Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Independent publisher
Peter Parker
Peter-A-P
Models in Library11
Datasets in Library0
Models on Hugging Face11
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Models
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E4B at revision 411aa17b749aa952df1359d2dcea73917a544d9a, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.1% (94.7% to 95.4%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E4B at revision 411aa17b749aa952df1359d2dcea73917a544d9a, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.1% (94.7% to 95.4%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E4B at revision 411aa17b749aa952df1359d2dcea73917a544d9a, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.1% (94.7% to 95.4%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.2% (94.7% to 95.5%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.2% (94.7% to 95.5%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…
Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.2% (94.7% to 95.5%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…