model-index:
- name: BEDAI-2B
  results:
  - task:
      type: multiple-choice
      name: Exams (TR)
    dataset:
      name: exams_tr
      type: exams_tr
      args: {split: validation}
    metrics:
    - name: accuracy_norm
      type: accuracy
      value: 25.70

  - task:
      type: question-answering-extractive
      name: TQuAD (TR)
    dataset:
      name: tquad
      type: tquad
      args: {split: validation}
    metrics:
    - name: exact_match
      type: exact_match
      value: 9.9807
    - name: f1
      type: f1
      value: 22.9314

  - task:
      type: question-answering-extractive
      name: XQuAD (TR)
    dataset:
      name: xquad_tr
      type: xquad_tr
      args: {split: validation}
    metrics:
    - name: exact_match
      type: exact_match
      value: 6.4706
    - name: f1
      type: f1
      value: 13.0114

  - task:
      type: text-classification
      name: Turkish PLU (overall)
    dataset:
      name: turkish_plu
      type: turkish_plu
      args: {split: test}
    metrics:
    - name: accuracy_norm
      type: accuracy
      value: 51.58


## Evaluation (CETVEL – Turkish subsets)

Raw artifacts: **[nurcunal/BEDAI-2B-cetvel-2025-10-31](https://huggingface.co/datasets/nurcunal/BEDAI-2B-cetvel-2025-10-31)**
This quick sweep covers **MCQA** (`exams_tr`), **QA** (mean F1 of `tquad` + `xquad_tr`), and **TC** (`turkish_plu` acc_norm).

**BEDAI-2B (this run):** MCQA **25.70**, QA **17.97**, TC **51.58**

<table>
<thead>
<tr><th style="text-align:left">Model</th><th>MCQA</th><th>QA</th><th>TC</th></tr>
</thead>
<tbody>

<tr><th style="text-align:left">BEDAI-2B (this work)</th>
<td style="background:#f4cccc">25.70</td>
<td style="background:#f8cbad">17.97</td>
<td style="background:#ffeb9c">51.58</td></tr>

<tr><th style="text-align:left">CohereLabs__aya-expanse-32b</th>
<td style="background:#ffeb9c">52.47</td>
<td style="background:#f8cbad">20.48</td>
<td style="background:#ffeb9c">50.67</td></tr>

<tr><th style="text-align:left">CohereLabs__aya-expanse-8b</th>
<td style="background:#f8cbad">44.09</td>
<td style="background:#f4cccc">0.19</td>
<td style="background:#ffeb9c">50.03</td></tr>

<tr><th style="text-align:left">google__gemma-2-9b-it</th>
<td style="background:#ffeb9c">48.20</td>
<td style="background:#f4cccc">4.46</td>
<td style="background:#f8cbad">45.38</td></tr>

<tr><th style="text-align:left">google__gemma-3-12b-it</th>
<td style="background:#ffeb9c">52.66</td>
<td style="background:#f4cccc">10.26</td>
<td style="background:#ffeb9c">54.38</td></tr>

<tr><th style="text-align:left">google__gemma-3-27b-it</th>
<td style="background:#c6efce">55.40</td>
<td style="background:#f4cccc">10.56</td>
<td style="background:#ffeb9c">53.65</td></tr>

<tr><th style="text-align:left">google__gemma-3-4b-it</th>
<td style="background:#f8cbad">42.33</td>
<td style="background:#f4cccc">8.22</td>
<td style="background:#f8cbad">46.15</td></tr>

<tr><th style="text-align:left">Kumru-2B</th>
<td style="background:#f8cbad">39.69</td>
<td style="background:#f4cccc">6.50</td>
<td style="background:#ffeb9c">47.57</td></tr>

<tr><th style="text-align:left">Llama-3.1-8B-Instruct</th>
<td style="background:#ffeb9c">45.77</td>
<td style="background:#c6efce">38.99</td>
<td style="background:#f8cbad">46.51</td></tr>

<tr><th style="text-align:left">Llama-3.3-70B-Instruct</th>
<td style="background:#c6efce">60.70</td>
<td style="background:#ffeb9c">23.97</td>
<td style="background:#c6efce">63.73</td></tr>

<tr><th style="text-align:left">meta-llama__Llama-3.2-11B-Vision-Instruct</th>
<td style="background:#ffeb9c">45.66</td>
<td style="background:#f4cccc">4.37</td>
<td style="background:#f8cbad">47.88</td></tr>

<tr><th style="text-align:left">meta-llama__Llama-3.2-3B-Instruct</th>
<td style="background:#f8cbad">37.00</td>
<td style="background:#f4cccc">7.52</td>
<td style="background:#f4cccc">39.00</td></tr>

<tr><th style="text-align:left">Qwen__Qwen2-72B-Instruct</th>
<td style="background:#c6efce">61.27</td>
<td style="background:#f4cccc">0.83</td>
<td style="background:#c6efce">60.47</td></tr>

<tr><th style="text-align:left">Qwen__Qwen2-7B-Instruct</th>
<td style="background:#ffeb9c">49.66</td>
<td style="background:#f4cccc">1.53</td>
<td style="background:#ffeb9c">52.52</td></tr>

<tr><th style="text-align:left">Trendyol__Llama-3-Trendyol-LLM-8b-chat-v2.0</th>
<td style="background:#c6efce">53.28</td>
<td style="background:#f4cccc">0.17</td>
<td style="background:#c6efce">54.06</td></tr>

<tr><th style="text-align:left">Trendyol__Trendyol-LLM-7B-chat-v4.1.0</th>
<td style="background:#c6efce">54.94</td>
<td style="background:#f4cccc">0.34</td>
<td style="background:#ffeb9c">52.12</td></tr>

<tr><th style="text-align:left">ytu-ce-cosmos__Turkish-Gemma-9b-v0.1</th>
<td style="background:#ffeb9c">51.85</td>
<td style="background:#f4cccc">11.11</td>
<td style="background:#f8cbad">46.97</td></tr>

<tr><th style="text-align:left">ytu-ce-cosmos__turkish-gpt2-large-750m-instruct-v0.1</th>
<td style="background:#f8cbad">35.20</td>
<td style="background:#f4cccc">0.28</td>
<td style="background:#ffeb9c">52.77</td></tr>

</tbody>
</table>

> **Notes**  
> • QA = mean F1 over **TQuAD** and **XQuAD-TR** for this run.  
> • CETVEL has more tasks (GEC/MT/NLI/SUM); this compares shared Turkish subsets only.  
> • For reproducibility, see the dataset repo above and the exact command used.