PyTorch
Romanian
llama
Eval Results
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- ---
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- license: llama2
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ 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
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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: 44
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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: 39.3
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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: 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:
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+ - 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:
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+ - name: Average spearman
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+ type: spearman
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+ value: 71.75
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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:
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+ - name: Average pearson
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+ type: pearson
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+ value: 71.99
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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: 35.56
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+ - name: 1-shot
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+ type: accuracy
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+ value: 36.42
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+ - name: 3-shot
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+ type: accuracy
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+ value: 38.56
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+ - 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
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+ - name: 1-shot
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+ type: accuracy
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+ value: 25.48
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+ - name: 3-shot
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+ type: accuracy
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+ value: 27.61
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+ - 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:
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+ - name: 0-shot
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+ type: accuracy
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+ value: 58.72
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+ - name: 1-shot
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+ type: accuracy
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+ value: 58.88
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+ - name: 3-shot
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+ type: accuracy
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+ value: 60.38
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+ - name: 5-shot
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+ type: accuracy
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+ value: 59.19
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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: 55.85
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+ - name: 1-shot
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+ type: accuracy
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+ value: 57.06
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+ - name: 3-shot
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+ type: accuracy
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+ value: 57.52
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+ - name: 5-shot
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+ type: accuracy
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+ value: 57.89
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+ - name: 10-shot
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+ type: accuracy
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+ value: 57.79
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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: 0-shot
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+ type: accuracy
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+ value: 0
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+ - name: 1-shot
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+ type: accuracy
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+ value: 2.96
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+ - name: 3-shot
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+ type: accuracy
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+ value: 4.62
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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: 42.78
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+ - name: 1-shot
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+ type: macro-f1
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+ value: 98
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+ - name: 3-shot
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+ type: macro-f1
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+ value: 95.13
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+ - name: 5-shot
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+ type: macro-f1
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+ 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
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+ - name: 1-shot
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+ type: macro-f1
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+ value: 67.36
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+ - name: 3-shot
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+ type: macro-f1
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+ value: 65.16
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+ - name: 5-shot
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+ type: macro-f1
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+ value: 65.23
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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: 4.45
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+ - name: 1-shot
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+ type: bleu
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+ value: 8.61
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+ - name: 3-shot
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+ type: bleu
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+ value: 12.25
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+ - name: 5-shot
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+ type: bleu
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+ value: 14.73
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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: 1.29
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+ - name: 1-shot
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+ type: bleu
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+ value: 10.78
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+ - name: 3-shot
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+ type: bleu
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+ value: 16.82
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+ - name: 5-shot
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+ type: bleu
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+ value: 23.24
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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: 5.29
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+ - name: 1-shot
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+ type: exact_match
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+ value: 33.95
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+ - name: 3-shot
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+ type: exact_match
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+ value: 39.24
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+ - name: 5-shot
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+ type: exact_match
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+ value: 42.1
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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: 16.17
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+ - name: 1-shot
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+ type: f1
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+ value: 51.84
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+ - name: 3-shot
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+ type: f1
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+ value: 58.82
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+ - name: 5-shot
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+ type: f1
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+ value: 61.29
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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: 0-shot
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+ type: spearman
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+ value: -1.74
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+ - name: 1-shot
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+ type: spearman
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+ value: 15.47
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+ - name: 3-shot
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+ type: spearman
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+ value: 9.93
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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: 0-shot
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+ type: pearson
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+ value: -1.4
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+ - name: 1-shot
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+ type: pearson
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+ value: 15
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+ - name: 3-shot
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+ type: pearson
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+ value: 10.33
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+ datasets:
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+ - uonlp/CulturaX
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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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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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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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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+
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+
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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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+
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+
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+ ### Model Sources
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+
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+ <!-- Provide the basic links for the model. -->
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+
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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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+
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+ ### Intended Use Cases
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+
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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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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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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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+
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14")
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+ model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14")
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+
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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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+
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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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+
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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>
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+ <tr>
571
+ <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>
572
+ </tr>
573
+ </tbody>
574
+ </table>
575
+
576
+
577
+ <table>
578
+ <tbody>
579
+ <tr>
580
+ <td></td>
581
+ <td colspan="4"><center><strong>XQuAD</strong></center></td>
582
+ <td colspan="4"><center><strong>STS</strong></center></td>
583
+ </tr>
584
+ <tr>
585
+ <td></td>
586
+ <td colspan="2"><center><strong>Few-shot</strong></center></td>
587
+ <td colspan="2"><center><strong>Finetuned</strong></center></td>
588
+ <td colspan="2"><center><strong>Few-shot</strong></center></td>
589
+ <td colspan="2"><center><strong>Finetuned</strong></center></td>
590
+ </tr>
591
+ <tr>
592
+ <td><strong>Model</strong></td>
593
+ <td><center><strong>(EM)</strong></center></td>
594
+ <td><center><strong>(F1)</strong></center></td>
595
+ <td><center><strong>(EM)</strong></center></td>
596
+ <td><center><strong>(F1)</strong></center></td>
597
+ <td><center><strong>(Spearman)</strong></center></td>
598
+ <td><center><strong>(Pearson)</strong></center></td>
599
+ <td><center><strong>(Spearman)</strong></center></td>
600
+ <td><center><strong>(Pearson)</strong></center></td>
601
+ </tr>
602
+ <tr>
603
+ <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>
604
+ </tr>
605
+ <tr>
606
+ <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>
607
+ </tr>
608
+ </tbody>
609
+ </table>
610
+
611
+
612
+ ## RoLlama2 Model Family
613
+
614
+ | Model | Link |
615
+ |--------------------|:--------:|
616
+ |RoLlama2-7b-Base-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14) |
617
+ |RoLlama2-7b-Instruct-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-05-14) |
618
+ |*RoLlama2-7b-Instruct-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09) |
619
+ |RoLlama2-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-DPO-2024-10-09) |
620
+
621
+ ## Citation
622
+
623
+ ```
624
+ @misc{masala2024vorbecstiromanecsterecipetrain,
625
+ title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions},
626
+ 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},
627
+ year={2024},
628
+ eprint={2406.18266},
629
+ archivePrefix={arXiv},
630
+ primaryClass={cs.CL},
631
+ url={https://arxiv.org/abs/2406.18266},
632
+ }
633
+ ```
634
+ <!-- **APA:**
635
+
636
+ [More Information Needed] -->
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