{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Wizualizacja atencji\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://github.com/jessevig/bertviz" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: bertviz in /home/kuba/anaconda3/lib/python3.8/site-packages (1.1.0)\n", "Requirement already satisfied: boto3 in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (1.17.93)\n", "Requirement already satisfied: requests in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (2.24.0)\n", "Requirement already satisfied: torch>=1.0 in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (1.8.1)\n", "Requirement already satisfied: sentencepiece in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (0.1.95)\n", "Requirement already satisfied: tqdm in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (4.47.0)\n", "Requirement already satisfied: transformers>=2.0 in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (4.2.2)\n", "Requirement already satisfied: regex in /home/kuba/anaconda3/lib/python3.8/site-packages (from bertviz) (2020.6.8)\n", "Requirement already satisfied: botocore<1.21.0,>=1.20.93 in /home/kuba/anaconda3/lib/python3.8/site-packages (from boto3->bertviz) (1.20.93)\n", "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/kuba/anaconda3/lib/python3.8/site-packages (from boto3->bertviz) (0.10.0)\n", "Requirement already satisfied: s3transfer<0.5.0,>=0.4.0 in /home/kuba/anaconda3/lib/python3.8/site-packages (from boto3->bertviz) (0.4.2)\n", "Requirement already satisfied: certifi>=2017.4.17 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (2020.6.20)\n", "Requirement already satisfied: idna<3,>=2.5 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (2.10)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (1.25.9)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /home/kuba/anaconda3/lib/python3.8/site-packages (from requests->bertviz) (3.0.4)\n", "Requirement already satisfied: typing-extensions in /home/kuba/anaconda3/lib/python3.8/site-packages (from torch>=1.0->bertviz) (3.7.4.2)\n", "Requirement already satisfied: numpy in /home/kuba/anaconda3/lib/python3.8/site-packages (from torch>=1.0->bertviz) (1.18.5)\n", "Requirement already satisfied: sacremoses in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (0.0.43)\n", "Requirement already satisfied: tokenizers==0.9.4 in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (0.9.4)\n", "Requirement already satisfied: packaging in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (20.4)\n", "Requirement already satisfied: filelock in /home/kuba/anaconda3/lib/python3.8/site-packages (from transformers>=2.0->bertviz) (3.0.12)\n", "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/kuba/anaconda3/lib/python3.8/site-packages (from botocore<1.21.0,>=1.20.93->boto3->bertviz) (2.8.1)\n", "Requirement already satisfied: six in /home/kuba/anaconda3/lib/python3.8/site-packages (from sacremoses->transformers>=2.0->bertviz) (1.15.0)\n", "Requirement already satisfied: joblib in /home/kuba/anaconda3/lib/python3.8/site-packages (from sacremoses->transformers>=2.0->bertviz) (0.16.0)\n", "Requirement already satisfied: click in /home/kuba/anaconda3/lib/python3.8/site-packages (from sacremoses->transformers>=2.0->bertviz) (7.1.2)\n", "Requirement already satisfied: pyparsing>=2.0.2 in /home/kuba/anaconda3/lib/python3.8/site-packages (from packaging->transformers>=2.0->bertviz) (2.4.7)\n" ] } ], "source": [ "!pip install bertviz" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from transformers import AutoTokenizer, AutoModel\n", "from bertviz import model_view, head_view" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "TEXT = \"This is a sample input sentence for a transformer model\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "MODEL = \"distilbert-base-uncased\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n", "model = AutoModel.from_pretrained(MODEL, output_attentions=True)\n", "inputs = tokenizer.encode(TEXT, return_tensors='pt')\n", "outputs = model(inputs)\n", "attention = outputs[-1]\n", "tokens = tokenizer.convert_ids_to_tokens(inputs[0]) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SELF ATTENTION MODELS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", "
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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "/**\n", " * @fileoverview Transformer Visualization D3 javascript code.\n", " *\n", " *\n", " * Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n", " *\n", " * Change log:\n", " *\n", " * 12/19/18 Jesse Vig Assorted cleanup. Changed orientation of attention matrices.\n", " * 12/29/20 Jesse Vig Significant refactor.\n", " * 12/31/20 Jesse Vig Support multiple visualizations in single notebook.\n", " * 02/06/21 Jesse Vig Move require config from separate jupyter notebook step\n", " * 05/03/21 Jesse Vig Adjust height of visualization dynamically\n", " **/\n", "\n", "require.config({\n", " paths: {\n", " d3: '//cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min',\n", " jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',\n", " }\n", "});\n", "\n", "requirejs(['jquery', 'd3'], function ($, d3) {\n", "\n", " const params = {\"attention\": [{\"name\": null, \"attn\": [[[[0.04504745081067085, 0.06121315807104111, 0.03234885632991791, 0.07350576668977737, 0.08869655430316925, 0.06299575418233871, 0.13025709986686707, 0.0724874883890152, 0.0789855495095253, 0.04788164049386978, 0.0528176911175251, 0.06413961201906204, 0.1896234154701233], [0.1642618179321289, 0.045601703226566315, 0.08717474341392517, 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BOXWIDTH = 110;\n", " const BOXHEIGHT = 22.5;\n", " const MATRIX_WIDTH = 115;\n", " const CHECKBOX_SIZE = 20;\n", " const TEXT_TOP = 30;\n", "\n", " console.log(\"d3 version\", d3.version)\n", " let headColors;\n", " try {\n", " headColors = d3.scaleOrdinal(d3.schemeCategory10);\n", " } catch (err) {\n", " console.log('Older d3 version')\n", " headColors = d3.scale.category10();\n", " }\n", " let config = {};\n", " initialize();\n", " renderVis();\n", "\n", " function initialize() {\n", " config.attention = params['attention'];\n", " config.filter = params['default_filter'];\n", " config.rootDivId = params['root_div_id'];\n", " config.nLayers = config.attention[config.filter]['attn'].length;\n", " config.nHeads = config.attention[config.filter]['attn'][0].length;\n", " if (params['heads']) {\n", " config.headVis = new Array(config.nHeads).fill(false);\n", " params['heads'].forEach(x => config.headVis[x] = true);\n", " } else {\n", " config.headVis = new Array(config.nHeads).fill(true);\n", " }\n", " config.initialTextLength = config.attention[config.filter].right_text.length;\n", " config.layer = (params['layer'] == null ? 0 : params['layer'])\n", "\n", "\n", " let layerEl = $(`#${config.rootDivId} #layer`);\n", " for (var i = 0; i < config.nLayers; i++) {\n", " layerEl.append($(\"