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---
language: vi
tags:
- spam-detection
- vietnamese
- bartpho
license: apache-2.0
datasets:
- visolex/ViSpamReviews
metrics:
- accuracy
- f1
model-index:
- name: bartpho-spam-classification
results:
- task:
type: text-classification
name: Spam Detection (Multi-Class)
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/bartpho-syllable
pipeline_tag: text-classification
---
# BARTPho-Spam-MultiClass
Fine-tuned from [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable) on **ViSpamReviews** (multi-class).
* **Task**: 4-way classification
* **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/bartpho-spam-classification")
model = AutoModelForSequenceClassification.from_pretrained("visolex/bartpho-spam-classification")
text = "Đánh giá quá chung chung, không liên quan."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
pred = model(**inputs).logits.argmax(dim=-1).item()
label_map = {0: "NO-SPAM",1: "SPAM-1",2: "SPAM-2",3: "SPAM-3"}
print(label_map[pred])
``` |