updating readme

README.md

@@ -1,57 +1,54 @@
 Hugging Face's logo
 ---
-language: ha
+language: yo
 datasets:
 
 ---
-# xlm-roberta-base-finetuned-swahili
+# xlm-roberta-base-finetuned-yoruba
 ## Model description
-**xlm-roberta-base-finetuned-swahili** is a **Swahili RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Swahili language texts.  It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.  
+**xlm-roberta-base-finetuned-yoruba** is a **Yoruba RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Yorùbá language texts.  It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets.  
 
-Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus. 
+Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Yorùbá 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/xlm-roberta-base-finetuned-swahili')
->>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa")
+>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-yoruba')
+>>> unmasker("Arẹmọ Phillip to jẹ ọkọ <mask> Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")
                     
-[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa', 
-'score': 0.5077782273292542, 
-'token': 190096, 
-'token_str': 'Ufaransa'}, 
-{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 
-'score': 0.3657738268375397, 
-'token': 7270, 
-'token_str': 'Paris'}, 
-{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa', 
-'score': 0.01592041552066803, 
-'token': 176392, 
-'token_str': 'Gabon'}, 
-{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 
-'score': 0.010881908237934113, 
-'token': 9942, 
-'token_str': 'France'}, 
-{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa', 
-'score': 0.009554869495332241, 
-'token': 185918, 
-'token_str': 'Marseille'}]
+[{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.24844281375408173, 
+'token': 44109, 
+'token_str': '▁Queen'}, 
+{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ile Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.1665010154247284, 
+'token': 1350, 
+'token_str': '▁ile'}, 
+{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.07604238390922546, 
+'token': 1053, 
+'token_str': '▁ti'}, 
+{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ baba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.06353845447301865, 
+'token': 12878, 
+'token_str': '▁baba'}, 
+{'sequence': '<s> Arẹmọ Phillip to jẹ ọkọ Oba Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun</s>', 'score': 0.03836742788553238, 
+'token': 82879, 
+'token_str': '▁Oba'}]
+
 
 
 ```
 #### 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 [Swahili CC-100](http://data.statmt.org/cc-100/)
+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.
 
 ## Training procedure
 This model was trained on a single NVIDIA V100 GPU
 
 ## Eval results on Test set (F-score, average over 5 runs)
-Dataset| XLM-R F1 | sw_roberta F1
+Dataset| XLM-R F1 | yo_roberta F1
 -|-|-
-[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.37 | 89.74 
+[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 77.58 | 83.66 
+[BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) |  | 
 
 ### BibTeX entry and citation info
 By David Adelani