Push Keras model using huggingface_hub.

.gitattributes

@@ -36,3 +36,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 model[[:space:]]v3.keras filter=lfs diff=lfs merge=lfs -text
 model-v3.keras filter=lfs diff=lfs merge=lfs -text
 model.keras filter=lfs diff=lfs merge=lfs -text
+variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,30 +1,50 @@
 ---
-language:
-- id
-datasets:
-- mabzak/indonesia-youtube-comment
-widget:
- - text: "Saya suka"
+library_name: keras
 ---
 
-# Generasi Komentar YouTube dengan Model LSTM
+## Model description
 
-![License](https://img.shields.io/badge/License-MIT-green.svg)
+More information needed
 
-## Deskripsi
+## Intended uses & limitations
 
-Halaman ini berisi implementasi dan penggunaan model LSTM (Long Short-Term Memory) untuk menghasilkan komentar YouTube berikutnya berdasarkan teks masukan. Model ini dilatih menggunakan dataset komentar YouTube untuk memprediksi kata-kata atau frasa berikutnya dalam komentar.
+More information needed
 
-## Penggunaan
+## Training and evaluation data
 
-### Menghasilkan Komentar Baru
+More information needed
 
-Untuk memprediksi komentar berikutnya, Anda dapat menggunakan fungsi `predict_next_sentence` pada model LSTM. Berikut adalah contoh penggunaannya:
+## Training procedure
 
-```python
-seed_text = "Saya suka"
-predicted_sentence = predict_next_sentence(seed_text, model, tokenizer, max_sequence_length, num_words=9)
-print("Kalimat yang Diprediksi:", predicted_sentence)
-```
+### Training hyperparameters
 
-masih di edit
+The following hyperparameters were used during training:
+
+| Hyperparameters | Value |
+| :-- | :-- |
+| name | Adam |
+| weight_decay | None |
+| clipnorm | None |
+| global_clipnorm | None |
+| clipvalue | None |
+| use_ema | False |
+| ema_momentum | 0.99 |
+| ema_overwrite_frequency | None |
+| jit_compile | True |
+| is_legacy_optimizer | False |
+| learning_rate | 0.0010000000474974513 |
+| beta_1 | 0.9 |
+| beta_2 | 0.999 |
+| epsilon | 1e-07 |
+| amsgrad | False |
+| training_precision | float32 |
+
+
+ ## Model Plot
+
+<details>
+<summary>View Model Plot</summary>
+
+![Model Image](./model.png)
+
+</details>

fingerprint.pb

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