Token Classification
spaCy
Tagalog
Eval Results
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@@ -57,8 +57,24 @@ model-index:
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  - name: Sentences F-Score
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  type: f_score
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  value: 0.9783715013
 
 
 
 
 
 
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  ---
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- Parsers for UD-NewsCrawl
 
 
 
 
 
 
 
 
 
 
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  | Feature | Description |
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  | --- | --- |
 
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  - name: Sentences F-Score
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  type: f_score
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  value: 0.9783715013
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+ datasets:
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+ - UD-Filipino/UD_Tagalog-NewsCrawl
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+ base_model:
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+ - jcblaise/roberta-tagalog-large
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+ pipeline_tag: token-classification
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+ library_name: spacy
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  ---
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+
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+ <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/634e20a0c1ce28f1de920cc4/k7SJny1M3lDa5CH_T1bp3.png" width="130" height="130" align="right" />
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+
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+ # UD Parser (Monolingual context-sensitive vectors + transition-based parser)
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+
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+ This is the spaCy pipeline trained on [UD-NewsCrawl](https://huggingface.co/datasets/UD-Filipino/UD_Tagalog-NewsCrawl).
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+ It uses context-sensitive vectors from [jcbalise/roberta-tagalog-large](https://huggingface.co/jcblaise/roberta-tagalog-large).
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+ It is trained using a transition-based parser based on [Honnibal and Johnson (2015)](https://aclanthology.org/D15-1162/) and can perform dependency parsing, lemmatization, and morphological annotation.
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+ The trainable lemmatizer is based on [Muller et al. (2015)](https://aclanthology.org/D15-1272/).
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+ More information can be found [in this blog post](https://explosion.ai/blog/edit-tree-lemmatizer).
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+
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  | Feature | Description |
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  | --- | --- |