update model card README.md

README.md

@@ -10,27 +10,50 @@ model-index:
   results: []
 ---
 
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
 # Kemenkeu-Sentiment-Classifier
 
-This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the MoF-DAC Mini Challenge#1 dataset.
+This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8224
-- Accuracy: 0.6857
-- F1: 0.6429
+- Loss: 0.8733
+- Accuracy: 0.64
+- F1: 0.5936
+
+## Model description
+
+More information needed
 
 ## Intended uses & limitations
 
-- This model can be used to classify text with four possible outputs [netral, tdk-relevan, negatif, and positif]
-- Only for specific cases related to the Ministry Of Finance Indonesia
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 6
+- eval_batch_size: 6
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
 
-## Training results
+### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| No log        | 1.0   | 394  | 0.8581          | 0.6857   | 0.6268 |
-| 0.9155        | 2.0   | 788  | 0.8224          | 0.6857   | 0.6429 |
-| 0.5238        | 3.0   | 1182 | 0.9928          | 0.6771   | 0.6459 |
-| 0.2662        | 4.0   | 1576 | 1.1901          | 0.6829   | 0.6572 |
+| 1.0146        | 1.0   | 500  | 0.8733          | 0.64     | 0.5936 |
+| 0.7047        | 2.0   | 1000 | 0.8814          | 0.634    | 0.6008 |
+| 0.5002        | 3.0   | 1500 | 0.9076          | 0.668    | 0.6446 |
+| 0.3531        | 4.0   | 2000 | 0.9730          | 0.664    | 0.6374 |
 
 
 ### Framework versions