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    "accelerator": "GPU"
  },
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    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/471.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m471.6/471.6 kB\u001b[0m \u001b[31m35.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "pip install -q datasets transformers[torch] evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"glue\", \"mrpc\")\n",
        "dataset"
      ],
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        "outputId": "111c6c7d-046f-4fbf-eaa8-c0f2e17dd00d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/35.3k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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              "train-00000-of-00001.parquet:   0%|          | 0.00/649k [00:00<?, ?B/s]"
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        {
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              "validation-00000-of-00001.parquet:   0%|          | 0.00/75.7k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test-00000-of-00001.parquet:   0%|          | 0.00/308k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained( \"bert-base-uncased\")\n",
        "mapped_dataset = dataset.map(lambda x: tokenizer(x[\"sentence1\"], x[\"sentence2\"]), batched = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169,
          "referenced_widgets": [
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            "5fc10befafa6449b9671e0cbc764b693"
          ]
        },
        "id": "agq0BbpP5uMw",
        "outputId": "4fbac3e7-42f6-4513-afc7-643eb19dae8d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce82045dbbf345ae8442d5f877543601"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3f11a3906294bfd908ac07026cb7d10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "kGhWXGRk5wmX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "7f7319b274a948759b8a9f4838e7803e",
            "e6598b0d6e284d5084eabf3491d245d5",
            "87b6602c02a548b1a3daf279cf64a7cc",
            "f1afd14fec5a4c1facd956f0eb7111bf",
            "11870fce83954cffafae68c95214de19",
            "8c3ff0c61137401da760a517fe117716",
            "2a63ae7e6b38444f879a6a739fa5f62f",
            "aaeb62c7715d451ab29b9de05b36a6c7",
            "e6c315735fab4e05b0a2b78bcb7e64a8",
            "4eaf98fd3b5840549cc00a5b039ecfa7",
            "722cc0cb191e4e3189deafbb86380d11"
          ]
        },
        "id": "1J2CCEuN5yPc",
        "outputId": "75fb1de1-aeef-4cc7-dd81-918557483d31"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f7319b274a948759b8a9f4838e7803e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "import evaluate\n",
        "import numpy as np\n",
        "def compute_metrics(eval_preds):\n",
        "  metric = evaluate.load(\"glue\", \"mrpc\")\n",
        "  logits, labels = eval_preds\n",
        "  predictions = np.argmax(logits, axis =-1)\n",
        "  return metric.compute(predictions=predictions, references = labels)\n",
        "#train_args = TrainingArguments(\"test-run-1\", evaluation_strategy= \"epoch\")\n",
        "\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "train_args = TrainingArguments(\n",
        "    output_dir='./results',                   # directory to save results\n",
        "    evaluation_strategy=\"epoch\",              # evaluation at the end of each epoch\n",
        "    learning_rate=1e-2,                      # learning rate\n",
        "    per_device_train_batch_size=16,           # batch size for training\n",
        "    weight_decay=0.01,                        # strength of weight decay\n",
        "    logging_dir='./logs',                     # directory for storing logs\n",
        "    logging_steps=10,\n",
        "    seed=42,                                   # random seed for reproducibility\n",
        "    adam_beta1=0.9,                           # beta1 for Adam optimizer\n",
        "    adam_beta2=0.999,                         # beta2 for Adam optimizer\n",
        "    adam_epsilon=1e-08,                       # epsilon for Adam optimizer\n",
        "    lr_scheduler_type=\"cosine_with_restarts\",\n",
        "    num_train_epochs=10,\n",
        "    warmup_steps=500,\n",
        "    gradient_accumulation_steps=2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7mSnEYi50XQ",
        "outputId": "807a8fd8-ab39-4b0b-a935-582d2b2bd58e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yGRhbVO51_Y",
        "outputId": "94a7e782-0c95-48a8-f42a-03a7927c6015"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSdpaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install loralib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3l8nukj53cx",
        "outputId": "b4d09a1c-217e-4af6-d791-84f5d9b5104e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting loralib\n",
            "  Downloading loralib-0.1.2-py3-none-any.whl.metadata (15 kB)\n",
            "Downloading loralib-0.1.2-py3-none-any.whl (10 kB)\n",
            "Installing collected packages: loralib\n",
            "Successfully installed loralib-0.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import loralib as lora\n",
        "def apply_lora_to_bert(bert_model, r=4, lora_alpha=8, lora_dropout=0.1):\n",
        "    for layer in bert_model.bert.encoder.layer:\n",
        "        # Apply LoRA to the query and key projection layers\n",
        "        layer.attention.self.query = lora.Linear(\n",
        "            layer.attention.self.query.in_features,\n",
        "            layer.attention.self.query.out_features,\n",
        "            r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout\n",
        "        )\n",
        "        layer.attention.self.key = lora.Linear(\n",
        "            layer.attention.self.key.in_features,\n",
        "            layer.attention.self.key.out_features,\n",
        "            r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout\n",
        "        )\n",
        "        layer.attention.self.value = lora.Linear(\n",
        "            layer.attention.self.value.in_features,\n",
        "            layer.attention.self.value.out_features,\n",
        "            r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout\n",
        "        )\n",
        "    return bert_model\n",
        "\n",
        "# Apply LoRA to the BERT model's attention layers\n",
        "model = apply_lora_to_bert(model)"
      ],
      "metadata": {
        "id": "r_FwSl6E56L7"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFV4ZKXy6J6Q",
        "outputId": "9a934447-04b9-441e-e7f8-500b03d9a9b5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSdpaSelfAttention(\n",
              "              (query): Linear(\n",
              "                in_features=768, out_features=768, bias=True\n",
              "                (lora_dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (key): Linear(\n",
              "                in_features=768, out_features=768, bias=True\n",
              "                (lora_dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (value): Linear(\n",
              "                in_features=768, out_features=768, bias=True\n",
              "                (lora_dropout): Dropout(p=0.1, inplace=False)\n",
              "              )\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lora.mark_only_lora_as_trainable(model)"
      ],
      "metadata": {
        "id": "OuDPFx486bMT"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def count_trainable_params(model):\n",
        "    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "    return trainable_params\n",
        "def show_trainable_params(model):\n",
        "    print(\"Trainable parameters (requires_grad=True):\")\n",
        "    for name, param in model.named_parameters():\n",
        "        if param.requires_grad:\n",
        "            print(f\"{name}: {param.shape}\")\n",
        "\n",
        "\n",
        "print(count_trainable_params(model))\n",
        "show_trainable_params(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEsPT2VO6iXl",
        "outputId": "45e7bfb8-9640-49e5-a6c9-756e17469e66"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "221184\n",
            "Trainable parameters (requires_grad=True):\n",
            "bert.encoder.layer.0.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.0.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.0.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.0.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.0.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.0.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.1.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.1.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.1.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.1.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.1.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.1.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.2.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.2.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.2.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.2.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.2.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.2.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.3.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.3.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.3.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.3.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.3.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.3.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.4.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.4.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.4.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.4.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.4.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.4.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.5.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.5.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.5.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.5.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.5.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.5.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.6.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.6.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.6.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.6.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.6.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.6.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.7.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.7.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.7.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.7.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.7.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.7.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.8.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.8.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.8.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.8.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.8.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.8.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.9.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.9.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.9.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.9.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.9.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.9.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.10.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.10.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.10.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.10.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.10.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.10.attention.self.value.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.11.attention.self.query.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.11.attention.self.query.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.11.attention.self.key.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.11.attention.self.key.lora_B: torch.Size([768, 4])\n",
            "bert.encoder.layer.11.attention.self.value.lora_A: torch.Size([4, 768])\n",
            "bert.encoder.layer.11.attention.self.value.lora_B: torch.Size([768, 4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model = model,\n",
        "    args = train_args,\n",
        "    train_dataset = mapped_dataset[\"train\"],\n",
        "    eval_dataset = mapped_dataset[\"validation\"],\n",
        "    data_collator = data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics = compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "rEZcVtdD6oTV"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "81341c7cb70b42ba80fc91b4bc72093a",
            "16dd837f664c4a08a78996bd9b8e034d",
            "c09de22919a64539beae5214fd031130",
            "d2e600ebe51f4f8fad5805d368dede1a",
            "cbccf104a4bc458abefa98853a192dca",
            "28bdbf9101534903b25f3d1be641ba04",
            "a581f87642b7448381e94d63d3108d10",
            "e408be4049fd433aa63e2753f286d48c",
            "abc4b2e7e4d74a2da1f1cc973f54ee94",
            "7a0db28618c64680bce9aaf9a66e7635",
            "d14827923e0b42e292149306c649d042"
          ]
        },
        "id": "Zt2J9Yq86rn1",
        "outputId": "5074e018-3d32-42e0-f772-3d68acaa429b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1150' max='1150' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1150/1150 06:57, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.605000</td>\n",
              "      <td>0.617748</td>\n",
              "      <td>0.700980</td>\n",
              "      <td>0.817365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.658700</td>\n",
              "      <td>0.628044</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.645400</td>\n",
              "      <td>0.624117</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.668500</td>\n",
              "      <td>0.626570</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.636100</td>\n",
              "      <td>0.623987</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.632200</td>\n",
              "      <td>0.624126</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.634500</td>\n",
              "      <td>0.623990</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.607100</td>\n",
              "      <td>0.623969</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.618600</td>\n",
              "      <td>0.623960</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.665300</td>\n",
              "      <td>0.623955</td>\n",
              "      <td>0.683824</td>\n",
              "      <td>0.812227</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1150, training_loss=0.6334076553842296, metrics={'train_runtime': 419.9254, 'train_samples_per_second': 87.349, 'train_steps_per_second': 2.739, 'total_flos': 1436395093366032.0, 'train_loss': 0.6334076553842296, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "preds = trainer.predict(mapped_dataset[\"test\"])\n",
        "logits, labels, _ = preds\n",
        "predictions = numpy.argmax(logits, axis =-1)\n",
        "metric = evaluate.load(\"glue\", \"mrpc\")\n",
        "metric.compute(predictions = predictions, references = labels)"
      ],
      "metadata": {
        "id": "2r7YPLkO6s8E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "021d2f88-76d2-4c2e-e5df-20a6ab2df430"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.664927536231884, 'f1': 0.7987465181058496}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3BoUmAHTbVIO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}