Update README.md

README.md

@@ -14,24 +14,44 @@ should probably proofread and complete it, then remove this comment. -->
 
 # xlm-roberta-base-finetuned-language-detection-new
 
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification dataset](https://huggingface.co/datasets/papluca/language-identification).
+
 It achieves the following results on the evaluation set:
 - Loss: 0.0436
 - Accuracy: 0.9959
 
 ## Model description
 
-More information needed
+The model used in this task is XLM-RoBERTa transformer model with a classification head on top.
 
 ## Intended uses & limitations
 
-More information needed
+It identifies the language a document is written in and it supports 20 different langauges:
+
+* Arabic (ar)
+* Bulgarian (bg)
+* German (de)
+* Modern greek (el)
+* English (en)
+* Spanish (es)
+* French (fr)
+* Hindi (hi)
+* Italian (it)
+* Japanese (ja)
+* Dutch (nl)
+* Polish (pl)
+* Portuguese (pt)
+* Russian (ru)
+* Swahili (sw)
+* Thai (th)
+* Turkish (tr)
+* Urdu (ur)
+* Vietnamese (vi)
+* Chinese (zh)
 
 ## Training and evaluation data
 
-More information needed
-
-## Training procedure
+The model is fine-tuned on the [Language Identification dataset](https://huggingface.co/datasets/papluca/language-identification), a corpus consists of text from 20 different languages. The dataset is split with 7000 sentences for training, 1000 for validating, and 1000 for testing. The accuracy on the test set is 99.5%.
 
 ### Training hyperparameters