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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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95 lines
2.2 KiB
95 lines
2.2 KiB
<template>
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<div style="display:inline;">
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<v-tooltip bottom>
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<template v-slot:activator="{ on }">
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<v-btn
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class="text-capitalize ps-1 pe-1"
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min-width="36"
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icon
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v-on="on"
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@click="dialog=true"
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>
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<v-icon>
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mdi-cog-outline
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</v-icon>
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</v-btn>
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</template>
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<span>Settings</span>
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</v-tooltip>
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<v-dialog
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v-model="dialog"
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width="800"
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>
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<base-card
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title="Settings"
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:cancel-text="$t('generic.close')"
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@cancel="dialog=false"
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>
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<template #content>
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<h3>Auto Labeling</h3>
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<p>
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The auto labeling allows users to annotate data automatically.
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It enables them to speed up annotating data.
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You only have to correct labels which are mislabeled by the system and annotate labels which aren’t labeled by it.
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</p>
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<p>
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Notice that you can't use this feature unless the project administrators configure the auto labeling.
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Also, depending on the configuration, it will take some cost for the administrators(e.g. In the case of configuring some paid service like AWS or GCP).
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</p>
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<v-switch
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v-model="value.onAutoLabeling"
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/>
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<v-alert
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v-if="errors.autoLabelingConfig"
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prominent
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type="error"
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>
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<v-row align="center">
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<v-col class="grow">
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{{ errors.autoLabelingConfig }}
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</v-col>
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</v-row>
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</v-alert>
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</template>
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</base-card>
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</v-dialog>
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</div>
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</template>
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<script>
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import BaseCard from '@/components/molecules/BaseCard'
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export default {
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components: {
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BaseCard
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},
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props: {
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value: {
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type: Object,
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default: () => {},
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required: true
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},
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errors: {
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type: Object,
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default: () => {},
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required: true
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}
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},
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data() {
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return {
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dialog: false
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}
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},
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watch: {
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value: {
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handler(val) {
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this.$emit('input', val)
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},
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deep: true
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}
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}
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}
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</script>
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