Update README.md

README.md

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+Hugging Face's logo
 ---
-license: apache-2.0
+language: 
+- ar
+- as
+- bn
+- ca
+- en
+- es
+- eu
+- fr
+- gu
+- hi
+- id
+- ig
+- mr
+- pa
+- pt
+- sw
+- ur
+- vi
+- yo
+- zh
+- multilingual
+
+
+datasets:
+- wikiann
 ---
+# xlm-roberta-base-wikiann-ner
+## Model description
+**xlm-roberta-base-wikiann-ner** is the first **Named Entity Recognition** model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi,  Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) 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 three types of entities: location (LOC), organizations (ORG), and person (PER). 
+Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of  languages datasets obtained from  [WikiANN](https://huggingface.co/datasets/wikiann) 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("xlm-roberta-base-wikiann-ner")
+model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-base-wikiann-ner")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Ìbọn ń ró kù kù gẹ́gẹ́ bí ọwọ́ ọ̀pọ̀ aráàlù ṣe tẹ ìbọn ní Kyiv láti dojú kọ Russia"
+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 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi,  Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)[wikiann](https://huggingface.co/datasets/wikiann). 
+
+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-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
+
+### BibTeX entry and citation info
+
+```
+
+
+