Evaluate_BLOOM_BLOOMZ_for_Machine_Translation_on_IWSLT15.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/trminhnam/CodeBERT/blob/master/Evaluate_BLOOM_BLOOMZ_for_Machine_Translation_on_IWSLT15.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJhQAdzscBUr"
      },
      "source": [
        "## Installing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w4zgT6lxWPQY"
      },
      "outputs": [],
      "source": [
        "!pip install -q peft transformers datasets evaluate seqeval wandb\n",
        "!pip install accelerate -q\n",
        "# !pip install bitsandbytes -q\n",
        "# !python -m bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global credential.helper store\n",
        "!huggingface-cli login --token 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 --add-to-git-credential"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvmN-mNxMqcm",
        "outputId": "d6130597-db58-4e3f-bb8f-e7aa417a02fd"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -q sacrebleu\n",
        "# import locale\n",
        "# locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "nHffOEGcP6Il"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdwHV5GBeBan"
      },
      "source": [
        "## Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "8l_KDWZbV8kL"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import os\n",
        "import json\n",
        "import html\n",
        "import evaluate\n",
        "import warnings\n",
        "import numpy as np\n",
        "\n",
        "from tqdm import tqdm\n",
        "from datasets import load_dataset\n",
        "from peft import PeftConfig, PeftModel\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import default_data_collator\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup\n",
        "from peft import get_peft_config, get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType, PeftType, LoraConfig\n",
        "\n",
        "from transformers import (\n",
        "    AutoModelForTokenClassification,\n",
        "    AutoTokenizer,\n",
        "    DataCollatorForTokenClassification,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        ")\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "device = \"cuda\"\n",
        "model_name_or_path = \"bigscience/bloomz-560m\"\n",
        "tokenizer_name_or_path = \"bigscience/bloomz-560m\"\n",
        "\n",
        "dataset_name = \"mt_eng_vietnamese\"\n",
        "dataset_config_name = \"iwslt2015-en-vi\"\n",
        "\n",
        "SOURCE_LANG, TARGET_LANG = dataset_config_name.split('-')[1:]\n",
        "FULL_LANG_MAPPING = {\n",
        "    \"en\": \"English\",\n",
        "    \"vi\": \"Vietnamese\",\n",
        "}\n",
        "FULL_SOURCE_LANG, FULL_TARGET_LANG = FULL_LANG_MAPPING[SOURCE_LANG], FULL_LANG_MAPPING[TARGET_LANG]\n",
        "\n",
        "MAX_LENGTH = 128\n",
        "BATCH_SIZE = 8\n",
        "\n",
        "SEED = 42"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Utils"
      ],
      "metadata": {
        "id": "Re_jQssZPB-i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def set_seed(seed):\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    if torch.cuda.device_count() > 0:\n",
        "        torch.cuda.manual_seed_all(seed)\n",
        "\n",
        "def loading_dataset_with_name(dataset_name, dataset_config_name):\n",
        "    dataset = load_dataset(dataset_name, dataset_config_name)\n",
        "    # print(dataset[\"train\"][0])\n",
        "    if \"validation\" not in dataset:\n",
        "        dataset[\"validation\"] = load_dataset(\n",
        "            dataset_name,\n",
        "            split=f\"train[:10%]\",\n",
        "        )\n",
        "\n",
        "        dataset[\"train\"] = load_dataset(\n",
        "            dataset_name,\n",
        "            split=f\"train[10%:20%]\",\n",
        "        )\n",
        "\n",
        "        dataset[\"train\"] = load_dataset(\n",
        "            dataset_name,\n",
        "            split=f\"train[20%:]\",\n",
        "        )\n",
        "    return dataset\n",
        "\n",
        "def generate_and_tokenize_prompt_for_evaluation(examples, tokenizer, prompt_template, max_length, src_lang, tgt_lang):\n",
        "    full_src_lang, full_tgt_lang = FULL_LANG_MAPPING[src_lang], FULL_LANG_MAPPING[tgt_lang]\n",
        "\n",
        "    if not isinstance(examples['translation'], list):\n",
        "        source_sentences = [html.unescape(examples['translation'][src_lang])]\n",
        "        target_sentences = [html.unescape(examples['translation'][tgt_lang])]\n",
        "    else:\n",
        "        source_sentences = [html.unescape(data[src_lang]) for data in examples['translation']]\n",
        "        target_sentences = [html.unescape(data[tgt_lang]) for data in examples['translation']]\n",
        "\n",
        "    # print(source_sentences)\n",
        "    # print(target_sentences)\n",
        "\n",
        "    batch_size = len(source_sentences)\n",
        "    # print(batch_size)\n",
        "\n",
        "    inputs = [\n",
        "        prompt_template.format(\n",
        "            full_source_lang=full_src_lang,\n",
        "            source_sent=source_sentences[i],\n",
        "            full_target_lang=full_tgt_lang,\n",
        "        )\n",
        "        for i in range(batch_size)\n",
        "    ]\n",
        "\n",
        "    model_inputs = tokenizer(inputs)\n",
        "    labels = tokenizer(target_sentences)\n",
        "\n",
        "    for i in range(batch_size):\n",
        "        # get input ids of current index\n",
        "        sample_input_ids = model_inputs[\"input_ids\"][i]\n",
        "\n",
        "        # add the end of string token\n",
        "        label_input_ids = labels[\"input_ids\"][i] + [tokenizer.eos_token_id]\n",
        "\n",
        "        # create attention mask for input ids\n",
        "        model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n",
        "\n",
        "    # create padding\n",
        "    for i in range(batch_size):\n",
        "        sample_input_ids = model_inputs[\"input_ids\"][i]\n",
        "        label_input_ids = labels[\"input_ids\"][i]\n",
        "\n",
        "        # padding input ids to max length\n",
        "        model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
        "            max_length - len(sample_input_ids)\n",
        "        ) + sample_input_ids\n",
        "\n",
        "        # padding attention mask to max length\n",
        "        model_inputs[\"attention_mask\"][i] = [0] * (\n",
        "            max_length - len(sample_input_ids)\n",
        "        ) + model_inputs[\"attention_mask\"][i]\n",
        "\n",
        "        # padding label ids to max length\n",
        "        labels[\"input_ids\"][i] = [-100] * (\n",
        "            max_length - len(label_input_ids)\n",
        "        ) + label_input_ids\n",
        "\n",
        "        # convert to torch tensor\n",
        "        model_inputs[\"input_ids\"][i] = torch.tensor(\n",
        "            model_inputs[\"input_ids\"][i][:max_length]\n",
        "        )\n",
        "        model_inputs[\"attention_mask\"][i] = torch.tensor(\n",
        "            model_inputs[\"attention_mask\"][i][:max_length]\n",
        "        )\n",
        "        labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "\n",
        "# post process function to decode the output ids and get the translated text from index: max_length\n",
        "def postprocess(predictions, labels, max_seq_len=None):\n",
        "    predictions = predictions.cpu().numpy()\n",
        "    labels = labels.cpu().numpy()\n",
        "\n",
        "    # predictions is in shape (batch_size, seq_len)\n",
        "    # we cut at the seq_len dimension to get the actual predictions because the model is trained with pre padding to max length\n",
        "    if max_seq_len:\n",
        "        predictions = predictions[:, max_seq_len:]\n",
        "\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",
        "    # Some simple post-processing\n",
        "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
        "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
        "    return decoded_preds, decoded_labels\n",
        "    return model_inputs\n",
        "\n",
        "def evaluate_model_with_bleu(model, data_loader, max_length):\n",
        "    model.eval()\n",
        "    bleu_metric = evaluate.load(\"bleu\")\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(data_loader):\n",
        "            batch = {k: v.to(device) for k, v in batch.items()}\n",
        "            generated_tokens = model.generate(\n",
        "                batch[\"input_ids\"],\n",
        "                attention_mask=batch[\"attention_mask\"],\n",
        "                max_length=MAX_LENGTH*2,\n",
        "                do_sample=True,\n",
        "                top_p=0.95,\n",
        "                top_k=60,\n",
        "                temperature=0.9,\n",
        "            )\n",
        "\n",
        "            labels = batch[\"labels\"]\n",
        "\n",
        "            decoded_preds, decoded_labels = postprocess(generated_tokens, labels, max_seq_len=max_length)\n",
        "            decoded_labels = [x[0] for x in decoded_labels]\n",
        "\n",
        "            bleu_metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
        "\n",
        "    bleu_result = bleu_metric.compute()\n",
        "    # print(f\"BLEU score on validation set: {bleu_result['bleu']*100:.2f}\")\n",
        "    return bleu_result"
      ],
      "metadata": {
        "id": "5mPfzfGIPEgD"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load tokenizer, model and PEFT parameters"
      ],
      "metadata": {
        "id": "iUojuVJ_OGUO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "oH-I2MuOWn9O"
      },
      "outputs": [],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
        "if tokenizer.pad_token_id is None:\n",
        "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=False)\n",
        "model = model.to(device)\n",
        "\n",
        "\n",
        "# peft_model_id = \"tmnam20/lora_16_8_0.1_lora_only_5e-5\"\n",
        "# config = PeftConfig.from_pretrained(peft_model_id)\n",
        "# inference_model = AutoModelForCausalLM.from_pretrained(\n",
        "#     config.base_model_name_or_path\n",
        "# )\n",
        "# tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "# model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
        "# model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pipeline"
      ],
      "metadata": {
        "id": "W9vLaMqfP81j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_machine_translation(model, tokenizer, dataset_names, prompt_templates, max_length):\n",
        "    for dataset_name in dataset_names:\n",
        "        if dataset_names.get(dataset_name, None) is None:\n",
        "            dataset_names[dataset_name] = [None]\n",
        "        for dataset_config_name in dataset_names[dataset_name]:\n",
        "            # loading dataset\n",
        "            print(\"#\" * 150)\n",
        "            print(f\"Running evaluation on dataset: {dataset_name}.{dataset_config_name}\")\n",
        "            src_lang, tgt_lang = dataset_config_name.split('-')[1:]\n",
        "            full_src_lang, full_tgt_lang = FULL_LANG_MAPPING[SOURCE_LANG], FULL_LANG_MAPPING[TARGET_LANG]\n",
        "            print()\n",
        "\n",
        "            for template in prompt_templates:\n",
        "                # reload dataset to apply new prompt\n",
        "                dataset = loading_dataset_with_name(dataset_name, dataset_config_name)\n",
        "                datasets_to_eval = {}\n",
        "                if \"validation\" in dataset:\n",
        "                    datasets_to_eval['validation'] = dataset['validation']\n",
        "                if \"test\" in dataset:\n",
        "                    datasets_to_eval['test'] = dataset['test']\n",
        "\n",
        "                # tokenizing subsets\n",
        "                for k, v in datasets_to_eval.items():\n",
        "                    v = v.map(\n",
        "                        generate_and_tokenize_prompt_for_evaluation,\n",
        "                        batched=True,\n",
        "                        num_proc=4,\n",
        "                        remove_columns=v.column_names,\n",
        "                        desc=f\"Creating prompt and tokenizing on {k} subset\",\n",
        "                        load_from_cache_file=False,\n",
        "                        fn_kwargs={\n",
        "                            \"tokenizer\": tokenizer,\n",
        "                            \"prompt_template\": template,\n",
        "                            \"max_length\": MAX_LENGTH,\n",
        "                            \"src_lang\": src_lang,\n",
        "                            \"tgt_lang\": tgt_lang,\n",
        "                        }\n",
        "                    )\n",
        "                    datasets_to_eval[k] = v\n",
        "                    len(v[100]['input_ids']) == max_length\n",
        "                    # print(tokenizer.decode(v[100]['input_ids']))\n",
        "                    assert len(v[100]['labels']) == max_length\n",
        "                    assert v[100]['input_ids'].count(3) == v[100]['attention_mask'].count(0)\n",
        "\n",
        "                eval_bleu = {}\n",
        "                for k, v in datasets_to_eval.items():\n",
        "                    dataloader = DataLoader(\n",
        "                        v, collate_fn=default_data_collator, batch_size=BATCH_SIZE\n",
        "                    )\n",
        "\n",
        "                    bleu_result = evaluate_model_with_bleu(model, dataloader, max_length)\n",
        "                    bleu_score = bleu_result['bleu']\n",
        "                    eval_bleu[k] = bleu_score\n",
        "\n",
        "                # print result\n",
        "                print(f'#' * 50)\n",
        "                print(f\"Dataset: {dataset_name}.{dataset_config_name}\")\n",
        "                print(f'{src_lang} --> {tgt_lang}')\n",
        "                print(f'Prompt template: {template}')\n",
        "                for k, v in eval_bleu.items():\n",
        "                    print(f\"BLEU score on {k} set: {v*100:.2f}\")\n",
        "                print()\n",
        "\n",
        "            print(\"#\" * 100)\n"
      ],
      "metadata": {
        "id": "P-d2ovbXP-jJ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Running pipeline"
      ],
      "metadata": {
        "id": "m-dVlzS-Wpg0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "set_seed(SEED)\n",
        "datasets = {\n",
        "    \"mt_eng_vietnamese\": [\"iwslt2015-en-vi\", \"iwslt2015-vi-en\"]\n",
        "    \"tmnam20/PhoMT\": [\"phomt-en-vi\", \"phomt-vi-en\"]\n",
        "}\n",
        "translation_prompt_templates = [\n",
        "    \"Translate the following text from {full_source_lang} to {full_target_lang} {source_sent} \",\n",
        "    \"Translate the following text from {full_source_lang} to {full_target_lang}: \\\"{source_sent}\\\". The {full_target_lang} text is: \"\n",
        "]\n",
        "\n",
        "eval_machine_translation(model, tokenizer, datasets, translation_prompt_templates, MAX_LENGTH)"
      ],
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            "ab3bc24e27cc489892f9f729fa7f41b7",
            "5186043c985c4840b64de726173fa4ec",
            "c1d19bbe4bed4c7eaef23eb97b3dafed",
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            "2fcef39ef6504376b190beafe828937f",
            "7649dbb3f32d4169bed61851c4ef613f",
            "040593601f6b41838f61cd041aa383d9",
            "c187adf3c37b4df5bbbfc8e64dbd09eb",
            "39d9b3e30e8246b7a67ab0550d355c7e",
            "c9e3cae72394419597a6d51e8f95ee02",
            "d9d43307c89c4e85acc10114e5a39cf5",
            "df3f009cb58a4515a3c514987203c610",
            "2cd7d2d1d8254804a0b0cd6aa45bf7bd",
            "375e6cccda814bf2ab04222c06a4ff8f",
            "21bde623706f48d2ba28dd9551658a0d",
            "b62413340634461296b271705a900e16",
            "6ae3e48eaf43476ba3bdeab48531d143",
            "5146c5c1789c4a2fbd5d501786cd4d2a",
            "976341a64e4c4a0d949901638e7166c6",
            "5e2da4e215a9476e8e6900ecbdba026c",
            "3190159d719d4edcaa720a43fecc6f15"
          ]
        },
        "id": "ACKicLcYWoeZ",
        "outputId": "d99b48e5-8f4c-4f2d-f1f3-b40744fb8a62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "######################################################################################################################################################\n",
            "Running evaluation on dataset: tmnam20/PhoMT.phomt-en-vi\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.87k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "daed01b000414eb4a5593afd3b2c9cf8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset pho_mt/phomt-en-vi to /root/.cache/huggingface/datasets/tmnam20___pho_mt/phomt-en-vi/1.0.0/28105c5f339b00b7069d0f5fc2e75a12386b6ea94bd4675faca47925930907ce...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9bd1164cc4774bb283a80b4b3f4b6b06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6dd8b18d81db49879e6948ce705e792b"
            }
          },
          "metadata": {}
        },
        {
          "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": "087b7be5467f4cb7b0cdd4939e2ec5b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7671f6c614e4aafba122ad11a52c410"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f55843ec1bd4911a662332993a7ae58"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset pho_mt downloaded and prepared to /root/.cache/huggingface/datasets/tmnam20___pho_mt/phomt-en-vi/1.0.0/28105c5f339b00b7069d0f5fc2e75a12386b6ea94bd4675faca47925930907ce. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73cfc39407bd4878b47c920f0a6a7767"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating prompt and tokenizing on validation subset (num_proc=4):   0%|          | 0/18719 [00:00<?, ? example…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92b57b26a7a54b48bd586323d8ec55c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating prompt and tokenizing on test subset (num_proc=4):   0%|          | 0/19151 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9d43307c89c4e85acc10114e5a39cf5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2340/2340 [1:38:51<00:00,  2.53s/it]\n",
            " 62%|██████▏   | 1477/2394 [50:26<25:17,  1.65s/it]"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8V8Ki7j1Fz7L"
      },
      "source": [
        "### Checking output of the processing method"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate_and_tokenize_prompt_for_evaluation(\n",
        "#     {\n",
        "#         \"translation\": {\n",
        "#             'en': 'In 4 minutes , atmospheric chemist Rachel Pike provides a glimpse of the massive scientific effort behind the bold headlines on climate change , with her team -- one of thousands who contributed -- taking a risky flight over the rainforest in pursuit of data on a key molecule .',\n",
        "#             'vi': 'Trong 4 phút , chuyên gia hoá học khí quyển Rachel Pike giới thiệu sơ lược về những nỗ lực khoa học miệt mài đằng sau những tiêu đề táo bạo về biến đổi khí hậu , cùng với đoàn nghiên cứu của mình -- hàng ngàn người đã cống hiến cho dự án này -- một chuyến bay mạo hiểm qua rừng già để tìm kiếm thông tin về một phân tử then chốt .'\n",
        "#         }\n",
        "#     }\n",
        "# )"
      ],
      "metadata": {
        "id": "_bPv72pXOAJv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print(len(eval_dataset[100]['input_ids']))\n",
        "# print(tokenizer.decode(eval_dataset[100]['input_ids']))\n",
        "# print(len(eval_dataset[100]['labels']))\n",
        "# print(eval_dataset[100]['input_ids'].count(3))\n",
        "# print(eval_dataset[100]['attention_mask'].count(0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ib4755EqNP_b",
        "outputId": "6d975e3f-06e3-4168-e5d2-9300bb44d09e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "128\n",
            "<pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>Translate the following text from English to Vietnamese Samuel is 16 . He 's tall . He 's very handsome . \n",
            "128\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J55E_eco0SRz"
      },
      "source": [
        "## Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # print an example of predictions and labels\n",
        "# model.eval()\n",
        "# with torch.no_grad():\n",
        "#     for batch in val_dataloader:\n",
        "#         batch = {k: v.to(device) for k, v in batch.items()}\n",
        "#         generated_tokens = model.generate(\n",
        "#             batch[\"input_ids\"],\n",
        "#             attention_mask=batch[\"attention_mask\"],\n",
        "#             max_length=max_length*2,\n",
        "#             do_sample=True,\n",
        "#             top_p=0.95,\n",
        "#             top_k=60,\n",
        "#             temperature=0.9,\n",
        "#         )\n",
        "\n",
        "#         labels = batch[\"labels\"]\n",
        "\n",
        "#         print(\"All generated token:\" + json.dumps(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True), indent=4, ensure_ascii=False))\n",
        "#         decoded_preds, decoded_labels = postprocess(generated_tokens, labels, max_seq_len=max_length)\n",
        "#         decoded_labels = [x[0] for x in decoded_labels]\n",
        "\n",
        "#         print(\"Decoded_preds:\" + json.dumps(decoded_preds, indent=4, ensure_ascii=False))\n",
        "#         print(\"Decoded_labels:\" + json.dumps(decoded_labels, indent=4, ensure_ascii=False))\n",
        "\n",
        "#         break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klpWmfwDPV0O",
        "outputId": "3f9cea68-e4aa-44ed-d157-e6702ab1d721"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All generated token:[\n",
            "    \"Translate the following text from English to Vietnamese When I was little , I thought my country was the best on the planet , and I grew up singing a song called \\\" Nothing To Envy . \\\"  Tôi nghĩ khi còn nhỏ, tôi nghĩ quốc gia của tôi là nước tốt nhất trên trái đất và tôi đã lớn lên hát một bài hát nói “Không nên sợ”.\",\n",
            "    \"Translate the following text from English to Vietnamese And I was very proud .  Và tôi rất vui được làm vậy.\",\n",
            "    \"Translate the following text from English to Vietnamese In school , we spent a lot of time studying the history of Kim Il-Sung , but we never learned much about the outside world , except that America , South Korea , Japan are the enemies .  Trong trường học, chúng ta dành nhiều thời gian nghiên cứu lịch sử của Kim Il-Sung, nhưng chúng ta chưa học được gì về thế giới ngoài nước Mỹ, Nhật Bản, nhưng lại là kẻ thù.\",\n",
            "    \"Translate the following text from English to Vietnamese Although I often wondered about the outside world , I thought I would spend my entire life in North Korea , until everything suddenly changed .  Mặc dù tôi thường thắc mắc về cuộc sống ngoài kia , tôi nghĩ rằng tôi sẽ sống tận hưởng cuộc sống của mình ở Bắc Hàn rồi thì mọi chuyện đột nhiên thay đổi .\"\n",
            "]\n",
            "Decoded_preds:[\n",
            "    \"Tôi nghĩ khi còn nhỏ, tôi nghĩ quốc gia của tôi là nước tốt nhất trên trái đất và tôi đã lớn lên hát một bài hát nói “Không nên sợ”.\",\n",
            "    \"Và tôi rất vui được làm vậy.\",\n",
            "    \"Trong trường học, chúng ta dành nhiều thời gian nghiên cứu lịch sử của Kim Il-Sung, nhưng chúng ta chưa học được gì về thế giới ngoài nước Mỹ, Nhật Bản, nhưng lại là kẻ thù.\",\n",
            "    \"Mặc dù tôi thường thắc mắc về cuộc sống ngoài kia , tôi nghĩ rằng tôi sẽ sống tận hưởng cuộc sống của mình ở Bắc Hàn rồi thì mọi chuyện đột nhiên thay đổi .\"\n",
            "]\n",
            "Decoded_labels:[\n",
            "    \"Khi tôi còn nhỏ , Tôi nghĩ rằng BắcTriều Tiên là đất nước tốt nhất trên thế giới và tôi thường hát bài \\\" Chúng ta chẳng có gì phải ghen tị . \\\"\",\n",
            "    \"Tôi đã rất tự hào về đất nước tôi .\",\n",
            "    \"Ở trường , chúng tôi dành rất nhiều thời gian để học về cuộc đời của chủ tịch Kim II- Sung , nhưng lại không học nhiều về thế giới bên ngoài , ngoại trừ việc Hoa Kỳ , Hàn Quốc và Nhật Bản là kẻ thù của chúng tôi .\",\n",
            "    \"Mặc dù tôi đã từng tự hỏi không biết thế giới bên ngoài kia như thế nào , nhưng tôi vẫn nghĩ rằng mình sẽ sống cả cuộc đời ở BắcTriều Tiên , cho tới khi tất cả mọi thứ đột nhiên thay đổi .\"\n",
            "]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "Kz2DYrMr4rv2",
        "outputId": "e47b7bee-fd8f-493b-e2a7-ad6cdc3b8c6b"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.94k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/1.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/3.34k [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 318/318 [10:36<00:00,  2.00s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BLEU score on validation set: 12.13\n"
          ]
        }
      ],
      "source": [
        "# # evaluate on val set\n",
        "\n",
        "# model.eval()\n",
        "# bleu_metric = evaluate.load(\"bleu\")\n",
        "# # sacrebleu_metric = evaluate.load(\"sacrebleu\")\n",
        "\n",
        "# with torch.no_grad():\n",
        "#     for batch in tqdm(val_dataloader):\n",
        "#         batch = {k: v.to(device) for k, v in batch.items()}\n",
        "#         generated_tokens = model.generate(\n",
        "#             batch[\"input_ids\"],\n",
        "#             attention_mask=batch[\"attention_mask\"],\n",
        "#             max_length=max_length*2,\n",
        "#             do_sample=True,\n",
        "#             top_p=0.95,\n",
        "#             top_k=60,\n",
        "#             temperature=0.9,\n",
        "#         )\n",
        "\n",
        "#         labels = batch[\"labels\"]\n",
        "\n",
        "#         decoded_preds, decoded_labels = postprocess(generated_tokens, labels, max_seq_len=max_length)\n",
        "#         decoded_labels = [x[0] for x in decoded_labels]\n",
        "\n",
        "#         bleu_metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
        "#         # sacrebleu_metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
        "\n",
        "# bleu_result = bleu_metric.compute()\n",
        "# print(f\"BLEU score on validation set: {bleu_result['bleu']*100:.2f}\")\n",
        "# # sacrebleu_result = sacrebleu_metric.compute()\n",
        "# # print(f\"SacreBLEU score on validation set: {sacrebleu_result['score']:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # evaluate on test set\n",
        "\n",
        "# model.eval()\n",
        "# bleu_metric = evaluate.load(\"bleu\")\n",
        "# # sacrebleu_metric = evaluate.load(\"sacrebleu\")\n",
        "\n",
        "# with torch.no_grad():\n",
        "#     for batch in tqdm(test_dataloader):\n",
        "#         batch = {k: v.to(device) for k, v in batch.items()}\n",
        "#         generated_tokens = model.generate(\n",
        "#             batch[\"input_ids\"],\n",
        "#             attention_mask=batch[\"attention_mask\"],\n",
        "#             max_length=max_length*2,\n",
        "#             do_sample=True,\n",
        "#             top_p=0.95,\n",
        "#             top_k=60,\n",
        "#             temperature=0.9,\n",
        "#         )\n",
        "\n",
        "#         labels = batch[\"labels\"]\n",
        "\n",
        "#         decoded_preds, decoded_labels = postprocess(generated_tokens, labels, max_seq_len=max_length)\n",
        "#         decoded_labels = [x[0] for x in decoded_labels]\n",
        "\n",
        "#         bleu_metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
        "#         # sacrebleu_metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
        "\n",
        "# bleu_result = bleu_metric.compute()\n",
        "# print(f\"BLEU score on test set: {bleu_result['bleu']*100:.2f}\")\n",
        "# # sacrebleu_result = sacrebleu_metric.compute()\n",
        "# # print(f\"SacreBLEU score on test set: {sacrebleu_result['score']:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "aBG3Zb8vVF_x",
        "outputId": "0b7612d4-5fcf-4044-e2f1-3284d47758b3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 56%|█████▋    | 179/318 [06:08<04:46,  2.06s/it]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-0fd59c2b4f69>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dataloader\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      9\u001b[0m         \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\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[0mbatch\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[0;32m---> 10\u001b[0;31m         generated_tokens = model.generate(\n\u001b[0m\u001b[1;32m     11\u001b[0m             \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"input_ids\"\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     12\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"attention_mask\"\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/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    114\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\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--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\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/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, **kwargs)\u001b[0m\n\u001b[1;32m   1586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1587\u001b[0m             \u001b[0;31m# 13. run sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1588\u001b[0;31m             return self.sample(\n\u001b[0m\u001b[1;32m   1589\u001b[0m                 \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1590\u001b[0m                 \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogits_processor\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/generation/utils.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m   2640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2641\u001b[0m             \u001b[0;31m# forward pass to get next token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2642\u001b[0;31m             outputs = self(\n\u001b[0m\u001b[1;32m   2643\u001b[0m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2644\u001b[0m                 \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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[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/models/bloom/modeling_bloom.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, head_mask, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, **deprecated_arguments)\u001b[0m\n\u001b[1;32m    906\u001b[0m         \u001b[0mreturn_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_return_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 908\u001b[0;31m         transformer_outputs = self.transformer(\n\u001b[0m\u001b[1;32m    909\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m             \u001b[0mpast_key_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpast_key_values\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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[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/models/bloom/modeling_bloom.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, head_mask, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict, **deprecated_arguments)\u001b[0m\n\u001b[1;32m    779\u001b[0m                 )\n\u001b[1;32m    780\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--> 781\u001b[0;31m                 outputs = block(\n\u001b[0m\u001b[1;32m    782\u001b[0m                     \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m                     \u001b[0mlayer_past\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayer_past\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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[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/models/bloom/modeling_bloom.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, alibi, attention_mask, layer_past, head_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m         \u001b[0;31m# Self attention.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m         attn_outputs = self.self_attention(\n\u001b[0m\u001b[1;32m    440\u001b[0m             \u001b[0mlayernorm_output\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m             \u001b[0mresidual\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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        "metric = evaluate.load(\"bleu\")\n",
        "metric.add_batch(\n",
        "    predictions=[\n",
        "        \"I go to school by public transportation\",\n",
        "        \"They sleep at night today\",\n",
        "    ],\n",
        "    references=[\n",
        "        \"I go to school by bus\",\n",
        "        \"They sleep at night\"\n",
        "    ]\n",
        ")\n",
        "\n",
        "results = metric.compute()\n",
        "print(f\"BLEU score: {results['bleu']:.2f}\")"
      ]
    },
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