updating hausa readme

README.md

@@ -1,43 +1,45 @@
 Hugging Face's logo
 ---
-language: yo
+language: ha
 datasets:
 
 ---
-# bert-base-multilingual-cased-finetuned-yoruba
+# bert-base-multilingual-cased-finetuned-hausa
 ## Model description
-**bert-base-multilingual-cased-finetuned-yoruba** is a **Yoruba BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Yorùbá language texts.  It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.  
+**bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa language texts.  It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets.  
 
-Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Yorùbá corpus. 
+Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Hausa corpus. 
 ## Intended uses & limitations
 #### How to use
 You can use this model with Transformers *pipeline* for masked token prediction.
 ```python
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba')
->>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")
-
-[{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Mary Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.1738305538892746, 
-'token': 12176, 
-'token_str': 'Mary'}, 
-{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.16382873058319092, 
-'token': 13704, 
-'token_str': 'Queen'}, 
-{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.13272495567798615, 
-'token': 14382, 
-'token_str': 'ti'}, 
-{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ King Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.12823280692100525, 
-'token': 11515, 
-'token_str': 'King'}, 
-{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Lady Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.07841219753026962, 
-'token': 14005, 
-'token_str': 'Lady'}]
+>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-hausa')
+>>> unmasker("Shugaban [MASK] Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci")
+                    
+[{'sequence': 
+'[CLS] Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 
+'score': 0.9762618541717529, 
+'token': 22045, 
+'token_str': 'Nigeria'}, 
+{'sequence': '[CLS] Shugaban Ka Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.007239189930260181, 
+'token': 25444, 
+'token_str': 'Ka'}, 
+{'sequence': '[CLS] Shugaban, Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001990817254409194, 
+'token': 117, 
+'token_str': ','}, 
+{'sequence': '[CLS] Shugaban Ghana Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001566368737258017, 
+'token': 28682, 
+'token_str': 'Ghana'}, 
+{'sequence': '[CLS] Shugabanmu Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.0009375187801197171, 
+'token': 11717, 
+'token_str': '##mu'}]
 
 ```
 #### 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 Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
+This model was fine-tuned on [Hausa CC-100] (http://data.statmt.org/cc-100/)
 
 ## Training procedure
 This model was trained on a single NVIDIA V100 GPU
@@ -45,9 +47,9 @@ This model was trained on a single NVIDIA V100 GPU
 ## Eval results on Test set (F-score)
 Dataset|F1-score
 -|-
-Yoruba GV NER |75.34
+Hausa GV NER |80.34
 MasakhaNER |80.82
-BBC Yoruba |80.66
+VOA Hausa Textclass |80.66
 
 ### BibTeX entry and citation info
 By David Adelani