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Update models directory [ci skip]
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@ -32,11 +32,17 @@ const MODEL_META = {
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las: 'Labelled dependencies',
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token_acc: 'Tokenization',
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tok: 'Tokenization',
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lemma: 'Statistical lemmatization',
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morph: 'Morphological analysis',
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tags_acc: 'Part-of-speech tags (fine grained tags, Token.tag)',
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tag: 'Part-of-speech tags (fine grained tags, Token.tag)',
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pos: 'Part-of-speech tags (coarse grained tags, Token.pos)',
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ents_f: 'Named entities (F-score)',
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ents_p: 'Named entities (precision)',
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ents_r: 'Named entities (recall)',
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ner_f: 'Named entities (F-score)',
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ner_p: 'Named entities (precision)',
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ner_r: 'Named entities (recall)',
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sent_f: 'Sentence segmentation (F-score)',
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sent_p: 'Sentence segmentation (precision)',
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sent_r: 'Sentence segmentation (recall)',
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@ -88,11 +94,12 @@ function formatVectors(data) {
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}
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function formatAccuracy(data) {
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const exclude = ['speed']
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if (!data) return []
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return Object.keys(data)
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.map(label => {
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const value = data[label]
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return isNaN(value)
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return isNaN(value) || exclude.includes(label)
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? null
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: {
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label,
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@ -109,6 +116,7 @@ function formatModelMeta(data) {
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version: data.version,
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sizeFull: data.size,
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pipeline: data.pipeline,
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components: data.components,
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notes: data.notes,
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description: data.description,
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sources: data.sources,
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@ -117,7 +125,8 @@ function formatModelMeta(data) {
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license: data.license,
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labels: isEmptyObj(data.labels) ? null : data.labels,
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vectors: formatVectors(data.vectors),
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accuracy: formatAccuracy(data.performance),
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// TODO: remove accuracy fallback
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accuracy: formatAccuracy(data.accuracy || data.performance),
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}
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}
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