Update README.md

README.md

@@ -22,7 +22,84 @@ It achieves the following results on the evaluation set:
 - Loss: 0.3242
 - Accuracy: 0.8997
 
-## Model description
+## How To use
+#### Load Base Model and Plateer Classifier Model.
+```python
+import joblib;
+from huggingface_hub import hf_hub_download;
+from peft import PeftModel, PeftConfig;
+from transformers import AutoTokenizer, TextClassificationPipeline, AutoModelForSequenceClassification;
+from huggingface_hub import HfApi, login
+with open('./api_key/HGF_TOKEN.txt', 'r') as hgf:
+    login(token=hgf.read())
+api = HfApi()
+repo_id = "x2bee/plateer_classifier_v0.1"
+data_id = "x2bee/plateer_category_data"
+
+# Load Config, Tokenizer, Label_Encoder
+config = PeftConfig.from_pretrained(repo_id, subfolder="last-checkpoint")
+tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="last-checkpoint")
+label_encoder_file = hf_hub_download(repo_id=data_id, repo_type="dataset", filename="label_encoder.joblib")
+label_encoder = joblib.load(label_encoder_file)
+
+# Load base_model
+base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen2.5-1.5B", num_labels=17)
+base_model.resize_token_embeddings(len(tokenizer))
+
+# Load Model
+model = PeftModel.from_pretrained(base_model, repo_id, subfolder="last-checkpoint")
+
+import torch
+class TextClassificationPipeline(TextClassificationPipeline):
+    def __call__(self, inputs, top_k=5, **kwargs):
+        inputs = self.tokenizer(inputs, return_tensors="pt", truncation=True, padding=True, **kwargs)
+        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        scores, indices = torch.topk(probs, top_k, dim=-1)
+        
+        results = []
+        for batch_idx in range(indices.shape[0]):
+            batch_results = []
+            for score, idx in zip(scores[batch_idx], indices[batch_idx]):
+                temp_list = []
+                label = self.model.config.id2label[idx.item()]
+                label = int(label.split("_")[1])
+                temp_list.append(label)
+                predicted_class = label_encoder.inverse_transform(temp_list)[0]
+                            
+                batch_results.append({
+                    "label": label,
+                    "label_decode": predicted_class,
+                    "score": score.item(),
+                })
+            results.append(batch_results)
+        
+        return results
+
+classifier_model = TextClassificationPipeline(tokenizer=tokenizer, model=model)
+
+def plateer_classifier(text, top_k=3):
+    result = classifier_model(text, top_k=top_k)
+    return result
+```
+
+#### Run
+```python
+user_input = "머리띠"
+result = plateer_classifier(user_input)[0]
+print(result)
+```
+
+```bash
+{'label': 6, 'label_decode': '뷰티/케어', 'score': 0.42996299266815186}
+{'label': 15, 'label_decode': '패션/의류/잡화', 'score': 0.1485249102115631}
+{'label': 8, 'label_decode': '스포츠', 'score': 0.1281907707452774}
+```
+
 
 More information needed