Add widget examples + link to cased model
#1
by
tomaarsen
HF staff
- opened
README.md
CHANGED
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@@ -33,6 +33,51 @@ metrics:
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- recall
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- f1
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pipeline_tag: token-classification
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language:
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- de
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- en
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@@ -52,7 +97,11 @@ should probably proofread and complete it, then remove this comment. -->
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# span-marker-bert-base-multilingual-uncased-multinerd
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This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an [Babelscape/multinerd](https://huggingface.co/datasets/Babelscape/multinerd) dataset.
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-
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- Loss: 0.0054
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- Overall Precision: 0.9275
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- Overall Recall: 0.9147
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- recall
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- f1
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pipeline_tag: token-classification
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widget:
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- text: "amelia earthart flog mit ihrer einmotorigen lockheed vega 5b über den atlantik nach paris."
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example_title: "German"
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- text: "amelia earhart flew her single engine lockheed vega 5b across the atlantic to paris."
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example_title: "English"
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- text: "amelia earthart voló su lockheed vega 5b monomotor a través del océano atlántico hasta parís."
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example_title: "Spanish"
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- text: "amelia earthart a fait voler son monomoteur lockheed vega 5b à travers l'ocean atlantique jusqu'à paris."
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example_title: "French"
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- text: "amelia earhart ha volato con il suo monomotore lockheed vega 5b attraverso l'atlantico fino a parigi."
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example_title: "Italian"
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- text: "amelia earthart vloog met haar één-motorige lockheed vega 5b over de atlantische oceaan naar parijs."
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example_title: "Dutch"
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- text: "amelia earthart przeleciała swoim jednosilnikowym samolotem lockheed vega 5b przez ocean atlantycki do paryża."
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example_title: "Polish"
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- text: "amelia earhart voou em seu monomotor lockheed vega 5b através do atlântico para paris."
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example_title: "Portuguese"
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- text: "амелия эртхарт перелетела на своем одномоторном самолете lockheed vega 5b через атлантический океан в париж."
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example_title: "Russian"
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- text: "amelia earthart flaug eins hreyfils lockheed vega 5b yfir atlantshafið til parísar."
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example_title: "Icelandic"
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- text: "η amelia earthart πέταξε το μονοκινητήριο lockheed vega 5b της πέρα από τον ατλαντικό ωκεανό στο παρίσι."
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example_title: "Greek"
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- text: "amelia earhartová přeletěla se svým jednomotorovým lockheed vega 5b přes atlantik do paříže."
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example_title: "Czech"
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- text: "amelia earhart lensi yksimoottorisella lockheed vega 5b:llä atlantin yli pariisiin."
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example_title: "Finnish"
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- text: "amelia earhart fløj med sin enmotoriske lockheed vega 5b over atlanten til paris."
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example_title: "Danish"
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- text: "amelia earhart flög sin enmotoriga lockheed vega 5b över atlanten till paris."
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example_title: "Swedish"
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- text: "amelia earhart fløy sin enmotoriske lockheed vega 5b over atlanterhavet til paris."
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example_title: "Norwegian"
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- text: "amelia earhart și-a zburat cu un singur motor lockheed vega 5b peste atlantic până la paris."
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example_title: "Romanian"
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- text: "amelia earhart menerbangkan mesin tunggal lockheed vega 5b melintasi atlantik ke paris."
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example_title: "Indonesian"
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- text: "амелія эрхарт пераляцела на сваім аднаматорным lockheed vega 5b праз атлантыку ў парыж."
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example_title: "Belarusian"
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- text: "амелія ергарт перелетіла на своєму одномоторному літаку lockheed vega 5b через атлантику до парижа."
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example_title: "Ukrainian"
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- text: "amelia earhart preletjela je svojim jednomotornim zrakoplovom lockheed vega 5b preko atlantika do pariza."
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example_title: "Croatian"
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- text: "amelia earhart lendas oma ühemootoriga lockheed vega 5b üle atlandi ookeani pariisi."
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example_title: "Estonian"
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language:
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- de
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- en
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# span-marker-bert-base-multilingual-uncased-multinerd
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This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an [Babelscape/multinerd](https://huggingface.co/datasets/Babelscape/multinerd) dataset.
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Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance:
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[lxyuan/span-marker-bert-base-multilingual-cased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-cased-multinerd).
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This model achieves the following results on the evaluation set:
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- Loss: 0.0054
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- Overall Precision: 0.9275
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- Overall Recall: 0.9147
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