update model card README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: mit
 tags:
 - generated_from_trainer
 datasets:
@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.5795454545454546
+      value: 0.7363636363636363
     - name: Precision
       type: precision
-      value: 0.5972816727924745
+      value: 0.7397155596092384
     - name: Recall
       type: recall
-      value: 0.5821402372137666
+      value: 0.7459489407651173
     - name: F1
       type: f1
-      value: 0.587634338762031
+      value: 0.741920437379511
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # sentiment_model
 
-This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the indonlu dataset.
+This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0627
-- Accuracy: 0.5795
-- Precision: 0.5973
-- Recall: 0.5821
-- F1: 0.5876
+- Loss: 0.7788
+- Accuracy: 0.7364
+- Precision: 0.7397
+- Recall: 0.7459
+- F1: 0.7419
 
 ## Model description
 
@@ -78,9 +78,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 1.3736        | 1.0   | 221  | 1.2279          | 0.475    | 0.4466    | 0.4635 | 0.4100 |
-| 1.0778        | 2.0   | 442  | 1.1328          | 0.5477   | 0.5786    | 0.5496 | 0.5452 |
-| 0.8717        | 3.0   | 663  | 1.0627          | 0.5795   | 0.5973    | 0.5821 | 0.5876 |
+| 1.1939        | 1.0   | 221  | 0.8261          | 0.6932   | 0.7203    | 0.7034 | 0.7056 |
+| 0.6866        | 2.0   | 442  | 0.7925          | 0.725    | 0.7378    | 0.7377 | 0.7346 |
+| 0.4791        | 3.0   | 663  | 0.7788          | 0.7364   | 0.7397    | 0.7459 | 0.7419 |
 
 
 ### Framework versions