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--- |
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license: llama2 |
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language: |
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- ro |
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base_model: meta-llama/Llama-2-7b-hf |
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datasets: |
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- uonlp/CulturaX |
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model-index: |
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- name: OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14 |
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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: 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: 38.03 |
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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: 37.95 |
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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: 27.22 |
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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: 59.29 |
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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: 57.22 |
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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_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: 2.53 |
|
- 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: 44.00 |
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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: 83.25 |
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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: 61.04 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_binary_finetuned |
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type: LaRoSeDa_binary_finetuned |
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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: 98.97 |
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- task: |
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type: text-generation |
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dataset: |
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name: LaRoSeDa_multiclass_finetuned |
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type: LaRoSeDa_multiclass_finetuned |
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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: 87.72 |
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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: 10.01 |
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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: 13.03 |
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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_finetuned |
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type: WMT_EN-RO_finetuned |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 27.85 |
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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_finetuned |
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type: WMT_RO-EN_finetuned |
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metrics: |
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- name: Average bleu |
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type: bleu |
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value: 39.30 |
|
- 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: 30.15 |
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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: 47.03 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_finetuned |
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type: XQuAD_finetuned |
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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: 67.06 |
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- task: |
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type: text-generation |
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dataset: |
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name: XQuAD_finetuned |
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type: XQuAD_finetuned |
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metrics: |
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- name: Average f1 |
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type: f1 |
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value: 79.96 |
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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: |
|
- name: Average spearman |
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type: spearman |
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value: 7.89 |
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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: 7.98 |
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- task: |
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type: text-generation |
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dataset: |
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name: STS_finetuned |
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type: STS_finetuned |
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metrics: |
|
- name: Average spearman |
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type: spearman |
|
value: 71.75 |
|
- task: |
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type: text-generation |
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dataset: |
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name: STS_finetuned |
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type: STS_finetuned |
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metrics: |
|
- name: Average pearson |
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type: pearson |
|
value: 71.99 |
|
- 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: 35.56 |
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- name: 1-shot |
|
type: accuracy |
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value: 36.42 |
|
- name: 3-shot |
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type: accuracy |
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value: 38.56 |
|
- name: 5-shot |
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type: accuracy |
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value: 38.39 |
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- name: 10-shot |
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type: accuracy |
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value: 39.07 |
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- name: 25-shot |
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type: accuracy |
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value: 39.67 |
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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: 25.82 |
|
- name: 1-shot |
|
type: accuracy |
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value: 25.48 |
|
- name: 3-shot |
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type: accuracy |
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value: 27.61 |
|
- name: 5-shot |
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type: accuracy |
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value: 29.96 |
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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: |
|
- name: 0-shot |
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type: accuracy |
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value: 58.72 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 58.88 |
|
- name: 3-shot |
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type: accuracy |
|
value: 60.38 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 59.19 |
|
- 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: |
|
- name: 0-shot |
|
type: accuracy |
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value: 55.85 |
|
- name: 1-shot |
|
type: accuracy |
|
value: 57.06 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 57.52 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 57.89 |
|
- name: 10-shot |
|
type: accuracy |
|
value: 57.79 |
|
- task: |
|
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: |
|
- name: 1-shot |
|
type: accuracy |
|
value: 0.00 |
|
- name: 3-shot |
|
type: accuracy |
|
value: 2.96 |
|
- name: 5-shot |
|
type: accuracy |
|
value: 4.62 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: LaRoSeDa_binary |
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type: LaRoSeDa_binary |
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metrics: |
|
- name: 0-shot |
|
type: macro-f1 |
|
value: 42.78 |
|
- name: 1-shot |
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type: macro-f1 |
|
value: 98.00 |
|
- name: 3-shot |
|
type: macro-f1 |
|
value: 95.13 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 97.07 |
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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: 46.41 |
|
- name: 1-shot |
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type: macro-f1 |
|
value: 67.36 |
|
- name: 3-shot |
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type: macro-f1 |
|
value: 65.16 |
|
- name: 5-shot |
|
type: macro-f1 |
|
value: 65.23 |
|
- 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: |
|
- name: 0-shot |
|
type: bleu |
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value: 4.45 |
|
- name: 1-shot |
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type: bleu |
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value: 8.61 |
|
- name: 3-shot |
|
type: bleu |
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value: 12.25 |
|
- name: 5-shot |
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type: bleu |
|
value: 14.73 |
|
- 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: |
|
- name: 0-shot |
|
type: bleu |
|
value: 1.29 |
|
- name: 1-shot |
|
type: bleu |
|
value: 10.78 |
|
- name: 3-shot |
|
type: bleu |
|
value: 16.82 |
|
- name: 5-shot |
|
type: bleu |
|
value: 23.24 |
|
- task: |
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type: text-generation |
|
dataset: |
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name: XQuAD_EM |
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type: XQuAD_EM |
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metrics: |
|
- name: 0-shot |
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type: exact_match |
|
value: 5.29 |
|
- name: 1-shot |
|
type: exact_match |
|
value: 33.95 |
|
- name: 3-shot |
|
type: exact_match |
|
value: 39.24 |
|
- name: 5-shot |
|
type: exact_match |
|
value: 42.10 |
|
- 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: |
|
- name: 0-shot |
|
type: f1 |
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value: 16.17 |
|
- name: 1-shot |
|
type: f1 |
|
value: 51.84 |
|
- name: 3-shot |
|
type: f1 |
|
value: 58.82 |
|
- name: 5-shot |
|
type: f1 |
|
value: 61.29 |
|
- 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: |
|
- name: 1-shot |
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type: spearman |
|
value: -1.74 |
|
- name: 3-shot |
|
type: spearman |
|
value: 15.47 |
|
- name: 5-shot |
|
type: spearman |
|
value: 9.93 |
|
- 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: |
|
- name: 1-shot |
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type: pearson |
|
value: -1.40 |
|
- name: 3-shot |
|
type: pearson |
|
value: 15.00 |
|
- name: 5-shot |
|
type: pearson |
|
value: 10.33 |
|
|
|
|
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--- |
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|
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model points/is identical to [RoLlama2-7b-Base-2024-05-14](https://huggingface.co./OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14). |
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RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **foundational 7B 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 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:** Llama2 Community License Agreement |
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- **Continual pretrained from model:** [Llama-2-7b](https://huggingface.co./meta-llama/Llama-2-7b-hf) |
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- **Trained using:** [CulturaX](https://huggingface.co./datasets/uonlp/CulturaX) |
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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-recipes |
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- **Paper:** https://arxiv.org/abs/2406.18266 |
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|
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## Intended Use |
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### Intended Use Cases |
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RoLlama2 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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|
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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/RoLlama2-7b-Base") |
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model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base") |
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input_text = "Mihai Eminescu a fost " |
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input_ids = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**input_ids, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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|
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## Academic Benchmarks |
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|
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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-2-7b</td><td><center>37.04</center></td><td><center>36.05</center></td><td><center><strong>33.66</strong></center></td><td><center>57.56</center></td><td><center>48.00</center></td><td><center><strong>4.75</strong></center></td><td><center>42.22</center></td> |
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</tr> |
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<tr> |
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<td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em><strong>38.03</strong></em></center></td><td><center><em><strong>37.95</strong></em></center></td><td><center><em>27.22</em></center></td><td><center><em><strong>59.29</strong></em></center></td><td><center><em><strong>57.22</strong></em></center></td><td><center><em>2.53</em></center></td><td><center><em><strong>44.00</strong></em></center></td> |
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</tr> |
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</tbody> |
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</table> |
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|
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## Downstream Tasks |
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|
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<table> |
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<tbody> |
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<tr> |
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<td></td> |
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<td colspan="4"><center><strong>LaRoSeDa</strong></center></td> |
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<td colspan="4"><center><strong>WMT</strong></center></td> |
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</tr> |
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<tr> |
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<td></td> |
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<td colspan="2"><center><strong>Few-shot</strong></center></td> |
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<td colspan="2"><center><strong>Finetuned</strong></center></td> |
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<td colspan="2"><center><strong>Few-shot</strong></center></td> |
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<td colspan="2"><center><strong>Finetuned</strong></center></td> |
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</tr> |
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<tr> |
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<td><strong>Model</strong></td> |
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<td><center><strong>Binary<br>(Macro F1)</strong></center></td> |
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<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
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<td><center><strong>Binary<br>(Macro F1)</strong></center></td> |
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<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> |
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<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
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<td><center><strong>RO-EN<br>(Bleu)</strong></center></td> |
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<td><center><strong>EN-RO<br>(Bleu)</strong></center></td> |
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<td><center><strong>RO-EN<br>(Bleu)</strong></center> |
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</tr> |
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<tr> |
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<td>Llama-2-7b</td><td><center><strong>93.19</strong></center></td><td><center>54.11</center></td><td><center>98.43</center></td><td><center>87.22</center></td><td><center><strong>14.90</strong></center></td><td><center><strong>26.61</strong></center></td><td><center>24.95</center></td><td><center>39.09</center></td> |
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</tr> |
|
<tr> |
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<td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em>83.25</em></center></td><td><center><em><strong>61.04</strong></em></center></td><td><center><em><strong>98.97</strong></em></center></td><td><center><em><strong>87.72</strong></em></center></td><td><center><em>10.01</em></center></td><td><center><em>13.03</em></center></td><td><center><em><strong>27.85</strong></em></center></td><td><center><em><strong>39.30</strong></em></center></td> |
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</tr> |
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</tbody> |
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</table> |
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|
|
|
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<table> |
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<tbody> |
|
<tr> |
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<td></td> |
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<td colspan="4"><center><strong>XQuAD</strong></center></td> |
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<td colspan="4"><center><strong>STS</strong></center></td> |
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</tr> |
|
<tr> |
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<td></td> |
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<td colspan="2"><center><strong>Few-shot</strong></center></td> |
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<td colspan="2"><center><strong>Finetuned</strong></center></td> |
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<td colspan="2"><center><strong>Few-shot</strong></center></td> |
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<td colspan="2"><center><strong>Finetuned</strong></center></td> |
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</tr> |
|
<tr> |
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<td><strong>Model</strong></td> |
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<td><center><strong>(EM)</strong></center></td> |
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<td><center><strong>(F1)</strong></center></td> |
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<td><center><strong>(EM)</strong></center></td> |
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<td><center><strong>(F1)</strong></center></td> |
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<td><center><strong>(Spearman)</strong></center></td> |
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<td><center><strong>(Pearson)</strong></center></td> |
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<td><center><strong>(Spearman)</strong></center></td> |
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<td><center><strong>(Pearson)</strong></center></td> |
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</tr> |
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<tr> |
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<td>Llama-2-7b</td><td><center><strong>38.91</strong></center></td><td><center><strong>56.82</strong></center></td><td><center>65.46</center></td><td><center>79.42</center></td><td><center><strong>9.08</strong></center></td><td><center><strong>9.07</strong></center></td><td><center><strong>79.93</strong></center></td><td><center><strong>81.08</strong></center></td> |
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</tr> |
|
<tr> |
|
<td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em>30.15</em></center></td><td><center><em>47.03</em></center></td><td><center><em><strong>67.06</strong></em></center></td><td><center><em><strong>79.96</strong></em></center></td><td><center><em>7.89</em></center></td><td><center><em>7.98</em></center></td><td><center><em>71.75</em></center></td><td><center><em>71.99</em></center></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
|
|
|
|
## RoLlama2 Model Family |
|
|
|
| Model | Link | |
|
|--------------------|:--------:| |
|
|*RoLlama2-7b-Base-2024-05-14* | [link](https://huggingface.co./OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14) | |
|
|RoLlama2-7b-Instruct-2024-05-14 | [link](https://huggingface.co./OpenLLM-Ro/RoLlama2-7b-Instruct-2024-05-14) | |
|
|RoLlama2-7b-Instruct-2024-10-09| [link](https://huggingface.co./OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09) | |
|
|RoLlama2-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co./OpenLLM-Ro/RoLlama2-7b-Instruct-DPO-2024-10-09) | |
|
|
|
## 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}, |
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eprint={2406.18266}, |
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archivePrefix={arXiv}, |
|
primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2406.18266}, |
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} |
|
``` |
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<!-- **APA:** |
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