8 yelp_review_classifier_using_transfomer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# install library"
      ],
      "metadata": {
        "id": "2OzSpGklg2rM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers accelerate datasets\n",
        "\n"
      ],
      "metadata": {
        "id": "TRY-B6T1NbiI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# load dataset"
      ],
      "metadata": {
        "id": "XokzT2dEg8az"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"yelp_review_full\")"
      ],
      "metadata": {
        "id": "Qck7kiRwNblQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "MUJbYJfzQTjI",
        "outputId": "f001f97c-5631-4e35-f954-09655326b7fd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'text'],\n",
              "        num_rows: 650000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['label', 'text'],\n",
              "        num_rows: 50000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# load model and tokenizer and prepare dataset for training"
      ],
      "metadata": {
        "id": "Y4wggEO4hBh3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
      ],
      "metadata": {
        "id": "YcKvE2zUVcPn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n",
        "\n",
        "\n",
        "tokenized_datasets = dataset.map(tokenize_function, batched=True,batch_size=1000)"
      ],
      "metadata": {
        "id": "glukYs_rZFrP",
        "outputId": "6bcfe1f3-0b4b-423e-d280-c302561117c9",
        "colab": {
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          "height": 49,
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            "28cea923b0f04d6c8a47d34b3de0d8cf",
            "7e18b3b12a244a5689aa696e96b49496"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8eef5c24fc6f4eefb9f787dff22706cf"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(1000))\n",
        "small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(1000))"
      ],
      "metadata": {
        "id": "IsGcIxMxZFzZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2l = { 0: \"1 stars\", 1: \"2 stars\",2: \"3 stars\",3:\"4 stars\",4: \"5 stars\"}\n",
        "l2id ={\"1 stars\": 0,\"2 stars\": 1,\"3 stars\": 2,\"4 stars\": 3,\"5 stars\": 4}"
      ],
      "metadata": {
        "id": "GMx5OU9gNboE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=5,id2label=id2l,label2id=l2id)"
      ],
      "metadata": {
        "id": "sukNsUs6Nbqo",
        "outputId": "42a3edc6-df01-451d-b2fe-d075335807ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\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": "markdown",
      "source": [
        "# accuracy metric"
      ],
      "metadata": {
        "id": "zCGsq7nyhWXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")"
      ],
      "metadata": {
        "id": "HqGOkH0oNb2u",
        "outputId": "ac9c1d85-ddc1-4d0d-94e3-57455e540576",
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          "height": 105,
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          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-56b0b4182bac>:4: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"accuracy\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "81c1c1fb89c74a83b826a24931ff38f8"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "qepedwBchVfB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "caCcqU11Nb5t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9lp-xOYchc_4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# train model"
      ],
      "metadata": {
        "id": "S3uzVmBGhdlU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments,Trainer\n",
        "\n",
        "training_args = TrainingArguments(output_dir=\"yelp-review-classification\", evaluation_strategy=\"epoch\")"
      ],
      "metadata": {
        "id": "p8RQWXrpPVLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=small_train_dataset,\n",
        "    eval_dataset=small_eval_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "rxx4g-wFPUpE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "t5jT-YfUNcCu",
        "outputId": "ad7a7b57-50c0-4f35-9ba2-8e5a571acbdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='375' max='375' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [375/375 06:10, Epoch 3/3]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.227781</td>\n",
              "      <td>0.455000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.013400</td>\n",
              "      <td>0.579000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.034980</td>\n",
              "      <td>0.598000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=375, training_loss=1.02489013671875, metrics={'train_runtime': 375.2105, 'train_samples_per_second': 7.996, 'train_steps_per_second': 0.999, 'total_flos': 789354427392000.0, 'train_loss': 1.02489013671875, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YSF8mFrPhkDp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# save model and load for inference"
      ],
      "metadata": {
        "id": "5md8f8wlhkW0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"review-classifier\"\n",
        "trainer.save_model(path)\n",
        "tokenizer.save_pretrained(path)"
      ],
      "metadata": {
        "id": "C6zYwIfnNcE7",
        "outputId": "44418437-2425-4990-e285-9f25f41d31a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('review-classifier/tokenizer_config.json',\n",
              " 'review-classifier/special_tokens_map.json',\n",
              " 'review-classifier/vocab.txt',\n",
              " 'review-classifier/added_tokens.json',\n",
              " 'review-classifier/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "Pipe = pipeline(\"sentiment-analysis\", model=path, tokenizer= path,top_k=5)\n"
      ],
      "metadata": {
        "id": "xekYyiFJZ3UM",
        "outputId": "2e209136-acb0-447a-fc9e-5c89903c1124",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "review = '''\n",
        "This place is great! Atmosphere is chill and cool but the staff is also really friendly.\n",
        "They know what they’re doing and what they’re talking about,\n",
        "and you can tell making the customers happy is their main priority.\n",
        "Food is pretty good, some italian classics and some twists,\n",
        "and for their prices it’s 100% worth it.\n",
        "\n",
        "'''"
      ],
      "metadata": {
        "id": "7x6bsfwSakE6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Pipe(review)[0]"
      ],
      "metadata": {
        "id": "6DWg8emqbHLS",
        "outputId": "bf31ecff-224e-44a9-c85d-6eed0c880692",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': '4 stars', 'score': 0.5979881286621094},\n",
              " {'label': '5 stars', 'score': 0.36606311798095703},\n",
              " {'label': '3 stars', 'score': 0.02637379989027977},\n",
              " {'label': '2 stars', 'score': 0.006953672971576452},\n",
              " {'label': '1 stars', 'score': 0.002621283521875739}]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ],
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