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"overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "20cd20f75a5d4ff684822aba64640939": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "**Creating a Transformer**" ], "metadata": { "id": "JrsFXq2mUYUS" } }, { "cell_type": "markdown", "source": [ "The first thing we’ll need to do to initialize a BERT model is load a configuration object:" ], "metadata": { "id": "wrCqHJhaUnop" } }, { "cell_type": "code", "source": [ "from transformers import BertConfig, BertModel\n", "\n", "# Building the config\n", "config = BertConfig()\n", "\n", "# Building the model from the config\n", "model = BertModel(config)\n", "#The configuration contains many attributes that are used to build the model:\n", "\n", "print(config)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FgH-8NxoUZgg", "outputId": "b606db94-4192-4dcb-ea42-47a17d800fee" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "BertConfig {\n", " \"_attn_implementation_autoset\": true,\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"classifier_dropout\": null,\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 512,\n", " \"model_type\": \"bert\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 12,\n", " \"pad_token_id\": 0,\n", " \"position_embedding_type\": \"absolute\",\n", " \"transformers_version\": \"4.48.3\",\n", " \"type_vocab_size\": 2,\n", " \"use_cache\": true,\n", " \"vocab_size\": 30522\n", "}\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Different loading methods" ], "metadata": { "id": "R2kbEE9iVYCZ" } }, { "cell_type": "markdown", "source": [ "Creating a model from the default configuration initializes it with random values:" ], "metadata": { "id": "Xq99jk_wVcWI" } }, { "cell_type": "markdown", "source": [ "**Loading a Transformer model that is already trained is simple — we can do this using the from_pretrained() method:**" ], "metadata": { "id": "-wFl0lQ2qXrp" } }, { "cell_type": "code", "source": [ "from transformers import BertModel\n", "\n", "model = BertModel.from_pretrained(\"bert-base-cased\")\n", "print(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 954, "referenced_widgets": [ "b3d817946b4a4461b7dc6ee1823c821a", 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"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/570 [00:00