Update README.md

README.md

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----
-license: mit
----
+---
+language:
+- min
+- id
+- en
+license: mit
+library_name: transformers
+tags:
+- xlm-roberta
+- sentiment-analysis
+- code-mixing
+- explainable-ai
+- xai
+- low-resource-language
+datasets:
+- custom-minangkabau-youtube-comments
+metrics:
+- f1
+- accuracy
+---
+
+# Explainable XLM-R for Code-Mixed Minangkabau Sentiment Analysis
+
+## Model Description
+This model is a fine-tuned version of **XLM-RoBERTa (XLM-R)** specifically designed to handle the complexities of **Code-Mixed Minangkabau** sentiment analysis. Social media data in Indonesia, particularly from regional areas like West Sumatra, often features a heavy mix of Minangkabau, Indonesian, and English. 
+
+Traditional models often struggle with this "low-resource gap" and "non-standard orthography." This model bridge those gaps while prioritizing **Explainability (XAI)** to demystify the "black-box" nature of Deep Learning.
+
+### Key Features:
+- **Multilingual Support:** Optimized for Indonesian, Minangkabau, and English code-mixing.
+- **Robustness:** Handles informal spelling, unique affixations, and phonetic typing common in YouTube comments.
+- **XAI-Ready:** Designed to be interpreted using feature attribution methods like **SHAP** or **LIME** to provide local and global explanations.
+
+## Intended Uses & Limitations
+- **Primary Use:** Sentiment classification (Positive, Neutral, Negative) for regional Indonesian languages.
+- **Limitations:** Performance might vary on purely formal Minang literature as the training data is derived from social media (YouTube) contexts.
+
+## How to Use
+You can use this model directly with the `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "bimobirra/explainable-xlmr-code-mixed-low-resource-lang"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "Rancak bana videonya, tapi agak sedikit nge-lag loadingnya."
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class_id = logits.argmax().item()
+print(f"Predicted class ID: {predicted_class_id}")