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--- |
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language: |
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- sw |
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license: apache-2.0 |
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datasets: |
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- wikiann |
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pipeline_tag: token-classification |
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examples: null |
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widget: |
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- text: Serikali imetangaza hali ya janga katika wilaya 10 za kusini ambazo zimeathiriwa zaidi na dhoruba. |
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example_title: Sentence_1 |
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- text: Asidi ya mafuta ya Omega-3 inachukuliwa kuwa muhimu kwa mwili wa binadamu. |
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example_title: Sentence_2 |
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- text: Tahadhari yatolewa kuhusu uwezekano wa mlipuko wa Volkano DR Congo. |
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example_title: Sentence_3 |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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library_name: transformers |
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--- |
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## Intended uses & limitations |
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#### How to use |
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You can use this model with Transformers *pipeline* for NER. |
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```python |
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from transformers import pipeline |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("eolang/Swahili-NER-BertBase-Cased") |
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model = AutoModelForTokenClassification.from_pretrained("eolang/Swahili-NER-BertBase-Cased") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Kwa nini Kenya inageukia mazao ya GMO kukabiliana na ukame" |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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## Training data |
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This model was fine-tuned on the Swahili Version of the WikiAnn dataset for cross-lingual name tagging and linking based on Wikipedia articles in 295 languages |
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## Training procedure |
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This model was trained on a single NVIDIA A 5000 GPU with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805) which trained & evaluated the model on CoNLL-2003 NER task. |
