| --- |
| license: mit |
| task_categories: |
| - text-classification |
| language: |
| - tr |
| tags: |
| - nli |
| - turkish |
| - error-analysis |
| - ensemble |
| pretty_name: Turkish NLI Wrong Samples (Ensemble Base Models + BiLSTM) |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: bert-allnli-tr |
| data_files: bert-allnli-tr/data.parquet |
| - config_name: mdeberta-v3-base-mnli-xnli |
| data_files: mdeberta-v3-base-mnli-xnli/data.parquet |
| - config_name: gemma-3-27b-it |
| data_files: gemma-3-27b-it/data.parquet |
| - config_name: qwen2-7b-instruct |
| data_files: qwen2-7b-instruct/data.parquet |
| - config_name: bilstm-16feat-entropy |
| data_files: bilstm-16feat-entropy/data.parquet |
| --- |
| |
| # Turkish NLI — Wrong Samples (4 Ensemble Bases + BiLSTM) |
|
|
| Wrongly predicted examples for the four ensemble base models and the best |
| meta-learner (**BiLSTM 16-feature + entropy**), from the Turkish NLI project |
| (`yilmazzey/sdp2-nli` evaluation splits). |
|
|
| ## Prediction source (consistent across all configs) |
|
|
| All **base-model** wrong sets use: |
|
|
| `argmax` over softmax arrays in |
| `stacking_cache_train_split_16feat_entropy/` |
|
|
| | Model | Softmax origin | |
| |------|----------------| |
| | BERT (`emrecan/bert-base-turkish-cased-allnli_tr`) | Encoder classifier softmax | |
| | mDeBERTa (`MoritzLaurer/mDeBERTa-v3-base-mnli-xnli`) | Encoder classifier softmax | |
| | Gemma 3 27B IT | **Verbalizer** softmax (last prompt position) | |
| | Qwen2 7B Instruct | **Verbalizer** softmax (last prompt position) | |
|
|
| **BiLSTM** uses the same cached softmax vectors as 16 features |
| (12 probabilities + 4 entropies) with the trained `*_bilstm.pt` meta-learner |
| per dataset config. So base-model wrongs and BiLSTM wrongs are comparable |
| on the same scoring path (not first-word generation / hard JSON). |
|
|
| ## Configs (load one model at a time) |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("yilmazzey/wrong_samples", "bert-allnli-tr") |
| ds = load_dataset("yilmazzey/wrong_samples", "mdeberta-v3-base-mnli-xnli") |
| ds = load_dataset("yilmazzey/wrong_samples", "gemma-3-27b-it") |
| ds = load_dataset("yilmazzey/wrong_samples", "qwen2-7b-instruct") |
| ds = load_dataset("yilmazzey/wrong_samples", "bilstm-16feat-entropy") |
| ``` |
|
|
| ## Evaluation splits covered (`split_key`) |
| |
| | split_key | dataset | split | |
| |-----------|---------|-------| |
| | `snli_tr_1_1::test` | snli_tr_1_1 | test | |
| | `multinli_tr_1_1::validation_matched` | multinli_tr_1_1 | validation_matched | |
| | `multinli_tr_1_1::validation_mismatched` | multinli_tr_1_1 | validation_mismatched | |
| | `trglue_mnli::test_matched` | trglue_mnli | test_matched | |
| | `trglue_mnli::test_mismatched` | trglue_mnli | test_mismatched | |
| |
| Columns `dataset`, `split`, `split_key`, and `pred_source` are present on every row. |
| |
| ## Notes |
| |
| - Each row is an example where **that config's model** predicted incorrectly. |
| - Peer predictions from the other base models are included for recovery analysis. |
| - This is **not** the Table-4 hard-vote JSON subset (first-word generation path). |
| |
