COMP8730_proposed_solution.ipynb

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      "metadata": {
        "id": "xFVjZS8JAkY7"
      },
      "outputs": [],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install apache_beam\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5-95VV-EYPC9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaVYy2vqBFvA",
        "outputId": "e2731dbb-9213-40b4-c64a-ea227258644c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8444218515250481\n"
          ]
        }
      ],
      "source": [
        "import random\n",
        "import re\n",
        "\n",
        "import gc\n",
        "\n",
        "random.seed(0)\n",
        "print(random.random()) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
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            "27a52a4af9414437b39321ec57d7768d",
            "5353fba47e2242d89aa522de3598f6cd"
          ]
        },
        "id": "bw0ZiibJBFsR",
        "outputId": "9a8114bb-7d69-49fb-e24f-085fa4d2c70f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading builder script: 14.8kB [00:00, 7.38MB/s]                   \n",
            "Downloading metadata: 3.07MB [00:00, 85.9MB/s]                  \n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset oscar/unshuffled_deduplicated_sa (download: 6.93 MiB, generated: 36.46 MiB, post-processed: Unknown size, total: 43.40 MiB) to C:\\Users\\surajp\\.cache\\huggingface\\datasets\\oscar\\unshuffled_deduplicated_sa\\1.0.0\\84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading data: 100%|██████████| 81.0/81.0 [00:00<00:00, 77.9kB/s]\n",
            "Downloading data: 100%|██████████| 7.27M/7.27M [00:00<00:00, 13.6MB/s]\n",
            "Downloading data files: 100%|██████████| 1/1 [00:00<00:00,  1.05it/s]\n",
            "                                                                                     \r"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset oscar downloaded and prepared to C:\\Users\\surajp\\.cache\\huggingface\\datasets\\oscar\\unshuffled_deduplicated_sa\\1.0.0\\84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1/1 [00:00<00:00, 90.93it/s]\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_sa\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WnTtloWm-aMk"
      },
      "outputs": [],
      "source": [
        "text = dataset[\"train\"][\"text\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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          ]
        },
        "id": "mycxf6NgFIiq",
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset wikipedia/20200301.sa/1.0.0 (download: 14.82 MiB, generated: 56.70 MiB, total: 71.52 MiB) to /root/tensorflow_datasets/wikipedia/20200301.sa/1.0.0...\u001b[0m\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:Dataset wikipedia is hosted on GCS. It will automatically be downloaded to your\n",
            "local data directory. If you'd instead prefer to read directly from our public\n",
            "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
            "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[1mDataset wikipedia downloaded and prepared to /root/tensorflow_datasets/wikipedia/20200301.sa/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "# Construct a tf.data.Dataset\n",
        "ds = tfds.load('wikipedia/20200301.sa', split=\"train\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "n-wx66tyFIeN",
        "outputId": "f0aabc54-28b9-4e4f-987e-a0d60c50d3e8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = tfds.as_dataframe(ds.take(len(ds)))\n",
        "txt = df.text.apply(lambda x: x.decode('utf-8'))\n",
        "gc.collect()\n",
        "\n",
        "txt_ = dataset[\"train\"][\"text\"]\n",
        "\n",
        "text = list(txt_)+list(txt)\n",
        "text = [re.sub(\"[a-zA-Z0-9]+\", \"\", txt_) for txt_ in text]\n",
        "gc.collect()\n",
        "\n",
        "del txt\n",
        "del txt_\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cj1Mxgpr71ln"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3DYGN9755JWH"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForMaskedLM"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "omH81bdE5JT0",
        "outputId": "eb8b6e52-010a-4fe7-fbe9-8f2914836d01"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "46819608641a47969f629d1622511193",
              "version_major": 2,
              "version_minor": 0
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        },
        {
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        },
        {
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            ]
          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at ai4bharat/indic-bert were not used when initializing AlbertForMaskedLM: ['sop_classifier.classifier.weight', 'sop_classifier.classifier.bias']\n",
            "- This IS expected if you are initializing AlbertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing AlbertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "model_name = \"ai4bharat/indic-bert\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, strip_accents=False)\n",
        "model = AutoModelForMaskedLM.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi0UEehtBMLc",
        "outputId": "155deb6f-d6ff-4df7-b7a7-78fb76c65631"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AlbertConfig {\n",
              "  \"_name_or_path\": \"ai4bharat/indic-bert\",\n",
              "  \"attention_probs_dropout_prob\": 0,\n",
              "  \"bos_token_id\": 2,\n",
              "  \"classifier_dropout_prob\": 0.1,\n",
              "  \"down_scale_factor\": 1,\n",
              "  \"embedding_size\": 128,\n",
              "  \"eos_token_id\": 3,\n",
              "  \"gap_size\": 0,\n",
              "  \"hidden_act\": \"gelu\",\n",
              "  \"hidden_dropout_prob\": 0,\n",
              "  \"hidden_size\": 768,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"inner_group_num\": 1,\n",
              "  \"intermediate_size\": 3072,\n",
              "  \"layer_norm_eps\": 1e-12,\n",
              "  \"max_position_embeddings\": 512,\n",
              "  \"model_type\": \"albert\",\n",
              "  \"net_structure_type\": 0,\n",
              "  \"num_attention_heads\": 12,\n",
              "  \"num_hidden_groups\": 1,\n",
              "  \"num_hidden_layers\": 12,\n",
              "  \"num_memory_blocks\": 0,\n",
              "  \"pad_token_id\": 0,\n",
              "  \"position_embedding_type\": \"absolute\",\n",
              "  \"transformers_version\": \"4.17.0\",\n",
              "  \"type_vocab_size\": 2,\n",
              "  \"vocab_size\": 200000\n",
              "}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "we5dYtfOBVCW",
        "outputId": "b6fa54f4-3539-4a4f-92a2-d22c18f06c75"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'bos_token': '[CLS]',\n",
              " 'cls_token': '[CLS]',\n",
              " 'do_lower_case': True,\n",
              " 'eos_token': '[SEP]',\n",
              " 'keep_accents': False,\n",
              " 'mask_token': AddedToken(\"[MASK]\", rstrip=False, lstrip=True, single_word=False, normalized=False),\n",
              " 'name_or_path': 'ai4bharat/indic-bert',\n",
              " 'pad_token': '<pad>',\n",
              " 'remove_space': True,\n",
              " 'sep_token': '[SEP]',\n",
              " 'sp_model_kwargs': {},\n",
              " 'special_tokens_map_file': None,\n",
              " 'strip_accents': False,\n",
              " 'tokenizer_file': None,\n",
              " 'unk_token': '<unk>'}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.init_kwargs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rwZm_F3MB9c4"
      },
      "outputs": [],
      "source": [
        "from tqdm.auto import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ENT1qOHjAdlI"
      },
      "outputs": [],
      "source": [
        "def batch_encode(text, max_seq_len, batch_size=4096):\n",
        "    encoded_sentences = []\n",
        "    for i in tqdm(range(0, len(text), batch_size)):\n",
        "        encoded_sent = tokenizer.batch_encode_plus(\n",
        "            text[i : i + batch_size],\n",
        "            max_length=max_seq_len,\n",
        "            add_special_tokens=True,\n",
        "            padding=True,\n",
        "            return_attention_mask=True,\n",
        "            pad_to_max_length=True,\n",
        "            truncation=True,\n",
        "            #return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "    encoded_sentences += encoded_sent\n",
        "    \n",
        "    return (input_ids_train, attention_masks_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h6pGRmV2Adih"
      },
      "outputs": [],
      "source": [
        "#encoded_sentences = batch_encode(text=text,\n",
        "#                                                      max_seq_len=128, \n",
        "#                                                      batch_size=4096)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "As8Nep6xDsjr"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ngzFYMOAHT8H"
      },
      "outputs": [],
      "source": [
        "class Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels=None):\n",
        "        self.encodings = encodings\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.encodings[\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xVxVevBYHgyD"
      },
      "outputs": [],
      "source": [
        "X_train_tokenized = tokenizer(text, padding=True, truncation=True, max_length=128)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EgPZQbydG77y"
      },
      "outputs": [],
      "source": [
        "train_dataset = Dataset(X_train_tokenized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z6iJcxE9JZB1"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForLanguageModeling\n",
        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "rITGgBeiCcR_",
        "outputId": "1634085a-91e0-42ad-f301-45d0fdedb3a5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 29090\n",
            "  Num Epochs = 40\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 72760\n"
          ]
        },
        {
          "data": {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2374' max='72760' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 2374/72760 10:58 < 5:25:48, 3.60 it/s, Epoch 1.30/40]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1819</td>\n",
              "      <td>2.754100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to ./results/checkpoint-1819\n",
            "Configuration saved in ./results/checkpoint-1819/config.json\n",
            "Model weights saved in ./results/checkpoint-1819/pytorch_model.bin\n",
            "tokenizer config file saved in ./results/checkpoint-1819/tokenizer_config.json\n",
            "Special tokens file saved in ./results/checkpoint-1819/special_tokens_map.json\n"
          ]
        }
      ],
      "source": [
        "#https://huggingface.co/docs/transformers/v4.17.0/en/main_classes/trainer#transformers.TrainingArguments\n",
        "\n",
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    learning_rate=3e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=40,\n",
        "    logging_strategy =\"epoch\",\n",
        "    weight_decay=0.01,\n",
        "    do_train=True,\n",
        "    save_strategy =\"epoch\",\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    #eval_dataset=tokenized_imdb[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
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        "model_name = \"ai4bharat/indic-bert\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, strip_accents=False)\n",
        "model = AutoModelForMaskedLM.from_pretrained(model_name)"
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