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--- |
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license: cc-by-nc-4.0 |
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language: |
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- ro |
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base_model: |
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- OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23 |
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datasets: |
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- OpenLLM-Ro/ro_dpo_helpsteer |
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- OpenLLM-Ro/ro_dpo_ultrafeedback |
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- OpenLLM-Ro/ro_dpo_magpie |
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- OpenLLM-Ro/ro_dpo_argilla_magpie |
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- OpenLLM-Ro/ro_dpo_helpsteer2 |
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model-index: |
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- name: OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23 |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: RoMT-Bench |
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type: RoMT-Bench |
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metrics: |
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- name: Score |
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type: Score |
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value: 7.00 |
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- task: |
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type: text-generation |
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dataset: |
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name: RoCulturaBench |
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type: RoCulturaBench |
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metrics: |
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- name: Score |
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type: Score |
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value: 4.73 |
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- task: |
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type: text-generation |
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dataset: |
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name: Romanian_Academic_Benchmarks |
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type: Romanian_Academic_Benchmarks |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 53.76 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_arc_challenge |
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type: OpenLLM-Ro/ro_arc_challenge |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 51.09 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_mmlu |
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type: OpenLLM-Ro/ro_mmlu |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 56.22 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_winogrande |
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type: OpenLLM-Ro/ro_winogrande |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 66.77 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_hellaswag |
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type: OpenLLM-Ro/ro_hellaswag |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 59.38 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_gsm8k |
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type: OpenLLM-Ro/ro_gsm8k |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 31.54 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_truthfulqa |
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type: OpenLLM-Ro/ro_truthfulqa |
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metrics: |
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- name: Average accuracy |
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type: accuracy |
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value: 57.56 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 96.87 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass |
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type: LaRoSeDa_multiclass |
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metrics: |
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- name: Average macro-f1 |
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type: macro-f1 |
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value: 60.75 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO |
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type: WMT_EN-RO |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 20.30 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN |
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type: WMT_RO-EN |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 18.57 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD |
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type: XQuAD |
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metrics: |
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- name: Average exact_match |
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type: exact_match |
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value: 9.22 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD |
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type: XQuAD |
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metrics: |
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- name: Average f1 |
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type: f1 |
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value: 22.75 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: Average spearman |
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type: spearman |
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value: 30.82 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS |
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type: STS |
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metrics: |
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- name: Average pearson |
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type: pearson |
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value: 20.25 |
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- task: |
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type: text-generation |
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dataset: |
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name: RoMT-Bench |
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type: RoMT-Bench |
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metrics: |
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- name: First turn |
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type: Score |
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value: 7.30 |
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- name: Second turn |
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type: Score |
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value: 6.70 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_arc_challenge |
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type: OpenLLM-Ro/ro_arc_challenge |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 51.59 |
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- name: 1-shot |
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type: accuracy |
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value: 52.10 |
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- name: 3-shot |
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type: accuracy |
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value: 50.99 |
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- name: 5-shot |
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type: accuracy |
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value: 50.81 |
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- name: 10-shot |
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type: accuracy |
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value: 49.70 |
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- name: 25-shot |
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type: accuracy |
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value: 51.33 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_mmlu |
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type: OpenLLM-Ro/ro_mmlu |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 56.88 |
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- name: 1-shot |
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type: accuracy |
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value: 55.61 |
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- name: 3-shot |
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type: accuracy |
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value: 56.06 |
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- name: 5-shot |
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type: accuracy |
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value: 56.31 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_winogrande |
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type: OpenLLM-Ro/ro_winogrande |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 65.67 |
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- name: 1-shot |
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|
type: accuracy |
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value: 66.30 |
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- name: 3-shot |
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|
type: accuracy |
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value: 67.40 |
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- name: 5-shot |
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type: accuracy |
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value: 67.72 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_hellaswag |
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type: OpenLLM-Ro/ro_hellaswag |
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metrics: |
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- name: 0-shot |
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type: accuracy |
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value: 60.53 |
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|
- name: 1-shot |
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type: accuracy |
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value: 60.37 |
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- name: 3-shot |
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|
type: accuracy |
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value: 58.20 |
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- name: 5-shot |
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|
type: accuracy |
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value: 58.18 |
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|
- name: 10-shot |
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|
type: accuracy |
|
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value: 59.61 |
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- task: |
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type: text-generation |
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dataset: |
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name: OpenLLM-Ro/ro_gsm8k |
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type: OpenLLM-Ro/ro_gsm8k |
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metrics: |
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- name: 1-shot |
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type: accuracy |
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value: 25.09 |
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- name: 3-shot |
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|
type: accuracy |
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value: 30.02 |
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- name: 5-shot |
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type: accuracy |
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value: 39.50 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
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- name: 0-shot |
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type: macro-f1 |
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value: 95.39 |
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- name: 1-shot |
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type: macro-f1 |
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value: 95.90 |
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- name: 3-shot |
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type: macro-f1 |
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value: 98.00 |
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- name: 5-shot |
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type: macro-f1 |
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value: 98.17 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass |
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type: LaRoSeDa_multiclass |
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metrics: |
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- name: 0-shot |
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type: macro-f1 |
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value: 60.30 |
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- name: 1-shot |
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type: macro-f1 |
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value: 64.73 |
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- name: 3-shot |
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type: macro-f1 |
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value: 58.69 |
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- name: 5-shot |
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type: macro-f1 |
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value: 59.30 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_EN-RO |
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type: WMT_EN-RO |
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metrics: |
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- name: 0-shot |
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type: bleu |
|
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value: 5.46 |
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- name: 1-shot |
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type: bleu |
|
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value: 26.08 |
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- name: 3-shot |
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type: bleu |
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value: 25.90 |
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- name: 5-shot |
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type: bleu |
|
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value: 23.76 |
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- task: |
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type: text-generation |
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dataset: |
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name: WMT_RO-EN |
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type: WMT_RO-EN |
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metrics: |
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- name: 0-shot |
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type: bleu |
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value: 2.74 |
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|
- name: 1-shot |
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type: bleu |
|
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value: 20.95 |
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- name: 3-shot |
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type: bleu |
|
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value: 31.53 |
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- name: 5-shot |
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type: bleu |
|
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value: 19.05 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_EM |
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type: XQuAD_EM |
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metrics: |
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- name: 0-shot |
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type: exact_match |
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value: 12.27 |
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- name: 1-shot |
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type: exact_match |
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value: 17.98 |
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- name: 3-shot |
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type: exact_match |
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value: 5.04 |
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- name: 5-shot |
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type: exact_match |
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value: 1.60 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_F1 |
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type: XQuAD_F1 |
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metrics: |
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- name: 0-shot |
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type: f1 |
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value: 26.24 |
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- name: 1-shot |
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type: f1 |
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value: 32.54 |
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- name: 3-shot |
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type: f1 |
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value: 18.00 |
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- name: 5-shot |
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type: f1 |
|
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value: 14.22 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_Spearman |
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type: STS_Spearman |
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metrics: |
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- name: 1-shot |
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type: spearman |
|
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value: 76.70 |
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- name: 3-shot |
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type: spearman |
|
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value: 2.82 |
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- name: 5-shot |
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type: spearman |
|
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value: 12.95 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_Pearson |
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type: STS_Pearson |
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metrics: |
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- name: 1-shot |
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type: pearson |
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value: 77.30 |
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- name: 3-shot |
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type: pearson |
|
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value: -14.56 |
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- name: 5-shot |
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type: pearson |
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value: -1.99 |
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--- |
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# Model Card for Model ID |
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*Built with Meta Llama 3.1* |
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<!-- Provide a quick summary of what the model is/does. --> |
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RoLlama3.1 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned instruct 8B model**. Links to other models can be found at the bottom of this page. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants. |
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- **Developed by:** OpenLLM-Ro |
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<!-- - **Funded by [optional]:** [More Information Needed] --> |
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<!-- - **Shared by [optional]:** [More Information Needed] --> |
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<!-- - **Model type:** [More Information Needed] --> |
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- **Language(s):** Romanian |
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- **License:** cc-by-nc-4.0 |
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- **Finetuned from model:** [RoLlama3.1-8b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23) |
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- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer), [RoUltraFeedback](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_ultrafeedback), [RoMagpieDPO](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_magpie), [RoArgillaMagpie](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_argilla_magpie), [RoHelpSteer2](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer2) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory |
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- **Paper:** https://arxiv.org/abs/2406.18266 |
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## Intended Use |
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### Intended Use Cases |
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RoLlama3.1 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23") |
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instruction = "Ce jocuri de societate pot juca cu prietenii mei?" |
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chat = [ |
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{"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."}, |
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{"role": "user", "content": instruction}, |
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] |
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="") |
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inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
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outputs = model.generate(input_ids=inputs, max_new_tokens=128) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Academic Benchmarks |
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<table> |
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<tbody> |
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<tr> |
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<td><strong>Model</strong></td> |
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<td><strong><center>Average</center></strong></td> |
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<td><strong><center>ARC</center></strong></td> |
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<td><strong><center>MMLU</center></strong></td> |
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<td><strong><center>Winogrande</center></strong></td> |
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<td><strong><center>Hellaswag</center></strong></td> |
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<td><strong><center>GSM8k</center></strong></td> |
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<td><strong><center>TruthfulQA</center></strong></td> |
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</tr> |
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<tr> |
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<td>Llama-3.1-8B-Instruct</td><td><center>49.87</center></td><td><center>42.86</center></td><td><center>53.73</center></td><td><center>59.71</center></td><td><center>56.82</center></td><td><center>35.56</center></td><td><center>50.54</center></td> |
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</tr> |
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<tr> |
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<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>53.03</center></td><td><center>47.69</center></td><td><center>54.57</center></td><td><center>65.84</center></td><td><center>59.94</center></td><td><center><strong>44.30</strong></center></td><td><center>45.82</center></td> |
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</tr> |
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<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>53.36</center></td><td><center>48.97</center></td><td><center>55.17</center></td><td><center>66.52</center></td><td><center><strong>60.73</strong></center></td><td><center>42.03</center></td><td><center>46.71</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>52.74</center></td><td><center>44.84</center></td><td><center>55.06</center></td><td><center>65.87</center></td><td><center>58.67</center></td><td><center>44.17</center></td><td><center>47.82</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>53.76</strong></em></center></td><td><center><em><strong>51.09</strong></em></center></td><td><center><em><strong>56.22</strong></em></center></td><td><center><em><strong>66.77</strong></em></center></td><td><center><em>59.38</em></center></td><td><center><em>31.54</em></center></td><td><center><em><strong>57.56</strong></em></center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
|
|
|
|
|
|
## Downstream tasks |
|
|
|
|
|
<table> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td></td> |
|
|
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td> |
|
|
<td colspan="4"><center><strong>WMT</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td></td> |
|
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><strong>Model</strong></td> |
|
|
<td><center><strong>Binary<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>Binary<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
|
|
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
|
|
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td> |
|
|
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
|
|
<td><center><strong>RO-EN<br>(Bleu)</strong></center> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Llama-3.1-8B-Instruct</td><td><center>95.74</center></td><td><center>59.49</center></td><td><center><strong>98.57</strong></center></td><td><center>82.41</center></td><td><center>19.01</center></td><td><center><strong>27.77</strong></center></td><td><center><strong>29.02</strong></center></td><td><center>39.80</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>94.56</center></td><td><center>60.10</center></td><td><center>95.12</center></td><td><center><strong>87.53</strong></center></td><td><center>21.88</center></td><td><center>23.99</center></td><td><center>28.27</center></td><td><center><strong>40.44</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>95.32</center></td><td><center><strong>60.84</strong></center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>23.18</strong></center></td><td><center>25.11</center></td><td><center>-</center></td><td><center>-</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>96.10</center></td><td><center>55.37</center></td><td><center>-</center></td><td><center>-</center></td><td><center>21.29</center></td><td><center>21.86</center></td><td><center>-</center></td><td><center>-</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>96.87</strong></em></center></td><td><center><em>60.75</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>20.30</em></center></td><td><center><em>18.57</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
|
|
|
<table> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td></td> |
|
|
<td colspan="4"><center><strong>XQuAD</strong></center></td> |
|
|
<td colspan="4"><center><strong>STS</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td></td> |
|
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
|
<td colspan="2"><center><strong>Few-shot</strong></center></td> |
|
|
<td colspan="2"><center><strong>Finetuned</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><strong>Model</strong></td> |
|
|
<td><center><strong>(EM)</strong></center></td> |
|
|
<td><center><strong>(F1)</strong></center></td> |
|
|
<td><center><strong>(EM)</strong></center></td> |
|
|
<td><center><strong>(F1)</strong></center></td> |
|
|
<td><center><strong>(Spearman)</strong></center></td> |
|
|
<td><center><strong>(Pearson)</strong></center></td> |
|
|
<td><center><strong>(Spearman)</strong></center></td> |
|
|
<td><center><strong>(Pearson)</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Llama-3.1-8B-Instruct</td><td><center><strong>44.96</strong></center></td><td><center><strong>64.45</strong></center></td><td><center><strong>69.50</strong></center></td><td><center><strong>84.31</strong></center></td><td><center>72.11</center></td><td><center>71.64</center></td><td><center>84.59</center></td><td><center>84.96</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>13.59</center></td><td><center>23.56</center></td><td><center>49.41</center></td><td><center>62.93</center></td><td><center>75.89</center></td><td><center>76.00</center></td><td><center><strong>86.86</strong></center></td><td><center><strong>87.05</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>10.74</center></td><td><center>19.75</center></td><td><center>-</center></td><td><center>-</center></td><td><center>73.53</center></td><td><center>74.93</center></td><td><center>-</center></td><td><center>-</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>21.58</center></td><td><center>36.54</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>78.01</strong></center></td><td><center><strong>77.98</strong></center></td><td><center>-</center></td><td><center>-</center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em>9.22</em></center></td><td><center><em>22.75</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>30.82</em></center></td><td><center><em>20.25</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
## MT-Bench |
|
|
|
|
|
<table> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td><strong>Model</strong></td> |
|
|
<td><strong><center>Average</center></strong></td> |
|
|
<td><strong><center>1st turn</center></strong></td> |
|
|
<td><strong><center>2nd turn</center></strong></td> |
|
|
<td><strong><center>Answers in Ro</center></strong></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Llama-3.1-8B-Instruct</td><td><center>5.69</center></td><td><center>5.85</center></td><td><center>5.53</center></td><td><center><strong>160/160</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>5.42</center></td><td><center>5.95</center></td><td><center>4.89</center></td><td><center><strong>160/160</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>6.43</center></td><td><center>6.78</center></td><td><center>6.09</center></td><td><center><strong>160/160</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>6.21</center></td><td><center>6.74</center></td><td><center>5.69</center></td><td><center><strong>160/160</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>7.00</strong></em></center></td><td><center><em><strong>7.30</strong></em></center></td><td><center><em><strong>6.70</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
|
|
|
## RoCulturaBench |
|
|
|
|
|
<table> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td><strong>Model</strong></td> |
|
|
<td><strong><center>Average</center></strong></td> |
|
|
<td><strong><center>Answers in Ro</center></strong></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Llama-3.1-8B-Instruct</td><td><center>3.54</center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>3.55</center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>4.28</center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>4.42</center></td><td><center><strong>100/100</strong></center></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>4.73</strong></em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
|
|
|
|
|
|
## RoLlama3.1 Model Family |
|
|
|
|
|
| Model | Link | |
|
|
|--------------------|:--------:| |
|
|
|RoLlama3.1-8b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09) | |
|
|
|RoLlama3.1-8b-Instruct-2025-04-23| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23) | |
|
|
|RoLlama3.1-8b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2024-10-09) | |
|
|
|*RoLlama3.1-8b-Instruct-DPO-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23) | |
|
|
|
|
|
|
|
|
## Citation |
|
|
|
|
|
``` |
|
|
@misc{masala2024vorbecstiromanecsterecipetrain, |
|
|
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, |
|
|
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea}, |
|
|
year={2024}, |
|
|
eprint={2406.18266}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL}, |
|
|
url={https://arxiv.org/abs/2406.18266}, |
|
|
} |
|
|
``` |
|
|
<!-- **APA:** |
|
|
|
|
|
[More Information Needed] --> |
