metadata
license: apache-2.0
base_model: phobert-v2
tags:
- vietnamese
- hate-speech
- span-detection
- token-classification
- nlp
datasets:
- visolex/ViHOS
model-index:
- name: phobert-v2-hsd-span
results:
- task:
type: token-classification
name: Hate Speech Span Detection
dataset:
name: visolex/ViHOS
type: visolex/ViHOS
metrics:
- type: f1
value: 0.6326
- type: precision
value: 0.6494
- type: recall
value: 0.6305
- type: exact_match
value: 0
phobert-v2-hsd-span: Hate Speech Span Detection (Vietnamese)
This model is a fine-tuned version of phobert-v2 for Vietnamese Hate Speech Span Detection.
Model Details
- Base Model:
phobert-v2 - Description: Vietnamese Hate Speech Span Detection
- Framework: HuggingFace Transformers
- Task: Hate Speech Span Detection (token/char-level spans)
Hyperparameters
- Max sequence length:
64 - Learning rate:
5e-6 - Batch size:
32 - Epochs:
100 - Early stopping patience:
5
Results
- F1:
0.6326 - Precision:
0.6494 - Recall:
0.6305 - Exact Match:
0.0000
Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch
model_name = "phobert-v2-hsd-span"
tok = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
text = "Ví dụ câu tiếng Việt có nội dung thù ghét ..."
enc = tok(text, return_tensors="pt", truncation=True, max_length=256, is_split_into_words=False)
with torch.no_grad():
logits = model(**enc).logits
pred_ids = logits.argmax(-1)[0].tolist()
# TODO: chuyển pred_ids -> spans theo scheme nhãn của bạn (BIO/BILOU/char-offset)
License
Apache-2.0
Acknowledgments
- Base model: phobert-v2