Update README.md

README.md

@@ -6,7 +6,87 @@ language:
 - th
 metrics:
 - f1
-pipeline_tag: text-classification
 tags:
 - roberta
----
+---
+
+# Traffy Complaint Classification
+
+This model is trained to automatically classify types of traffic complaints in Thai text, aiming to reduce the need for manual classification by humans.
+
+### Model Details
+
+    Model Name: KDAI-NLP/wangchanberta-traffy-multi
+    Tokenizer: airesearch/wangchanberta-base-att-spm-uncased
+    License: Apache License 2.0
+
+### How to Use
+
+```python
+
+!pip install sentencepiece
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from torch.nn.functional import sigmoid
+import json
+
+# Target lists
+target_list = [
+    'ความสะอาด', 'สายไฟ', 'สะพาน', 'ถนน', 'น้ำท่วม',
+    'ร้องเรียน', 'ท่อระบายน้ำ', 'ความปลอดภัย', 'คลอง', 'แสงสว่าง',
+    'ทางเท้า', 'จราจร', 'กีดขวาง', 'การเดินทาง', 'เสียงรบกวน',
+    'ต้นไม้', 'สัตว์จรจัด', 'เสนอแนะ', 'คนจรจัด', 'ห้องน้ำ',
+    'ป้ายจราจร', 'สอบถาม', 'ป้าย', 'PM2.5'
+]
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("airesearch/wangchanberta-base-att-spm-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("KDAI-NLP/wangchanberta-traffy-multi")
+
+# Example text to classify
+text = "ช่วยด้วยครับถนนน้ำท่วมอีกแล้ว ต้นไม้ก็ล้มขวางทาง กลับบ้านไม่ได้"
+
+# Encode the text using the tokenizer
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256)
+
+# Get model predictions (logits)
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# Apply sigmoid function to convert logits to probabilities
+probabilities = sigmoid(logits)
+
+# Map probabilities to corresponding labels
+probabilities = probabilities.squeeze().tolist()
+label_probabilities = zip(target_list, probabilities)
+
+# Print labels with probabilities
+for label, probability in label_probabilities:
+    print(f"{label}: {probability:.4f}")
+
+# Or JSON
+# Create a dictionary for labels and probabilities
+results_dict = {label: probability for label, probability in label_probabilities}
+
+# Convert dictionary to JSON string
+results_json = json.dumps(results_dict, ensure_ascii=False, indent=4)
+
+# Print the JSON string
+print(results_json)
+```
+
+## Training Details
+
+The model was trained on traffic complaint data API (included stopwords) using the airesearch/wangchanberta-base-att-spm-uncased base model. This is a multi-label classification task with a total of 24 classes.
+
+## Training Scores
+
+| Model                              | Stopword | Epoch | Training Loss | Validation Loss | F1      | Accuracy |
+| ---------------------------------- | -------- | ----- | ------------- | --------------- | ------- | -------- |
+| wangchanberta-base-att-spm-uncased | Included | 0     | 0.0322        | 0.034822        | 0.7015  | 0.7569   |
+| wangchanberta-base-att-spm-uncased | Included | 2     | 0.0207        | 0.026364        | 0.8405  | 0.7821   |
+| wangchanberta-base-att-spm-uncased | Included | 4     | 0.0165        | 0.025142        | 0.8458  | 0.7934   |
+
+
+Feel free to customize the README further if needed.