bleu_mt5.ipynb

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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "# import os\n",
        "# os.chdir('/content/drive/My Drive/translate')"
      ]
    },
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      "source": [
        "drive.flush_and_unmount()"
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        "outputId": "49965a47-c004-4600-ecdb-814244daa7f0"
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      "outputs": [],
      "source": [
        "!pip install sacrebleu transformers sentencepiece"
      ]
    },
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        "id": "0LKdsji4MDYv"
      },
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      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import json\n",
        "from sacrebleu import BLEU\n",
        "import transformers"
      ]
    },
    {
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      "source": [
        "bleu = BLEU()\n",
        "import os\n",
        "os.environ['TF_GPU_ALLOCATOR'] = 'cuda_malloc_async'\n",
        "\n",
        "checkpoint = 'google/mt5-small'\n",
        "model_name = 'mT5_small_PTT_2023_08_06_Thu_Aug_17_081645_2023.ckpt'\n",
        "split_char = '⫯'\n",
        "\n",
        "datasets_name = 'PTT_2023_08_06'\n",
        "prefix = \"translate engTyping to Traditional Chinese:\".split()"
      ]
    },
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      "source": [
        "model = transformers.TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)\n",
        "model.load_weights(f'models/{model_name}')\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(checkpoint)"
      ]
    },
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      "source": [
        "with open(f'datasets/{datasets_name}_engTyping_inserted_lines.txt', encoding='utf8') as file:\n",
        "    engTyping_inserted_lines = file.readlines()\n",
        "with open(f'datasets/{datasets_name}_zh_lines.txt', encoding='utf8') as file:\n",
        "    zh_lines = file.readlines()\n",
        "lines_len = len(engTyping_inserted_lines)\n",
        "assert lines_len == len(zh_lines)\n",
        "engTyping_inserted_lines = engTyping_inserted_lines[int(lines_len * 0.85):]\n",
        "engTyping_lines = [prefix + line.split(split_char) for line in engTyping_inserted_lines]\n",
        "zh_lines = zh_lines[int(lines_len * 0.85):]\n",
        "for i in range(len(zh_lines)):\n",
        "    zh_lines[i] = ' '.join(zh_lines[i].split(split_char)[1:-1])"
      ]
    },
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "299/300"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ]
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      ],
      "source": [
        "pred_sentences = []\n",
        "loop_times = 300\n",
        "eng_len = len(engTyping_lines)\n",
        "split_point = eng_len // loop_times\n",
        "for k in range(loop_times + 1):\n",
        "    lines = engTyping_lines[k * split_point:(k + 1) * split_point]\n",
        "    len_lines = len(lines)\n",
        "    inputs = tokenizer(lines, is_split_into_words=True, return_tensors=\"tf\", truncation=True, padding=True).input_ids\n",
        "    outputs = model.generate(inputs, max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)\n",
        "    decoded_sentences = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "    pred_sentences.extend(decoded_sentences)\n",
        "    print(f'\\r{k}/{loop_times}', end='')\n",
        "\n",
        "\n",
        "print(pred_sentences)"
      ]
    },
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      "outputs": [
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          "text": [
            "BLEU = 83.17 92.7/86.9/81.4/76.2 (BP = 0.989 ratio = 0.989 hyp_len = 2878258 ref_len = 2909826)\n"
          ]
        }
      ],
      "source": [
        "result = bleu.corpus_score(pred_sentences, [zh_lines])\n",
        "print(result)"
      ]
    },
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          "execution_count": 18,
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      "source": [
        "len(pred_sentences)"
      ]
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          "execution_count": 19,
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      ],
      "source": [
        "len(zh_lines)"
      ]
    },
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        },
        "id": "4_--g2ibCJOb"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "with open('translator_mT5_lines.pkl', 'wb') as file:\n",
        "    pickle.dump(pred_sentences, file)"
      ]
    }
  ],
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