Update README.md

README.md

@@ -81,15 +81,33 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.
+
+- **Developed by:** See [associated paper](https://arxiv.org/abs/1911.02116)
+- **Model type:** Multi-lingual model
+- **Language(s) (NLP) or Countries (images):** XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in English
+- **License:** More information needed
+- **Related Models:** [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm)
+    - **Parent Model:** [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base)
+- **Resources for more information:** 
+  - [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr)
 
 ## Intended uses & limitations
 
-More information needed
+This model can be used to extract multilingual information such as location, date and time on social media (Twitter, etc.). This model is limited by an Indonesian language training data set to be tested in 4 languages (English, Spanish, Italian and Slovak) using zero-shot transfer learning techniques to extract multilingual information.
 
 ## Training and evaluation data
 
-More information needed
+This model was fine-tuned on Indonesian NER datasets.
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
+B-DAT Beginning of a date right after another date
+I-DAT |Date
+B-TIM |Beginning of a time right after another time
+I-TIM |Time
 
 ## Training procedure