MT5_finetuning(Nepali_News_title_generation).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bimarshak7/finetuning-hf-models/blob/main/MT5_finetuning(Nepali_News_title_generation).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "fa6e0d74-847d-4c93-ebe1-97924b3edb42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m30.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m36.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -U transformers datasets accelerate SentencePiece -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from datasets import load_dataset, load_metric, Dataset\n",
        "from transformers import (\n",
        "    T5Tokenizer,\n",
        "    MT5ForConditionalGeneration,\n",
        "    TrainingArguments,\n",
        "    Trainer\n",
        ")\n",
        "import re\n",
        "import string\n",
        "import time\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "id": "wP_ADunKw7ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e85559cc-51e9-4a94-d40b-550e285e756a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1-71LzjRgPJblmNEs9t8XjmEU9qoh2CyW"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdVDTc1ixkMZ",
        "outputId": "2dc69deb-2107-4ae4-9054-9d60598f9327"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-71LzjRgPJblmNEs9t8XjmEU9qoh2CyW\n",
            "To: /content/news_big.csv\n",
            "100% 1.30G/1.30G [00:11<00:00, 116MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds = Dataset.from_csv(\"news_big.csv\").train_test_split(test_size=0.2)\n",
        "ds = ds.remove_columns([\"id\", \"date_published\"])\n",
        "ds"
      ],
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        "id": "x4GX-_b8ycWe",
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9ed68de782784fd2a9b5df336d0e30de"
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['title', 'news'],\n",
              "        num_rows: 181145\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['title', 'news'],\n",
              "        num_rows: 45287\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds = ds.filter(lambda eg: (len(eg['news'].split()) >= 1000))\n",
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260,
          "referenced_widgets": [
            "3992e59a58204614851590da6d5f9754",
            "eda134adc4b3444f89d9ba5c9d75ab02",
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            "9f1cf4f6dbed428f86bab714e0cf5a58"
          ]
        },
        "id": "D1gqnWaWixbZ",
        "outputId": "da942822-75e6-4722-8663-d80ddb28ba56"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/181145 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3992e59a58204614851590da6d5f9754"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/45287 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51d7c2f57536475d88131cc162e151b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['title', 'news'],\n",
              "        num_rows: 10482\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['title', 'news'],\n",
              "        num_rows: 2585\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_train = ds[\"train\"]\n",
        "ds_test = ds[\"test\"]"
      ],
      "metadata": {
        "id": "dFtnvPWZW32N"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL = \"google/mt5-base\""
      ],
      "metadata": {
        "id": "V8RdsLYPbzzi"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = T5Tokenizer.from_pretrained(MODEL)\n",
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397,
          "referenced_widgets": [
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            "f29c96c03b9e485d92d0b1042d816a80",
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        },
        "id": "1MktdnDAaZr_",
        "outputId": "8a50a0aa-e827-4e28-a5a7-f1e88f7a04f3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/376 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9734eb865e6e4223b51c58be2fc905cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/4.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4147e6a4bad84068b07a2bece8cef6b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/65.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73ce9140b58e4ade912a87ce213ec0fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/702 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51a487a2509243dc92be971cdbd1dff6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T5Tokenizer(name_or_path='google/mt5-base', vocab_size=250100, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
              "\t0: AddedToken(\"<pad>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t1: AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t2: AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(\"प्रतिनिधिसभा सदस्य तथा पूर्व मन्त्री दीपक खड्का चढेको गाडी धनकुटामा दुर्घटना हुँदा धरान–धनकुटा सडक अवरुद्ध भएको छ ।\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65k5iwwX8aQ_",
        "outputId": "21f5f93c-3cc3-459b-b349-0c40f1c00afd"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [259, 68727, 19350, 29102, 22355, 2375, 7688, 26980, 259, 54868, 16973, 38926, 4648, 37314, 1770, 6158, 11812, 3596, 1462, 76312, 69799, 142102, 53740, 56089, 96961, 1437, 2948, 64168, 18395, 25993, 1326, 33180, 57455, 7247, 1577, 54568, 15530, 232107, 2573, 4956, 2237, 259, 378, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words = stopwords.words('nepali')"
      ],
      "metadata": {
        "id": "1q97OPTDgW4t"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(eg,isNews=True):\n",
        "    #remove punctuations\n",
        "    text = eg[\"news\"]\n",
        "    title = eg[\"title\"]\n",
        "\n",
        "    sentences = text.replace(\"\\n\", \" \").split(u\"।\")\n",
        "    sentences_title = title.replace(\"\\n\", \" \").split(u\"।\")\n",
        "\n",
        "    f = [sentence.translate(str.maketrans('', '', string.punctuation)) for sentence in sentences]\n",
        "    f_title = [sentence.translate(str.maketrans('', '', string.punctuation)) for sentence in sentences_title]\n",
        "\n",
        "    f = [x.strip() for x in f if len(x)>0]\n",
        "    f_title = [x.strip() for x in f_title if len(x)>0]\n",
        "\n",
        "    if isNews:\n",
        "        f = [word for words in f for word in words.split() if word not in stop_words]\n",
        "    text =  \" \".join(f)\n",
        "    text = re.sub(r\"\\d\", \"\", text)\n",
        "\n",
        "    title =  \" \".join(f_title)\n",
        "    title = re.sub(r\"\\d\", \"\", title)\n",
        "\n",
        "    eg[\"title\"] = title\n",
        "    eg[\"news\"] = text\n",
        "\n",
        "    return eg"
      ],
      "metadata": {
        "id": "jJ4_37gRb28G"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds_train = ds_train.map(clean_text)\n",
        "ds_test = ds_test.map(clean_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "a7a30e87d9d34795ac238d1a047f7471"
          ]
        },
        "id": "EvB_wDOXfWWD",
        "outputId": "4c2a2e57-1067-4e18-ff30-c58e5a672d31"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10482 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffaba70530c048cab87b3ec64eb0512d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2585 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4431e14317f2456f83455c0dc4c5b054"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_input_length = 1000\n",
        "max_target_length = 28"
      ],
      "metadata": {
        "id": "VCPh3F94kd9v"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PREFIX = \"summarize: \"\n",
        "def preprocess_data(examples):\n",
        "  inputs = [PREFIX + doc for doc in examples[\"news\"]]\n",
        "  model_inputs = tokenizer(examples[\"news\"], max_length=max_input_length, truncation=True)\n",
        "\n",
        "  # Setup the tokenizer for targets\n",
        "  with tokenizer.as_target_tokenizer():\n",
        "    labels = tokenizer(examples[\"title\"], max_length=max_target_length, truncation=True)\n",
        "\n",
        "  model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "  return model_inputs"
      ],
      "metadata": {
        "id": "I6S0OTpIh6Ny"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_prep = ds_train.map(preprocess_data, batched=True)\n",
        "test_prep = ds_test.map(preprocess_data, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "ade7b4fe1a554b6592dd330f3c561f9f",
            "f2f21741b6144ce299c5d19341f9e831",
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            "de9a14eb47f849a59d5947f2bfbe9897",
            "a60661d714a2470d832502b503e9dae6",
            "67e15464ff034df9a21cfe94472c33a2",
            "ab1c874ffa754d608efdb04b14591df0",
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            "455297ca152c45bcae558c726edee496"
          ]
        },
        "id": "0vg3ZpM0konx",
        "outputId": "2aa6dfd8-8b4f-4e89-d477-31391e6fb722"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/10482 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ade7b4fe1a554b6592dd330f3c561f9f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3860: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2585 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a60661d714a2470d832502b503e9dae6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer"
      ],
      "metadata": {
        "id": "In2jNQ0SmzaF"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 4\n",
        "model_dir = f\"{MODEL}\""
      ],
      "metadata": {
        "id": "0rbkec_EoK30"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "args = Seq2SeqTrainingArguments(\n",
        "    output_dir = \"nepali_news_title\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=4e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=1,\n",
        "    predict_with_generate=True,\n",
        "    fp16=True,\n",
        ")"
      ],
      "metadata": {
        "id": "pHktrP4ZoAGU"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer,model=MODEL)"
      ],
      "metadata": {
        "id": "eKU0sVMUoTpS"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rouge_score -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sIj-srpRrG5l",
        "outputId": "aef57fa9-f7df-4738-dd94-0c913d310e3c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rouge = load_metric(\"rouge\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRgDmygiq4Je",
        "outputId": "14e128e3-8681-4594-edc9-0b94fc406fc5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:752: FutureWarning: The repository for rouge contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.16.1/metrics/rouge/rouge.py\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# def compute_metrics(eval_pred):\n",
        "#     predictions, labels = eval_pred\n",
        "#     decoded_preds = tokenizer.batch_decode(predictions, 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",
        "\n",
        "#     # Rouge expects a newline after each sentence\n",
        "#     decoded_preds = [\"\\n\".join(nltk.sent_tokenize(pred.strip()))\n",
        "#                       for pred in decoded_preds]\n",
        "#     decoded_labels = [\"\\n\".join(nltk.sent_tokenize(label.strip()))\n",
        "#                       for label in decoded_labels]\n",
        "\n",
        "#     # Compute ROUGE scores\n",
        "#     result = metric.compute(predictions=decoded_preds, references=decoded_labels,\n",
        "#                             use_stemmer=True)\n",
        "\n",
        "#     # Extract ROUGE f1 scores\n",
        "#     result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
        "\n",
        "#     # Add mean generated length to metrics\n",
        "#     prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id)\n",
        "#                       for pred in predictions]\n",
        "#     result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "\n",
        "#     return {k: round(v, 4) for k, v in result.items()}"
      ],
      "metadata": {
        "id": "WHwynJgpq61e"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_pred):\n",
        "\n",
        "    predictions, labels = eval_pred\n",
        "\n",
        "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
        "\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "\n",
        "    return {k: round(v, 4) for k, v in result.items()}"
      ],
      "metadata": {
        "id": "EDJ77PPC-TRI"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained(MODEL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208,
          "referenced_widgets": [
            "1fb93ae651b74e579c43f3ddb741989e",
            "ad5af061e8b240a78587a663bfc93427",
            "e557780b4f0346a7a3e43c213484e311",
            "9fa024f1dfdb49598d73fe4e3a83d0d2",
            "d25a6ed72246440cacc571b247f03b4b",
            "490964426e72439eaf4d48bb282c9386",
            "bfceefa49922421bb2faa56cf982796a",
            "ea7978ea56e4465aa7480b0edb0917f4",
            "f47aaa7059184f8392192255647c00a4",
            "9c06b8a859a642bab79c792693b1dd62",
            "29336e495761472a98aff08ccdccebc8",
            "cc246cb85dce4c199d66d8867d1bc1fc",
            "4b823b9289e6462d8c4f77331b4fb095",
            "0bbfa20d2d0f4fe58ef71d4e1d534367",
            "fd3567cd804b45afa31c3a245649cdcc",
            "1131d4b6f9b84620929325cb852f06d8",
            "0987f7685b264481b9b99d8e3c54b6d6",
            "1039b454a078424faa4968b11783687c",
            "833b011e1dd7456193a55de99d336542",
            "051293ecc9d4431ea6d5da5f456258b9",
            "2c3eef6eeebb4c58b7acc6b0508a38ea",
            "99f2d3032171431397cded637bcb6c53"
          ]
        },
        "id": "e4IqmVhwrOsX",
        "outputId": "e1283494-fcf2-4d80-ef6a-1870fdd54bdc"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/2.33G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fb93ae651b74e579c43f3ddb741989e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc246cb85dce4c199d66d8867d1bc1fc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMLRS3eLxYox",
        "outputId": "a20edcc0-5dfd-469a-e49a-26f79e8c3320"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "220"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_prep,\n",
        "    eval_dataset=test_prep,\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "-kKIdBSErVqN"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zj-MZiAhAu-R",
        "outputId": "10854d66-c464-4506-a7da-2f2e7d6c1ba6"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "IGpvUFqArqum",
        "outputId": "70f14578-c293-4f53-974f-ca98d99b5146"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2622' max='2621' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2621/2621 37:02, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='647' max='647' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [647/647 14:42]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "type AggregateScore doesn't define __round__ method",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1535\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1536\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1537\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1538\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1928\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1929\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_log_save_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1931\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mDebugOption\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTPU_METRICS_DEBUG\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2266\u001b[0m                     \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_metrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2267\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2268\u001b[0;31m                 \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mignore_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2269\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_report_to_hp_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer_seq2seq.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, eval_dataset, ignore_keys, metric_key_prefix, **gen_kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gen_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric_key_prefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetric_key_prefix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m     def predict(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, eval_dataset, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m   3017\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3018\u001b[0m         \u001b[0meval_loop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprediction_loop\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_legacy_prediction_loop\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluation_loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3019\u001b[0;31m         output = eval_loop(\n\u001b[0m\u001b[1;32m   3020\u001b[0m             \u001b[0meval_dataloader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3021\u001b[0m             \u001b[0mdescription\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Evaluation\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mevaluation_loop\u001b[0;34m(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)\u001b[0m\n\u001b[1;32m   3308\u001b[0m                 )\n\u001b[1;32m   3309\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3310\u001b[0;31m                 \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEvalPrediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3311\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3312\u001b[0m             \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-31-3bd907dab94b>\u001b[0m in \u001b[0;36mcompute_metrics\u001b[0;34m(eval_pred)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"gen_len\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction_lens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-31-3bd907dab94b>\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"gen_len\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction_lens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: type AggregateScore doesn't define __round__ method"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "news = \"\"\"\n",
        "प्रतिनिधिसभा सदस्य तथा पूर्व मन्त्री दीपक खड्का चढेको गाडी धनकुटामा दुर्घटना हुँदा धरान–धनकुटा सडक अवरुद्ध भएको छ । खड्का सवार स्कोरपियो गाडी टिपरसँग ठोकिएर सडकमै थन्किएपछि सडक अवरुद्ध भएको हो । अर्को पनि दुर्घटना एकै ठाउँमा हुँदा तीन वटा गाडी सडकमै रहेकाले सडक अवरुद्ध हुँदा हजारौ यात्रु अलपत्र परेका छन् ।\n",
        "सङ्खुवासभाको चैनपुरबाट विराटनगर जाँदै गरेको प्रदेश नं.१–०२–००२ च ३५४५ नम्बरको स्कर्पियो गाडी धनकुटाको साँगुरी गढी गाउँपालिकाको कोल बोटेमा इटहरीबाट धनकुटा आउँदै गरेको प्रदेश नं.२–०३–००१ क १५२१ नम्बरको टिपरसँग ठोकिएको जिल्ला प्रहरी कार्यालय धनकुटाका प्रहरी प्रमुख कृष्ण कोइरालाले बताउनुभयो ।\n",
        "सडकबाट दुर्घटनाग्रस्त गाडी र ट्रक निकाल्न समस्या भएका कारण सडक अवरुद्ध भएको प्रहरीले जनाएको छ ।\"\"\""
      ],
      "metadata": {
        "id": "y8QwuG-AA1xb"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news_clean = clean_text({\"news\":news,\"title\":\"\"})"
      ],
      "metadata": {
        "id": "MxJZ5gzaBZTr"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(news_clean[\"news\"], max_length=max_input_length, truncation=True, return_tensors=\"pt\").to(\"cuda\")"
      ],
      "metadata": {
        "id": "8kbcE10KBoH7"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = model.generate(**inputs, num_beams=10, do_sample=True, min_length=10, max_length=64)\n",
        "decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)\n",
        "print(decoded_output)"
      ],
      "metadata": {
        "id": "QdaJO0mGrfcA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "742424f9-ea70-4b86-e582-49a0ffca3a87"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<extra_id_0> नम्बरको टिपरसँग ठोकिएको']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_pred(news):\n",
        "  news_clean = clean_text({\"news\":news,\"title\":\"\"})\n",
        "  inputs = tokenizer(news_clean[\"news\"], max_length=max_input_length, truncation=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "  output = model.generate(**inputs, num_beams=10, do_sample=True, min_length=28, max_length=64)\n",
        "  decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)\n",
        "  return decoded_output"
      ],
      "metadata": {
        "id": "SnpL2U8Mtekv"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news2 = \"\"\"\n",
        "सर्वोच्च अदालतले नेपाल वायुसेवा निगममा कार्यरत विमान परिचारक र परिचारिका (क्याबिन क्रू) लाई सेवा सुविधाका आधारमा कुनै पनि विभेद नगर्न अन्तरिम आदेश जारी गरेको छ।\n",
        "न्यायाधीश तिलप्रसाद श्रेष्ठको एकल इजलासले आइतबार निगमका नाममा परिचारक र परिचारिकालाई सेवा सुविधामा फरक पर्ने गरी कुनै पनि कदम नचाल्न अन्तरिम आदेश गरेको हो। निवेदकका तर्फबाट वरिष्ठ अधिवक्ता डा चन्द्रकान्त ज्ञवाली, अधिवक्ताहरू अनन्तराज लुइँटेल, कीर्तिनाथ शर्मा पौडेल, लीलाधर उपाध्याय, प्रतिभा उप्रेती, विशाल थापालगायतले बहस गर्नुभएको थियो।\n",
        "अदालतले निवेदक करारका कर्मचारी भएकाले कारबाही गर्ने, करार भङ्ग गर्ने तथा सेवा सुविधामासमेत हस्तक्षेप गर्न सक्ने कुनै पनि काम नगर्न भनेको छ। निगममा कार्यरत सबै विमान परिचारक र परिचारिकाले आफूहरूमाथि अन्याय र विभेदपूर्ण व्यवहार भएको उल्लेख गर्दै यही पुस १८ गते सर्वोच्च अदालतमा रिट निवेदन दायर गरेका थिए।\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "eOvsAmNW0CAl"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "make_pred(news)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEgIt4GBYv_m",
        "outputId": "276c9a00-9584-41cf-c112-33733116e65a"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<extra_id_0> नम्बरको टिपरसँग ठोकिएपछि सडक अवरुद्ध भ <extra_id_1>कोइरालालाई जानकारी गरायो']"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_pred(news2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfDJavqy0orN",
        "outputId": "fb2fb93d-c931-44d1-d14f-9a83c29bed3a"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<extra_id_0> तेस्रो चरणको रिट निवेदन <extra_id_1>मा विभेद नगर्न अन्तरिम आदेश जारी <extra_id_2>े']"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QFQ6HoBD0qbc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}