Soma-v5

nivas25/Soma-v5

Soma-v5 is a text classification model from nivas25. Use it when you need a label for a piece of text. The card lists the license as mit.

A SPEAK | SILENT helper-bot gate for multi-party Ubuntu IRC. A 7B LoRA student decides whether an extra helper should talk after the last line of a 12-line window. It is not a Theory-of-Mind stack, not PPO, not emotio…

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Updated Aug 30, 2026

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461 MB

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Hugging Face

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.safetensors162 MB · 93%

At a glance

Task: Text Classification
License: mit
Access: Public
Created: Aug 30, 2026
Updated: Aug 30, 2026
SHA: 7add4733

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Datasets

jkkummerfeld/irc_disentangle

Model card

From the Hugging Face model README

SOMA gate

A SPEAK | SILENT helper-bot gate for multi-party Ubuntu IRC. A 7B LoRA student decides whether an extra helper should talk after the last line of a 12-line window. It is not a Theory-of-Mind stack, not PPO, not emotion, and not a reply writer.

Default is SILENT. A frozen writer (out of scope here) would run only if the gate says SPEAK.

Data

Upstream logs: jkkummerfeld/irc_disentangle — Kummerfeld et al., ACL 2019 (P19-1374 / arXiv:1810.11118). CC-BY-4.0. One Hub row is one IRC line (id, raw, ascii, tokenized, date, connections).

We add SPEAK/SILENT labels. Hub has none. Not Lowe/McGill Ubuntu Dialogue Corpus.

You must accept the Hugging Face dataset license to download raw dumps. This clone does not ship those parquets.

What is in this clone vs not

In the clone	Not in the clone (rebuild or request)
Code (`src/`, `scripts/01–17`, `tests/`)	`data/raw/*.parquet` (IRC dumps)
200 gold windows (`data/silver/audit_200.csv`, `sft_test.jsonl`)	~21k teacher silver jsonl
Friend holdout CSVs (gold empty)	LoRA adapter / `.safetensors`
Reports + figures	`sft_train_1to3.jsonl` (~28 MB silver; rebuild after harvest)

Train file is gitignored even though it is under 50 MB: it is teacher-labeled IRC text, not gold. Rebuild with harvest + scripts/15_export_sft_and_eda.py + scripts/16_freeze_friends_and_splits.py, or request from the authors.

Pipeline

script	role
`01_download.py`	Hub snapshots → `data/raw/`
`02_analyze.py`	parse + EDA → `messages.parquet`
`03_build_windows.py`	exact W=12 windows
`04_export.py`	30k `label_queue` (gold empty)
`05_select_pilot_2k.py`	freeze 2k ids
`06_silver_label_pilot.py`	Qwen-32B v1 silver on 2k (not mixed into train)
`07_audit_sample.py`	freeze 200 for human audit
`07_label_ui.py`	local gold UI
`08_apply_reviewed_gold.py`	copy 200 gold onto the queue
`09` / `10` / `11` Gate 1	teacher vs gold: v2 73.5% → v2.1 77.5% → v2.2 85.5% PASS
`12`–`14` harvest	v2.2 silver 5,100 SPEAK / 16,250 SILENT
`15_export_sft_and_eda.py`	1:1 SFT + paper EDA
`16_freeze_friends_and_splits.py`	1:3 train, 400 friend holdout
`17_launch_lora_train.py`	7B LoRA on Modal H100

No stemming, lemmatizing, stopwords, or lowercasing. See reports/data_preprocessing.md.

Setup

Python 3.12 (3.11+). uv.

uv sync --extra dev
cp .env.example .env   # HF_TOKEN if you download Hub data
uv run pytest

Download + windows (needs Hub token, writes large parquets locally):

uv run python scripts/01_download.py
uv run python scripts/02_analyze.py
uv run python scripts/03_build_windows.py
uv run python scripts/04_export.py

Teacher harvest and LoRA need Modal credentials. Do not commit .env.

Results (200 gold test)

Best LoRA checkpoint step 1200. Mapping Silent=0, Speak=1.

method	Macro-F1	Speak-F1	Silent-F1
LoRA 7B	0.878	0.861	0.895
B3 question ∧ ¬helper	0.775	0.716	0.833
B2 question	0.682	0.652	0.712
B1 always SILENT	0.355	0.000	0.710

Confusion: TN 102, FP 8, FN 16, TP 74. Details: reports/training_eval.md.

License

Code: MIT (LICENSE)
Upstream IRC data: CC-BY-4.0 (Kummerfeld et al. 2019). Users must accept the Hub license before 01_download.py.

Authors

PES University capstone — SOMA (Synthetic Orchestrator for Meaningful Assistance). Gate only: SPEAK/SILENT on Ubuntu IRC, not ToM/PPO.

Task: Text Classification
License: mit
Languages: en
Created: Aug 30, 2026
Updated: Aug 30, 2026