Text Classification
TensorRT
Core ML
ONNX
Safetensors
OpenVINO
English
Russian
bert
feature-extraction
vocabulary-filtering
denoising
multilingual
quantized
int8
Instructions to use faxenoff/code-daemon-denoise-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use faxenoff/code-daemon-denoise-v1 with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
denoise head: temperature calibration T=2.13 + re-measured card
Browse files
README.md
CHANGED
|
@@ -157,20 +157,35 @@ ids — do not remap them yourself.
|
|
| 157 |
|
| 158 |
## 6. Evaluation
|
| 159 |
|
| 160 |
-
|
|
|
|
| 161 |
|
| 162 |
| metric | value |
|
| 163 |
|---|--:|
|
| 164 |
-
| SAFE (keep) F1 | **0.
|
| 165 |
-
| BALLAST (drop) F1 | **0.
|
| 166 |
-
| Strip precision | **0.
|
| 167 |
-
|
| 168 |
-
|
| 169 |
-
38% of the size — the pruning removes rows the two target languages never reach, so there is nothing
|
| 170 |
-
to lose by it.
|
| 171 |
|
| 172 |
Strip precision is the number to watch if you tune the threshold: it says how often a word the model
|
| 173 |
-
drops really was ballast.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 174 |
|
| 175 |
---
|
| 176 |
|
|
|
|
| 157 |
|
| 158 |
## 6. Evaluation
|
| 159 |
|
| 160 |
+
Re-measured 2026-09-07 on the shipped INT8 engine against the frozen held-out set
|
| 161 |
+
(224 words, 95 keep / 129 drop), at `strip_threshold = 0.95`:
|
| 162 |
|
| 163 |
| metric | value |
|
| 164 |
|---|--:|
|
| 165 |
+
| SAFE (keep) F1 | **0.70** |
|
| 166 |
+
| BALLAST (drop) F1 | **0.82** |
|
| 167 |
+
| Strip precision | **0.89** |
|
| 168 |
+
| Strip recall | **0.77** |
|
| 169 |
+
| Technical words wrongly dropped | **12 / 95** |
|
|
|
|
|
|
|
| 170 |
|
| 171 |
Strip precision is the number to watch if you tune the threshold: it says how often a word the model
|
| 172 |
+
drops really was ballast. The count of technical words wrongly dropped is the one to watch if you
|
| 173 |
+
care about the expensive direction.
|
| 174 |
+
|
| 175 |
+
**Head calibration (2026-09-07).** The head shipped before this date was fitted against an earlier
|
| 176 |
+
build of the encoder, and its scores saturated: 174 of the 224 held-out words landed at P < 0.01 or
|
| 177 |
+
P > 0.99, so `strip_threshold` decided only twelve of them and every useful operating point sat above
|
| 178 |
+
t = 0.99 — out of reach of the documented knob. The head has been **temperature-scaled** (w and b
|
| 179 |
+
divided by T = 2.13). That is a monotone transform, so the model's ranking, its ROC and all SAFE
|
| 180 |
+
metrics are unchanged by construction; only the probability scale moves, which puts the threshold
|
| 181 |
+
back in charge. At the default 0.95 this halves the technical words wrongly dropped (24 → 12) for
|
| 182 |
+
15 fewer ballast words removed (114 → 99).
|
| 183 |
+
|
| 184 |
+
**On the numbers previously published here (SAFE F1 0.79 / strip precision 0.88).** They are not
|
| 185 |
+
reproducible from the artifacts in this repository, and re-measuring is what surfaced the saturation
|
| 186 |
+
above. Two independent implementations — the daemon's own harness and a standalone script — agree on
|
| 187 |
+
the table above to the row. The earlier figures appear to belong to an encoder build that predates
|
| 188 |
+
the vocabulary prune and is no longer published; they are corrected rather than defended.
|
| 189 |
|
| 190 |
---
|
| 191 |
|