README.md

---
license: mit
language:
- code
- en
- ru
tags:
- code
- token-classification
- named-entity-recognition
- ner
- xlm-roberta
- weak-supervision
- software-engineering
pipeline_tag: token-classification
base_model:
- nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
---

# code-daemon-ner-v1

A small **NER model that finds software-engineering entities in prose** — the sentences of a
README, a design doc, an issue thread, a commit message. In
[Code-Daemon](https://code-daemon.dev) the spans it emits become the concept nodes
that link a paragraph of documentation to the code it talks about.

**What makes it different:** this is prose NER, not code parsing. A parser already extracts every
identifier in a source file, exactly. What it cannot do is read *"the IVF rescoring path in
`faiss_index.zig` regressed nDCG@10 by 4 points"* and tell you that `IVF` is an algorithm,
`faiss_index.zig` a file and `nDCG@10` a metric. No public model exists for these labels, and the
zero-shot ones miss the conventions that matter here — so this one was trained for them, and it
runs faster on a CPU than they do on a GPU.

- **~117M parameters** — XLM-RoBERTa, 12 layers × 384 hidden, multilingual vocabulary.
- **English and Russian** prose; **128 tokens** — one or two sentences per call.
- **2-input ONNX** (`input_ids`, `attention_mask`) → `logits [batch, seq, 17]` (8 types in BIO).

## Labels

| tier | type | what | examples |
|---|---|---|---|
| Core | `component` | a named part of a system, **including model names** | `DocStore`, `mMiniLMv2` |
| Core | `api_endpoint` | a callable surface: function, method, route, **agent tool name** | `semantic_search`, `POST /v1/messages` |
| Core | `file_path` | a path or file name | `src/semantic/faiss_index.zig` |
| Core | `tool` | something you invoke: executable, runtime, service, language, hardware | `TensorRT`, `rclone`, `SQL` |
| Soft | `algorithm` | a named method | `BM25`, `Louvain` |
| Soft | `data_structure` | a named container or layout | `CSR`, `Prolly tree` |
| Soft | `config` | a knob, flag, env var | `journal_mode=MEMORY`, `--epochs` |
| Soft | `metric` | a measurement or unit | `nDCG@10`, `p95 latency` |

Core types are what a knowledge graph links on; Soft types are tolerated at lower precision.

## Numbers

1 500 hand-reviewed sentences, entity level (exact span **and** type). The honest alternative for a
private label set is a zero-shot model you hand the label names to — GLiNER, each run at its best
of four prompt/threshold settings:

| model | params | Core macro-F1 | P / R | chunks/s |
|---|--:|--:|--:|--:|
| **this model** | **117M** | **0.426** | 0.38 / 0.55 | **68 on a CPU** |
| gliner-multitask-v1.0 | 909M | 0.284 | 0.23 / 0.41 | 10 on a GPU |
| gliner_multi-v2.1 | 289M | 0.155 | 0.17 / 0.23 | 39 on a GPU |

Per Core type, F1:

| type | this model | gliner-multitask | gliner_multi |
|---|--:|--:|--:|
| `api_endpoint` | **0.521** | 0.382 | 0.006 |
| `file_path` | **0.497** | 0.300 | 0.248 |
| `tool` | **0.425** | 0.312 | 0.286 |
| `component` | **0.263** | 0.143 | 0.080 |

- **Fine-tuning buys the label convention.** Where a label name explains itself (`file_path`,
  `tool`), zero-shot gets within ~1.5×; where it is a project rule (`api_endpoint` includes agent
  tool names), it collapses. If your types are ordinary ones, a zero-shot model may serve you better.
- **The trade: recall over precision.** On the strongest types it over-predicts (P 0.39–0.41,
  R 0.68–0.72). Use it as a **candidate generator behind a filter**, not as a labeller you trust
  unattended.
- **Soft types are weak** (macro-F1 0.21; `config` 0.12), and multi-word spans mostly come back
  as single tokens.

Inside the daemon on TensorRT, a 32-sentence batch takes 3 ms of GPU and 15 ms of tokenisation —
parallelise the tokenizer before looking for a faster engine.

## How to use it

- **Pre-split identifiers** (`camelCase` / `snake_case` → words) before tokenizing and keep
  **seq 128**: that is how it was trained, and scoring it any other way gives lower numbers.
- **Calibrated probabilities:** divide the logits by `T` from `temperature.json` (0.665) before
  the softmax; `argmax` alone does not need it.
- **Shapes:** TensorRT INT8 single profile seq 128; OpenVINO CPU / iGPU INT8 at batch 32, NPU INT4
  at batch 8; Core ML `b32 s128`.

```python
import onnxruntime as ort, numpy as np, json
from transformers import AutoTokenizer

tok    = AutoTokenizer.from_pretrained(".")            # bundled XLM-R SentencePiece
labels = json.load(open("code-daemon-ner-v1_label_map.json"))["id2label"]
T      = json.load(open("temperature.json"))["T"]
sess   = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])

def tag(text, max_len=128):
    enc = tok(text, return_tensors="np", truncation=True, max_length=max_len,
              return_offsets_mapping=True, return_token_type_ids=False)
    logits = sess.run(None, {"input_ids":      enc["input_ids"].astype(np.int64),
                             "attention_mask": enc["attention_mask"].astype(np.int64)})[0][0]
    probs  = np.exp(logits / T) / np.exp(logits / T).sum(-1, keepdims=True)   # calibrated
    for (a, b), row in zip(enc["offset_mapping"][0], probs):
        lab = labels[str(int(row.argmax()))]
        if lab != "O" and b > a:
            yield text[a:b], lab, float(row.max())

for span, label, p in tag("The IVF rescoring path in src/semantic/faiss_index.zig cost 4 nDCG@10 points."):
    print(f"{span!r:40} {label:18} {p:.2f}")
```

**Out of scope:** symbols in source code (use a parser), general-domain NER, any high-stakes
automatic decision.

## How it was made

- **Backbone:** `mMiniLMv2-L12-H384`, a multilingual MiniLM distilled from XLM-RoBERTa-Large.
- **Labels by weak supervision:** with no corpus for this label set, several independent labellers
  voted on each chunk and the votes were reconciled into one BIO sequence; the backbone was then
  fine-tuned on them and its output temperature-calibrated.
- **Evaluation is human-labelled** and disjoint from the training labels.

## Files

| file | what it is |
|---|---|
| `model.onnx` | FP32, dynamic shape — the standalone path |
| `model_static.onnx`, `model_int8qdt.onnx` | seq pinned to 128 / INT8 Q/DQ — engine-build inputs |
| `model.safetensors`, `config.json` | the weights as `XLMRobertaForTokenClassification`; `AutoModelForTokenClassification` loads them as-is; the Apple (MLX) build is prepared from this pair |
| `sentencepiece.bpe.model`, `tokenizer_config.json`, `code-daemon-ner-v1_tokenizer.json` | the tokenizer (SentencePiece + fast tokenizer) |
| `code-daemon-ner-v1_label_map.json` | the 17 BIO classes |
| `temperature.json` | the calibration scalar `T` |
| `eval_metrics.json`, `manifest.json` | per-type scores; build metadata |
| `code-daemon-ner-v1_{win_x64,linux_x64}_trt11.0_sm_{75,80,86,89,120}.engine` | TensorRT INT8 per OS × GPU; `sm_90` (H100/H200) Linux only |
| `code-daemon-ner-v1_ov2026.4_{cpu,igpu_lnl}_int8_b32_s128.{xml,bin}`, `…_npu_int4_b8_s128.*` | OpenVINO 2026.4 for Intel CPU, iGPU, NPU |
| `coreml_ane/embed.mlpackage/` | Core ML for the Apple Neural Engine, function `b32 s128`; load with `cpuAndNeuralEngine` |

A TensorRT engine loads only on the GPU architecture and TensorRT version that built it; any
other machine uses OpenVINO or the ONNX.

## License

Released under the **MIT license**. The backbone is a re-upload of Microsoft's mMiniLMv2
([microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm), MIT); the re-upload
repository itself declares no license. Neither the training corpus nor the evaluation set ships
here. Not legal advice.

## Attribution

Backbone: **[nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large)**
(itself distilled from XLM-RoBERTa-Large).