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106 lines
3.4 KiB
Plaintext
106 lines
3.4 KiB
Plaintext
//- 💫 DOCS > USAGE > FACTS & FIGURES > BENCHMARKS > MODEL COMPARISON
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| In this section, we provide benchmark accuracies for the pre-trained
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| model pipelines we distribute with spaCy. Evaluations are conducted
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| end-to-end from raw text, with no "gold standard" pre-processing, over
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| text from a mix of genres where possible. For are more detailed
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| comparison of the available models, see the new
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| #[+a("/models/comparison") model comparison tool].
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+aside("Methodology")
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| The evaluation was conducted on raw text with no gold standard
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| information. The parser, tagger and entity recognizer were trained on the
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| #[+a("https://www.gabormelli.com/RKB/OntoNotes_Corpus") OntoNotes 5]
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| corpus, the word vectors on #[+a("http://commoncrawl.org") Common Crawl].
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+h(4, "benchmarks-models-english") English
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+table(["Model", "spaCy", "Type", "UAS", "NER F", "POS", "WPS", "Size"])
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+row
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+cell #[+a("/models/en#en_core_web_sm") #[code en_core_web_sm]] 2.0.0
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+cell("num") 2.x
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+cell neural
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+cell("num") 91.7
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+cell("num") 85.3
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+cell("num") 97.0
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+cell("num") 10.1k
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+cell("num") #[strong 35MB]
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+row
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+cell #[+a("/models/en#en_core_web_md") #[code en_core_web_md]] 2.0.0
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+cell("num") 2.x
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+cell neural
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+cell("num") 91.7
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+cell("num") #[strong 85.9]
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+cell("num") 97.1
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+cell("num") 10.0k
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+cell("num") 115MB
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+row
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+cell #[+a("/models/en#en_core_web_lg") #[code en_core_web_lg]] 2.0.0
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+cell("num") 2.x
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+cell neural
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+cell("num") #[strong 91.9]
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+cell("num") #[strong 85.9]
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+cell("num") #[strong 97.2]
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+cell("num") 10.0k
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+cell("num") 812MB
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+row("divider")
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+cell #[code en_core_web_sm] 1.2.0
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+cell("num") 1.x
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+cell linear
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+cell("num") 86.6
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+cell("num") 78.5
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+cell("num") 96.6
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+cell("num") #[strong 25.7k]
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+cell("num") 50MB
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+row
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+cell #[code en_core_web_md] 1.2.1
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+cell("num") 1.x
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+cell linear
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+cell("num") 90.6
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+cell("num") 81.4
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+cell("num") 96.7
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+cell("num") 18.8k
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+cell("num") 1GB
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+h(4, "benchmarks-models-spanish") Spanish
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+aside("Evaluation note")
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| The NER accuracy refers to the "silver standard" annotations in the
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| WikiNER corpus. Accuracy on these annotations tends to be higher than
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| correct human annotations.
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+table(["Model", "spaCy", "Type", "UAS", "NER F", "POS", "WPS", "Size"])
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+row
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+cell #[+a("/models/es#es_core_news_sm") #[code es_core_news_sm]] 2.0.0
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+cell("num") 2.x
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+cell("num") neural
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+cell("num") 89.8
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+cell("num") 88.7
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+cell("num") #[strong 96.9]
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+cell("num") #[em n/a]
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+cell("num") #[strong 35MB]
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+row
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+cell #[+a("/models/es#es_core_news_md") #[code es_core_news_md]] 2.0.0
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+cell("num") 2.x
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+cell("num") neural
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+cell("num") #[strong 90.2]
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+cell("num") 89.0
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+cell("num") 97.8
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+cell("num") #[em n/a]
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+cell("num") 93MB
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+row("divider")
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+cell #[code es_core_web_md] 1.1.0
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each data in ["1.x", "linear", 87.5]
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+cell("num")=data
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+cell("num") #[strong 94.2]
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+cell("num") 96.7
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+cell("num") #[em n/a]
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+cell("num") 377MB
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