Eng_to_Hinglish.ipynb

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      "execution_count": 1,
      "metadata": {
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        "outputId": "e256fccd-9436-4cf3-f745-2ac650de4382"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q sentencepiece xformers transformers bitsandbytes accelerate peft trl -qqq"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, BitsAndBytesConfig, pipeline, logging, HfArgumentParser, TrainingArguments\n",
        "import transformers\n",
        "import torch\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer\n",
        "from datasets import Dataset"
      ],
      "metadata": {
        "id": "-VCj9OYGIqNr"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Use GPT-3, BLOOM or LLama2 for better results\n",
        "\n",
        "modelname = \"ai-forever/mGPT\"\n",
        "new_model = \"/content/drive/MyDrive/Hinglish/weights\""
      ],
      "metadata": {
        "id": "Zh-HEueOItFL"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#path to custom dataset, refine my custom_dataset.txt if you want\n",
        "\n",
        "with open(\"/content/drive/MyDrive/custom_dataset.txt\", \"r\") as f:\n",
        "    text = f.read()\n",
        "    pairs = text.split(\"\\n\\n\")\n",
        "    for i in range(len(pairs)):\n",
        "        pairs[i] = pairs[i].split(\"\\n\")"
      ],
      "metadata": {
        "id": "pwyK7bFVI4Vc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#give the system prompt, the purpose why you are fine tuning and training\n",
        "\n",
        "system_prompt = \"Convert English to Hinglish\"\n",
        "\n",
        "def gen():\n",
        "  for i in range(len(pairs)-2):\n",
        "    yield {\"text\": f\"<s>[INST]<<SYS>>{system_prompt}<</SYS>>{pairs[i][0].strip('English: ')}[/INST]{pairs[i][1].strip('Hinglish: ')}</s>\"}\n",
        "\n",
        "dataset = Dataset.from_generator(gen)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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      "execution_count": 6,
      "outputs": [
        {
          "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,
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    {
      "cell_type": "code",
      "source": [
        "# QLoRA configuration\n",
        "compute_dtype = getattr(torch, \"float16\")\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    compute_dtype=\"float16\",\n",
        "    bnb_4bit_use_double_quant=False,\n",
        ")\n",
        "device_map = {\"\": 0}\n",
        "# Load base model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    modelname,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map,\n",
        "    use_auth_token=\"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\"\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(modelname, trust_remote_code=True, use_auth_token=\"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\")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\"\n",
        "\n",
        "#LoRA configuration\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.05,\n",
        "    r=64,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "# training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=\"/content/drive/MyDrive/Hinglish\",\n",
        "    num_train_epochs=100,\n",
        "    per_device_train_batch_size=16,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    logging_steps=100,\n",
        "    learning_rate=0.0001,\n",
        "    weight_decay=0.0001,\n",
        "    fp16=False,\n",
        "    bf16=False,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=0.03,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=\"constant\",\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=None,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=False,\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "trainer.train()\n",
        "\n",
        "# Save the trained model\n",
        "trainer.model.save_pretrained(new_model)"
      ],
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      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:479: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/725 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94b26e675fe24543ba052b431424ad24"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/3.45G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6dcc04da52524125b3ce55de4b5401b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:640: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.89M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b431dbe5587b4e948238cc42d6fe47aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12b95e2762014cc48b0772408384cfb9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:122: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:166: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/113 [00:00<?, ? examples/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2900' max='2900' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2900/2900 55:24, Epoch 100/100]\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>100</td>\n",
              "      <td>0.642600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.291300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.191200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.153400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.140400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.138200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.131100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.122000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.151400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.111600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.114400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.106400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.108100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.108300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.103600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.103700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.102400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.098700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.101000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.100800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.099100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.099700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.096800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.099200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.101200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2600</td>\n",
              "      <td>0.113100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>0.103300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>0.097800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2900</td>\n",
              "      <td>0.099300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "                                                  modelname,\n",
        "                                                  low_cpu_mem_usage=True,\n",
        "                                                  return_dict=True,\n",
        "                                                  torch_dtype=torch.float16,\n",
        "                                                  device_map=device_map,\n",
        "                                                  use_auth_token=\"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\"\n",
        "                                                  )\n",
        "model = PeftModel.from_pretrained(base_model, new_model)\n",
        "model = model.merge_and_unload()\n",
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(modelname, trust_remote_code=True, use_auth_token=\"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\")\n",
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NudwlMA9W0RX",
        "outputId": "d8da12e0-436e-4d7f-9e8e-d3e7d1c76eac"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:479: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora.py:475: UserWarning: fan_in_fan_out is set to False but the target module is `Conv1D`. Setting fan_in_fan_out to True.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:640: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "def translate_to_hinglish(query, max_length=64):\n",
        "    non_english_chars_pattern = re.compile(r'[^a-zA-Z]+')\n",
        "    system_prompt = \"Convert English to Hinglish\"\n",
        "    pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=max_length)\n",
        "    result = pipe(f\"<s>[INST] <<SYS>> {system_prompt} <</SYS>>{query}[/INST]\")\n",
        "    result = result[0]['generated_text'].split('[/INST]')[1].split('।')[0]+ \"।\"\n",
        "    return result"
      ],
      "metadata": {
        "id": "Hl2c_VUgW3Ev"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "queries = [\"Definitely share your feedback in the comment section.\",\n",
        "         \"So even if it's a big video, I will clearly mention all the products.\",\n",
        "          \"I was waiting for my bag.\",]"
      ],
      "metadata": {
        "id": "JUdwAc-XXPXW"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for query in queries:\n",
        "    print(f\"English: {query}\\n Hinglish: {translate_to_hinglish(query, max_length=100)}\\n\")"
      ],
      "metadata": {
        "id": "Q8aAabyN02P-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ],
      "metadata": {
        "id": "Ao2g-sPJfSk-"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#test the model\n",
        "\n",
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "\n",
        "# Prepare test set\n",
        "with open(\"/content/test_dataset.txt\", \"r\", encoding='utf-8') as f:\n",
        "    test_text = f.read()\n",
        "    test_pairs = test_text.split(\"\\n\\n\")\n",
        "    for i in range(len(test_pairs)):\n",
        "        test_pairs[i] = test_pairs[i].split(\"\\n\")\n",
        "\n",
        "# Generate translations\n",
        "translations = []\n",
        "for pair in test_pairs:\n",
        "    english_sentence = pair[0].strip('English: ')\n",
        "    hinglish_translation = translate_to_hinglish(english_sentence)\n",
        "    translations.append(hinglish_translation)\n",
        "\n",
        "# Prepare references and candidates for BLEU score calculation\n",
        "references = [[pair[1].strip('Hinglish: ').split()] for pair in test_pairs]\n",
        "candidates = [translation.split() for translation in translations]\n",
        "\n",
        "\n",
        "score = corpus_bleu(references, candidates)\n",
        "print(f\"BLEU Score: {score}\")\n"
      ],
      "metadata": {
        "id": "13mKC4TO1_2l"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}