conversation_terminator_classifier.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ChakshuGautam/transformers/blob/main/conversation_terminator_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "FIzsObxv0sqI"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install openai\n",
        "!pip install tiktoken\n",
        "!pip install datasets\n",
        "!pip install transformers==4.28.0\n",
        "!pip install git+https://github.com/huggingface/accelerate\n",
        "!pip install evaluate\n",
        "!pip install git+https://github.com/huggingface/huggingface_hub.git@main\n",
        "!sudo apt -qq install git-lfs\n",
        "!git config --global credential.helper store\n",
        "!pip install numpy\n",
        "!pip install accelerate\n",
        "!pip install bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "from datasets import list_datasets, load_dataset\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer, BertTokenizerFast, TFBertForSequenceClassification, BertTokenizer\n",
        "import numpy as np\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('google/mobilebert-uncased')\n",
        "\n",
        "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)\n",
        "\n",
        "# def tokenize(batch):\n",
        "#     return tokenizer(batch['text'], padding='max_length', truncation=True, max_length=512)\n",
        "\n",
        "dataset = load_dataset('Chakshu/conversation_ender')\n",
        "dataset = dataset[\"train\"]\n",
        "tokenized_data = tokenizer(dataset[\"text\"], return_tensors=\"np\", padding=True)\n",
        "tokenized_data = dict(tokenized_data)\n",
        "labels = np.array(dataset[\"is_terminator\"])\n",
        "\n",
        "model = TFBertForSequenceClassification.from_pretrained('google/mobilebert-uncased', num_labels=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280,
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        "id": "MuQUw1or3R4_",
        "outputId": "bf9503b1-7d9b-4fe0-8c9b-4c4c3e099413"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'MobileBertTokenizer'. \n",
            "The class this function is called from is 'BertTokenizer'.\n",
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/Chakshu___csv/Chakshu--conversation_ender-afeed2b305add17b/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using a model of type mobilebert to instantiate a model of type bert. This is not supported for all configurations of models and can yield errors.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/164M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a44f0770c82d4ce98b3ce3ac48bffbf1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at google/mobilebert-uncased were not used when initializing TFBertForSequenceClassification: ['mobilebert', 'seq_relationship___cls', 'predictions___cls']\n",
            "- This IS expected if you are initializing TFBertForSequenceClassification 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 TFBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at google/mobilebert-uncased and are newly initialized: ['classifier', 'bert']\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": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "nCdUh8dP9qXT",
        "outputId": "f57d8ad8-4b85-44d8-9095-c1942a58fdc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from transformers import PushToHubCallback\n",
        "\n",
        "push_to_hub_callback = PushToHubCallback(\n",
        "    output_dir=\"./conversation_terminator_classifier\", tokenizer=tokenizer, hub_model_id=\"Chakshu/conversation_terminator_classifier\"\n",
        ")\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=tf.keras.metrics.BinaryAccuracy())\n",
        "\n",
        "model.fit(tokenized_data, labels, epochs=10, batch_size=32, callbacks=push_to_hub_callback)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeHg0I3WK3Bz",
        "outputId": "2fd93866-ac3d-48e9-d697-1cbcf3ab6396"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/conversation_terminator_classifier is already a clone of https://huggingface.co/Chakshu/conversation_terminator_classifier. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/conversation_terminator_classifier is already a clone of https://huggingface.co/Chakshu/conversation_terminator_classifier. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "8/8 [==============================] - 71s 2s/step - loss: 0.2552 - binary_accuracy: 0.9444\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 4s 507ms/step - loss: 0.1295 - binary_accuracy: 0.9872\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.0707 - binary_accuracy: 0.9872\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 5s 575ms/step - loss: 0.0859 - binary_accuracy: 0.9829\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 4s 515ms/step - loss: 0.0484 - binary_accuracy: 0.9872\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 4s 490ms/step - loss: 0.0363 - binary_accuracy: 0.9957\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 4s 520ms/step - loss: 0.0209 - binary_accuracy: 1.0000\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 4s 525ms/step - loss: 0.0268 - binary_accuracy: 0.9957\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 15s 2s/step - loss: 0.0364 - binary_accuracy: 0.9915\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 4s 493ms/step - loss: 0.0108 - binary_accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f4a58313640>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.saved_model.save(model, 'save_model')"
      ],
      "metadata": {
        "id": "1i6hxM82YV2Y",
        "outputId": "e01a9d49-7be0-4ce9-976f-ff98bb98bc3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla, embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses while saving (showing 5 of 829). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = load_dataset('Chakshu/conversation_ender')\n",
        "test_dataset = test_dataset[\"test\"]\n",
        "test_tokenized_data = tokenizer(dataset[\"text\"], return_tensors=\"np\", padding=True)\n",
        "test_tokenized_data = dict(test_tokenized_data)\n",
        "labels = np.array(dataset[\"is_terminator\"])\n",
        "\n",
        "# loaded_model = tf.saved_model.load('save_model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87,
          "referenced_widgets": [
            "a03649f576bd4f75bb5cd41976d8f816",
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        "id": "SyKdLzLfYZoQ",
        "outputId": "2b60d8a4-e8e0-473b-e79d-96add0c67c37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/Chakshu___csv/Chakshu--conversation_ender-afeed2b305add17b/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "a03649f576bd4f75bb5cd41976d8f816"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_correct = 0\n",
        "\n",
        "for i in range(len(test_dataset)):\n",
        "  single_string = test_dataset[i]['text']\n",
        "  inputs = tokenizer(single_string, return_tensors=\"np\", padding=True)\n",
        "  predictions = model(inputs.input_ids, inputs.attention_mask)\n",
        "  probabilities = tf.nn.sigmoid(predictions.logits)\n",
        "\n",
        "  # Round the probabilities to the nearest integer to get the class prediction\n",
        "  predicted_class = tf.round(probabilities)\n",
        "\n",
        "  # Extract the predicted class as a Python integer and print it\n",
        "  total_correct += int(predicted_class.numpy()) == test_dataset[i]['is_terminator']\n",
        "  # print(single_string, test_dataset[i]['is_terminator'], \" ||| Correct Prediction?\", int(predicted_class.numpy()), int(predicted_class.numpy()) == test_dataset[i]['is_terminator'])\n",
        "\n",
        "print(\"Total Correct Classifications =\", 100*total_correct/len(test_dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vm_ca5zeYuwJ",
        "outputId": "d9de03b6-38e8-452f-b22e-aface3c4fca3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Correct Classifications = 100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exmaple Usage"
      ],
      "metadata": {
        "id": "4BAUiQbm5nRd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, TFBertForSequenceClassification, BertTokenizer\n",
        "import tensorflow as tf\n",
        "\n",
        "model_name = 'Chakshu/conversation_terminator_classifier' \n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = TFBertForSequenceClassification.from_pretrained(model_name)\n",
        "inputs = tokenizer(\"I will talk to you later\", return_tensors=\"np\", padding=True)\n",
        "outputs = model(inputs.input_ids, inputs.attention_mask)\n",
        "probabilities = tf.nn.sigmoid(outputs.logits)\n",
        "\n",
        "# Round the probabilities to the nearest integer to get the class prediction\n",
        "predicted_class = tf.round(probabilities)\n",
        "print(\"The last message by the user indicates that the conversation has\", \"'ENDED'\" if int(predicted_class.numpy()) == 1 else \"'NOT ENDED'\")"
      ],
      "metadata": {
        "id": "cZ0v5ypTm_KM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baef0c18-ad63-4cc9-f18b-f76690c1a815"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at Chakshu/conversation_terminator_classifier were not used when initializing TFBertForSequenceClassification: ['dropout_73']\n",
            "- This IS expected if you are initializing TFBertForSequenceClassification 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 TFBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFBertForSequenceClassification were initialized from the model checkpoint at Chakshu/conversation_terminator_classifier.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The last message by the user indicates that the conversation has 'ENDED'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "iHNaRWH8FH82"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Quantization"
      ],
      "metadata": {
        "id": "-EvnkydIFLUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from transformers import TFAutoModel, AutoTokenizer\n",
        "\n",
        "# Step 1: Download model from HuggingFace\n",
        "model_name = 'Chakshu/conversation_terminator_classifier'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = TFAutoModel.from_pretrained(model_name)\n",
        "\n",
        "# Step 2: Convert the model to TFLite format\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Step 3: Quantize the TFLite model\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "tflite_quant_model = converter.convert()\n",
        "\n",
        "# Step 4: Save the quantized model\n",
        "with open('quant_model.tflite', 'wb') as f:\n",
        "    f.write(tflite_quant_model)\n"
      ],
      "metadata": {
        "id": "hBjirMmhJlc1",
        "outputId": "b125a39d-3599-473f-efa1-6ee08bce14e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322,
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        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/394 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f82ad27c534046a9b4f5f422dde351f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc74fb9ac7da4792a30631e655bbdfa9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39b7028695554d059a051588e9817f02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.01k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55ad325f97a34984889aafa417257de6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/217M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50ce97308bd84be1ba23b10520982a75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at Chakshu/conversation_terminator_classifier were not used when initializing TFBertModel: ['classifier', 'dropout_73']\n",
            "- This IS expected if you are initializing TFBertModel 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 TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFBertModel were initialized from the model checkpoint at Chakshu/conversation_terminator_classifier.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n",
            "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, pooler_layer_call_fn while saving (showing 5 of 828). These functions will not be directly callable after loading.\n",
            "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, pooler_layer_call_fn while saving (showing 5 of 828). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "def get_model_size(model_path):\n",
        "    model_size = os.path.getsize(model_path)\n",
        "    return model_size / (1024 * 1024) # Convert bytes to megabytes\n",
        "\n",
        "# Save the original model\n",
        "model.save_pretrained(\"Chakshu/conversation_terminator_classifier\")\n",
        "\n",
        "original_model_path = \"Chakshu/conversation_terminator_classifier\"\n",
        "quantized_model_path = \"quant_model.tflite\"\n",
        "\n",
        "original_model_size = get_model_size(original_model_path)\n",
        "quantized_model_size = get_model_size(quantized_model_path)\n",
        "\n",
        "print(f\"Original model size: {original_model_size} MB\")\n",
        "print(f\"Quantized model size: {quantized_model_size} MB\")"
      ],
      "metadata": {
        "id": "tsz5HgzwKU0q",
        "outputId": "79a56e83-8c29-45e5-f592-e7ebff472133",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model size: 0.00390625 MB\n",
            "Quantized model size: 52.35237121582031 MB\n"
          ]
        }
      ]
    }
  ]
}