mirror of https://github.com/explosion/spaCy.git
Fix vector details in model overview
This commit is contained in:
ines
parent
9baab241b4
commit
43512c68b2
|
|
@ -40,6 +40,8 @@ for id in CURRENT_MODELS
|
|||
|
||||
each label in ["Pipeline", "Vectors", "Sources", "Author", "License"]
|
||||
- var field = label.toLowerCase()
|
||||
if field == "vectors"
|
||||
- field = "vecs"
|
||||
+row
|
||||
+cell.u-nowrap
|
||||
+label=label
|
||||
|
|
|
|||
|
|
@ -20,21 +20,33 @@ const CHART_FONTS = {
|
|||
* @property {function} vectors - Format vector data (entries and dimensions).
|
||||
* @property {function} version - Format model version number.
|
||||
*/
|
||||
export const formats = {
|
||||
const formats = {
|
||||
author: (author, url) => url ? `<a href="${url}" target="_blank">${author}</a>` : author,
|
||||
license: (license, url) => url ? `<a href="${url}" target="_blank">${license}</a>` : license,
|
||||
sources: sources => (sources instanceof Array) ? sources.join(', ') : sources,
|
||||
pipeline: pipes => (pipes && pipes.length) ? pipes.map(p => `<code>${p}</code>`).join(', ') : '-',
|
||||
vectors: vec => vec ? `${abbrNumber(vec.keys)} keys, ${abbrNumber(vec.vectors)} unique vectors (${vec.width} dimensions)` : 'n/a',
|
||||
vectors: vec => formatVectors(vec),
|
||||
version: version => `<code>v${version}</code>`
|
||||
};
|
||||
|
||||
/**
|
||||
* Format word vectors data depending on contents.
|
||||
* @property {Object} data - The vectors object from the model's meta.json.
|
||||
*/
|
||||
const formatVectors = data => {
|
||||
if (!data) return 'n/a';
|
||||
if (Object.values(data).every(n => n == 0)) return 'context vectors only';
|
||||
const { keys, vectors: vecs, width } = data;
|
||||
return `${abbrNumber(keys)} keys, ${abbrNumber(vecs)} unique vectors (${width} dimensions)`;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Find the latest version of a model in a compatibility table.
|
||||
* @param {string} model - The model name.
|
||||
* @param {Object} compat - Compatibility table, keyed by spaCy version.
|
||||
*/
|
||||
export const getLatestVersion = (model, compat = {}) => {
|
||||
const getLatestVersion = (model, compat = {}) => {
|
||||
for (let [spacy_v, models] of Object.entries(compat)) {
|
||||
if (models[model]) return models[model][0];
|
||||
}
|
||||
|
|
@ -90,7 +102,7 @@ export class ModelLoader {
|
|||
const tpl = new Templater(modelId);
|
||||
tpl.get('table').removeAttribute('data-loading');
|
||||
tpl.get('error').style.display = 'block';
|
||||
for (let key of ['sources', 'pipeline', 'vectors', 'author', 'license']) {
|
||||
for (let key of ['sources', 'pipeline', 'vecs', 'author', 'license']) {
|
||||
tpl.get(key).parentElement.parentElement.style.display = 'none';
|
||||
}
|
||||
}
|
||||
|
|
@ -120,8 +132,8 @@ export class ModelLoader {
|
|||
if (author) tpl.fill('author', formats.author(author, url), true);
|
||||
if (license) tpl.fill('license', formats.license(license, this.licenses[license]), true);
|
||||
if (sources) tpl.fill('sources', formats.sources(sources));
|
||||
if (vectors) tpl.fill('vectors', formats.vectors(vectors));
|
||||
else tpl.get('vectors').parentElement.parentElement.style.display = 'none';
|
||||
if (vectors) tpl.fill('vecs', formats.vectors(vectors));
|
||||
else tpl.get('vecs').parentElement.parentElement.style.display = 'none';
|
||||
if (pipeline && pipeline.length) tpl.fill('pipeline', formats.pipeline(pipeline), true);
|
||||
else tpl.get('pipeline').parentElement.parentElement.style.display = 'none';
|
||||
}
|
||||
|
|
@ -223,8 +235,9 @@ export class ModelComparer {
|
|||
const version = getLatestVersion(name, this.compat);
|
||||
const modelName = `${name}-${version}`;
|
||||
return new Promise((resolve, reject) => {
|
||||
if (!version) reject();
|
||||
// resolve immediately if model already loaded, e.g. in this.models
|
||||
if (this.models[name]) resolve(this.models[name]);
|
||||
else if (this.models[name]) resolve(this.models[name]);
|
||||
else fetch(`${this.url}/meta/${modelName}.json`)
|
||||
.then(res => handleResponse(res))
|
||||
.then(json => json.ok ? resolve(this.saveModel(name, json)) : reject())
|
||||
|
|
@ -306,12 +319,13 @@ export class ModelComparer {
|
|||
this.tpl.fill(`size${i}`, size);
|
||||
this.tpl.fill(`desc${i}`, description || 'n/a');
|
||||
this.tpl.fill(`pipeline${i}`, formats.pipeline(pipeline), true);
|
||||
this.tpl.fill(`vectors${i}`, formats.vectors(vectors));
|
||||
this.tpl.fill(`vecs${i}`, formats.vectors(vectors));
|
||||
this.tpl.fill(`sources${i}`, formats.sources(sources));
|
||||
this.tpl.fill(`author${i}`, formats.author(author, url), true);
|
||||
this.tpl.fill(`license${i}`, formats.license(license, this.licenses[license]), true);
|
||||
// check if model accuracy or speed includes one of the pre-set keys
|
||||
for (let key of [...metaKeys, ...Object.keys(this.benchKeys.speed)]) {
|
||||
const allKeys = [].concat(...Object.entries(this.benchKeys).map(([_, v]) => Object.keys(v)));
|
||||
for (let key of allKeys) {
|
||||
if (accuracy[key]) this.tpl.fill(`${key}${i}`, accuracy[key].toFixed(2))
|
||||
else if (speed[key]) this.tpl.fill(`${key}${i}`, convertNumber(Math.round(speed[key])))
|
||||
else this.tpl.fill(`${key}${i}`, 'n/a')
|
||||
|
|
|
|||
|
|
@ -68,6 +68,7 @@
|
|||
"gpu": "words per second on GPU",
|
||||
"pipeline": "Processing pipeline components in order",
|
||||
"sources": "Sources of training data",
|
||||
"vecs": "Word vectors included in the model. Models that only support context vectors compute similarity via the tensors shared with the pipeline.",
|
||||
"benchmark_parser": "Parser accuracy",
|
||||
"benchmark_ner": "NER accuracy",
|
||||
"benchmark_speed": "Speed"
|
||||
|
|
|
|||
|
|
@ -53,6 +53,8 @@ div(data-tpl=TPL data-tpl-key="result" style="display: none")
|
|||
|
||||
for label in ["Version", "Size", "Pipeline", "Vectors", "Sources", "Author", "License"]
|
||||
- var field = label.toLowerCase()
|
||||
if field == "vectors"
|
||||
- field = "vecs"
|
||||
+row
|
||||
+cell.u-nowrap
|
||||
+label=label
|
||||
|
|
|
|||
|
|
@ -4,9 +4,9 @@ p
|
|||
| Similarity is determined by comparing #[strong word vectors] or "word
|
||||
| embeddings", multi-dimensional meaning representations of a word. Word
|
||||
| vectors can be generated using an algorithm like
|
||||
| #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. Most of spaCy's
|
||||
| #[+a("/models") default models] come with
|
||||
| #[strong 300-dimensional vectors] that look like this:
|
||||
| #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. spaCy's medium
|
||||
| #[code md] and large #[code lg] #[+a("/models") models] come with
|
||||
| #[strong multi-dimensional vectors] that look like this:
|
||||
|
||||
+code("banana.vector", false, false, 250).
|
||||
array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01,
|
||||
|
|
|
|||
|
|
@ -4,12 +4,9 @@
|
|||
| Dense, real valued vectors representing distributional similarity
|
||||
| information are now a cornerstone of practical NLP. The most common way
|
||||
| to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
|
||||
| family of algorithms. The default
|
||||
| #[+a("/models/en") English model] installs
|
||||
| 300-dimensional vectors trained on the
|
||||
| #[+a("http://commoncrawl.org") Common Crawl] corpus.
|
||||
| If you need to train a word2vec model, we recommend the implementation in
|
||||
| the Python library #[+a("https://radimrehurek.com/gensim/") Gensim].
|
||||
| family of algorithms. If you need to train a word2vec model, we recommend
|
||||
| the implementation in the Python library
|
||||
| #[+a("https://radimrehurek.com/gensim/") Gensim].
|
||||
|
||||
include ../_spacy-101/_similarity
|
||||
include ../_spacy-101/_word-vectors
|
||||
|
|
|
|||
Loading…
Reference in New Issue