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toolathon123/logistics-southamerica-quality

Logistics Quality – South America (Brazil & Argentina) Live shipment records collected from the South American (Brazil / Argentina) logistics network for service-quality monitoring and improvement. This dataset supports complaint type classification of customer feedback, on-time/damage KPI reporting, and route-level hotspot analysis (with special attention to cross-border corridors such as São Paulo → Buenos Aires). Collection & real-time nature Records are… See the full description on the dataset page: https://huggingface.co/datasets/toolathon123/logistics-southamerica-quality.

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Logistics Quality – South America (Brazil & Argentina)

Live shipment records collected from the South American (Brazil / Argentina) logistics network for service-quality monitoring and improvement. This dataset supports complaint type classification of customer feedback, on-time/damage KPI reporting, and route-level hotspot analysis (with special attention to cross-border corridors such as São Paulo → Buenos Aires).

Collection & real-time nature

Records are ingested from the transport management system in near real-time. Each snapshot is timestamped in UTC. New records can be appended incrementally — see Incremental append — without re-processing historical rows.

Schema (columns)

Column	Type	Description
`transport_id`	string	Unique shipment id (e.g. `BR-20260813-000001`)
`timestamp`	string	ISO-8601 UTC timestamp of the shipment event
`origin_country`	string	`BR` or `AR`
`origin_city`	string	Origin city
`destination_country`	string	`BR` or `AR`
`destination_city`	string	Destination city
`route_key`	string	`"{origin} -> {destination}"` corridor key
`is_cross_border`	bool	Whether the shipment crosses the BR–AR border
`transport_mode`	string	`road` \	`rail` \	`air`
`carrier_name`	string	Carrier operating the shipment
`on_time_delivery`	bool	Delivered within agreed deadline
`delay_hours`	float	Delay in hours (0 when on time)
`damage_status`	string	`none` \	`minor` \	`major`
`feedback_language`	string	Language of the customer feedback (`pt` / `es`)
`customer_feedback`	string	Free-text customer feedback (Portuguese or Spanish)
`gt_complaint`	string	Reference complaint label used for validation: `none`\	`delay`\	`damage`\	`service`\	`cost`\	`loss`
`predicted_complaint`	string	Model-predicted complaint category (see models below)
`confidence`	float	Confidence of the top predicted category
`prob_delay` … `prob_positive`	float	Full model probability vector over the 6 categories
`segment`	string	Ingestion batch (`live_batch`, `incremental_*`)

Complaint categories

Category	Meaning
`delay`	延误 — late / delayed delivery
`damage`	货损 — damaged or spoiled goods
`service`	服务态度 — poor service / attitude
`cost`	费用争议 — billing / cost dispute
`loss`	包裹丢失 — lost / missing package
`positive`	no complaint / positive feedback

Models used for inference

—Primary: `MoritzLaurer/mDeBERTa-v3-base-mnli-xnli` — multilingual zero-shot NLI (XLM-R/DeBERTa family). One model handles both Portuguese (BR) and Spanish (AR) feedback via the Hugging Face Inference API.
—Cross-check: `joeddav/xlm-roberta-large-xnli` — XLM-RoBERTa-large NLI, used to validate predictions on a sample.
—Language-specific pretrained models considered: `neuralmind/bert-base-portuguese-cased` (BERTimbau, pt) and `dccuchile/bert-base-spanish-wwm-cased` (BETO, es).

Incremental append

python

from datasets import load_dataset
from huggingface_hub import HfApi

api = HfApi()
# append a small batch of new records (only new transport_ids)
new_records = [...]  # list of dicts with the schema above (new transport_ids)
api.upload_file(
    path_or_fileobj=... ,  # or use push_to_hub on the dataset
    path_in_repo="data/incremental_20260813.parquet",
    repo_id="toolathon123/logistics-southamerica-quality",
    repo_type="dataset",
)
# Re-run inference only on records lacking a `predicted_complaint`.

A fully worked append + re-classification example is provided in the analysis pipeline (see logistics_project/scripts/).