added readme

README.md

@@ -1,3 +1,226 @@
 ---
 license: bsd-3-clause
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- bert
+- bert-mini
+- classification
+- decision-model
+- dynamic-classification
+- calibrated-classification
+- option-scoring
+- oryn
+datasets:
+- zefang-liu/phishing-email-dataset
+- fancyzhx/ag_news
+- google/boolq
+- SetFit/sst5
+metrics:
+- accuracy
+- brier_score
+- expected_calibration_error
+base_model:
+- boltuix/bert-mini
 ---
+
+# Oryn
+
+Oryn is a compact, low-latency decision model built on
+[`boltuix/bert-mini`](https://huggingface.co/boltuix/bert-mini).
+
+Unlike a conventional classifier with a fixed output layer, Oryn receives the
+classification question and candidate answers at inference time. This allows
+the same model to handle binary decisions, multiclass classification, ordinal
+scoring, and other option-based tasks.
+
+Oryn is inspired by the publicly described architecture and training ideas
+behind Laya.
+
+## Highlights
+
+- Approximately 21 MB of trained weights
+- Approximately 3 ms warmed-up single-request latency in the author's local test
+- Dynamically supplied classification options
+- Supports binary, categorical, and ordinal questions
+- Uses one `[MASK]` marker for each candidate answer
+- Scores all supplied options relative to one another
+- Trained on phishing detection, BoolQ, AG News, and SST-5
+- Designed for local and CPU-friendly inference
+
+Latency depends on the device, runtime, input length, and measurement method.
+The reported 3 ms result should not be treated as a universal benchmark.
+
+## Architecture
+
+Oryn consists of:
+
+1. A pretrained BERT Mini encoder
+2. Two additional Transformer encoder layers
+3. One `[MASK]` marker for each candidate answer
+4. A learned linear option scorer
+5. A softmax over the options provided with the request
+
+A request is represented approximately as:
+
+```text
+question: Which category best describes this text?
+state: The company reported record quarterly revenue.
+options: business [MASK], sports [MASK], technology [MASK], politics [MASK]
+```
+
+The contextual representation at each option's `[MASK]` token is passed through
+the shared scoring layer. The resulting logits are normalized across the
+candidate options.
+
+Because the answer space is supplied at request time, it is not permanently
+encoded into a fixed classification head.
+
+## Evaluation
+
+Oryn was evaluated on a random sample of 1,000 held-out examples from its
+multitask validation set.
+
+| Metric | Result |
+|---|---:|
+| Overall accuracy | 71.7% |
+| Brier score | 0.3468 |
+| Negative log-likelihood | 0.6228 |
+| Expected calibration error | 0.0381 |
+| Average confidence | 0.7518 |
+| Batched throughput | 129.9 examples/second |
+
+### Results by question type
+
+| Question type | Examples | Accuracy | Brier score |
+|---|---:|---:|---:|
+| Binary / `noul` | 591 | 85.62% | 0.2083 |
+| Categorical / `choice` | 196 | 79.59% | 0.2793 |
+| Ordinal / `score` | 213 | 25.82% | 0.7933 |
+
+The ordinal sentiment task is currently the model's main weakness. The model is
+substantially stronger on binary and categorical decisions.
+
+The benchmark was performed with batched GPU inference. Batched latency is not
+equivalent to end-to-end single-request latency.
+
+## Training data
+
+Oryn was trained on a mixture of:
+
+- `zefang-liu/phishing-email-dataset`
+- `fancyzhx/ag_news`
+- `google/boolq`
+- `SetFit/sst5`
+
+These datasets expose the model to:
+
+- Phishing and fraud detection
+- Binary reading comprehension
+- News topic classification
+- Five-level ordinal sentiment classification
+
+## Training method
+
+Training used two phases:
+
+1. Supervised cross-entropy warmup
+2. Calibration-oriented optimization using differentiable proper-scoring
+   objectives
+
+For nominal decisions, the calibration objective combines cross-entropy with a
+spherical scoring term. For ordinal decisions, cross-entropy is combined with
+ranked probability score.
+
+This training procedure is Laya-inspired, but it does not reproduce the
+original RLCD/REINFORCE procedure exactly.
+
+## Intended uses
+
+Oryn is suitable for experimentation with:
+
+- Message triage
+- Phishing detection
+- Topic classification
+- Sentiment classification
+- Support-request routing
+- Urgency classification
+- Small local decision systems
+- Dynamic classification research
+- Low-latency edge or local inference
+
+## General classification
+
+Oryn can technically receive any question and any set of options:
+
+```python
+result = predict(
+    text="The company reported record quarterly revenue.",
+    question="Which category best describes this text?",
+    options=[
+        "business",
+        "sports",
+        "technology",
+        "politics",
+    ],
+)
+```
+
+However, architectural support for arbitrary options does not guarantee
+reliable knowledge of arbitrary domains.
+
+Oryn is most reliable on tasks similar to its training mixture. New domains
+should be evaluated and usually fine-tuned before production use.
+
+## Example output
+
+```python
+{
+    "prediction": "business",
+    "confidence": 0.8421,
+    "probabilities": {
+        "business": 0.8421,
+        "sports": 0.0314,
+        "technology": 0.0987,
+        "politics": 0.0278
+    }
+}
+```
+
+## Files
+
+
+The custom `inference.py` implementation is required because Oryn uses a custom
+option-scoring head rather than a standard Transformers sequence-classification
+head.
+
+## Installation
+
+```bash
+pip install torch transformers
+```
+
+## Inference
+
+```bash
+python inference.py \
+  --text "The company reported record quarterly revenue." \
+  --question "Which category best describes this text?" \
+  --options business sports technology politics
+```
+
+## Citation
+
+If you use Oryn, cite this model repository:
+
+```bibtex
+@misc{oryn_bert_mini,
+  title        = {Oryn: A compact low-latency BERT Mini option-scoring model},
+  author       = {Timo Sarkar},
+  year         = {2026},
+  howpublished = {Hugging Face},
+  note         = {A compact low-latency BERT Mini option-scoring model}
+}
+```