barga v0.1.0: G14 decision model (English, Nepali)

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LICENSE

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NOTICE

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+barga
+Copyright 2026 Ampixa
+
+This product is licensed under the Apache License, Version 2.0 (see LICENSE).
+
+The barga model weights are fine-tuned from the encoder of Julia-1 by Supersonic Labs
+(https://huggingface.co/SupersonicLabs/Julia-1, revision a85b127321d580d65176c89ced8273f305745d85),
+licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0).
+
+Julia-1 is in turn built on mmBERT-small by the Johns Hopkins University CLSP group
+(https://huggingface.co/jhu-clsp/mmBERT-small), licensed under the MIT License.
+
+The model code drives transformers' ModernBERT implementation (Hugging Face transformers, Apache License 2.0).

README.md

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+---
+license: apache-2.0
+language:
+- en
+- ne
+library_name: barga
+pipeline_tag: zero-shot-classification
+base_model: SupersonicLabs/Julia-1
+tags:
+- nepali
+- decision-model
+- multiple-choice
+- system-one
+- modernbert
+- mmbert
+model-index:
+- name: barga
+  results:
+  - task:
+      type: multiple-choice
+      name: Typed decisions
+    dataset:
+      name: typed-decisions (test split)
+      type: LocalLLaMA/typed-decisions
+      split: test
+    metrics:
+    - type: accuracy
+      value: 76.3
+      name: Accuracy (released checkpoint)
+  - task:
+      type: natural-language-inference
+      name: Natural language inference
+    dataset:
+      name: MultiNLI (300-question sample)
+      type: nyu-mll/multi_nli
+    metrics:
+    - type: accuracy
+      value: 66.0
+      name: Accuracy (released checkpoint)
+  - task:
+      type: natural-language-inference
+      name: Natural language inference (Nepali)
+    dataset:
+      name: MNLI-Nepali (300-question sample)
+      type: IRIIS-RESEARCH/MNLI-Nepali
+    metrics:
+    - type: accuracy
+      value: 64.3
+      name: Accuracy (released checkpoint)
+  - task:
+      type: question-answering
+      name: Answerability / extractive QA as choice
+    dataset:
+      name: SQuAD 2.0 (300-question sample)
+      type: rajpurkar/squad_v2
+    metrics:
+    - type: accuracy
+      value: 61.0
+      name: Accuracy (released checkpoint)
+  - task:
+      type: text-classification
+      name: Intent classification
+    dataset:
+      name: CLINC150 (300-question sample)
+      type: clinc/clinc_oos
+    metrics:
+    - type: accuracy
+      value: 90.7
+      name: Accuracy (released checkpoint)
+---
+
+# barga
+
+**barga** (Nepali बर्ग, "category") is a small **system-one decision model** for English and Nepali. It reads a
+**state** (a conversation, a call transcript, a document, a game position) and one or more **questions**, each with
+its own candidate **options**, and returns a probability for every option in a single forward pass. It does not
+generate text. It decides.
+
+- **Size:** 140.6M parameters (ModernBERT / mmBERT-small encoder + a small option scorer), fp32 safetensors, 562 MB.
+- **Languages:** English, Nepali (Devanagari and romanized), and code-mixed Nepali–English.
+- **Architecture:** split-layer. The lower 11 of 22 encoder layers read the state once; the top 11 layers read each
+  question jointly with the state. Every question about the same state shares one state encoding.
+- **Speed:** 0.87 s per turn (median; 1.38 s p95) on an Apple M3 Pro CPU with 4 threads, for simulator call turns of
+  about 6 questions over states of up to 1,024 tokens; a short input with one question takes 0.05–0.2 s. Peak
+  process memory 2.6 GB.
+- **Licence:** Apache-2.0. Built on [Julia-1](https://huggingface.co/SupersonicLabs/Julia-1) (Apache-2.0), which is
+  built on [mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) (MIT).
+- **Made by** [Ampixa](https://ampixa.com).
+
+## Quickstart
+
+```bash
+pip install barga
+```
+
+```python
+import barga
+
+model = barga.load("ampixa/barga")          # device="cuda" for a GPU
+
+state = ("Caller: Hello, I want to book a check-up for my dog tomorrow morning.\n"
+         "Agent: Sure. We have 9:30 or 11:00 tomorrow.\n"
+         "Caller: 9:30 is fine.")
+
+for d in model.decide(state, [
+    {"id": "intent", "text": "What does the caller want?",
+     "options": [{"id": "book", "description": "Book an appointment"},
+                 {"id": "cancel", "description": "Cancel an appointment"},
+                 {"id": "info", "description": "Only ask for information"}]},
+    {"id": "intent_ne", "text": "कल गर्नेले के चाहनुहुन्छ?",
+     "options": [{"id": "book", "description": "अपोइन्टमेन्ट बुक गर्न"},
+                 {"id": "cancel", "description": "अपोइन्टमेन्ट रद्द गर्न"},
+                 {"id": "info", "description": "जानकारी मात्र सोध्न"}]},
+]):
+    print(d.question_id, d.choice, d.probs)
+# intent    book  {'book': 0.989, 'cancel': 0.009, 'info': 0.003}
+# intent_ne book  {'book': 0.983, 'cancel': 0.002, 'info': 0.015}
+```
+
+Questions can be `"choice"` (default), `"boolean"` (two options valued `True`/`False`) or `"ordinal"` (numeric
+option values; the result also carries an `expected_value`). Each takes 2–20 options and an optional `rubric`. The
+model reads only the state, the question, the rubric and each option's description, never the ids. The input
+format is documented in the [barga package](https://pypi.org/project/barga/).
+
+## How to read the output
+
+`choice` is the most probable option; `probs` gives every option's probability (they sum to 1 within a question).
+Probabilities compare options within one question. A flat distribution (say a top probability below 0.6) means the
+model is unsure, which is often correct when the state does not answer the question. Set your own threshold on
+your own data before acting on a decision automatically.
+
+## Intended uses
+
+- Turn-by-turn decisions in voice and chat agents: intent, slot values, whether the caller confirmed, what to do next.
+- Classification, routing and yes/no checks over English or Nepali text, with the label set given at run time.
+- Reading-comprehension style checks (does the text support this statement? is this question answerable?).
+- Rule and strategy questions about Nepali-played card and board games (see the limits below).
+
+## Evaluation
+
+All numbers are accuracy (%) on held-out test sets that were never used for training or checkpoint selection. The
+released checkpoint is **seed 17**; **two-seed** is the mean of seed 17 and an identically trained seed-18
+replicate, shown to indicate seed variance. Sampled sets use a fixed seed (17).
+
+### Calls and agent decisions
+
+| test set | questions | released | two-seed |
+|---|---:|---:|---:|
+| Held-out real customer-service calls, set A (English questions) | 2,589 | 77.0 | 76.6 |
+| same calls, Nepali questions | 2,589 | 77.1 | 76.7 |
+| same calls rendered in the output style of a speech recognizer (English / Nepali questions) | 2,589 | 72.1 / 71.6 | 72.1 / 71.9 |
+| Held-out real calls, set B (English) | 135 | 64.4 | 64.8 |
+| set B in speech-recognizer style | 135 | 62.2 | 60.7 |
+| Receptionist simulator, template phrasing (300 calls) | 1,779 | 94.4 | 94.4 |
+| Receptionist simulator, LLM phrasing (300 calls) | 1,678 | 96.1 | 96.1 |
+| typed-decisions test split | 2,000 | 76.3 | 76.0 |
+
+### Reading comprehension and classification (300-question samples unless noted)
+
+| test set | released | two-seed |
+|---|---:|---:|
+| MultiNLI | 66.0 | 65.5 |
+| MNLI-Nepali | 64.3 | 63.0 |
+| SQuAD 2.0 | 61.0 | 60.2 |
+| BoolQ | 60.7 | 62.3 |
+| ShARC | 62.7 | 64.2 |
+| PAWS | 56.7 | 56.8 |
+| CLINC150 intents | 90.7 | 91.3 |
+| AG News (100) | 95.0 | 94.0 |
+| Emotion (100) | 81.0 | 84.5 |
+| Hard cases (1,500; the hard slice of an Ampixa support-ticket decision set) | 26.3 | 26.1 |
+
+### Games (rule and strategy questions; positions from engines and self-play)
+
+| game | questions | released | two-seed |
+|---|---:|---:|---:|
+| Dhumbal | 1,440 | 62.0 | 62.1 |
+| Ludo | 1,592 | 62.9 | 62.7 |
+| Marriage | 1,494 | 47.7 | 47.6 |
+| Call Break | 1,828 | 47.8 | 46.7 |
+| Chess | 1,674 | 42.8 | 41.3 |
+| Bagh-Chal | 3,726 | 24.0 | 24.1 |
+
+Game questions have 2–20 options, so chance differs by set: about 14% on Bagh-Chal, and up to 50% on yes/no
+families.
+
+### Against Julia-1 on identical questions
+
+Same questions and the same scoring for both models; a question a model cannot encode counts as wrong.
+
+| | Julia-1 | barga (released) |
+|---|---:|---:|
+| typed-decisions test | 72.5 | **76.3** |
+| CLINC150 | 55.7 | **90.7** |
+| MultiNLI / MNLI-Nepali | 30.7 / 31.0 | **66.0 / 64.3** |
+| ShARC / BoolQ / SQuAD 2.0 | 31.0 / 45.7 / 51.0 | **62.7 / 60.7 / 61.0** |
+| PAWS | 54.3 | **56.7** |
+| AG News (100) | 94.0 | **95.0** |
+| Emotion (100) | **86.0** | 81.0 |
+| Hard cases | **33.7** | 26.3 |
+| Julia-1's own evaluation families (12 Open-Jev families, macro) | **87.8** | 58.5 |
+
+On the real-call and simulator sets barga scores 77 and 94–96, against Julia-1's 22 and 23. Read that gap with care:
+- **Encoding:** Julia-1 cannot encode some of those questions; on the questions both models answer it scores 17.7–24.8.
+- **Training data:** barga was trained on the training splits of those same sources, and Julia-1 was not.
+
+Julia-1 leads on its own evaluation families: game-playing agents (tile platformer, ViZDoom, Snake), workflow and
+customer controls, and reasoning controls. barga was not trained on those families.
+
+## Limitations
+
+- **Near chance on some game families.** Chess move legality (yes/no), tic-tac-toe, Bagh-Chal, Marriage discards,
+  Ludo safe squares and Call Break card play score within a few points of chance. Do not use barga as a game engine;
+  use a rules engine for legality and barga, at most, to rank legal moves.
+- **Out of domain.** Julia-1's agent-control families (above) and the hard support-ticket cases are weak spots.
+- **Real-call accuracy is about 77%.** Speech-recognizer errors cost about 5 points (72%). Keep a human or a
+  confirmation step in the loop for anything with consequences.
+- **Calibration.** Probabilities are informative but not calibrated to your data; threshold them on your own data.
+- **Context.** 1,024 state tokens (when a state is longer, barga keeps its beginning and its most recent end) and
+  768 tokens per question with its options. Longer questions are rejected, not cut.
+- **Languages.** Tested on English, Nepali (Devanagari and romanized) and code-mixed text. Other languages inherit
+  some ability from mmBERT but were not evaluated.
+- **Seed variance.** Two identically trained seeds differ by up to 3.5 points on small sets (135–300 questions);
+  differences smaller than that between models are not meaningful.
+
+## Out-of-scope uses
+
+Generating text; open-ended question answering without candidate options; medical, legal, financial or safety-critical
+decisions without human review; profiling or surveillance of people; any use that violates the licences of the
+training data sources listed below.
+
+## Training data
+
+barga was fine-tuned from the encoder of Julia-1 on a weighted mix of 32 training sets:
+
+| source | share | licence / terms |
+|---|---:|---|
+| Nepali/English customer-service calls from partners, used under agreement; not released | 13.1% | private; used under agreement |
+| Receptionist simulator: synthetic calls; template phrasing and LLM-written phrasing | 13.9% | generated by Ampixa |
+| typed-decisions (train split) | 8.8% | Apache-2.0 |
+| MultiNLI, SQuAD 2.0, ShARC, CLINC150, BoolQ, PAWS (English) | 19.5% | CC BY 3.0 / CC BY-SA 3.0 / MIT / other (MultiNLI), CC BY-SA 4.0, CC BY-SA 3.0, CC BY 3.0, CC BY-SA 3.0, "may be freely used for any purpose" |
+| MultiNLI, SQuAD 2.0, ShARC, CLINC150, BoolQ machine-translated to Nepali (Sarvam-Translate) | 14.1% | as the originals |
+| MNLI-Nepali (IRIIS-RESEARCH) | 4.8% | see the dataset |
+| Bagh-Chal, Marriage, Dhumbal, Ludo, Call Break positions with rule-checked labels | 12.4% | generated by Ampixa |
+| Chess positions from the Lichess open database with engine labels | 3.0% | CC0 (Lichess database) |
+| Ampixa support-ticket decision sets (routing, negation, urgency, emotion, abstention) | 4.8% | Ampixa |
+| Typed-decision replays with teacher soft labels | 2.4% | generated by Ampixa |
+| AG News, Emotion (dair-ai), Banking77, MASSIVE (scenario) | 3.2% | research / non-commercial (AG News), educational and research purposes only (Emotion), CC BY 4.0, CC BY 4.0 |
+
+Protected test calls and the typed-decisions test split were never used for training. No call audio or transcripts
+are released.
+
+## Files
+
+- `backbone/`: ModernBERT encoder (config.json, model.safetensors)
+- `heads.pt`: option scorer weights
+- `tokenizer/`: tokenizer (the mmBERT/Gemma-style 256k vocabulary)
+- `bundle.json`: architecture, serialization config and the sha256 of every file above; `barga.load` verifies
+  them before loading. Its backbone reference was rewritten from a local path to its public source; every hashed
+  file is byte-identical to the evaluated checkpoint.
+
+## Citation
+
+```bibtex
+@misc{barga2026,
+  title  = {barga: a system-one decision model for English and Nepali},
+  author = {Ampixa},
+  year   = {2026},
+  url    = {https://huggingface.co/ampixa/barga}
+}
+```
+
+barga builds on Julia-1 (Supersonic Labs) and mmBERT (Johns Hopkins University CLSP).

backbone/config.json

@@ -0,0 +1,79 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 1,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 1,
+  "global_attn_every_n_layers": 3,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 384,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
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