---
language:
- de
- en
license: mit
tags:
- logistics
- shipping
- container
- classification
- port
- terminal
size_categories:
- n<100
---

# Container Status Classification Dataset

Dataset for training container status classification models in port logistics.

## Labels (15 categories)

| Label | Description | Urgency |
|-------|-------------|---------|
| DAMAGED | Container damaged, inspection required | high |
| DELAYED | Container delayed, schedule change | medium |
| CUSTOMS_HOLD | Customs inspection or hold | high |
| READY_PICKUP | Available for collection | low |
| GATE_IN | Container entered terminal | low |
| GATE_OUT | Container left terminal | low |
| LOADED | Loaded on vessel | low |
| DISCHARGED | Unloaded from vessel | medium |
| MISSING_SEAL | Seal missing or broken | high |
| WEIGHT_MISMATCH | Weight discrepancy | high |
| TEMP_ALERT | Temperature alert for reefers | critical |
| DOCUMENTS_MISSING | Missing paperwork | high |
| BLOCKED | Container blocking access | medium |
| RELEASED | Customs cleared, free to go | low |
| INSPECTION_REQUIRED | Survey/inspection needed | high |

## Data Structure

```json
{
  "text": "Container MSCU1234567 beschädigt",
  "label": "DAMAGED",
  "urgency": "high",
  "action_required": "inspect_and_claim",
  "container_id": "MSCU1234567"
}
```

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("mindchain/container-status-de")
```

## Intended Models

- google/t5gemma-2-270m (edge deployment)
- google/t5gemma-2-1b (serverless)
- Fine-tuned for text-to-text classification