adding rreadme

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+You're absolutely right — label flipping is **critical** here since the base model you fine-tuned uses a **non-standard label mapping** (`0 = Positive`, `2 = Negative`). Here's the **updated and corrected** model card with:
 
-## How to Get Started with the Model
+* 🔁 Clear warning about label flipping
+* 🧪 Updated usage code with correct interpretation
+* ✅ Rewritten explanation for clarity
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
+---
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+# 🇮🇩 Fine-Tuned IndoRoBERTa for Indonesian Financial Sentiment Classification
 
-[More Information Needed]
+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**.
 
-#### Metrics
+---
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+## 🧠 Model Objective
 
-[More Information Needed]
+This model classifies Indonesian financial news articles into:
 
-### Results
+* `0` → **Positive**
+* `1` → **Neutral**
+* `2` → **Negative**
 
-[More Information Needed]
+⚠️ **Important: Label Mapping is Flipped**
+This label order follows the base model's unexpected configuration. During training and evaluation, the dataset was relabeled accordingly.
 
-#### Summary
+> ⚠️ Always interpret model output using this mapping:
+>
+> * `0`: Positive
+> * `1`: Neutral
+> * `2`: Negative
 
+---
 
+## 📊 Dataset & Preprocessing Pipeline
 
-## Model Examination [optional]
+### 🔹 Source Dataset
 
-<!-- Relevant interpretability work for the model goes here -->
+* [`intanm/indonesian-financial-sentiment-analysis`](https://huggingface.co/datasets/intanm/indonesian-financial-sentiment-analysis)
+* Labeled financial news (imbalanced and limited)
 
-[More Information Needed]
+### 📈 Data Augmentation & Balancing
 
-## Environmental Impact
+#### 1. 🧪 Gemini Synthetic Generation
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+* Generated structured financial news samples using `gemini-2.0-flash-lite`
+* Targeted generation for underrepresented classes
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+#### 2. ✍️ GPT-2 Prompt Completion
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+* Used [`indonesian-nlp/gpt2-medium-indonesian`](https://huggingface.co/indonesian-nlp/gpt2-medium-indonesian)
+* Prompt templates varied and strictly separated between train/test sets
 
-## Technical Specifications [optional]
+#### 3. 🧩 Roberta-Based Masked Augmentation
 
-### Model Architecture and Objective
+* Strategic masking/filling while protecting key financial terms
+* Iterative masking to increase diversity and context coverage
 
-[More Information Needed]
+#### 📊 Final Label Distribution
 
-### Compute Infrastructure
+**Train Set**:
 
-[More Information Needed]
+```
+2 (Negative): 22906
+1 (Neutral): 23374
+0 (Positive): 23423
+```
 
-#### Hardware
+**Test Set**:
 
-[More Information Needed]
+```
+2 (Negative): 9817
+1 (Neutral): 10018
+0 (Positive): 10039
+```
 
-#### Software
+---
 
-[More Information Needed]
+## 🏋️ Training Details
+
+### 🔁 Label Flipping
+
+> The base model uses **non-standard labels**:
+>
+> * `0`: Positive
+> * `1`: Neutral
+> * `2`: Negative
+>
+> Training data was relabeled accordingly.
+
+### 🔧 TrainingArguments
+
+```python
+TrainingArguments(
+    output_dir="./results-roberta",
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    logging_strategy="epoch",
+    per_device_train_batch_size=256,
+    per_device_eval_batch_size=256,
+    num_train_epochs=15,
+    learning_rate=2e-5,
+    weight_decay=0.01,
+    load_best_model_at_end=True,
+    metric_for_best_model="accuracy",
+    save_total_limit=4,
+)
+```
+
+* Early stopping (`patience=2`)
+* Training completed at **epoch 5**, best model from **epoch 3**
+
+### ✅ Evaluation Results
+
+```
+eval_accuracy  = 0.9749
+eval_precision = 0.9749
+eval_recall    = 0.9749
+eval_f1        = 0.9749
+```
 
-## Citation [optional]
+---
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## 🔎 Usage
 
-**BibTeX:**
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-[More Information Needed]
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("ihsan31415/finetuned-indo-roBERTa-financial-sentiment")
+tokenizer = AutoTokenizer.from_pretrained("ihsan31415/finetuned-indo-roBERTa-financial-sentiment")
 
-**APA:**
+# Example input
+text = "IHSG diprediksi melemah karena sentimen global negatif"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+outputs = model(**inputs)
 
-[More Information Needed]
+# Get predicted class
+predicted_label = torch.argmax(outputs.logits, dim=1).item()
 
-## Glossary [optional]
+# Interpret using flipped label mapping
+label_map = {
+    0: "Positive",
+    1: "Neutral",
+    2: "Negative"
+}
+print(f"Predicted sentiment: {label_map[predicted_label]}")
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+---
 
-[More Information Needed]
+## 📌 Citation
 
-## More Information [optional]
+If you use this model in your research or application, please cite or link to this model card.
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## 📬 Contact
 
-[More Information Needed]
+Created by 
+[`ihsan31415`](https://huggingface.co/ihsan31415)
+[`ihsan31415`](https://github.com/ihsan31415)
+[`Khoirul Ihsan`](https://www.linkedin.com/in/khoirul-ihsan-387115288/)
 
-## Model Card Contact
+For collaborations or questions, feel free to reach out via Hugging Face or GitHub.
 
-[More Information Needed]
+---