

## Intended uses & limitations

#### How to use

You can use this model with Transformers *pipeline* for NER.

```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("eolang/Swahili-NER-BertBase-Cased")
model = AutoModelForTokenClassification.from_pretrained("eolang/Swahili-NER-BertBase-Cased")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Kwa nini Kenya inageukia mazao ya GMO kukabiliana na ukame"

ner_results = nlp(example)
print(ner_results)
```

## Training data

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


## Training procedure

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.
{"language": ["sw"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["wikiann"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "token-classification", "widget": [{"text": "Serikali imetangaza hali ya janga katika wilaya 10 za kusini ambazo zimeathiriwa zaidi na dhoruba.", "example_title": "Sentence_1"}, {"text": "Asidi ya mafuta ya Omega-3 inachukuliwa kuwa muhimu kwa mwili wa binadamu.", "example_title": "Sentence_2"}, {"text": "Tahadhari yatolewa kuhusu uwezekano wa mlipuko wa Volkano DR Congo.", "example_title": "Sentence_3"}]}
transformers pytorch bert token-classification sw dataset:wikiann arxiv:1810.04805 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #sw #dataset-wikiann #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 



## Intended uses & limitations

#### How to use

You can use this model with Transformers *pipeline* for NER.



## Training data

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


## Training procedure

This model was trained on a single NVIDIA A 5000 GPU with recommended hyperparameters from the original BERT paper which trained & evaluated the model on CoNLL-2003 NER task.
## Intended uses & limitations #### How to use

You can use this model with Transformers *pipeline* for NER. ## Training data

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 ## Training procedure

This model was trained on a single NVIDIA A 5000 GPU with recommended hyperparameters from the original BERT paper which trained & evaluated the model on CoNLL-2003 NER task.
TAGS
#transformers #pytorch #bert #token-classification #sw #dataset-wikiann #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Intended uses & limitations #### How to use

You can use this model with Transformers *pipeline* for NER. ## Training data

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 ## Training procedure

This model was trained on a single NVIDIA A 5000 GPU with recommended hyperparameters from the original BERT paper which trained & evaluated the model on CoNLL-2003 NER task.
TAGS
#transformers #pytorch #bert #token-classification #sw #dataset-wikiann #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## Intended uses & limitations#### How to use

You can use this model with Transformers *pipeline* for NER.## Training data

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## Training procedure

This model was trained on a single NVIDIA A 5000 GPU with recommended hyperparameters from the original BERT paper which trained & evaluated the model on CoNLL-2003 NER task.