Update readme

README.md

@@ -9,10 +9,54 @@ language:
 metrics:
 - seqeval
 widget:
+- text: >- 
+    Hala Madrid y nada más. It means Go Madrid and nothing more.
 - text: >- 
     Hallo, Guten Tag! how are you?
 - text: >- 
     Sie sind gut. How about you? Comment va ta mère? And what about your school? Estoy aprendiendo español. Thanks.
-- text: >- 
-    Hala Madrid y nada más. It means Hala Madrid and nothing more.
----
+---
+
+# Code-Mixed Language Detection using XLM-RoBERTa
+
+## Description
+This model detects Languages with its boundary by classifying each token. Currently, it supports German (DE), English (EN), Spanish (ES), and French (FR) languages.
+
+## Training Dataset
+The training dataset is based on [The Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi). The preprocessed dataset can be found [here](https://huggingface.co/datasets/msislam/marc-code-mixed-small).
+
+## Results
+
+```
+'DE': {'precision': 0.9870741390453328, 'recall': 0.9883516686696866, 'f1': 0.9877124907612713}
+'EN': {'precision': 0.9901617633147289, 'recall': 0.9914748508098892, 'f1': 0.9908178720181748}
+'ES': {'precision': 0.9912407007439404, 'recall': 0.9912407007439404, 'f1': 0.9912407007439406}
+'FR': {'precision': 0.9872469872469872, 'recall': 0.9871314927468414, 'f1': 0.9871892366188945}
+
+'overall_precision': 0.9888723454274744
+'overall_recall': 0.9895702634880803
+'overall_f1': 0.9892211813585232
+'overall_accuracy': 0.9993651810717168
+```
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+tokenizer = AutoTokenizer.from_pretrained("msislam/code-mixed-language-detection-XLMRoberta")
+
+model = AutoModelForTokenClassification.from_pretrained("msislam/code-mixed-language-detection-XLMRoberta")
+
+text = 'Hala Madrid y nada más. It means Go Madrid and nothing more.'
+
+tokens = tokenizer(text, add_special_tokens= False, return_tensors="pt")
+
+with torch.no_grad():
+  logits = model(**inputs).logits
+
+labels_predicted = logits.argmax(-1)
+
+lang_tag_predicted = [model_best.config.id2label[t.item()] for t in labels_predicted[0]]
+lang_tag_predicted
+```