Wisesight Thai sentiment — III-BERT
Fine-tuned downstream checkpoint built from KordAI/III-BERT.
- Task: Wisesight Thai sentiment
- Labels (4): pos, neu, neg, q
- Base model:
KordAI/III-BERT - Custom code: this repository contains III-BERT model code. Load it with
trust_remote_code=True.
Test metrics
| Metric | Value |
|---|---|
| test_loss | 0.729800 |
| test_accuracy | 0.725200 |
| test_f1_macro | 0.606600 |
| test_f1_weighted | 0.717400 |
| test_f1_neg | 0.749500 |
| test_f1_neu | 0.781200 |
| test_f1_pos | 0.518000 |
| test_f1_q | 0.377800 |
The Wisesight scores are the held-out test results from the local training run.
Loading
from transformers import CamembertTokenizer, AutoModelForSequenceClassification
model_id = "KordAI/III-BERT-Wisesight-Sentiment"
tokenizer = CamembertTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
trust_remote_code=True,
)
For token classification, the model outputs one label distribution per tokenized subword. Map subword predictions back to words according to your tokenizer alignment strategy.