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Initial Rust integration bundle (test F1 0.968, bf16 safetensors, candle-transformers compatible)

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Files changed (9) hide show
  1. README.md +125 -0
  2. calibrator.json +26 -0
  3. config.json +93 -0
  4. model.safetensors +3 -0
  5. spec.json +133 -0
  6. special_tokens_map.json +37 -0
  7. tensor_index.json +1200 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +944 -0
README.md ADDED
@@ -0,0 +1,125 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: apache-2.0
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+ base_model: answerdotai/ModernBERT-large
4
+ tags:
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+ - modernbert
6
+ - episodic-ingestion-compiler
7
+ - ingestion-model-spec-v1
8
+ - rust
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+ - candle
10
+ language:
11
+ - en
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+ library_name: candle
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+ ---
14
+
15
+ # episodic-ingestion-compiler — Rust integration bundle
16
+
17
+ Rust-native packaging of the `ingestion-model-spec-v1` encoder
18
+ (ModernBERT-large + 5 heads) for consumers using `candle-transformers`.
19
+ Zero Python runtime needed.
20
+
21
+ **Full training checkpoint** (best.pt + calibrator.joblib) lives at
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+ [`Avifenesh/episodic-ingestion-compiler-modernbert-large-span-5000`](https://huggingface.co/Avifenesh/episodic-ingestion-compiler-modernbert-large-span-5000).
23
+
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+ ## Headline metrics (13,786-row held-out test split)
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+
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+ | Metric | Value |
27
+ |---|---:|
28
+ | schema_validity | **1.000** |
29
+ | abstention_accuracy | **0.986** |
30
+ | claim_present F1 | 0.968 |
31
+ | false_emission_rate | 0.008 |
32
+ | span_token_IoU | 0.970 |
33
+ | predicate_accuracy_macro | 0.960 |
34
+ | subject_type_accuracy_macro | 0.998 |
35
+
36
+ ## Contents
37
+
38
+ ```
39
+ model.safetensors — ModernBERT backbone + 5 heads, bf16, 790 MB
40
+ config.json — arch + trained args + head schema
41
+ tokenizer.json — HF tokenizers-crate compatible
42
+ spec.json — spec-v1 contract (predicates, subject_types,
43
+ object_value shapes, literal fields)
44
+ calibrator.json — isotonic regression as piecewise-linear points
45
+ tensor_index.json — per-tensor shape/dtype (debug aid)
46
+ ```
47
+
48
+ Backbone keys are remapped to `backbone.model.*` so
49
+ `candle_transformers::models::modernbert::ModernBert::load(vb.pp("backbone"), ...)`
50
+ resolves without any glue code.
51
+
52
+ ## Loading in Rust
53
+
54
+ ```toml
55
+ [dependencies]
56
+ candle-core = "0.9"
57
+ candle-nn = "0.9"
58
+ candle-transformers = "0.9"
59
+ tokenizers = { version = "0.22", default-features = false, features = ["onig"] }
60
+ safetensors = "0.4"
61
+ ```
62
+
63
+ Working crate + example at
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+ https://github.com/avifenesh/episodic-ingestion-compiler/tree/main/rust/ingestion-model
65
+
66
+ ```rust
67
+ use candle_core::Device;
68
+ use ingestion_model::{Bundle, IngestionEncoder, Role};
69
+
70
+ let bundle = Bundle::load("ingestion_model_v1")?;
71
+ let device = Device::cuda_if_available(0).unwrap_or(Device::Cpu);
72
+ let encoder = IngestionEncoder::load(&bundle, device)?;
73
+
74
+ let raw = encoder.predict(
75
+ Role::Assistant,
76
+ "I edited crates/foo/src/lib.rs and ran cargo test which passed.",
77
+ )?;
78
+ // raw = claim_prob, predicate, class_hint, subject_type,
79
+ // confidence_raw, span_char (in original blurb), span_tok
80
+ let calibrated = bundle.calibrator.as_ref()
81
+ .map(|c| c.apply(raw.confidence_raw))
82
+ .unwrap_or(raw.confidence_raw);
83
+ ```
84
+
85
+ On CPU (single-core gemm), warm-start is ~100 ms/req with bf16 weights
86
+ automatically upcast to fp32 (candle's CPU matmul doesn't support bf16).
87
+ Batched / CUDA paths exposed via `predict_batch` and `--features cuda`.
88
+
89
+ ## Spec-v1 contract
90
+
91
+ Closed vocabularies are in `spec.json`:
92
+
93
+ - **21 predicates** — Class S (declarative) + Class E (evidence) +
94
+ reserved (`reverted_file`, `deleted_file`, `created_file`,
95
+ `committed`, `deployed`, `incident_observed`)
96
+ - **13 subject_types** — `objective`, `command`, `file`, `pr`,
97
+ `incident`, `policy`, `person`, `repo`, `team`, `service`, `document`,
98
+ `ticket`, `thread`
99
+ - **2 deferred predicates** that the model MUST NEVER emit —
100
+ `has_current_input`, `has_phase`
101
+ - **object_value shapes** per predicate family, for the wrapper
102
+
103
+ The model emits the information-bearing subset; the caller's
104
+ deterministic wrapper fills in literal fields (`status="candidate"`,
105
+ `source_authority="model_draft"`, `extraction_method="ingestion_model_v1"`),
106
+ per-predicate `object_value` dicts, `subject_id` extraction from the
107
+ span, and spec validation (silent-drop on failure).
108
+
109
+ Python reference implementation:
110
+ [`scripts/ingestion_wrapper.py`](https://github.com/avifenesh/episodic-ingestion-compiler/blob/main/scripts/ingestion_wrapper.py).
111
+ Rust crate ships only the encoder for now; the wrapper lives in the
112
+ caller's crate so it can evolve independently of the model.
113
+
114
+ ## Calibration
115
+
116
+ `calibrator.json` is a piecewise-linear isotonic map. Raw confidence
117
+ ECE 0.340 → post-calibration ECE 0.000 on the 2879-row cal split.
118
+ Apply with `(c - x[i]) / (x[i+1] - x[i])` linear interp between the
119
+ knots, clip to endpoints out of range.
120
+
121
+ ## License / lineage
122
+
123
+ ModernBERT-large is Apache-2.0. The 5 adapter heads and calibrator are
124
+ derivatives; training code is at
125
+ https://github.com/avifenesh/episodic-ingestion-compiler.
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+ {
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+ "schema_version": "ingestion_model_v1",
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+ "backbone": "answerdotai/ModernBERT-large",
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+ "backbone_type": "modernbert",
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+ "weights_dtype": "bf16",
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+ "trained_args": {
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+ "backbone": "answerdotai/ModernBERT-large",
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+ "train_file": "data/bakeoff/splits/span_slot.train.jsonl",
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+ "val_file": "data/bakeoff/splits/span_slot.val.jsonl",
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+ "out_dir": "models/phase2/modernbert-large-mh-span",
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+ "summary": "runs/phase2/modernbert-large-mh-span/summary.json",
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+ "max_length": 512,
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+ "batch_size": 16,
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+ "eval_batch_size": 32,
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+ "lr": 3e-05,
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+ "warmup_steps": 300,
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+ "max_steps": 5000,
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+ "span": {
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+ "pool": "per_token",
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+ "interpretation": "start/end logits; softmax over seq"
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+ }
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+ },
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+ "role_prefix": "required: '[USER] ', '[ASSISTANT] ', '[TOOL_RESULT] ' prepended to blurb text before tokenization",
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+ "max_length": 512
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+ }
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+ {
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+ "predicates": [
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+ "thread"
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+ },
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