Instructions to use marcosgg/bert-base-gl-SLI-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcosgg/bert-base-gl-SLI-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="marcosgg/bert-base-gl-SLI-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("marcosgg/bert-base-gl-SLI-NER") model = AutoModelForTokenClassification.from_pretrained("marcosgg/bert-base-gl-SLI-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Named Entity Recognition (NER) model for Galician
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This is a NER model for Galician (ILG/RAG spelling) which uses the standard
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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.
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# Named Entity Recognition (NER) model for Galician
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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).
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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.
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