| --- |
| license: cc-by-nc-4.0 |
| language: |
| - bn |
| - en |
| base_model: xlm-roberta-base |
| pipeline_tag: text-classification |
| library_name: onnxruntime |
| tags: |
| - bengali |
| - bangla |
| - information-extraction |
| - event-extraction |
| - onnx |
| - int8 |
| - quantized |
| - news |
| --- |
| |
| # newsintel-event-type |
|
|
| Multi-label event-type router: accident / disaster / crime (a document may fire several). |
|
|
| 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 **2 · router — accident/disaster/crime** of that chain: |
|
|
| ``` |
| document → relevance gate → event-type router → evidence selection |
| → NER → relation extraction → knowledge graph → structured event JSON |
| ``` |
|
|
| ## Model details |
|
|
| | | | |
| |---|---| |
| | Base model | `xlm-roberta-base` | |
| | Task | `event_type` | |
| | Input | `text` | |
| | Max length | 256 | |
| | Format | ONNX INT8 (dynamic quantization), ~279 MB | |
| | Version | `cf651b4757647` | |
| | Languages | Bengali (primary, ~96% of training corpus), English | |
|
|
| ### Labels |
| - `accident` |
| - `disaster` |
| - `crime` |
|
|
| ### Thresholds |
| - `accident`: 0.675 |
| - `disaster`: 0.75 |
| - `crime`: 0.55 |
|
|
| ### Decision rule |
| ``` |
| p = sigmoid(logits); fired[i] = p[i] >= thresholds[labels[i]] |
| ``` |
| These values also ship machine-readable in `model_manifest.json`, so a serving process can |
| consume the model without hardcoding anything. |
|
|
| ## Evaluation |
|
|
| | Metric | Value | |
| |---|---| |
| | macro-F1 | 0.8841 | |
|
|
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import onnxruntime as ort, numpy as np |
| from transformers import AutoTokenizer |
| |
| d = snapshot_download("saidylive/newsintel-event-type", revision="cf651b4757647", |
| 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): |
| # p = sigmoid(logits); fired[i] = p[i] >= thresholds[labels[i]] |
| ``` |
|
|
| ## 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 |
| |
| ```bibtex |
| @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-event-type} |
| } |
| ``` |
| |
