updating readme

README.md

@@ -1 +1,62 @@
-hello
+Hugging Face's logo
+---
+language: amh, hau, ibo, kin, lug, luo, pcm, swa, wol, yor
+datasets:
+- masakhaner
+---
+# xlm-roberta-large-masakhaner
+## Model description
+**xlm-roberta-large-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned  XLM-RoBERTa large model.  It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER). 
+Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. 
+## Intended uses & limitations
+#### How to use
+You can use this model with Transformers *pipeline* for NER.
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-large-masakhaner")
+model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-large-masakhaner")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
+ner_results = nlp(example)
+print(ner_results)
+```
+#### Limitations and bias
+This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.  
+## Training data
+This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
+
+The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-DATE |Beginning of a DATE entity right after another DATE entity
+I-DATE |DATE entity
+B-PER |Beginning of a person’s name right after another person’s name
+I-PER |Person’s name
+B-ORG |Beginning of an organisation right after another organisation
+I-ORG |Organisation
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
+## Training procedure
+This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper]() which trained & evaluated the model on MasakhaNER corpus. 
+## Eval results on Test set (F-score)
+language|F1-score
+-|-
+amh |75.76
+hau |91.75
+ibo |86.26
+kin |76.38
+lug |84.64
+luo |80.65
+pcm |89.55
+swa |89.48
+wol |70.70
+yor |82.05
+
+### BibTeX entry and citation info
+```
+
+```
+
+