Instructions to use dsfsi/PuoBERTa-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsfsi/PuoBERTa-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dsfsi/PuoBERTa-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dsfsi/PuoBERTa-NER") model = AutoModelForTokenClassification.from_pretrained("dsfsi/PuoBERTa-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: cc-by-4.0
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datasets:
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- masakhane/masakhaner2
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language:
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- tn
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metrics:
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- f1
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library_name: transformers
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pipeline_tag: token-classification
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---
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# PuoBERTa-NER: A Setswana Langage Model Finetuned on MasakhaNER-2 for Named Entity Recognition.
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license: cc-by-4.0
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datasets:
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- masakhane/masakhaner2
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- dsfsi/PuoData
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language:
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- tn
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metrics:
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- f1
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library_name: transformers
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pipeline_tag: token-classification
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tags:
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- ner
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- african nlp
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- africanlp
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---
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# PuoBERTa-NER: A Setswana Langage Model Finetuned on MasakhaNER-2 for Named Entity Recognition.
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