Update README.md

README.md

@@ -4,11 +4,56 @@ widget:
 - text: "Pelayanan lama dan tidak ramah."
   example_title: "Sentiment analysis"
 ---
-| Hyperparameter | Default value |
-|---|---|
-| batch_size | 32 |
-| lr | 1e-4 |
-| eps | 1e-8 |
-| epochs | 3 |
-| step_size | 1 |
-| gamma | 0.1 |
+
+### Training hyperparameters
+* train_batch_size: 32 
+* eval_batch_size: 32
+* learning_rate: 1e-4
+* optimizer: AdamW with betas=(0.9, 0.999), eps=1e-8, and weight_decay=0.01
+* epochs: 3
+* learning_rate_scheduler: StepLR with step_size=592, gamma=0.1
+
+### Training Results
+
+The following table shows the training results for the model:
+
+| Epoch | Loss | Accuracy |
+|---|---|---|
+| 1 | 0.2936 | 0.9310 |
+| 2 | 0.1212 | 0.9526 |
+| 3 | 0.0795 | 0.9569 |
+
+### How to Use
+
+You can load the model and perform inference as follows:
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("taufiqdp/indonesian-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("taufiqdp/indonesian-sentiment")
+
+class_names = ['negatif', 'netral', 'positif']
+
+text = "Pelayanan lama dan tidak ramah"
+tokenized_text = tokenizer(text, return_tensors='pt')
+
+with torch.inference_mode():
+    logits = model(**tokenized_text)['logits']
+
+result = class_names[logits.argmax(dim=1)]
+print(result)
+```
+
+### Citation
+
+```
+@misc{koto2020indolem,
+      title={IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP}, 
+      author={Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin},
+      year={2020},
+      eprint={2011.00677},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```