marcosgg
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bert-base-gl-SLI-NER

Token Classification

Inference Endpoints

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marcosgg commited on Feb 9, 2023

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 ---
-license: gpl-3.0
 language:
 - gl
 - pt
 widget:
-- text: "A miña amiga Rosa, de Lisboa, estudou en Montreal. Agora traballa en Nova Pescanova."
 ---
 # Named Entity Recognition (NER) model for Galician
 This is a NER model for Galician (ILG/RAG spelling) which uses the standard 'enamex' classes: LOC (geographical locations); PER (people); ORG (organizations); MISC (other entities).
-The model is based on [BERT-base-gl-cased](https://huggingface.co/marcosgg/bert-base-gl-cased), which has been fine-tuned using custom splits of the [SLI_NERC dataset](https://github.com/xavier-gz/SLI_Galician_Corpora). On the test split of this dataset (not used for training), the model obtained the following results (Precision/Recall/F-score): 87.69 / 89.7 / 88.68.

 ---
+license: agpl-3.0
 language:
 - gl
 - pt
 widget:
+- text: >-
+    A miña amiga Rosa, de Lisboa, estudou en Montreal. Agora traballa en Nova
+    Pescanova.
 ---
 # Named Entity Recognition (NER) model for Galician
 This is a NER model for Galician (ILG/RAG spelling) which uses the standard 'enamex' classes: LOC (geographical locations); PER (people); ORG (organizations); MISC (other entities).
+The model is based on [BERT-base-gl-cased](https://huggingface.co/marcosgg/bert-base-gl-cased), which has been fine-tuned using custom splits of the [SLI_NERC dataset](https://github.com/xavier-gz/SLI_Galician_Corpora). On the test split of this dataset (not used for training), the model obtained the following results (Precision/Recall/F-score): 87.69 / 89.7 / 88.68.