Table Question Answering
Transformers
ONNX
Safetensors
English
modernbert
fill-mask
table-grounding
number-verification
clinical-study-report
Instructions to use decosaai/decosa-cell-pointer-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use decosaai/decosa-cell-pointer-modernbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="decosaai/decosa-cell-pointer-modernbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("decosaai/decosa-cell-pointer-modernbert-base") model = AutoModelForMaskedLM.from_pretrained("decosaai/decosa-cell-pointer-modernbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_summary.json from decosaai/decosa-cell-pointer-modernbert-base: direct link, hf CLI and curl.
- Browser
- Download file 3.67 kB
-
https://huggingface.co/decosaai/decosa-cell-pointer-modernbert-base/resolve/main/eval_summary.json
- Command line
-
hf download hf://decosaai/decosa-cell-pointer-modernbert-base/eval_summary.json
-
curl -L -o eval_summary.json https://huggingface.co/decosaai/decosa-cell-pointer-modernbert-base/resolve/main/eval_summary.json
3.67 kB
| { | |
| "model": "decosa-cell-pointer-modernbert-base", | |
| "default": "v2-blind (root)", | |
| "twin": "v1-aware (aware/)", | |
| "date": "2026-09-27", | |
| "setup": "CSR number checker end to end, only the pointer changes; BM25 table retrieval on every row; held-out sets never trained on; thresholds and model choice on the model's own dev split", | |
| "baseline": "Qwen3.8-27B as the pointer, values shown", | |
| "sets": { | |
| "synthetic_test": { | |
| "desc": "12 fictional CSRs from the training generator (different seeds), 96 planted errors", | |
| "qwen3.8-27b": { | |
| "caught": 93, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.11, | |
| "right_cell": 0.9898, | |
| "right_cell_planted": 0.8542, | |
| "s_per_100_pages": 79.3 | |
| }, | |
| "v2-blind": { | |
| "caught": 96, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.0, | |
| "right_cell": 1.0, | |
| "right_cell_planted": 1.0, | |
| "s_per_100_pages": 69.1 | |
| }, | |
| "v1-aware": { | |
| "caught": 96, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.0, | |
| "right_cell": 1.0, | |
| "right_cell_planted": 1.0, | |
| "s_per_100_pages": 139 | |
| }, | |
| "cascade_v2_plus_qwen_below_0.6": { | |
| "caught": 95, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.0, | |
| "right_cell": 0.999, | |
| "right_cell_planted": 0.99 | |
| } | |
| }, | |
| "rewritten_test": { | |
| "desc": "the same reports with paragraphs rewritten by Qwen3.8-27B, 96 planted errors", | |
| "qwen3.8-27b": { | |
| "caught": 93, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.0, | |
| "right_cell": 0.9869, | |
| "right_cell_planted": 0.8211 | |
| }, | |
| "v2-blind": { | |
| "caught": 95, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.0, | |
| "right_cell": 1.0, | |
| "right_cell_planted": 1.0, | |
| "s_per_100_pages": 71.5 | |
| }, | |
| "v1-aware": { | |
| "caught": 95, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.0, | |
| "right_cell": 0.999, | |
| "right_cell_planted": 0.989, | |
| "s_per_100_pages": 156 | |
| }, | |
| "cascade_v2_plus_qwen_below_0.6": { | |
| "caught": 93, | |
| "planted": 96, | |
| "false_flags_per_100_numbers": 0.0, | |
| "right_cell": 0.996, | |
| "right_cell_planted": 0.958 | |
| } | |
| }, | |
| "clinicaltrials_gov": { | |
| "desc": "30 real phase 3 trials (conditions not in training), posted results as report tables with template narratives, 178 planted errors", | |
| "qwen3.8-27b": { | |
| "caught": 172, | |
| "planted": 178, | |
| "false_flags_per_100_numbers": 2.79, | |
| "right_cell": 0.9717, | |
| "right_cell_planted": 0.8757, | |
| "s_per_100_pages": 307.2 | |
| }, | |
| "v2-blind": { | |
| "caught": 176, | |
| "planted": 178, | |
| "false_flags_per_100_numbers": 4.08, | |
| "right_cell": 0.9778, | |
| "right_cell_planted": 0.9774, | |
| "s_per_100_pages": 31.3 | |
| }, | |
| "v1-aware": { | |
| "caught": 176, | |
| "planted": 178, | |
| "false_flags_per_100_numbers": 3.65, | |
| "right_cell": 0.9817, | |
| "right_cell_planted": 0.9774, | |
| "s_per_100_pages": 61.7 | |
| }, | |
| "cascade_v2_plus_qwen_below_0.6": { | |
| "caught": 176, | |
| "planted": 178, | |
| "false_flags_per_100_numbers": 3.76, | |
| "right_cell": 0.981, | |
| "right_cell_planted": 0.977 | |
| } | |
| } | |
| }, | |
| "transfer_10k_mdna": { | |
| "desc": "two FY2025 10-Ks not trained on plus a synthetic sample, 69 figures; gold from a value-aware code tie-out", | |
| "right_cell": { | |
| "qwen3.8-27b_values_shown": 0.957, | |
| "qwen3.8-27b_values_hidden": 0.71, | |
| "v1-blind": 0.391, | |
| "v1-aware": 0.435, | |
| "v2-blind": 0.565 | |
| }, | |
| "note": "v1-aware pointed at the cell holding the written value on a planted swapped-period figure; the blind models did not" | |
| }, | |
| "cascade_verdict": "does not help; use the model alone", | |
| "latency": "0.2-0.45 s CPU per checked number, shared server, ONNX Runtime 4 threads", | |
| "source": "docs/evals/cell-pointer.md in the Decosa API repository" | |
| } |