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plateer_classifier_v0.1

PEFT
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plateer_classifier_v0.1
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---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: plateer_classifier_test
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# plateer_classifier_test

This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on [x2bee/plateer_category_data](https://huggingface.co/datasets/x2bee/plateer_category_data).
It achieves the following results on the evaluation set:
- [MLflow Result(https://polar-mlflow.x2bee.com/#/experiments/27/runs/baa7269894b14f91b8a8ea3822474476)]
- Loss: 0.3242
- Accuracy: 0.8997

## 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

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step   | Validation Loss | Accuracy |
|:-------------:|:------:|:------:|:---------------:|:--------:|
| 0.5023        | 0.0292 | 5000   | 0.5044          | 0.8572   |
| 0.4629        | 0.0585 | 10000  | 0.4571          | 0.8688   |
| 0.4254        | 0.0878 | 15000  | 0.4201          | 0.8770   |
| 0.4025        | 0.1171 | 20000  | 0.4016          | 0.8823   |
| 0.3635        | 0.3220 | 55000  | 0.3623          | 0.8905   |
| 0.3192        | 0.6441 | 110000 | 0.3242          | 0.8997   |

### Framework versions

- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.2.1
- Datasets 3.1.0
- Tokenizers 0.20.3