Datasets:

Mead0w1ark
/

HSClassify-benchmark

language:
  - en
  - th
  - vi
  - zh
pretty_name: HSClassify Benchmark
size_categories:
  - n<1K
task_categories:
  - text-classification
tags:
  - hs-codes
  - trade
  - customs
  - harmonized-system
  - benchmark
dataset_info:
  features:
    - name: text
      dtype: string
    - name: expected_hs_code
      dtype: string
    - name: category
      dtype: string
    - name: language
      dtype: string
    - name: notes
      dtype: string
  config_name: default
  splits:
    - name: test
      num_examples: 78
configs:
  - config_name: default
    data_files:
      - split: test
        path: benchmark_cases.csv

HSClassify Benchmark

Benchmark suite for evaluating the HSClassify HS code classifier.

Results (latest)

Metric	All Cases	In-Label-Space
Top-1 Accuracy	79.5%	88.6%
Top-3 Accuracy	82.0%	91.4%
Top-5 Accuracy	83.3%	92.9%
Chapter Accuracy	89.7%	95.7%

By Category

Category	N	Top-1	Top-3	Top-5
easy	27	96.3%	100%	100%
edge_case	21	71.4%	76.2%	81.0%
multilingual	20	100%	100%	100%
known_failure	10	10.0%	10.0%	10.0%

Test Cases

78 hand-crafted cases in benchmark_cases.csv across four categories:

easy (27): Common goods the model should classify correctly
edge_case (21): Ambiguous queries, short text, brand names
multilingual (20): Thai, Vietnamese, and Chinese queries
known_failure (10): Documents current blind spots and label-space gaps

Usage

Requires a trained HSClassify_micro model directory as a sibling folder (or pass --model-dir).

# Basic benchmark (~10s)
python benchmark.py

# Custom output path
python benchmark.py --output results/out.json

# With per-class split analysis
python benchmark.py --split-analysis

# Point to model directory explicitly
python benchmark.py --model-dir /path/to/HSClassify_micro

Split Analysis (training data)

Replicates the 80/20 stratified split from model training to report:

Worst 15 HS codes by F1 score
Top 20 cross-chapter confusions
Overall accuracy: 77.2% (matches training baseline)