LHDN Table Classifier · BERT · Development
A BERT text classifier for the table-selection part of the acceler-technology team's LHDN document-processing project. This development checkpoint supports the team's work on identifying relevant table-related text.
中文简介： 团队用于 LHDN 文档表格选择相关工作的 BERT 文本分类开发模型，输入是文本。
Purpose and model details
The model encodes text and predicts a class. It provides a classification approach alongside the team's Donut image parser and T5 text parser. It does not directly detect table boundaries in an image or extract individual cells.
| Item | Value |
|---|---|
| Role | Development checkpoint |
| Architecture | BertForSequenceClassification |
| Task setting | Single-label classification |
| Encoder layers / hidden size | 12 / 768 |
| Maximum input positions | 512 |
| Tokenizer | Lowercasing BertTokenizer |
| Saved Transformers version | 4.52.4 |
Sources: config.json and the saved tokenizer configuration. Class meanings are not documented in the configuration; confirm the training label map before interpreting predictions.
Load the model
Install torch and transformers first.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "acceler-technology/dev-lhdn-table-bert-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()
This example loads the model. Prepare input text as in training. Long text needs a chunking or truncation policy within the 512-position limit.
Project and limitations
Related project data is held in lhdn-dataset, which remains private. The exact training subset, full recipe, and quantitative evaluation results are not documented here.
Unfamiliar documents and text-extraction errors can affect classification. Review predictions before downstream use. The model does not determine tax or legal outcomes.
Team and links
Developed within acceler-technology. Henry (HenryTeo27) led this project area, executed the data work, and participated in model training and technical discussions. kwyong handled model uploads.
Project showcase · Production BERT snapshot
A license has not been specified for this checkpoint. Public visibility alone does not grant unrestricted reuse rights.
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