mBart.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3rlC7FT45_M",
        "outputId": "36ee2fc8-1bea-4b13-ed49-ec612ee2c1a2"
      },
      "outputs": [],
      "source": [
        "!pip install transformers -U -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TtiySNlE8jG3",
        "outputId": "80348d6b-bfdc-41ca-a4be-1e2c6aa84442"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sentencepiece in c:\\users\\my pc\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.1.99)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "! pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "vi6I7hyu8k9e"
      },
      "outputs": [],
      "source": [
        "from transformers import MBartForConditionalGeneration, MBart50TokenizerFast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "7febd1b246894b1f9326361e1863106b",
            "6f77a12bb20f4487aebf6a9f3665dd67",
            "30e1dd7224d84dd1854fd8587e926558",
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            "0041c745acbe4b0f9dcd232740a3cfa5",
            "741301900cc04618b9ea589d620ca85e",
            "c56b2e892fb24c25b2a2e575aa111d53",
            "2f68b98cf9cf4469b25b01f1fcee6634",
            "2973395231d840ceada94f247f0c13b1",
            "6384dd246e5043299aedac1a23f34adc",
            "36c2fb7f9c1543cfacfec79f3da1e3dc",
            "12dd8849a23b4fba8d52f71b579c4f76",
            "27328f7ba71c438c82399ae977bb1a5d",
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            "849ef8e766c045e981ca778e3f129c16",
            "695ef48dc65d40ed815b102d1869a3d5",
            "8e9f9cc49ae84cc4aef331e06fdbd22c",
            "e749bb8369e14ab1b23212351d497357",
            "301e13182c4345608e5e3504e26f6658",
            "a684644a36544f509a5465671b0ec628",
            "0573d81e557c434a8e9e9d2e3bd4b23e",
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            "eb27438c2cdc4b2b873d2b3b05261c0b",
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            "3eba48737e16458c80b868efa1ad4e4d",
            "e5ef59c0e9804e1ca75ca02ad516772a",
            "dd1c8a0d457543779ebdfb1bdb6635cd",
            "cc07ea4bcdea419f9fb1b8c751af167f",
            "5b3a7664ddb64e97980f468391a4726c",
            "610b18d3559a4e59a383d020e610ab78",
            "227a49440ad44e20ab507b044ede2510",
            "63efdaaa58a24f5e91d4ef4569577595",
            "f1fd4654ff7a41049da6ecdfbb6e3501"
          ]
        },
        "id": "FhD6JKSN9JrZ",
        "outputId": "d6008119-3a3c-446d-a12b-32c21c258a61"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47a58068d09c431f9853496c1e184293",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.44G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "ename": "OSError",
          "evalue": "Consistency check failed: file should be of size 2444714899 but has size 19912379 (pytorch_model.bin).\nWe are sorry for the inconvenience. Please retry download and pass `force_download=True, resume_download=False` as argument.\nIf the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub.",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[1;32mc:\\Users\\My PC\\Documents\\SIH\\translator\\mBart.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/My%20PC/Documents/SIH/translator/mBart.ipynb#W3sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m model \u001b[39m=\u001b[39m MBartForConditionalGeneration\u001b[39m.\u001b[39;49mfrom_pretrained(\u001b[39m\"\u001b[39;49m\u001b[39mfacebook/mbart-large-50-one-to-many-mmt\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
            "File \u001b[1;32mc:\\Users\\My PC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\modeling_utils.py:2793\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m   2790\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   2791\u001b[0m         \u001b[39m# This repo has no safetensors file of any kind, we switch to PyTorch.\u001b[39;00m\n\u001b[0;32m   2792\u001b[0m         filename \u001b[39m=\u001b[39m _add_variant(WEIGHTS_NAME, variant)\n\u001b[1;32m-> 2793\u001b[0m         resolved_archive_file \u001b[39m=\u001b[39m cached_file(\n\u001b[0;32m   2794\u001b[0m             pretrained_model_name_or_path, filename, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcached_file_kwargs\n\u001b[0;32m   2795\u001b[0m         )\n\u001b[0;32m   2796\u001b[0m \u001b[39mif\u001b[39;00m resolved_archive_file \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m filename \u001b[39m==\u001b[39m _add_variant(WEIGHTS_NAME, variant):\n\u001b[0;32m   2797\u001b[0m     \u001b[39m# Maybe the checkpoint is sharded, we try to grab the index name in this case.\u001b[39;00m\n\u001b[0;32m   2798\u001b[0m     resolved_archive_file \u001b[39m=\u001b[39m cached_file(\n\u001b[0;32m   2799\u001b[0m         pretrained_model_name_or_path,\n\u001b[0;32m   2800\u001b[0m         _add_variant(WEIGHTS_INDEX_NAME, variant),\n\u001b[0;32m   2801\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcached_file_kwargs,\n\u001b[0;32m   2802\u001b[0m     )\n",
            "File \u001b[1;32mc:\\Users\\My PC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\utils\\hub.py:429\u001b[0m, in \u001b[0;36mcached_file\u001b[1;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[0;32m    426\u001b[0m user_agent \u001b[39m=\u001b[39m http_user_agent(user_agent)\n\u001b[0;32m    427\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m    428\u001b[0m     \u001b[39m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[1;32m--> 429\u001b[0m     resolved_file \u001b[39m=\u001b[39m hf_hub_download(\n\u001b[0;32m    430\u001b[0m         path_or_repo_id,\n\u001b[0;32m    431\u001b[0m         filename,\n\u001b[0;32m    432\u001b[0m         subfolder\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m \u001b[39mif\u001b[39;49;00m \u001b[39mlen\u001b[39;49m(subfolder) \u001b[39m==\u001b[39;49m \u001b[39m0\u001b[39;49m \u001b[39melse\u001b[39;49;00m subfolder,\n\u001b[0;32m    433\u001b[0m         repo_type\u001b[39m=\u001b[39;49mrepo_type,\n\u001b[0;32m    434\u001b[0m         revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[0;32m    435\u001b[0m         cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[0;32m    436\u001b[0m         user_agent\u001b[39m=\u001b[39;49muser_agent,\n\u001b[0;32m    437\u001b[0m         force_download\u001b[39m=\u001b[39;49mforce_download,\n\u001b[0;32m    438\u001b[0m         proxies\u001b[39m=\u001b[39;49mproxies,\n\u001b[0;32m    439\u001b[0m         resume_download\u001b[39m=\u001b[39;49mresume_download,\n\u001b[0;32m    440\u001b[0m         token\u001b[39m=\u001b[39;49mtoken,\n\u001b[0;32m    441\u001b[0m         local_files_only\u001b[39m=\u001b[39;49mlocal_files_only,\n\u001b[0;32m    442\u001b[0m     )\n\u001b[0;32m    443\u001b[0m \u001b[39mexcept\u001b[39;00m GatedRepoError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    444\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mEnvironmentError\u001b[39;00m(\n\u001b[0;32m    445\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mYou are trying to access a gated repo.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mMake sure to request access at \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    446\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhttps://huggingface.co/\u001b[39m\u001b[39m{\u001b[39;00mpath_or_repo_id\u001b[39m}\u001b[39;00m\u001b[39m and pass a token having permission to this repo either \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    447\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mby logging in with `huggingface-cli login` or by passing `token=<your_token>`.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    448\u001b[0m     ) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
            "File \u001b[1;32mc:\\Users\\My PC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\huggingface_hub\\utils\\_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[39mif\u001b[39;00m check_use_auth_token:\n\u001b[0;32m    116\u001b[0m     kwargs \u001b[39m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[39m=\u001b[39mfn\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, has_token\u001b[39m=\u001b[39mhas_token, kwargs\u001b[39m=\u001b[39mkwargs)\n\u001b[1;32m--> 118\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\My PC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\huggingface_hub\\file_download.py:1429\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[1;34m(repo_id, filename, subfolder, repo_type, revision, endpoint, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout)\u001b[0m\n\u001b[0;32m   1426\u001b[0m         \u001b[39mif\u001b[39;00m local_dir \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   1427\u001b[0m             _check_disk_space(expected_size, local_dir)\n\u001b[1;32m-> 1429\u001b[0m     http_get(\n\u001b[0;32m   1430\u001b[0m         url_to_download,\n\u001b[0;32m   1431\u001b[0m         temp_file,\n\u001b[0;32m   1432\u001b[0m         proxies\u001b[39m=\u001b[39;49mproxies,\n\u001b[0;32m   1433\u001b[0m         resume_size\u001b[39m=\u001b[39;49mresume_size,\n\u001b[0;32m   1434\u001b[0m         headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[0;32m   1435\u001b[0m         expected_size\u001b[39m=\u001b[39;49mexpected_size,\n\u001b[0;32m   1436\u001b[0m     )\n\u001b[0;32m   1438\u001b[0m \u001b[39mif\u001b[39;00m local_dir \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   1439\u001b[0m     logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mStoring \u001b[39m\u001b[39m{\u001b[39;00murl\u001b[39m}\u001b[39;00m\u001b[39m in cache at \u001b[39m\u001b[39m{\u001b[39;00mblob_path\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
            "File \u001b[1;32mc:\\Users\\My PC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\huggingface_hub\\file_download.py:557\u001b[0m, in \u001b[0;36mhttp_get\u001b[1;34m(url, temp_file, proxies, resume_size, headers, timeout, max_retries, expected_size)\u001b[0m\n\u001b[0;32m    554\u001b[0m         temp_file\u001b[39m.\u001b[39mwrite(chunk)\n\u001b[0;32m    556\u001b[0m \u001b[39mif\u001b[39;00m expected_size \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m expected_size \u001b[39m!=\u001b[39m temp_file\u001b[39m.\u001b[39mtell():\n\u001b[1;32m--> 557\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mEnvironmentError\u001b[39;00m(\n\u001b[0;32m    558\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mConsistency check failed: file should be of size \u001b[39m\u001b[39m{\u001b[39;00mexpected_size\u001b[39m}\u001b[39;00m\u001b[39m but has size\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    559\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m \u001b[39m\u001b[39m{\u001b[39;00mtemp_file\u001b[39m.\u001b[39mtell()\u001b[39m}\u001b[39;00m\u001b[39m (\u001b[39m\u001b[39m{\u001b[39;00mdisplayed_name\u001b[39m}\u001b[39;00m\u001b[39m).\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mWe are sorry for the inconvenience. Please retry download and\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    560\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m pass `force_download=True, resume_download=False` as argument.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mIf the issue persists, please let us\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    561\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m know by opening an issue on https://github.com/huggingface/huggingface_hub.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    562\u001b[0m     )\n\u001b[0;32m    564\u001b[0m progress\u001b[39m.\u001b[39mclose()\n",
            "\u001b[1;31mOSError\u001b[0m: Consistency check failed: file should be of size 2444714899 but has size 19912379 (pytorch_model.bin).\nWe are sorry for the inconvenience. Please retry download and pass `force_download=True, resume_download=False` as argument.\nIf the issue persists, please let us know by opening an issue on https://github.com/huggingface/huggingface_hub."
          ]
        }
      ],
      "source": [
        "model = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50-one-to-many-mmt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "a6cde7e4772a4ef592920caff6622fd1",
            "9d912a20dc5f4c02a9d125f9c0318168",
            "8fb04a0b84c744de8994ad4540d55e18",
            "f1ecdb0d78d64e77a0b7620feb8c5fe1",
            "ce5ccbfe168c4258a79532b5cb9dd991",
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            "98383f6ea7b64915a82176750ae1955e",
            "9478a3eb5dae4c3b9286e83ffd7dd642"
          ]
        },
        "id": "5AbD5wRP9M-F",
        "outputId": "289d88ba-5d94-42c6-ba91-37b828bf1fb5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "27419699932d47768881af398b4e1258",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/528 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "09d7d8059d2a4a7b8be0bc03ccc08429",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "01e86e6bab2549dfa1919f8bb5a8f168",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/717 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = MBart50TokenizerFast.from_pretrained(\"facebook/mbart-large-50-one-to-many-mmt\", src_lang=\"en_XX\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Wd9kXRs59btw"
      },
      "outputs": [],
      "source": [
        "e_txt = \"Good morning Guys\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "tK70bcvb9i0E"
      },
      "outputs": [],
      "source": [
        "model_inputs = tokenizer(e_txt, return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "YBTbRa099nuC"
      },
      "outputs": [],
      "source": [
        "generated_tokens = model.generate(\n",
        "    **model_inputs,\n",
        "    forced_bos_token_id=tokenizer.lang_code_to_id[\"hi_IN\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "F7MrpcmO9tYc"
      },
      "outputs": [],
      "source": [
        "translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7e6Bq4Iq9xqq",
        "outputId": "2bfbe9a9-5441-4283-8134-6020c73e0272"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['नमस्ते लोग']"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "translation"
      ]
    },
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      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "NTJy3w9390bT"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "nZomaHN_AV0z"
      },
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      "source": [
        "filename='trained_model.sav'"
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      "execution_count": 13,
      "metadata": {
        "id": "P6_TVlUFAfBx"
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      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "IAxyxJfWBCZN"
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      "source": [
        "loaded_model=pickle.load(open('trained_model.sav','rb'))"
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    },
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      "execution_count": 15,
      "metadata": {
        "colab": {
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        "id": "KBBIeu5tBY8R",
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        {
          "data": {
            "text/plain": [
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            ]
          },
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        "e_txt = \"Good morning Guys\"\n",
        "\n",
        "model_inputs = tokenizer(e_txt, return_tensors=\"pt\")\n",
        "\n",
        "generated_tokens = loaded_model.generate(\n",
        "    **model_inputs,\n",
        "    forced_bos_token_id=tokenizer.lang_code_to_id[\"hi_IN\"]\n",
        ")\n",
        "\n",
        "translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n",
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        "translation"
      ]
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