eternalGenius
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rubert_level1_v2

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 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+base_model: DeepPavlov/rubert-base-cased
+language:
+  - ru
+tags:
+  - text-classification
+  - bert
+  - safetensors
+  - multilabel-classification
+  - requirements-engineering
+  - generated_from_trainer
+model-index:
+  - name: rubert_level1_v2
+    results:
+      - task:
+          type: text-classification
+        metrics:
+          - type: loss
+            value: 0.0727
+            name: Validation Loss
+          - type: f1
+            value: 0.9749
+            name: F1 Micro
+          - type: f1
+            value: 0.9750
+            name: F1 Macro
+          - type: f1
+            value: 0.9750
+            name: F1 Weighted
 ---
+# rubert_level1_v2
+This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) for multilabel classification of software requirements in Russian (Level 1).
+It achieves the following results on the evaluation set:
+* Loss: 0.0727
+* F1 Micro: 0.9749
+* F1 Macro: 0.9750
+* F1 Weighted: 0.9750
+## Model description
+Level 1 classifier in a cascaded requirements classification pipeline. Classifies Russian-language text fragments from meeting recordings into three categories:
+| Label | Description |
+|---|---|
+| `IsFunctional` | Functional requirements — what the system must do |
+| `IsBusiness` | Business requirements — budgets, KPIs, deadlines, regulations |
+| `Other (OT)` | Non-requirements — organizational remarks, transition phrases, context |
+`IsNonFunctional` is derived automatically as OR over Level 2 predictions and is not predicted by this model directly.
+The model is part of a cascaded pipeline:
+`Audio → GigaAM-v3 (ASR) → rubert_level1_v2 (L1) → rubert_level2_v2 (L2) → Report`
+Per-class classification thresholds are stored in `thresholds.json` in this repository.
+## Intended uses & limitations
+Intended for classification of Russian-language software requirements extracted from meeting audio recordings. Not suitable for general-purpose text classification or non-Russian languages.
+## Training and evaluation data
+Custom Russian-language requirements dataset compiled from:
+- PROMISE dataset (translated to Russian)
+- PURE dataset (parsed from XML, translated to Russian)
+- Synthetically generated examples (Grok, Claude Sonnet) across 14 domain areas
+Total: ~9800 labeled examples. Train/test split: 80/20, stratified, seed=42.
+## Training procedure
+### Training hyperparameters
+* learning_rate: 2e-05
+* train_batch_size: 16
+* eval_batch_size: 16
+* seed: 42
+* optimizer: AdamW with betas=(0.9, 0.999), epsilon=1e-08
+* lr_scheduler_type: linear
+* lr_scheduler_warmup_ratio: 0.06
+* num_epochs: 15 (early stopping patience=3)
+* max_length: 96
+### Training results
+| Training Loss | Epoch | Validation Loss | F1 Micro | F1 Macro | F1 Weighted |
+|---|---|---|---|---|---|
+| 0.1007 | 1 | 0.1046 | 0.9030 | 0.8907 | 0.8906 |
+| 0.0462 | 2 | 0.0471 | 0.9669 | 0.9671 | 0.9671 |
+| 0.0215 | 3 | 0.0467 | 0.9698 | 0.9697 | 0.9697 |
+| 0.0170 | 4 | 0.0556 | 0.9689 | 0.9689 | 0.9689 |
+| 0.0072 | 5 | 0.0784 | 0.9607 | 0.9604 | 0.9605 |
+| 0.0055 | 6 | 0.0608 | 0.9724 | 0.9727 | 0.9724 |
+Early stopping triggered after epoch 6.
+### Per-class results (test set)
+| Class | Precision | Recall | F1 | Support |
+|---|---|---|---|---|
+| IsFunctional | 0.934 | 0.948 | 0.941 | 420 |
+| IsBusiness | 0.993 | 0.978 | 0.985 | 416 |
+| Other (OT) | 1.000 | 1.000 | 1.000 | 421 |
+| **micro avg** | **0.975** | **0.975** | **0.975** | 1257 |
+### Framework versions
+* Transformers 4.57.1
+* PyTorch 2.8.0+cu128
+* Datasets 4.0.0
+* Tokenizers 0.22.2