30:T60b,{ const bar_height = 16 const bar_padding = 4 var svg = d3.create('svg') .attr('width', width) svg.selectAll('.barbg') .data(results) .join('rect') .attr('class','barbg') .attr('width', 300) .attr('height', bar_height) .attr('y', (d,i) => (bar_height+bar_padding)*i) .attr('fill-opacity', 0.1) svg.selectAll('.bar') .data(results) .join('rect') .attr('class','bar') .attr('width', (d) => d.probability * 300) .attr('height', bar_height) .attr('y', (d,i) => (bar_height+bar_padding)*i) .attr('fill', (d,i) => d3.schemeCategory10[i]) svg.selectAll('.count') .data(results) .join('text') .attr('class','count') .attr('x', (d) => d.probability * 300) .attr('y', (d,i) => (bar_height+bar_padding)*(i+0.42)) .attr('dx', d => d.probability > 0.75 ? -5 : 5) .attr('dy','.35em') .style('text-anchor', d => d.probability > 0.75 ? 'end' : 'start') .style('font-family','sans-serif') .style('font-size', bar_height/1.5+'px') .style('font-weight', d => d.probability > 0.75 ? 'bold' : 'normal') .text(d => format_count(d.probability)) svg.selectAll('.label') .data(results) .join('text') .attr('class','label') .attr('x', 305) .attr('y', (d,i) => (bar_height+bar_padding)*(i+0.42)) .attr('dy','.35em') .style('font-family','sans-serif') .style('font-size', bar_height/1.5+'px') .style('font-weight', d => d.probability > 0.75 ? 'bold' : 'normal') .text(d => d.label) return svg.node() }31:T406,html`

${parse_predictions(0)}

`32:[] 2e:{"title":"Toxicity bars","cells":[{"id":117,"value":"# Toxicity bars\nHeavily based on this work by Shubhanshu Mishra: https://observablehq.com/@napsternxg/tensorflow-js-toxicity-model","pinned":false,"hidden":false,"mode":"md"},{"id":122,"value":"viewof text_input = Inputs.text({label: \"Text\", value: \"You are an idiot.\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":165,"value":"## Implementation","pinned":false,"hidden":false,"mode":"md"},{"id":188,"value":"format_count = d3.format(\".0%\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":141,"value":"color = d3.scaleOrdinal(results.map(d => d.label), d3.schemeCategory10)","pinned":true,"hidden":false,"mode":"ojs"},{"id":127,"value":"results = predictions[0].results.length <= 0 ? [] : predictions.map(d => ({label: d.label, probability: d.results[0].probabilities[1]}))","pinned":true,"hidden":false,"mode":"ojs"},{"id":205,"value":"predictions","pinned":true,"hidden":false,"mode":"ojs"},{"id":13,"value":"/*html`

\n${comments.map((c, i) => {\n return `

${c}

${parse_predictions(i)}

`\n})}\n

`*/","pinned":true,"hidden":false,"mode":"ojs"},{"id":70,"value":"function parse_predictions(i){\n return predictions.map(l => `${l.label}=${l.results[i].match} (${l.results[i].probabilities[1].toFixed(2)})`).join(\" \")\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":10,"value":"// Finding the answers\npredictions = model.classify(comments);","pinned":true,"hidden":false,"mode":"ojs"},{"id":35,"value":"model.tokenizer","pinned":true,"hidden":false,"mode":"ojs"},{"id":54,"value":"comments = text_input.replace(/\\.\\s+([A-Z])/g, \".\\n$1\").split(\"\\n\").filter(x => x.length > 0)","pinned":true,"hidden":false,"mode":"ojs"},{"id":58,"value":"text_input","pinned":true,"hidden":false,"mode":"ojs"},{"id":77,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"// Load the model.\nmodel = toxicity.load();","pinned":true,"hidden":false,"mode":"ojs"},{"id":5,"value":"toxicity = require('@tensorflow-models/toxicity');","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}