Create README.md

README.md

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+---
+language:
+- sw
+license: apache-2.0
+datasets:
+- masakhaner
+pipeline_tag: token-classification
+examples: null
+widget:
+  - text: Joe Bidden ni rais wa marekani.
+    example_title: Sentence 1
+  - text: Tumefanya mabadiliko muhimu katika sera zetu za faragha na vidakuzi.
+    example_title: Sentence 2
+  - text: Mtoto anaweza kupoteza muda kabisa.
+    example_title: Sentence 3
+metrics:
+- accuracy
+---
+
+# TUS Named Entity Recognition
+
+- **TUS-NER-sw** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance 😀** 
+- Finetuned from model: [eolang/SW-v1](https://huggingface.co/eolang/SW-v1)
+
+## 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/SW-NER-v1")
+model = AutoModelForTokenClassification.from_pretrained("eolang/SW-NER-v1")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Tumefanya mabadiliko muhimu katika sera zetu za faragha na vidakuzi"
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
+## Training data
+
+This model was fine-tuned on the Swahili Version of the [Masakhane Dataset](https://github.com/masakhane-io/masakhane-ner/tree/main/MasakhaNER2.0/data/swa) from the [MasakhaneNER Project](https://github.com/masakhane-io/masakhane-ner).
+MasakhaNER is a collection of Named Entity Recognition (NER) datasets for 10 different African languages.
+The languages forming this dataset are: Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Luo, Nigerian-Pidgin, Swahili, Wolof, and Yorùbá.
+
+
+## Training procedure
+
+This model was trained on a single NVIDIA RTX 3090 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.