Update README.md

README.md

@@ -12,6 +12,12 @@ metrics:
 model-index:
 - name: indobert-lite-base-p2
   results: []
+datasets:
+- indonlp/indonlu
+- SEACrowd/id_google_play_review
+language:
+- id
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -20,24 +26,36 @@ should probably proofread and complete it, then remove this comment. -->
 # indobert-lite-base-p2
 
 This model is a fine-tuned version of [indobenchmark/indobert-lite-base-p2](https://huggingface.co/indobenchmark/indobert-lite-base-p2) on the None dataset.
-It achieves the following results on the evaluation set:
+It achieves the following results on the evaluation set for combined dataset from indonlu-smsa and id_google_play_review:
 - Loss: 0.4257
 - Accuracy: 0.9291
 - Precision: 0.8637
 - Recall: 0.8651
 - F1: 0.8643
 
+Seperate evaluation
+indonlu/indonlu-smsa
+- Accuracy: 0.9269
+- Precision: 0.9067
+- Recall: 0.8948
+- F1: 0.89995
+
 ## Model description
 
-More information needed
+https://huggingface.co/indobenchmark/indobert-lite-base-p2
+
+To Do:
+- Add optimized model from optimum
 
 ## Intended uses & limitations
 
-More information needed
+Sentiment Analysis, this model more lightweight than bert base and roberta base
+ofc because this is lite model haha
 
 ## Training and evaluation data
 
-More information needed
+The training combined all training data from indonlu-smsa and id google play review
+The evaluation is conducted using the validation split
 
 ## Training procedure
 
@@ -68,4 +86,4 @@ The following hyperparameters were used during training:
 - Transformers 4.48.2
 - Pytorch 2.6.0+cu126
 - Datasets 3.2.0
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0