huggingFaceFineTuning.ipynb

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    {
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      ],
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      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C40YSsR593rH"
      },
      "source": [
        "from datasets import load_dataset,load_metric\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification,Trainer,TrainingArguments\n",
        "checkpoint='bert-base-uncased'"
      ],
      "execution_count": null,
      "outputs": []
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        "model=AutoModelForSequenceClassification.from_pretrained(checkpoint,num_labels=2)\n",
        "tokenzier=AutoTokenizer.from_pretrained(checkpoint)"
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      "source": [
        "\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
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          "text": [
            "Reusing dataset glue (/root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
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            "Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-d50264bc56a2829e.arrow\n",
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-049a95e30376791f.arrow\n"
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      "source": [
        "training_args=TrainingArguments('test-trainer',evaluation_strategy=\"epoch\",\n",
        "                                per_device_train_batch_size=16,\n",
        "                                per_device_eval_batch_size=16,\n",
        "                                learning_rate=2e-5,\n",
        "                                weight_decay=0.01,\n",
        "                                num_train_epochs=5)\n",
        "trainer= Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "  \n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "_55KaB_P_hUd",
        "outputId": "748e70b7-5783-43fa-8f7b-e721144b1547"
      },
      "source": [
        "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
        "print(predictions.predictions.shape, predictions.label_ids.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='54' max='51' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [51/51 02:11]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(408, 2) (408,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9RW6loJ_ooy"
      },
      "source": [
        "import numpy as np\n",
        "preds=np.argmax(predictions.predictions,axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "3f925429860c4c788d462963770ff655",
            "552d1433284a4d8398c1dc37927b759b",
            "70f2e900bd764ecf849af5505d93f9f1",
            "7da2f1ab2dcc4630bd4a7eaf048cb06e",
            "c27981fbc66d4c07a1e1206e4045973a",
            "672cb6c7c4924cad9e570aae72471994",
            "02581beb5aa44822818e642d28a74d83",
            "c39f248d7bf24cddb465561460f2a7c8"
          ]
        },
        "id": "adIRQkZhAK_N",
        "outputId": "96ef41e7-3b66-4bf9-aad8-0db536eba883"
      },
      "source": [
        "metric = load_metric(\"glue\", \"mrpc\")\n",
        "metric.compute(predictions=preds, references=predictions.label_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3f925429860c4c788d462963770ff655",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1848.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnioSyrLAcCM"
      },
      "source": [
        "def compute_metrics(eval_preds):\n",
        "    metric = load_metric(\"glue\", \"mrpc\")\n",
        "    logits, labels = eval_preds\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5P6MwDYAtHa"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Hho7lqKBCBq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}