xlm-roberta-base-UITVSFC-T

xlm-roberta-base-UITVSFC-T

This model is xlm-roberta-base fine-tuned for UITVSFC-T on UIT-VSFC.

Evaluation protocol

• Dataset size: 16,175 examples.
• Shared fixed splits for S and T: 12,940 train / 1,617 development / 1,618 test.
• Split seed: 42; jointly stratified by sentiment–topic labels with exact duplicate groups kept in one split.
• Fine-tuning seeds: [42, 22, 202].
• Training: 3 epoch(s), AdamW, learning rate 2e-05, weight decay 0.01, warmup ratio 0.1.
• Effective train batch size: 8.
• Maximum sequence length: 256.
• Input mode: raw normalized Vietnamese text.
• No class weighting, resampling, external metadata, images, engagement features, or test-time model selection.
• Checkpoints are selected by development Macro-F1. The representative published checkpoint is seed 202, selected only by development Macro-F1.

Results

Test metrics are reported as mean ± sample standard deviation over seeds [42, 22, 202].

Per-seed results

Label mapping

{
  "0": "lecturer",
  "1": "training_program",
  "2": "facility",
  "3": "others"
}

Usage

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "BaoNhan/xlm-roberta-base-UITVSFC-T"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "Đây là nội dung tiếng Việt cần phân loại."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    probabilities = model(**inputs).logits.softmax(dim=-1)[0]
predicted_id = int(probabilities.argmax())
print(model.config.id2label[predicted_id], probabilities.tolist())

Files

• aggregate_metrics.json: aggregate metrics and training manifest.
• artifacts/per_seed_results.csv: one row per fine-tuning seed.
• artifacts/seed_*_confusion_matrix.csv: confusion matrix for each seed.
• artifacts/seed_*_classification_report.json: per-class metrics.
• artifacts/seed_*_test_predictions.csv: IDs, gold/predicted labels and probabilities; raw text is excluded.

Limitations

UIT-VSFC contains student feedback from a specific educational context. Performance may not transfer to other institutions, domains, informal writing styles, or newly emerging vocabulary. Predictions should not be treated as explanations of student intent.

Dataset citation

@InProceedings{8573337,
  author={Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu X. V. and Truong, Tham T. H. and Nguyen, Ngan Luu-Thuy},
  booktitle={2018 10th International Conference on Knowledge and Systems Engineering (KSE)},
  title={UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis},
  year={2018},
  pages={19--24},
  doi={10.1109/KSE.2018.8573337}
}