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models/pretrain/fasttextwiki.pt 307 MBmodels/depparse/combined_camembert-large.pt 169 MBmodels/depparse/gsd_camembert-large.pt 165 MBmodels/depparse/rhapsodie_camembert-large.pt 162 MBmodels/depparse/sequoia_camembert-large.pt 161 MBmodels/depparse/parisstories_camembert-large.pt 160 MBmodels/depparse/partut_camembert-large.pt 157 MBmodels/depparse/combined_charlm.pt 149 MBmodels/depparse/alts_camembert-large.pt 147 MBmodels/depparse/gsd_charlm.pt 145 MBmodels/depparse/rhapsodie_charlm.pt 142 MBmodels/depparse/sequoia_charlm.pt 142 MBmodels/depparse/parisstories_charlm.pt 140 MBmodels/depparse/partut_charlm.pt 137 MBmodels/coref/udcoref_xlm-roberta-lora.pt 136 MBmodels/depparse/alts_charlm.pt 127 MBmodels/depparse/combined_nocharlm.pt 115 MBmodels/depparse/gsd_nocharlm.pt 112 MBmodels/depparse/rhapsodie_nocharlm.pt 108 MBmodels/depparse/sequoia_nocharlm.pt 108 MBmodels/pretrain/conll17.pt 107 MBmodels/depparse/parisstories_nocharlm.pt 106 MBmodels/depparse/partut_nocharlm.pt 103 MBmodels/depparse/alts_nocharlm.pt 94 MBmodels/ner/wikinergold_camembert-large.pt 93 MBmodels/ner/wikinergold_charlm.pt 63 MBmodels/pos/alts_camembert-large.pt 45 MBmodels/pos/combined_camembert-large.pt 44 MBmodels/pos/gsd_camembert-large.pt 44 MBmodels/pos/sequoia_camembert-large.pt 43 MBmodels/pos/rhapsodie_camembert-large.pt 42 MBmodels/pos/parisstories_camembert-large.pt 42 MBmodels/pos/partut_camembert-large.pt 42 MBmodels/pos/alts_charlm.pt 35 MBmodels/pos/combined_charlm.pt 35 MBmodels/pos/gsd_charlm.pt 34 MBmodels/pos/sequoia_charlm.pt 33 MBmodels/pos/rhapsodie_charlm.pt 33 MBmodels/pos/parisstories_charlm.pt 32 MBmodels/pos/partut_charlm.pt 32 MBmodels/pos/alts_nocharlm.pt 20 MBmodels/backward_charlm/newswiki.pt 20 MBmodels/forward_charlm/newswiki.pt 20 MBmodels/pos/combined_nocharlm.pt 20 MBmodels/pos/gsd_nocharlm.pt 19 MBmodels/pos/sequoia_nocharlm.pt 18 MBmodels/pos/rhapsodie_nocharlm.pt 18 MBmodels/pos/parisstories_nocharlm.pt 17 MBmodels/pos/partut_nocharlm.pt 17 MBmodels/lemma/combined_charlm.pt 9 MBmodels/lemma/gsd_charlm.pt 9 MBmodels/lemma/sequoia_charlm.pt 9 MBmodels/lemma/alts_charlm.pt 9 MBmodels/lemma/partut_charlm.pt 9 MBmodels/lemma/rhapsodie_charlm.pt 9 MBmodels/lemma/parisstories_charlm.pt 9 MBmodels/lemma/combined_nocharlm.pt 3 MBmodels/tokenize/combined_charlm.pt 3 MBmodels/tokenize/gsd_charlm.pt 3 MBmodels/tokenize/sequoia_charlm.pt 3 MBmodels/tokenize/partut_charlm.pt 3 MBmodels/tokenize/alts_charlm.pt 3 MBmodels/tokenize/parisstories_charlm.pt 3 MBmodels/tokenize/rhapsodie_charlm.pt 3 MBmodels/lemma/gsd_nocharlm.pt 3 MBmodels/lemma/sequoia_nocharlm.pt 2 MBmodels/lemma/alts_nocharlm.pt 2 MBmodels/lemma/partut_nocharlm.pt 2 MBmodels/lemma/rhapsodie_nocharlm.pt 2 MBmodels/lemma/parisstories_nocharlm.pt 2 MBmodels/tokenize/combined_nocharlm.pt 665.7 kBmodels/tokenize/combined.pt 664.9 kBmodels/tokenize/gsd_nocharlm.pt 664.5 kBmodels/tokenize/gsd.pt 664.3 kBmodels/tokenize/sequoia_nocharlm.pt 635.1 kBmodels/tokenize/sequoia.pt 634.5 kBmodels/tokenize/partut_nocharlm.pt 632.5 kBmodels/tokenize/partut.pt 632.2 kBmodels/tokenize/alts_nocharlm.pt 631.2 kBmodels/tokenize/parisstories_nocharlm.pt 629.6 kBmodels/tokenize/rhapsodie_nocharlm.pt 629.3 kBmodels/tokenize/parisstories.pt 629.2 kBmodels/tokenize/rhapsodie.pt 628.8 kBmodels/mwt/alts.pt 548 kBmodels/mwt/combined.pt 544.8 kBmodels/mwt/sequoia.pt 544.7 kBmodels/mwt/partut.pt 542.7 kBmodels/mwt/gsd.pt 541.6 kBmodels/mwt/rhapsodie.pt 541.3 kBmodels/mwt/parisstories.pt 540.1 kBStanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in our website and our GitHub repository.
This card and repo were automatically prepared with hugging_stanza.py in the stanfordnlp/huggingface-models repo
Last updated 2026-09-29 19:42:01.141