| --- |
| library_name: transformers |
| pipeline_tag: text-classification |
| tags: |
| - bert |
| - document-processing |
| - table-selection |
| - lhdn |
| - ebc |
| --- |
| |
| # LHDN Table Classifier · BERT · Development |
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| 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. |
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| **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。 |
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| ## Purpose and model details |
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| 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. |
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| | 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 | |
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| Sources: [config.json](https://huggingface.co/acceler-technology/dev-lhdn-table-bert-model/blob/main/config.json) and the saved tokenizer configuration. Class meanings are not documented in the configuration; confirm the training label map before interpreting predictions. |
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| ## Load the model |
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| Install `torch` and `transformers` first. |
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| ```python |
| 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() |
| ``` |
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| This example loads the model. Prepare input text as in training. Long text needs a chunking or truncation policy within the 512-position limit. |
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| ## Project and limitations |
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| Related project data is held in [lhdn-dataset](https://huggingface.co/datasets/acceler-technology/lhdn-dataset), which remains private. The exact training subset, full recipe, and quantitative evaluation results are not documented here. |
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| Unfamiliar documents and text-extraction errors can affect classification. Review predictions before downstream use. The model does not determine tax or legal outcomes. |
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| ## Team and links |
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| Developed within [acceler-technology](https://huggingface.co/acceler-technology). Henry ([HenryTeo27](https://huggingface.co/HenryTeo27)) led this project area, executed the data work, and participated in model training and technical discussions. [kwyong](https://huggingface.co/kwyong) handled model uploads. |
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| [Project showcase](https://github.com/HenryTeo27/EBC-HuggingFace-Showcase) · [Production BERT snapshot](https://huggingface.co/acceler-technology/prod-lhdn-table-bert-model) |
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| A license has not been specified for this checkpoint. Public visibility alone does not grant unrestricted reuse rights. |
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