Create README.md

README.md

@@ -0,0 +1,50 @@
+---
+language:
+- sw
+license: apache-2.0
+datasets:
+- wikiann
+pipeline_tag: token-classification
+examples: null
+widget:
+- text: Uhuru Kenyatta ni rais wa Kenya.
+  example_title: Sentence_1
+- text: Tumefanya mabadiliko muhimu za sera zetu za faragha na vidakuzi
+  example_title: Sentence_2
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+library_name: transformers
+---
+
+
+## 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.