README.md

---
language: vi
tags:
- spam-detection
- vietnamese
- phobert
license: apache-2.0
datasets:
- visolex/ViSpamReviews
metrics:
- accuracy
- f1
model-index:
- name: phobert-spam-binary
  results:
  - task:
      type: text-classification
      name: Spam Detection (Binary)
    dataset:
      name: ViSpamReviews
      type: custom
    metrics:
    - name: Accuracy
      type: accuracy
      value: <INSERT_ACCURACY>
    - name: F1 Score
      type: f1
      value: <INSERT_F1_SCORE>
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
---

# PhoBERT-Spam-Binary

Fine-tuned from [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on **ViSpamReviews** (binary).

* **Task**: Binary classification (0 = non-spam, 1 = spam)
* **Dataset**: [ViSpamReviews](https://huggingface.co/datasets/visolex/ViSpamReviews)
* **Hyperparameters**

  * Batch size: 32
  * LR: 3e-5
  * Epochs: 100
  * Max seq len: 256
## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("visolex/phobert-spam-binary")
model = AutoModelForSequenceClassification.from_pretrained("visolex/phobert-spam-binary")

text = "Đánh giá ảo hoàn toàn!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
pred = model(**inputs).logits.argmax(dim=-1).item()
print("Spam" if pred==1 else "Non-spam")
```