---
language:
- vi
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
tags:
- vietnamese
- phobert
- disaster-response
- emergency-detection
- text-classification
- peft
- lora
base_model: vinai/phobert-base
---

# PhoBERT Vietnamese Cau Cuu Classifier

PhoBERT-based Vietnamese Facebook comment classifier for detecting **"cầu cứu"** comments during natural-disaster situations.

## Labels

- `0`: `khong_cau_cuu`
- `1`: `cau_cuu`

## Intended use

This model is designed to prioritize **high recall** for emergency rescue requests in Vietnamese social-media comments, especially when comments may contain distress language, location hints, phone numbers, or SOS markers.

## Training setup

- Base model: `vinai/phobert-base`
- Fine-tuning method: LoRA / PEFT
- Evaluation checkpoint source: `/content/phobert-cau-cuu/saved_model/checkpoint-171`
- Decision threshold for deployment: `0.4941`
- Threshold selection policy: `target_recall` with validation target recall `0.88`

## Validation metrics at selected threshold

- Accuracy: `0.8469`
- F1 macro: `0.8380`
- F1 (`cau_cuu`): `0.8000`
- Recall (`cau_cuu`): `0.8955`
- Precision (`cau_cuu`): `0.7229`

## Test metrics

- Accuracy: `0.8520`
- F1 macro: `0.8430`
- F1 (`cau_cuu`): `0.8054`
- Recall (`cau_cuu`): `0.9091`
- Precision (`cau_cuu`): `0.7229`

## Confusion matrix on test set

```text
107 23
6 60
```

## Recommended inference rule

Convert logits to probabilities and classify as `cau_cuu` when:

```python
prob_cau_cuu >= 0.4941
```

This threshold was chosen on the validation set to preserve strong recall while improving `F1(cau_cuu)` and overall accuracy.

## Example loading code

```python
import torch
from peft import AutoPeftModelForSequenceClassification
from transformers import AutoTokenizer

repo_id = "dat201204/phobert-vi-caucu-classifier"
threshold = 0.4941

tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=False)
model = AutoPeftModelForSequenceClassification.from_pretrained(repo_id)
model.eval()

text = "Cuu voi, nha em dang ngap va co nguoi gia bi ket"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    logits = model(**inputs).logits
    prob_cau_cuu = torch.softmax(logits, dim=-1)[0, 1].item()

label = "cau_cuu" if prob_cau_cuu >= threshold else "khong_cau_cuu"
print({"label": label, "prob_cau_cuu": prob_cau_cuu})
```

## Limitations

- The dataset was weakly supervised in the first labeling stage and may contain residual noise.
- The model is optimized for disaster-response triage, not for general sentiment or topic classification.
- Human verification is still recommended for high-stakes rescue coordination.