Update README.md

README.md

@@ -22,7 +22,7 @@ metrics:
 - precision
 - recall
 model-index:
-- name: finetuned-indo-roBERTa-financial-sentiment
+- name: indo-roBERTa-financial-sentiment
   results:
   - task:
       type: text-classification
@@ -52,6 +52,21 @@ license: mit
 
 This is a fine-tuned version of [`w11wo/indonesian-roberta-base-sentiment-classifier`](https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier), specialized for **Indonesian financial news sentiment classification** since i cant find any financial sentiment models for indonesian market, i decided to make my self.
 
+### 🧠 Model Summary
+
+| Field             | Value                                                                 |
+|------------------|-----------------------------------------------------------------------|
+| **Model Name**    | `ihsan31415/indo-roBERTa-financial-sentiment`              |
+| **Base Model**    | [`w11wo/indonesian-roberta-base-sentiment-classifier`](https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier) |
+| **Language**      | Indonesian (`id`)                                                    |
+| **Task**          | Sentiment Analysis (Financial)                                       |
+| **Labels**        | `0`: Positive, `1`: Neutral, `2`: Negative *(⚠️ flipped label order)* |
+| **Dataset**       | [`intanm/indonesian-financial-sentiment-analysis`](https://huggingface.co/datasets/intanm/indonesian-financial-sentiment-analysis) + synthetic and augmented samples |
+| **Fine-tuned by** | [`ihsan31415`](https://huggingface.co/ihsan31415)                   |
+| **Training Epochs** | 5 (Early stopping at epoch 5, best at epoch 3)                    |
+| **Eval Accuracy** | `97.49%`                                                             |
+
+
 ---
 
 ## 🧠 Model Objective
@@ -151,27 +166,36 @@ TrainingArguments(
 * Early stopping (`patience=2`)
 * Training completed at **epoch 5**, best model from **epoch 3**
 
-### ✅ Evaluation Results
+### 📊 Training Progress
+
+| Epoch | Training Loss | Validation Loss | Accuracy   | Precision  | Recall     | F1 Score   |
+|-------|----------------|------------------|------------|------------|------------|------------|
+| 1     | 0.104500       | 0.085562         | 0.969402   | 0.969715   | 0.969402   | 0.969356   |
+| 2     | 0.029100       | 0.088392         | 0.974859   | 0.974914   | 0.974859   | 0.974860   |
+| 3     | 0.012700       | 0.102305         | 0.974926   | 0.974949   | 0.974926   | 0.974933   |
+| 4     | 0.008900       | 0.125707         | 0.972816   | 0.972959   | 0.972816   | 0.972846   |
+| 5     | 0.004400       | 0.157659         | 0.966690   | 0.966902   | 0.966690   | 0.966676   |
 
-```
 
-eval_loss =                  0.10230540484189987
-eval_accuracy =              0.9749255130394028
-eval_precision =             0.9749490510899772
-eval_recall =                0.9749255130394028
-eval_f1 =                    0.9749326327197978
-eval_runtime =               71.9098
-eval_samples_per_second =    415.395
-eval_steps_per_second =      1.627
-epoch =                      5.0
+### ✅ Evaluation Results
 
+```bash
+eval_loss                 = 0.10230540484189987
+eval_accuracy             = 0.9749255130394028
+eval_precision            = 0.9749490510899772
+eval_recall               = 0.9749255130394028
+eval_f1                   = 0.9749326327197978
+eval_runtime              = 71.9098
+eval_samples_per_second   = 415.395
+eval_steps_per_second     = 1.627
+epoch                     = 5.0
 ```
 
 ---
 
 ## 🔎 Usage
 
-# Using Pipeline
+#### Using Pipeline
 ```python
 from transformers import pipeline
 
@@ -185,7 +209,7 @@ nlp = pipeline(
 
 nlp("IHSG diprediksi melemah karena sentimen global negatif")
 ```
-# RAW
+#### RAW
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
@@ -234,8 +258,8 @@ this indonesian RoBERTa base financial CLassifier was trained and evaluated by K
 ## 📬 Contact
 
 Created with love and tears by ihsan:\
-[![HuggingFace](https://img.shields.io/badge/HuggingFace-orange?style=flat&logo=huggingface&logoColor=white)][`ihsan31415`](https://huggingface.co/ihsan31415)\
-[![GitHub](https://img.shields.io/badge/GitHub-black?style=flat&logo=github&logoColor=white)][`ihsan31415`](https://github.com/ihsan31415)\
-[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue?style=flat&logo=linkedin&logoColor=white)][`Khoirul Ihsan`](https://www.linkedin.com/in/khoirul-ihsan-387115288/)\
+[![HuggingFace](https://img.shields.io/badge/HuggingFace-orange?style=flat&logo=huggingface&logoColor=white)] [`ihsan31415`](https://huggingface.co/ihsan31415)\
+[![GitHub](https://img.shields.io/badge/GitHub-black?style=flat&logo=github&logoColor=white)] [`ihsan31415`](https://github.com/ihsan31415)\
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue?style=flat&logo=linkedin&logoColor=white)] [`Khoirul Ihsan`](https://www.linkedin.com/in/khoirul-ihsan-387115288/)
 
 For collaborations or questions, feel free to reach out via Hugging Face or GitHub.