newsintel-re

newsintel-re

Relation extraction over [E1]/[E2]-marked sentences (OCCURRED_IN, HAS_DEATH_COUNT, …).

Part of NewsIntel AI — a CPU-deployable, LLM-free pipeline that extracts structured public-safety events (accidents, disasters, crimes) from Bangladeshi news in Bengali and English. This model is stage 5 · relations (19 types) of that chain:

document → relevance gate → event-type router → evidence selection
         → NER → relation extraction → knowledge graph → structured event JSON

Model details

Labels

• NO_RELATION
• OCCURRED_IN
• OCCURRED_ON
• INVOLVES_PERSON
• INVOLVES_ORG
• INVOLVES_VEHICLE
• HAS_DEATH_COUNT
• HAS_INJURY_COUNT
• HAS_DAMAGE_COUNT
• HAS_AFFECTED_COUNT
• HAS_CROP
• HAS_PRODUCTION_AMOUNT
• HAS_CRIME_TYPE
• HAS_DISASTER_TYPE
• ARRESTED_BY
• REPORTED_BY
• LOCATED_IN
• PART_OF
• ALIAS_OF

Thresholds

None — this model emits raw scores; the caller ranks or thresholds.

Decision rule

mark head/tail [E1]/[E2]; relation = id2label[argmax(softmax(logits))]

These values also ship machine-readable in model_manifest.json, so a serving process can consume the model without hardcoding anything.

Evaluation

Usage

from huggingface_hub import snapshot_download
import onnxruntime as ort, numpy as np
from transformers import AutoTokenizer

d = snapshot_download("saidylive/newsintel-re", revision="c3350e03a50db",
                      allow_patterns=["model_int8.onnx", "*.json", "*.model"])
tok = AutoTokenizer.from_pretrained(d)
sess = ort.InferenceSession(f"{d}/model_int8.onnx", providers=["CPUExecutionProvider"])

enc = tok("সাভারে বাস-ট্রাকের সংঘর্ষে নিহত ২", return_tensors="np")
logits = sess.run(None, {k: v for k, v in enc.items()
                         if k in {i.name for i in sess.get_inputs()}})[0]
# decision rule (from model_manifest.json):
#   mark head/tail [E1]/[E2]; relation = id2label[argmax(softmax(logits))]

Training data & provenance

Trained on the bd_eng_news_daily Kaggle corpus of Bangladeshi news (~713k articles, ~96% Bengali by character ratio). Labels are silver, not human-annotated: a teacher LLM produced structured event annotations, which were distilled into these small models. No manual annotation was performed at any stage.

This matters for how you read the metrics: they measure agreement with LLM-generated labels, not with human ground truth. There is no human-labelled evaluation set.

Limitations & bias

• Silver labels cap the ceiling. Systematic teacher-LLM errors are inherited.
• Domain-specific. Tuned to Bangladeshi public-safety news; expect degradation on other domains, regions, or registers.
• Opinion pieces leak through. Editorials and foreign wire stories are sometimes classified as events by the upstream gate/router.
• Entity noise. NER tags some generic Bengali nouns (e.g. রাজধানীর "of the capital", সদর "HQ") as locations.
• No calibration. Confidence-style outputs are uncalibrated; do not read them as probabilities of correctness.
• INT8 quantization trades a little accuracy for ~4× size reduction and CPU speed.
• Not for high-stakes use. Casualty counts and event classifications are unverified model output and must not be used for emergency response, journalism, or policy without human review.

License

Released under cc-by-nc-4.0 — free to share and adapt for non-commercial purposes with attribution. Note that the training corpus consists of copyrighted news articles and the labels were LLM-distilled; downstream users are responsible for their own compliance.

Citation

@software{newsintel_ai,
  title  = {NewsIntel AI: distilled multilingual event extraction for Bangladeshi news},
  author = {Md. Sheikh Saidy},
  year   = {2026},
  url    = {https://huggingface.co/saidylive/newsintel-re}
}