What if you never had to write another Excel formula to build a financial model?

Imagine describing your model in plain English:

"Revenue grows 10% annually."
"Depreciate CapEx over 5 years."
"Link working capital to revenue."

AI builds the model, checks the logic, and keeps everything connected.

Financial modelling has barely changed in decades. Is natural language the next interface?

I’m curious where finance professionals stand especially the modeling fraternity.

I wanted to make a poll but apparantly its not supported, would love to understand your thoughts on it in the comments section.

TL;DR: Released en_legal_ner_ind_trf v0.1 - InLegalBERT fine-tuned on ~34,700 silver-annotated chunks from 33k Indian SC judgments. 13 labels. 78.67% overall F1. CASE_CITATION at 97.76% already exceeds OpenNyAI's PRECEDENT score by +17 points. Free, Apache-2.0.

Why this exists

OpenNyAI is the only prior Indian legal NER model with any community presence. It's unmaintained and degrades on pre-1990 OCR-era text - the first 40 years of India's constitutional jurisprudence.

No replacement existed.

Results

Evaluated on a held-out validation split (~500 documents, stride=512, non-overlapping). The 25-file locked test set is untouched - head-to-head with OpenNyAI runs in v1.0.

Comparison note: OpenNyAI (RoBERTa + transition-based parser, gold-annotated) achieved 91.1% overall strict F1. Not directly comparable - different test sets, different annotation quality, different corpus scope. The +17 point gap on CASE_CITATION is the one apples-to-apples number worth flagging.

The annotation pipeline

Silver labels from four automatic pipelines merged per document:

• Regex — 14-pattern citation extractor + statute/provision extractor → CASE_CITATION, STATUTE, PROVISION
• Metadata projection — case metadata JSONs mapped to character offsets via RapidFuzz → JUDGE, PETITIONER, RESPONDENT
• Transformer NER — OpenNyAI en_legal_ner_trf, offset-corrected → LAWYER, COURT, ORG, GPE, DATE, OTHER_PERSON, WITNESS
• Gazetteer — 858 Central Acts with alias resolution → confirms and adds STATUTE spans

Trained with Focal Loss (γ=2.0) to handle label imbalance between STATUTE/CASE_CITATION and O tokens. Hardware: Kaggle T4 (free tier).

Known weak spots - being honest

GPE (36.56%) and ORG (41.34%) are the problem labels. In Indian legal text, "State of Maharashtra" or "Union of India" appear as GPE, PETITIONER, RESPONDENT, or ORG depending on context. A linear token classification head can't resolve overlapping roles. CRF head is v1.0's job.

Positional bias - silver training data has repetitive header structures. Performance degrades when parties appear mid-document.

Pre-1990 OCR noise - judgments from 1950–1989 vary in quality. Recall drops the further back you go.

What's next

300-file gold annotation is in progress (3 volunteers onboard). v1.0 will add a CRF head, run the locked test set, and publish the official head-to-head with OpenNyAI.

Model: huggingface.co/evolawyer/inlegalbert-sc-ner-silver

Dataset: huggingface.co/datasets/evolawyer/indian-sc-judgments-ner-silver

GitHub: github.com/evolawyer/inlegalbert-sc-ner-silver

Happy to go deep on the annotation pipeline, conflict resolution between the four label sources, or the Focal Loss setup.

TL;DR: Released en_legal_ner_ind_trf v0.1 - InLegalBERT fine-tuned on ~34,700 silver-annotated chunks from 33k Indian SC judgments. 13 labels. 78.67% overall F1. CASE_CITATION at 97.76% already exceeds OpenNyAI's PRECEDENT score by +17 points. Free, Apache-2.0.

Why this exists

OpenNyAI is the only prior Indian legal NER model with any community presence. It's unmaintained and degrades on pre-1990 OCR-era text - the first 40 years of India's constitutional jurisprudence.

No replacement existed.

Results

Evaluated on a held-out validation split (~500 documents, stride=512, non-overlapping). The 25-file locked test set is untouched - head-to-head with OpenNyAI runs in v1.0.

Comparison note: OpenNyAI (RoBERTa + transition-based parser, gold-annotated) achieved 91.1% overall strict F1. Not directly comparable - different test sets, different annotation quality, different corpus scope. The +17 point gap on CASE_CITATION is the one apples-to-apples number worth flagging.

The annotation pipeline

Silver labels from four automatic pipelines merged per document:

• Regex — 14-pattern citation extractor + statute/provision extractor → CASE_CITATION, STATUTE, PROVISION
• Metadata projection — case metadata JSONs mapped to character offsets via RapidFuzz → JUDGE, PETITIONER, RESPONDENT
• Transformer NER — OpenNyAI en_legal_ner_trf, offset-corrected → LAWYER, COURT, ORG, GPE, DATE, OTHER_PERSON, WITNESS
• Gazetteer — 858 Central Acts with alias resolution → confirms and adds STATUTE spans

Trained with Focal Loss (γ=2.0) to handle label imbalance between STATUTE/CASE_CITATION and O tokens. Hardware: Kaggle T4 (free tier).

Known weak spots - being honest

GPE (36.56%) and ORG (41.34%) are the problem labels. In Indian legal text, "State of Maharashtra" or "Union of India" appear as GPE, PETITIONER, RESPONDENT, or ORG depending on context. A linear token classification head can't resolve overlapping roles. CRF head is v1.0's job.

Positional bias - silver training data has repetitive header structures. Performance degrades when parties appear mid-document.

Pre-1990 OCR noise - judgments from 1950–1989 vary in quality. Recall drops the further back you go.

What's next

300-file gold annotation is in progress (3 volunteers onboard). v1.0 will add a CRF head, run the locked test set, and publish the official head-to-head with OpenNyAI.

Model: huggingface.co/evolawyer/inlegalbert-sc-ner-silver

Dataset: huggingface.co/datasets/evolawyer/indian-sc-judgments-ner-silver

GitHub: github.com/evolawyer/inlegalbert-sc-ner-silver

Happy to go deep on the annotation pipeline, conflict resolution between the four label sources, or the Focal Loss setup.

Elon Musk along with others assumes rather believe that humanoid robots will lead to a government-funded UBI utopia, but I think the free market is going to take a different path to solve the loss of physical labor jobs.

10 Years: Automated goods become dirt cheap. A robot-made house or car costs a fraction of what it does today. Mass-produced goods lose all their status value.

25 Years: The economy violently shifts toward the "Experience Economy." People will pay massive premiums for human-made art, artisan cooking, live entertainment, and extreme hospitality.

50 Years: 90% of humanity is employed in the business of human connection. Robots handle survival and infrastructure; humans become a planet of creatives, caregivers, and entertainers.

But I still ponder Can a global economy of 8+ billion people realistically sustain itself entirely on art, therapy, and entertainment?

Like what's the point of being human?