Update README.md

README.md

@@ -14,4 +14,63 @@ widget:
 - text: "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n’u Rwanda, bushingiye nanone ku bufatanye hagati y’imigabane ya Afurika n’u Burayi."
 ---
 
-Model description
+# Model description
+**mbert-base-uncased-ner-kin** is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities:
+
+- dates & time (DATE)
+- Location (LOC)
+- Organizations (ORG)
+- Person (PER)
+
+# Intended Use
+- Intended to be used for research purposes concerning Named Entity Recognition for African Languages.
+- Not intended for practical purposes.
+
+# Training Data
+This model was fine-tuned on the Kinyarwanda corpus **(kin)** of the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups.
+
+# Training procedure
+This model was trained on a single NVIDIA P5000 from [Paperspace](https://www.paperspace.com)
+#### Hyperparameters
+- **Learning Rate:** 5e-5
+- **Batch Size:** 32
+- **Maximum Sequence Length:** 164
+- **Epochs:** 30
+
+
+# Evaluation Data
+We evaluated this model on the test split of the Kinyarwandan corpus **(kin)** present in the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) with no thresholding.
+
+# Metrics
+- Precision
+- Recall
+- F1-score
+
+# Limitations
+- The size of the pre-trained language model prevents its usage in anything other than research.
+- Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
+- The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.
+
+# Caveats and Recommendations
+- The topics in the dataset corpus are centered around **News**. Future training could be done with a more diverse corpus.
+
+# Results
+Model Name| Precision | Recall | F1-score
+-|-|-|-
+**mbert-base-uncased-ner-kin**| 81.35 |83.98 |82.64
+
+# Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-ner-kin")
+model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-ner-kin")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Rayon Sports yasinyishije rutahizamu w’Umurundi"
+
+ner_results = nlp(example)
+print(ner_results)
+```