Text Classification
PEFT
lora
document-question-answering
structured-decisions
calibration
synthetic-evaluation
Instructions to use botp/Solomon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use botp/Solomon with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download serving/evidence-head.json from botp/Solomon: direct link, hf CLI and curl.
- Browser
- Download file 3.11 kB
-
https://huggingface.co/botp/Solomon/resolve/1d2de8ab5a4b08374d9aa4f3d41cc34d3abf3a88/serving/evidence-head.json
- Command line
-
hf download hf://botp/Solomon@1d2de8ab5a4b08374d9aa4f3d41cc34d3abf3a88/serving/evidence-head.json
-
curl -L -o evidence-head.json https://huggingface.co/botp/Solomon/resolve/1d2de8ab5a4b08374d9aa4f3d41cc34d3abf3a88/serving/evidence-head.json
3.11 kB
| { | |
| "arch": "scope10-evidence-mlp-v1", | |
| "hidden_size": 5120, | |
| "width": 512, | |
| "input": "concat(q, u, q*u), LayerNorm(3H, elementwise_affine=False)", | |
| "activation": "gelu", | |
| "hidden_layer": "lm.norm(output of language_model.layers[42]) (the frozen final norm applied to a mid-layer tap)", | |
| "branch_state": "last position of question branch, adapter active (question placement)", | |
| "unit_pooling": "mean over document-prefill tokens overlapping the unit char range (adapter off)", | |
| "output": "logit per unit; probability = sigmoid(logit)", | |
| "dtype": "float32", | |
| "variant": "refit-mlp-mid+lex", | |
| "layer": "mid", | |
| "layer_index": 42, | |
| "lexical_residual": { | |
| "alpha": 9.770263671875, | |
| "rule": "logit = head_logit + alpha * lexical; probability = sigmoid(logit)", | |
| "lexical": "solomon.evidence_selector.lexical: question words minus stop words, document-IDF weighted overlap per sentence unit, divided by the row max", | |
| "question": "solomon.evidence_selector.question_of(the answer branch prompt)", | |
| "warning": "solomon.evidence_head.load alone returns head_logit WITHOUT the lexical term; the serving selector adds it" | |
| }, | |
| "refit": { | |
| "dev": { | |
| "gold_rows": 753, | |
| "hit@1": 0.5285524568393094, | |
| "hit@3": 0.8167330677290837, | |
| "hit@5": 0.8539176626826029, | |
| "recall@5": 0.8253652058432935 | |
| }, | |
| "thresholded_dev": { | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "f1": 0.0 | |
| }, | |
| "thresholds": { | |
| "policy": "scope10-evidence-thresholds-v1", | |
| "min_no_support_accuracy": 0.95, | |
| "rule": "unit kept iff max(unit probs) >= absent and prob >= select (not used in serving: v1.1 serves ranked pointers, see evidence-policy.json)", | |
| "tasks": { | |
| "boolean": { | |
| "select": 0.0, | |
| "absent": 1.0, | |
| "constraint_met": true, | |
| "rows": 1375, | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "f1": 0.0, | |
| "tp": 0, | |
| "fp": 0, | |
| "fn": 291, | |
| "no_support_rows": 1122, | |
| "no_support_accuracy": 1.0 | |
| }, | |
| "multilabel": { | |
| "select": 0.0, | |
| "absent": 1.0, | |
| "constraint_met": true, | |
| "rows": 931, | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "f1": 0.0, | |
| "tp": 0, | |
| "fp": 0, | |
| "fn": 385, | |
| "no_support_rows": 569, | |
| "no_support_accuracy": 1.0 | |
| }, | |
| "ordered": { | |
| "select": 0.0, | |
| "absent": 1.0, | |
| "constraint_met": true, | |
| "rows": 134, | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "f1": 0.0, | |
| "tp": 0, | |
| "fp": 0, | |
| "fn": 120, | |
| "no_support_rows": 36, | |
| "no_support_accuracy": 1.0 | |
| }, | |
| "single": { | |
| "select": 0.0, | |
| "absent": 1.0, | |
| "constraint_met": true, | |
| "rows": 134, | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "f1": 0.0, | |
| "tp": 0, | |
| "fp": 0, | |
| "fn": 48, | |
| "no_support_rows": 94, | |
| "no_support_accuracy": 1.0 | |
| } | |
| } | |
| } | |
| } | |
| } |