Update README.md

README.md

@@ -1,61 +1,24 @@
----
-license: mit
-base_model: indobenchmark/indobert-large-p2
-tags:
-- generated_from_trainer
-model-index:
-- name: Indonesian-LegalBERT-lite
-  results: []
----
+# Model Card for Indo-LegalBERT
 
-<!-- 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. -->
+<!-- Provide a quick summary of what the model is/does. [Optional] -->
+Adaptasi IndoBERT pada korpus hukum sebesar 10.000 dokumen yang terdiri dari dokumen peraturan perundang-undangan Indonesia. Model ini memungkinkan penerapannya di berbagai downstream task di bidang hukum seperti klasifikasi dokumen hukum, menemukenali ketidakselarasan peraturan perundang-undangan, dan masih banyak lagi!
 
-# Indonesian-LegalBERT-lite
 
-This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.4760
 
-## Model description
+# Model Details
 
-More information needed
+## Model Description
 
-## Intended uses & limitations
+<!-- Provide a longer summary of what this model is/does. -->
+Adaptasi IndoBERT pada korpus hukum sebesar 10.000 dokumen yang terdiri dari dokumen peraturan perundang-undangan Indonesia. Model ini memungkinkan penerapannya di berbagai downstream task di bidang hukum seperti klasifikasi dokumen hukum, menemukenali ketidakselarasan peraturan perundang-undangan, dan masih banyak lagi!
 
-More information needed
+- **Developed by:** Maula Irfani, Netri Alia Rahmi, Edric Boby Tri Raharjo
+- **Model type:** Language model
+- **Language(s) (NLP):** id
+- **License:** mit
+- **Parent Model:** More information needed
+- **Resources for more information:** More information needed
 
-## Training and evaluation data
+# How to Get Started with the Model
 
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 128
-- eval_batch_size: 128
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 5
-
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss |
-|:-------------:|:-----:|:-----:|:---------------:|
-| 2.4828        | 1.0   | 2266  | 1.7891          |
-| 1.6374        | 2.0   | 4532  | 1.6163          |
-| 1.499         | 3.0   | 6798  | 1.5313          |
-| 1.4269        | 4.0   | 9064  | 1.4926          |
-| 1.3913        | 5.0   | 11330 | 1.4768          |
-
-
-### Framework versions
-
-- Transformers 4.31.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.0
-- Tokenizers 0.13.3
+Use the code below to get started with the model.