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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
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