arange(n_length, dtype=torch.float32) == index\n",
+        "        mask = mask.view(1, -1, 1)\n",
+        "\n",
+        "        # Broadcast mask\n",
+        "        masked = torch.multiply(hidden, mask)\n",
+        "\n",
+        "        # Reduce along 1st dimension\n",
+        "        hidden = torch.sum(masked, 1, keepdims=True)\n",
+        "\n",
+        "        # Compute final linear layer for token probabilities\n",
+        "        logits = F.linear(\n",
+        "            hidden,\n",
+        "            self.weight,\n",
+        "            bias=self.bias\n",
+        "        )\n",
+        "        return logits"
       ],
       "metadata": {
-        "id": "8_aqvTrW4a0A",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f0567027-a23f-4ec3-d5cf-8a1c04e377d8"
+        "id": "qlUq_uPfkGI1"
+      },
+      "id": "qlUq_uPfkGI1",
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import nltk\n",
+        "source_lang = \"src\"\n",
+        "target_lang = \"trg\"\n"
+      ],
+      "metadata": {
+        "id": "8_aqvTrW4a0A"
       },
       "id": "8_aqvTrW4a0A",
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
-          ]
-        }
-      ]
+      "execution_count": 36,
+      "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 37,
       "metadata": {
         "id": "54efc54f"
       },
       "outputs": [],
       "source": [
-        "batch_size = 16\n",
+        "batch_size = 7\n",
         "model_name = model_checkpoint.split(\"/\")[-1]\n",
         "args = Seq2SeqTrainingArguments(\n",
         "    f\"{model_name}-finetuned-{source_lang}-to-{target_lang}\",\n",
@@ -2158,27 +1964,18 @@
         "    per_device_eval_batch_size=batch_size,\n",
         "    weight_decay=0.01,\n",
         "    save_total_limit=3,\n",
-        "    num_train_epochs=1,\n",
+        "    num_train_epochs=10,\n",
         "    predict_with_generate=True,\n",
         "    fp16=True,\n",
         "    push_to_hub=True,\n",
+        "    seed=7575\n",
         ")"
       ],
       "id": "54efc54f"
     },
     {
       "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "XN9jS9G6B8Pc"
-      },
-      "id": "XN9jS9G6B8Pc",
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 38,
       "metadata": {
         "id": "356dd743"
       },
@@ -2190,7 +1987,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 39,
       "metadata": {
         "id": "2e966c25"
       },
@@ -2199,31 +1996,48 @@
         "import numpy as np\n",
         "\n",
         "def postprocess_text(preds, labels):\n",
-        "    preds = [pred.strip() for pred in preds]\n",
-        "    labels = [[label.strip()] for label in labels]\n",
+        "  preds = [pred.strip() for pred in preds]\n",
+        "  labels = [label.strip() for label in labels]\n",
+        "\n",
+        "  bleu_labels = [[label] for label in labels]\n",
+        "\n",
+        "  # rougeLSum expects newline after each sentence\n",
+        "  rouge_preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n",
+        "  rouge_labels = [\"\\n\".join(nltk.sent_tokenize(label))\n",
+        "                  for label in labels]\n",
         "\n",
-        "    return preds, labels\n",
+        "  return {\n",
+        "      \"bleu\": [preds, bleu_labels],\n",
+        "      \"meteor\": [preds, labels],\n",
+        "      \"rouge\": [rouge_preds, rouge_labels]\n",
+        "  }\n",
         "\n",
         "def compute_metrics(eval_preds):\n",
-        "    preds, labels = eval_preds\n",
-        "    if isinstance(preds, tuple):\n",
-        "        preds = preds[0]\n",
-        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
+        "  preds, labels = eval_preds\n",
+        "  if isinstance(preds, tuple):\n",
+        "    preds = preds[0]\n",
+        "  decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
+        "  labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
+        "  decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
         "\n",
-        "    # Replace -100 in the labels as we can't decode them.\n",
-        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
+        "  # Some simple post-processing\n",
+        "  decoded = postprocess_text(decoded_preds, decoded_labels)\n",
         "\n",
-        "    # Some simple post-processing\n",
-        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
+        "  result = rouge.compute(predictions=decoded[\"rouge\"][0], references=decoded[\"rouge\"][1], use_stemmer=True)\n",
+        "  # Extract a few results from ROUGE\n",
+        "  result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
         "\n",
-        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
-        "    result = {\"bleu\": result[\"score\"]}\n",
+        "  prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
+        "  result[\"gen_len\"] = np.mean(prediction_lens)\n",
+        "  result = {k: round(v, 4) for k, v in result.items()}\n",
         "\n",
-        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
-        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
-        "    result = {k: round(v, 4) for k, v in result.items()}\n",
-        "    return result"
+        "  bleu_result = bleu.compute(predictions=decoded[\"bleu\"][0], references=decoded[\"bleu\"][1])\n",
+        "  result[\"bleu\"] = bleu_result[\"score\"]\n",
+        "\n",
+        "  meteor_result = meteor.compute(predictions=decoded[\"meteor\"][0], references=decoded[\"meteor\"][1])\n",
+        "  result[\"meteor\"] = meteor_result[\"meteor\"]\n",
+        "\n",
+        "  return result\n"
       ],
       "id": "2e966c25"
     },
@@ -2251,7 +2065,7 @@
         "id": "gLImGXjFMM0Z"
       },
       "id": "gLImGXjFMM0Z",
-      "execution_count": null,
+      "execution_count": 40,
       "outputs": []
     },
     {
@@ -2263,54 +2077,54 @@
         "id": "IouICVkCMrbt"
       },
       "id": "IouICVkCMrbt",
-      "execution_count": null,
+      "execution_count": 41,
       "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 42,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 71,
           "referenced_widgets": [
-            "cb8c0ceed812400fb80bf3c0884e801c",
-            "a2913f4bf9b3404599806582ea9c9dda",
-            "bebdd6f9a8c2468499071ec7ffca4901",
-            "f87b2a4f927a4722b3ddff4e7178c04d",
-            "acbda0d9cf184a0080d744ce0ce2595b",
-            "fc874d5ea0ad4219938e5f422cc873ed",
-            "3a7d13eb24fb4115a28f7c441e1cc9c1",
-            "52119c2bd1c94d0d97dfb0ad43630dc8",
-            "da2927f85c1d4b52be3874dc9dd36eb6",
-            "abfa2d27e4ff415ab34a604e26449074",
-            "0eeb2be5f05446ff8fd9cc269659d4e5",
-            "2130a0fbe4984acda5fd414a89c62fa3",
-            "a2a4d09245d54f42a5d3bfa137c56b7e",
-            "bc37a3e226344398a4f7138db1fc30fe",
-            "54139c41c04741bc93b5166b328849b9",
-            "96ae99b730524fdaa9c401df061f5a30",
-            "e7f08e6d90a3479bba6c6b277f89bc5d",
-            "7191c9f7e28441038a72b5638ff8f5a2",
-            "c7f84a71647d4a89a45883b25b1506ec",
-            "601620faee2a4d71afc80a17a2c0307d",
-            "aae91005c47845ca8742dbe190c9ee3e",
-            "dae5a5254ba549098fd272de8ee606f6",
-            "d6b5e75c74b040f48d8c1fbda869a531",
-            "6973320e39f64d6cb9e34f1f73398ed8",
-            "7682b51b2020477db401e27d0fb08807",
-            "eaa95f0f657542d081991dd9fde21203",
-            "1bad1354c5da4a5b96686b83009fa8cd",
-            "040965cbde964230a9feee6068b6cfa0",
-            "74baaacd944d46aeb938e5119d7d74b5",
-            "3d11d8fb76fc4a08921ba892f5155460",
-            "cf9c55c0f3e145d9b7882625f0c9569e",
-            "de085a18d4e147459b37aa66545adf1b",
-            "086945381e0a4dc79d5e8e62bf6ba66b"
+            "1df80b3abc274dbf93ea2a4eebacbde6",
+            "b7538f52c6fa4cd195e16d6485857abb",
+            "f4eeebaaee334998ace7743a98a50b30",
+            "36ac9c8eb00e4126a68a932540197ff5",
+            "c36db17efc524aa89b002a3f02001ee2",
+            "c6b8a1a8258d4ab6b095fc0675324b85",
+            "ebef8616913146fab4944d3d951cd523",
+            "6837a7ce023a4a4ca362656ea69ef4c0",
+            "1e4578ff6ead48519c0db4d451f27d87",
+            "09db804a979b40ccaef47e9cc49656d9",
+            "41573f42ba6c4b94a801391fbcaddb0f",
+            "5398bd28ca7544fb8e3967dffc4c0415",
+            "42114f6617a547b29da88a64232750bb",
+            "c126d1c5426540278829c52a721c57cb",
+            "46ed0b8035dc4a209707a0d3515443e0",
+            "3b69984f8e3248f5aaed2fc211768fbd",
+            "ec7a331e84c54ef58f4d7d78a64854a0",
+            "7e2b86223cd5468eb2c26e487442ac36",
+            "88ecabbc0d474bcd9be6cbcded33a3be",
+            "7e9dc18ac017489e99986c90f5a84bfc",
+            "6e6de21ab0f54333af9d6e13474bba97",
+            "a621c2381db04e43beaeae5565e73344",
+            "84424c71b5a44889aae3180123b6240c",
+            "6bbb4d00b5414320b2689e482e7e6164",
+            "eb6e0776f87f4b18974b86a7f09439ce",
+            "bad880f391ba4692bb3022ead9c7c5b0",
+            "16e2233465ab4c24babc4e18cc525bca",
+            "7b0fd30e02e4469eb2d4361b8f085805",
+            "c1845b85765a4f34b9a4d65158ee44b8",
+            "9259cdeb93744e63b2c8eeb4f8431e6e",
+            "06b8bd1b4a794b608203b0e7c96ebfbc",
+            "33ad576d6ee94677a1dec49f9141df15",
+            "2a74ac140d5c4279b9540ebc8c650f5e"
           ]
         },
         "id": "OI5mNgIdJM7V",
-        "outputId": "435956bb-7f74-420b-986f-055d84a4d00d"
+        "outputId": "3fcfa27d-e59b-4bef-e985-fde56e8da2ab"
       },
       "outputs": [
         {
@@ -2322,7 +2136,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "cb8c0ceed812400fb80bf3c0884e801c"
+              "model_id": "1df80b3abc274dbf93ea2a4eebacbde6"
             }
           },
           "metadata": {}
@@ -2331,7 +2145,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:3586: 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",
+            "/usr/local/lib/python3.9/dist-packages/transformers/tokenization_utils_base.py:3596: 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"
           ]
         },
@@ -2344,7 +2158,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "2130a0fbe4984acda5fd414a89c62fa3"
+              "model_id": "5398bd28ca7544fb8e3967dffc4c0415"
             }
           },
           "metadata": {}
@@ -2358,7 +2172,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "d6b5e75c74b040f48d8c1fbda869a531"
+              "model_id": "84424c71b5a44889aae3180123b6240c"
             }
           },
           "metadata": {}
@@ -2371,33 +2185,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 43,
       "metadata": {
         "id": "6ljCme0qVaHw",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 332,
           "referenced_widgets": [
-            "b6eb39e5dd804fd7a63ffaa9af8251a5",
-            "4a1803205f9b4202b9b232b3f4c48632",
-            "3e82d4873bd94c588db9d1508897c971",
-            "5f54e0ada1fa4f27a803ec5131146cdf",
-            "59fe677dd25447c5ada55266f2ce6aff",
-            "67ebf8816b964e79aa4bde8a99247999",
-            "d0ef75af6ff24a1a912d1df4995efc9a",
-            "5f82c45527b7442b9e4cb66a2c48976a",
-            "a3b096140289433c8fc2c1649ce7e9eb",
-            "934aa40fc80e478ebd7da1a517da5eeb",
-            "0cd5cd660e4649b9836c0a5df525a8bf",
-            "f89415baac0d4b9a8f5a878edf98f4cb",
-            "a0a62435f8c34ac4bbcf9600af8455cb",
-            "a821cf09f7d744df9c064c37bae1910b",
-            "fa6e7e0636a84d8c83650965e8a4516a",
-            "4b88274b4abb43b1acbdf0d4933ddab4",
-            "6a5b6ff06f2b4592a7e96995f072adfe"
+            "355889d3a1f14eed91c511cd80ad7681",
+            "485bcd63d70347adaaeea305596b3afc",
+            "7e36006fa073432790a138c62027ea86",
+            "1a613697ab344314b8383ce91fddd4d5",
+            "e055d6c25c384390a0326167bc4815d3",
+            "2146c4271e7543ada4d0b2c9d16a627d",
+            "f1a2ebbffec049509e170f2a5ac3d4cb",
+            "1be8743f97ba478c9d037898c4573fe8",
+            "5570e4c5c5d64d538279b772f2a726a2",
+            "afcad6b0e1734247a588c658a9e34ac0",
+            "33325968e44242338e2294a80af87be9",
+            "f4c6a8d6db9c47d684d4edbd2473bc42",
+            "833442b28e1f4c1b818e6f99a2efd3dd",
+            "8859844f385740eda1358d95bfba45cd",
+            "85d96084e47149e3a49db2190020e6e7",
+            "3086a4ea116246beb5107367ad2b417e",
+            "d8a8b1e75fc44dd49cf087627bd90832"
           ]
         },
-        "outputId": "49a78a0d-19e9-4f53-dcf1-34b262f7ac9b"
+        "outputId": "108d450e-c3c5-45f4-adf0-025ef58e8c3c"
       },
       "outputs": [
         {
@@ -2420,13 +2234,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 44,
       "metadata": {
         "id": "5fc6ae4e",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "09807873-b827-4194-d438-3e683242af7a"
+        "outputId": "412889f5-9587-46b2-c1ec-2907d5259467"
       },
       "outputs": [
         {
@@ -2453,14 +2267,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 45,
       "metadata": {
         "id": "66b5eed0",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 194
+          "height": 1000
         },
-        "outputId": "f6c92cab-b4e3-4535-fe8e-fc2250674841"
+        "outputId": "aea3dbc5-8911-4709-fd91-718dcee65997"
       },
       "outputs": [
         {
@@ -2481,8 +2295,8 @@
               "\n",
               "    <div>\n",
               "      \n",
-              "      <progress value='372' max='372' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [372/372 02:36, Epoch 1/1]\n",
+              "      <progress value='8500' max='8500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [8500/8500 32:53, Epoch 10/10]\n",
               "    </div>\n",
               "    <table border=\"1\" class=\"dataframe\">\n",
               "  <thead>\n",
@@ -2490,17 +2304,135 @@
               "      <th>Epoch</th>\n",
               "      <th>Training Loss</th>\n",
               "      <th>Validation Loss</th>\n",
-              "      <th>Bleu</th>\n",
+              "      <th>Rouge1</th>\n",
+              "      <th>Rouge2</th>\n",
+              "      <th>Rougel</th>\n",
+              "      <th>Rougelsum</th>\n",
               "      <th>Gen Len</th>\n",
+              "      <th>Bleu</th>\n",
+              "      <th>Meteor</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
               "      <td>1</td>\n",
-              "      <td>No log</td>\n",
-              "      <td>1.454074</td>\n",
-              "      <td>10.470900</td>\n",
-              "      <td>28.509800</td>\n",
+              "      <td>2.104300</td>\n",
+              "      <td>1.089074</td>\n",
+              "      <td>60.349300</td>\n",
+              "      <td>28.138800</td>\n",
+              "      <td>57.395500</td>\n",
+              "      <td>57.360700</td>\n",
+              "      <td>22.419100</td>\n",
+              "      <td>20.462998</td>\n",
+              "      <td>0.480913</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>1.061200</td>\n",
+              "      <td>0.950218</td>\n",
+              "      <td>62.775500</td>\n",
+              "      <td>30.557900</td>\n",
+              "      <td>59.656900</td>\n",
+              "      <td>59.580100</td>\n",
+              "      <td>23.037800</td>\n",
+              "      <td>21.343876</td>\n",
+              "      <td>0.511960</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>0.906300</td>\n",
+              "      <td>0.904251</td>\n",
+              "      <td>63.634000</td>\n",
+              "      <td>31.630100</td>\n",
+              "      <td>60.599000</td>\n",
+              "      <td>60.540900</td>\n",
+              "      <td>22.649000</td>\n",
+              "      <td>22.464125</td>\n",
+              "      <td>0.522868</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>4</td>\n",
+              "      <td>0.839700</td>\n",
+              "      <td>0.876706</td>\n",
+              "      <td>63.586600</td>\n",
+              "      <td>31.942800</td>\n",
+              "      <td>60.261800</td>\n",
+              "      <td>60.211400</td>\n",
+              "      <td>24.237500</td>\n",
+              "      <td>22.273840</td>\n",
+              "      <td>0.528200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>5</td>\n",
+              "      <td>0.763600</td>\n",
+              "      <td>0.867112</td>\n",
+              "      <td>64.032700</td>\n",
+              "      <td>31.957300</td>\n",
+              "      <td>60.779300</td>\n",
+              "      <td>60.739400</td>\n",
+              "      <td>24.230000</td>\n",
+              "      <td>23.071687</td>\n",
+              "      <td>0.532730</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>6</td>\n",
+              "      <td>0.720700</td>\n",
+              "      <td>0.867724</td>\n",
+              "      <td>64.134200</td>\n",
+              "      <td>31.802500</td>\n",
+              "      <td>60.925400</td>\n",
+              "      <td>60.844000</td>\n",
+              "      <td>23.298000</td>\n",
+              "      <td>23.042680</td>\n",
+              "      <td>0.530030</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>7</td>\n",
+              "      <td>0.683900</td>\n",
+              "      <td>0.866991</td>\n",
+              "      <td>64.564700</td>\n",
+              "      <td>32.209100</td>\n",
+              "      <td>61.116700</td>\n",
+              "      <td>61.079700</td>\n",
+              "      <td>23.193600</td>\n",
+              "      <td>23.535714</td>\n",
+              "      <td>0.533777</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>8</td>\n",
+              "      <td>0.645500</td>\n",
+              "      <td>0.866668</td>\n",
+              "      <td>64.357400</td>\n",
+              "      <td>31.991800</td>\n",
+              "      <td>61.040000</td>\n",
+              "      <td>60.996700</td>\n",
+              "      <td>23.202700</td>\n",
+              "      <td>23.123911</td>\n",
+              "      <td>0.532127</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>9</td>\n",
+              "      <td>0.635500</td>\n",
+              "      <td>0.865385</td>\n",
+              "      <td>64.248600</td>\n",
+              "      <td>32.146400</td>\n",
+              "      <td>61.126200</td>\n",
+              "      <td>61.081000</td>\n",
+              "      <td>23.639900</td>\n",
+              "      <td>23.356753</td>\n",
+              "      <td>0.534063</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>10</td>\n",
+              "      <td>0.619600</td>\n",
+              "      <td>0.869250</td>\n",
+              "      <td>64.393400</td>\n",
+              "      <td>32.114000</td>\n",
+              "      <td>61.261500</td>\n",
+              "      <td>61.210100</td>\n",
+              "      <td>23.627800</td>\n",
+              "      <td>23.348993</td>\n",
+              "      <td>0.535556</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table><p>"
@@ -2508,15 +2440,55 @@
           },
           "metadata": {}
         },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n",
+            "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['source.spm', 'target.spm']. This may take a bit of time if the files are large.\n",
+            "Several commits (2) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
+            "Several commits (3) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (3) will be pushed upstream.\n",
+            "Several commits (4) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (4) will be pushed upstream.\n",
+            "Several commits (5) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (5) will be pushed upstream.\n",
+            "Several commits (6) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (6) will be pushed upstream.\n",
+            "Several commits (7) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (7) will be pushed upstream.\n",
+            "Several commits (8) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (8) will be pushed upstream.\n",
+            "Several commits (9) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (9) will be pushed upstream.\n",
+            "Several commits (10) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (10) will be pushed upstream.\n",
+            "Several commits (11) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (11) will be pushed upstream.\n",
+            "Several commits (12) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (12) will be pushed upstream.\n",
+            "Several commits (13) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (13) will be pushed upstream.\n",
+            "Several commits (14) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (14) will be pushed upstream.\n",
+            "Several commits (15) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (15) will be pushed upstream.\n",
+            "Several commits (16) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (16) will be pushed upstream.\n",
+            "Several commits (17) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (17) will be pushed upstream.\n"
+          ]
+        },
         {
           "output_type": "execute_result",
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-              "TrainOutput(global_step=372, training_loss=2.2586322087113575, metrics={'train_runtime': 157.158, 'train_samples_per_second': 37.822, 'train_steps_per_second': 2.367, 'total_flos': 59945021079552.0, 'train_loss': 2.2586322087113575, 'epoch': 1.0})"
+              "TrainOutput(global_step=8500, training_loss=0.8617506498448989, metrics={'train_runtime': 1973.5473, 'train_samples_per_second': 30.118, 'train_steps_per_second': 4.307, 'total_flos': 527206704021504.0, 'train_loss': 0.8617506498448989, 'epoch': 10.0})"
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