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Datasets:
yilmazzey
/
wrong_samples

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
parquet
Languages:
Turkish
Size:
10K - 100K
Tags:
nli
turkish
error-analysis
ensemble
Libraries:
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pandas
Polars
License:
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wrong_samples / README.md
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yilmazzey
Add cache-consistent wrong samples for 4 ensemble bases + BiLSTM 16feat
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metadata 
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) 

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).