vad-macbert

Name: vad-macbert
Rating: 2.5 (71 reviews)

Chinese VAD (valence/arousal/dominance) regression on top of chinese-macbert-base.

The model predicts 3 continuous values aligned to the VAD scale produced by RobroKools/vad-bert (teacher model).

Quickstart

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_path = "Pectics/vad-macbert"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.eval()

text = "这部电影让我很感动。"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
with torch.no_grad():
    outputs = model(**inputs)
vad = outputs.logits.squeeze().tolist()
print("VAD:", vad)

Model Details

Base model: hfl/chinese-macbert-base
Task: VAD regression (3 outputs: valence, arousal, dominance)
Head: AutoModelForSequenceClassification with num_labels=3, problem_type=regression

Data Sources & Labeling

en-zh_cn_vad_clean.csv

Source: OpenSubtitles EN-ZH parallel corpus.
Labeling: English side fed into RobroKools/vad-bert to obtain VAD values, then assigned to the paired Chinese text.

en-zh_cn_vad_long.csv

Derived from en-zh_cn_vad_clean.csv by filtering for longer texts using a length threshold (original threshold was not recorded).
Inferred from statistics: minimum length is 32 characters, so the filter likely kept samples with length >= 32 chars.

en-zh_cn_vad_long_clean.csv

Cleaned from en-zh_cn_vad_long.csv by removing subtitle formatting noise:
- ASS/SSA tag blocks like {\\fs..\\pos(..)} (including broken { blocks)
- HTML-like tags (e.g. <i>...</i>)
- Escape codes like \\N, \\n, \\h, \\t
- Extra whitespace normalization
Non-CJK rows were dropped.

en-zh_cn_vad_mix.csv

Mixed dataset created for replay training:
- 200k samples from en-zh_cn_vad_clean.csv
- 200k samples from en-zh_cn_vad_long_clean.csv
- Shuffled after sampling

Training Summary

The final model (vad-macbert-mix/best) was obtained in three stages:

Base training on en-zh_cn_vad_clean.csv
Long-text adaptation on en-zh_cn_vad_long_clean.csv
Replay mix on en-zh_cn_vad_mix.csv (resume from stage 2)

Final-stage Command (Replay Mix)

--model_name hfl/chinese-macbert-base
--output_dir train/vad-macbert-mix
--data_path train/en-zh_cn_vad_mix.csv
--epochs 4
--batch_size 32
--grad_accum_steps 4
--learning_rate 0.00001
--weight_decay 0.01
--warmup_ratio 0.1
--warmup_steps 0
--max_length 512
--eval_ratio 0.01
--eval_every 100
--eval_batches 200
--loss huber
--huber_delta 1.0
--shuffle_buffer 4096
--min_chars 2
--save_every 100
--log_every 1
--max_steps 5000
--seed 42
--dtype fp16
--num_rows 400000
--resume_from train/vad-macbert-long/best
--encoding utf-8

Training environment (conda llm):

Python 3.10.19
torch 2.9.1+cu130
transformers 4.57.6

Evaluation

Benchmark script: train/vad_benchmark.py

Evaluation uses a fixed stride derived from eval_ratio=0.01 (roughly 1 out of 100 samples).
Length buckets by character count: 0–20, 20–40, 40–80, 80–120, 120–200, 200–400, 400+

Results (vad-macbert-mix/best)

en-zh_cn_vad_clean.csv

mse_mean=0.043734
mae_mean=0.149322
pearson_mean=0.7335

en-zh_cn_vad_long_clean.csv

mse_mean=0.031895
mae_mean=0.131320
pearson_mean=0.7565

Notes:

400+ bucket Pearson is unstable due to small sample size; interpret with care.

Limitations

Labels are derived from an English VAD teacher and transferred via parallel alignment, so they reflect the teacher’s bias and may not match human Chinese annotations.
Subtitle corpora include translation artifacts and formatting noise; cleaned versions mitigate but do not fully remove this.
Extreme-length sentences are under-represented; performance on 400+ chars is not reliable.

Files in This Repo

config.json
model.safetensors
tokenizer.json, tokenizer_config.json, special_tokens_map.json, vocab.txt
training_args.json