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moreevals
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- compile-results.ipynb +30 -30
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +132 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +161 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +2249 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/blimp/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/cmmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +0 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/cmmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/copa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +58 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/copa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/glue/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +374 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/glue/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/hellaswag/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +67 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/hellaswag/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/lambada/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +126 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/lambada/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +252 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/logiqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +66 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/logiqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/mmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +2594 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/mmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/nq_open/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +80 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/nq_open/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/openbookqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +66 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/openbookqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +283 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/piqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +64 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/piqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/pythia/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +0 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/pythia/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/record/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +67 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/record/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/sciq/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +65 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/sciq/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/truthfulqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +282 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/truthfulqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +58 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +390 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +548 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +423 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +248 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
- lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-40/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +132 -0
compile-results.ipynb
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"Defaulting to user installation because normal site-packages is not writeable\n",
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"Requirement already satisfied: pandas in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (2.2.0)\n",
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"\u001b[33mWARNING: You are using pip version 21.2.4; however, version 24.0 is available.\n",
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"['mistralai/Mistral-7B-v0.1', 'mosaicml/mpt-7b-instruct', 'mosaicml/mpt-7b', 'mosaicml/mpt-7b-chat', 'bigscience/bloom-7b1', 'bigscience/bloomz-7b1-mt', 'bigscience/bloomz-7b1', 'EleutherAI/pythia-2.8b', 'EleutherAI/pythia-1.4b', 'EleutherAI/gpt-j-6b', 'EleutherAI/pythia-6.9b', 'google/flan-t5-base', 'google/gemma-2b', 'google/gemma-2b-it', 'google/gemma-7b', 'google/gemma-7b-it', 'google/flan-t5-large', 'microsoft/phi-1_5', 'microsoft/phi-2', 'microsoft/phi-1', 'allenai/OLMo-7B', 'TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T', 'TinyLlama/TinyLlama-1.1B-Chat-v1.0', 'RWKV/rwkv-5-world-1b5', 'RWKV/rwkv-5-world-3b', 'RWKV/rwkv-4-world-3b', 'RWKV/rwkv-4-world-1b5', 'RWKV/v5-Eagle-7B-HF', 'RWKV/rwkv-4-world-7b', 'aisingapore/sealion7b', 'aisingapore/sealion3b', './rwkv-x-dev/chunk4-0_85_pth', './rwkv-x-dev/chunk1-0_8_pth', './rwkv-x-dev/chunk0-0_8_pth', './rwkv-x-dev/blink4-final_pth', './rwkv-x-dev/chunk2-0_8_pth', './rwkv-x-dev/chunk3-0_8_pth', './rwkv-x-dev/r3-4k-test2-fix3-blink-final_pth', './rwkv-x-dev/R4-7B-15t-With-Mask_pth', './rwkv-x-dev/r3-testchunk-1-8_pth', './rwkv-x-dev/R4-with-shuffle-rwkv-53_pth', './rwkv-x-dev/chunk7-2-0_85_pth', './rwkv-x-dev/r3-testchunk2-blink-fixed_pth', './rwkv-x-dev/r3-testchunk2-blink_pth', './rwkv-x-dev/rwkv-230_pth', './rwkv-x-dev/chunk5-0_85_pth', './rwkv-x-dev/R4-7B-Base-No-Mask_pth', './rwkv-x-dev/RWKV-5-World-1B5-v2-20231025-ctx4096', './rwkv-x-dev/R4-1B5-No-Mask_pth', './rwkv-x-dev/RWKV-32K-5B-RW_pth', './rwkv-x-dev/R4-7B-15t-32k-No-Mask_pth', './rwkv-x-dev/chunk8-1-0_85_pth', './rwkv-x-dev/R4-7B-Base-32k-No-Mask_pth', './rwkv-x-dev/R4-no-shuffle-rwkv-53_pth', './rwkv-x-dev/r3-testchunk-1-8-no-cuda-with-warmup_pth', './rwkv-x-dev/r3-c1-8_pth', './rwkv-x-dev/RWKV-5-World-3B-v2-20231118-ctx16k', './rwkv-x-dev/RWKV-5-World-7B-v2-20240128-ctx4096', './rwkv-x-dev/R4-7B-15t-No-Mask_pth', './rwkv-x-dev/1_0-c1-290_pth', './rwkv-x-dev/R4-1B5-With-Mask_pth', './rwkv-x-dev/chunk6-0_85_pth', './rwkv-x-dev/R4-7B-Base-With-Mask_pth', 'rwkv-x-dev/v5-Eagle-7B-1_0T-HF', './rwkv-x-dev/chunk7-1-0_85_pth', './rwkv-x-dev/R4-7B-15t-extd-e3_pth', './rwkv-x-dev/r3-testchunk2_pth', './rwkv-x-dev/Hermes-RWKV-v5-7B_pth', './rwkv-x-dev/R4-7B-15t-extd-e2_pth', './rwkv-x-dev/r3-testchunk-blink_pth', 'togethercomputer/RedPajama-INCITE-7B-Base', 'togethercomputer/RedPajama-INCITE-7B-Instruct', 'togethercomputer/RedPajama-INCITE-7B-Chat', 'facebook/opt-2.7b', 'facebook/opt-6.7b', 'facebook/opt-1.3b', 'tiiuae/falcon-7b-instruct', 'tiiuae/falcon-rw-1b', 'tiiuae/falcon-rw-7b', 'tiiuae/falcon-7b', 'huggyllama/llama-7b', 'meta-llama/Llama-2-7b-chat-hf', 'meta-llama/Llama-2-7b-hf']\n",
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"eng_grp_sorted.to_csv('summary/bf16-sorted-eng-summary.csv', index=False)\n",
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"# English focused subset\n",
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"eng_focus_mixed=[\"lambada_openai\", \"lambada_standard\", \"blimp\", \"piqa\", \"copa\", \"sciq\", \"
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"eng_focus_tGrps=[\"anli\", \"glue\", \"mmlu\" ]\n",
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"eng_focus_tTest=[\"blimp\", \"arc_*\", \"logiqa\", \"winogrande\", \"openbookqa\", \"hellaswag\"]\n",
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"\n",
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"execution_count": 25,
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"text": [
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"Defaulting to user installation because normal site-packages is not writeable\n",
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"Requirement already satisfied: pandas in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (2.2.0)\n",
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"Requirement already satisfied: tzdata>=2022.7 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (2024.1)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2020.1 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (2024.1)\n",
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"Requirement already satisfied: numpy<2,>=1.22.4 in /Users/picocreator/Library/Python/3.9/lib/python/site-packages (from pandas) (1.26.1)\n",
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"\u001b[33mWARNING: You are using pip version 21.2.4; however, version 24.0 is available.\n",
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"You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
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+
"Found 4220 results.json files\n"
|
47 |
]
|
48 |
}
|
49 |
],
|
|
|
71 |
},
|
72 |
{
|
73 |
"cell_type": "code",
|
74 |
+
"execution_count": 27,
|
75 |
"metadata": {},
|
76 |
"outputs": [
|
77 |
{
|
|
|
156 |
},
|
157 |
{
|
158 |
"cell_type": "code",
|
159 |
+
"execution_count": 28,
|
160 |
"metadata": {},
|
161 |
"outputs": [
|
162 |
{
|
163 |
"name": "stdout",
|
164 |
"output_type": "stream",
|
165 |
"text": [
|
166 |
+
"Found 90 models\n",
|
167 |
"Models: \n",
|
168 |
+
"['mistralai/Mistral-7B-v0.1', 'mosaicml/mpt-7b-instruct', 'mosaicml/mpt-7b', 'mosaicml/mpt-7b-chat', 'bigscience/bloom-7b1', 'bigscience/bloomz-7b1-mt', 'bigscience/bloomz-7b1', 'EleutherAI/pythia-2.8b', 'EleutherAI/pythia-1.4b', 'EleutherAI/gpt-j-6b', 'EleutherAI/pythia-6.9b', 'google/flan-t5-base', 'google/gemma-2b', 'google/gemma-2b-it', 'google/gemma-7b', 'google/gemma-7b-it', 'google/flan-t5-large', 'microsoft/phi-1_5', 'microsoft/phi-2', 'microsoft/phi-1', 'allenai/OLMo-7B', 'TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T', 'TinyLlama/TinyLlama-1.1B-Chat-v1.0', 'RWKV/rwkv-5-world-1b5', 'RWKV/rwkv-5-world-3b', 'RWKV/rwkv-4-world-3b', 'RWKV/rwkv-4-world-1b5', 'RWKV/v5-Eagle-7B-HF', 'RWKV/rwkv-4-world-7b', 'aisingapore/sealion7b', 'aisingapore/sealion3b', './rwkv-x-dev/chunk4-0_85_pth', './rwkv-x-dev/chunk1-0_8_pth', './rwkv-x-dev/chunk0-0_8_pth', './rwkv-x-dev/blink4-final_pth', './rwkv-x-dev/chunk2-0_8_pth', './rwkv-x-dev/chunk3-0_8_pth', './rwkv-x-dev/r3-4k-test2-fix3-blink-final_pth', './rwkv-x-dev/R4-7B-15t-With-Mask_pth', './rwkv-x-dev/r3-testchunk-1-8_pth', './rwkv-x-dev/R4-with-shuffle-rwkv-53_pth', './rwkv-x-dev/chunk7-2-0_85_pth', './rwkv-x-dev/r3-testchunk2-blink-fixed_pth', './rwkv-x-dev/r3-testchunk2-blink_pth', './rwkv-x-dev/rwkv-230_pth', './rwkv-x-dev/1_3-C0-rwkv-60_pth', './rwkv-x-dev/chunk5-0_85_pth', './rwkv-x-dev/R4-7B-Base-No-Mask_pth', './rwkv-x-dev/RWKV-5-World-1B5-v2-20231025-ctx4096', './rwkv-x-dev/R4-1B5-No-Mask_pth', './rwkv-x-dev/RWKV-32K-5B-RW_pth', './rwkv-x-dev/R4-7B-15t-32k-No-Mask_pth', './rwkv-x-dev/1_3-C0-PRERUN-rwkv-60_pth', './rwkv-x-dev/chunk8-1-0_85_pth', './rwkv-x-dev/R4-7B-Base-32k-No-Mask_pth', './rwkv-x-dev/R4-no-shuffle-rwkv-53_pth', './rwkv-x-dev/r3-testchunk-1-8-no-cuda-with-warmup_pth', './rwkv-x-dev/r3-c1-8_pth', './rwkv-x-dev/1_3-C0-PRERUN-rwkv-450_pth', './rwkv-x-dev/RWKV-5-World-3B-v2-20231118-ctx16k', './rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-40_pth', './rwkv-x-dev/RWKV-5-World-7B-v2-20240128-ctx4096', './rwkv-x-dev/R4-7B-15t-No-Mask_pth', './rwkv-x-dev/1_0-c1-290_pth', './rwkv-x-dev/R4-1B5-With-Mask_pth', './rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30_pth', './rwkv-x-dev/1_3-C0-rwkv-70_pth', './rwkv-x-dev/chunk6-0_85_pth', './rwkv-x-dev/R4-7B-Base-With-Mask_pth', 'rwkv-x-dev/v5-Eagle-7B-1_0T-HF', './rwkv-x-dev/1_3-C0-PRERUN-rwkv-30_pth', './rwkv-x-dev/chunk7-1-0_85_pth', './rwkv-x-dev/R4-7B-15t-extd-e3_pth', './rwkv-x-dev/r3-testchunk2_pth', './rwkv-x-dev/Hermes-RWKV-v5-7B_pth', './rwkv-x-dev/R4-7B-15t-extd-e2_pth', './rwkv-x-dev/r3-testchunk-blink_pth', 'togethercomputer/RedPajama-INCITE-7B-Base', 'togethercomputer/RedPajama-INCITE-7B-Instruct', 'togethercomputer/RedPajama-INCITE-7B-Chat', 'facebook/opt-2.7b', 'facebook/opt-6.7b', 'facebook/opt-1.3b', 'tiiuae/falcon-7b-instruct', 'tiiuae/falcon-rw-1b', 'tiiuae/falcon-rw-7b', 'tiiuae/falcon-7b', 'huggyllama/llama-7b', 'meta-llama/Llama-2-7b-chat-hf', 'meta-llama/Llama-2-7b-hf']\n",
|
169 |
"Saved to compiled-lm-eval-results.json\n"
|
170 |
]
|
171 |
}
|
|
|
199 |
},
|
200 |
{
|
201 |
"cell_type": "code",
|
202 |
+
"execution_count": 29,
|
203 |
"metadata": {},
|
204 |
"outputs": [
|
205 |
{
|
|
|
691 |
"44 0.052515 0.566727 0.052515 "
|
692 |
]
|
693 |
},
|
694 |
+
"execution_count": 29,
|
695 |
"metadata": {},
|
696 |
"output_type": "execute_result"
|
697 |
}
|
|
|
892 |
},
|
893 |
{
|
894 |
"cell_type": "code",
|
895 |
+
"execution_count": 30,
|
896 |
"metadata": {},
|
897 |
"outputs": [
|
898 |
{
|
899 |
"name": "stdout",
|
900 |
"output_type": "stream",
|
901 |
"text": [
|
902 |
+
"total 27808\n",
|
903 |
+
"-rw-r--r--@ 1 picocreator staff 1.1M Mar 12 00:29 bf16-all-results-and-groups.csv\n",
|
904 |
+
"-rw-r--r--@ 1 picocreator staff 274K Mar 12 00:29 bf16-all-simplified-results-and-groups.csv\n",
|
905 |
+
"-rw-r--r--@ 1 picocreator staff 274K Mar 12 00:29 bf16-all-sorted-results-and-groups.csv\n",
|
906 |
+
"-rw-r--r--@ 1 picocreator staff 62K Mar 12 00:29 bf16-eng-focus.csv\n",
|
907 |
+
"-rw-r--r--@ 1 picocreator staff 989K Mar 12 00:29 bf16-eng-results.csv\n",
|
908 |
+
"-rw-r--r--@ 1 picocreator staff 83K Mar 12 00:29 bf16-eng-summary.csv\n",
|
909 |
+
"-rw-r--r--@ 1 picocreator staff 103K Mar 12 00:29 bf16-multilang-results.csv\n",
|
910 |
+
"-rw-r--r--@ 1 picocreator staff 15K Mar 12 00:29 bf16-multilang-summary.csv\n",
|
911 |
+
"-rw-r--r--@ 1 picocreator staff 62K Mar 12 00:29 bf16-sorted-eng-focus.csv\n",
|
912 |
+
"-rw-r--r--@ 1 picocreator staff 989K Mar 12 00:29 bf16-sorted-eng-results.csv\n",
|
913 |
+
"-rw-r--r--@ 1 picocreator staff 83K Mar 12 00:29 bf16-sorted-eng-summary.csv\n",
|
914 |
+
"-rw-r--r--@ 1 picocreator staff 15K Mar 12 00:29 bf16-sorted-multilang-summary.csv\n",
|
915 |
+
"-rw-r--r-- 1 picocreator staff 7.0M Mar 12 00:29 compiled-lm-eval-results.json\n",
|
916 |
+
"-rw-r--r--@ 1 picocreator staff 271K Mar 12 00:29 rwkv-x-dev-bf16-sorted-eng-all.csv\n",
|
917 |
+
"-rw-r--r--@ 1 picocreator staff 19K Mar 12 00:29 rwkv-x-dev-bf16-sorted-eng-focus.csv\n",
|
918 |
+
"-rw-r--r--@ 1 picocreator staff 17K Mar 12 00:29 rwkv-x-dev-bf16-sorted-multilang-summary.csv\n"
|
919 |
]
|
920 |
}
|
921 |
],
|
|
|
966 |
"eng_grp_sorted.to_csv('summary/bf16-sorted-eng-summary.csv', index=False)\n",
|
967 |
"\n",
|
968 |
"# English focused subset\n",
|
969 |
+
"eng_focus_mixed=[\"lambada_openai\", \"lambada_standard\", \"blimp\", \"piqa\", \"copa\", \"sciq\", \"truthfulqa\", \"pythia\"] #\"np_open\", \"cmmlu\", \"record\"\n",
|
970 |
"eng_focus_tGrps=[\"anli\", \"glue\", \"mmlu\" ]\n",
|
971 |
"eng_focus_tTest=[\"blimp\", \"arc_*\", \"logiqa\", \"winogrande\", \"openbookqa\", \"hellaswag\"]\n",
|
972 |
"\n",
|
lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,132 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"ai2_arc": {
|
4 |
+
"acc,none": 0.62119503945885,
|
5 |
+
"acc_stderr,none": 0.1029658289668019,
|
6 |
+
"acc_norm,none": 0.6180947012401353,
|
7 |
+
"acc_norm_stderr,none": 0.08639538666596923,
|
8 |
+
"alias": "ai2_arc"
|
9 |
+
},
|
10 |
+
"arc_challenge": {
|
11 |
+
"acc,none": 0.4035836177474403,
|
12 |
+
"acc_stderr,none": 0.014337158914268455,
|
13 |
+
"acc_norm,none": 0.43600682593856654,
|
14 |
+
"acc_norm_stderr,none": 0.014491225699230914,
|
15 |
+
"alias": " - arc_challenge"
|
16 |
+
},
|
17 |
+
"arc_easy": {
|
18 |
+
"acc,none": 0.7285353535353535,
|
19 |
+
"acc_stderr,none": 0.009125362970360623,
|
20 |
+
"acc_norm,none": 0.7079124579124579,
|
21 |
+
"acc_norm_stderr,none": 0.009330705616569065,
|
22 |
+
"alias": " - arc_easy"
|
23 |
+
}
|
24 |
+
},
|
25 |
+
"groups": {
|
26 |
+
"ai2_arc": {
|
27 |
+
"acc,none": 0.62119503945885,
|
28 |
+
"acc_stderr,none": 0.1029658289668019,
|
29 |
+
"acc_norm,none": 0.6180947012401353,
|
30 |
+
"acc_norm_stderr,none": 0.08639538666596923,
|
31 |
+
"alias": "ai2_arc"
|
32 |
+
}
|
33 |
+
},
|
34 |
+
"configs": {
|
35 |
+
"arc_challenge": {
|
36 |
+
"task": "arc_challenge",
|
37 |
+
"group": [
|
38 |
+
"ai2_arc"
|
39 |
+
],
|
40 |
+
"dataset_path": "allenai/ai2_arc",
|
41 |
+
"dataset_name": "ARC-Challenge",
|
42 |
+
"training_split": "train",
|
43 |
+
"validation_split": "validation",
|
44 |
+
"test_split": "test",
|
45 |
+
"doc_to_text": "Question: {{question}}\nAnswer:",
|
46 |
+
"doc_to_target": "{{choices.label.index(answerKey)}}",
|
47 |
+
"doc_to_choice": "{{choices.text}}",
|
48 |
+
"description": "",
|
49 |
+
"target_delimiter": " ",
|
50 |
+
"fewshot_delimiter": "\n\n",
|
51 |
+
"metric_list": [
|
52 |
+
{
|
53 |
+
"metric": "acc",
|
54 |
+
"aggregation": "mean",
|
55 |
+
"higher_is_better": true
|
56 |
+
},
|
57 |
+
{
|
58 |
+
"metric": "acc_norm",
|
59 |
+
"aggregation": "mean",
|
60 |
+
"higher_is_better": true
|
61 |
+
}
|
62 |
+
],
|
63 |
+
"output_type": "multiple_choice",
|
64 |
+
"repeats": 1,
|
65 |
+
"should_decontaminate": true,
|
66 |
+
"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
|
67 |
+
"metadata": {
|
68 |
+
"version": 1.0
|
69 |
+
}
|
70 |
+
},
|
71 |
+
"arc_easy": {
|
72 |
+
"task": "arc_easy",
|
73 |
+
"group": [
|
74 |
+
"ai2_arc"
|
75 |
+
],
|
76 |
+
"dataset_path": "allenai/ai2_arc",
|
77 |
+
"dataset_name": "ARC-Easy",
|
78 |
+
"training_split": "train",
|
79 |
+
"validation_split": "validation",
|
80 |
+
"test_split": "test",
|
81 |
+
"doc_to_text": "Question: {{question}}\nAnswer:",
|
82 |
+
"doc_to_target": "{{choices.label.index(answerKey)}}",
|
83 |
+
"doc_to_choice": "{{choices.text}}",
|
84 |
+
"description": "",
|
85 |
+
"target_delimiter": " ",
|
86 |
+
"fewshot_delimiter": "\n\n",
|
87 |
+
"metric_list": [
|
88 |
+
{
|
89 |
+
"metric": "acc",
|
90 |
+
"aggregation": "mean",
|
91 |
+
"higher_is_better": true
|
92 |
+
},
|
93 |
+
{
|
94 |
+
"metric": "acc_norm",
|
95 |
+
"aggregation": "mean",
|
96 |
+
"higher_is_better": true
|
97 |
+
}
|
98 |
+
],
|
99 |
+
"output_type": "multiple_choice",
|
100 |
+
"repeats": 1,
|
101 |
+
"should_decontaminate": true,
|
102 |
+
"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
|
103 |
+
"metadata": {
|
104 |
+
"version": 1.0
|
105 |
+
}
|
106 |
+
}
|
107 |
+
},
|
108 |
+
"versions": {
|
109 |
+
"ai2_arc": "N/A",
|
110 |
+
"arc_challenge": 1.0,
|
111 |
+
"arc_easy": 1.0
|
112 |
+
},
|
113 |
+
"n-shot": {
|
114 |
+
"ai2_arc": 0,
|
115 |
+
"arc_challenge": 0,
|
116 |
+
"arc_easy": 0
|
117 |
+
},
|
118 |
+
"config": {
|
119 |
+
"model": "hf",
|
120 |
+
"model_args": "pretrained=./rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30_pth,dtype=bfloat16,trust_remote_code=True",
|
121 |
+
"batch_size": "auto",
|
122 |
+
"batch_sizes": [
|
123 |
+
64
|
124 |
+
],
|
125 |
+
"device": null,
|
126 |
+
"use_cache": null,
|
127 |
+
"limit": null,
|
128 |
+
"bootstrap_iters": 100000,
|
129 |
+
"gen_kwargs": null
|
130 |
+
},
|
131 |
+
"git_hash": "173e67b"
|
132 |
+
}
|
lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/ai2_arc/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b934bfff21a539383d32a43ba0e211bf08916a03afb51b3b5723dbc08782bbac
|
3 |
+
size 44691
|
lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/anli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,161 @@
|
|
|
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|
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|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"blimp": {
|
4 |
+
"acc,none": 0.8161194029850747,
|
5 |
+
"acc_stderr,none": 0.16925179374295443,
|
6 |
+
"alias": "blimp"
|
7 |
+
},
|
8 |
+
"blimp_adjunct_island": {
|
9 |
+
"acc,none": 0.91,
|
10 |
+
"acc_stderr,none": 0.00905439020486644,
|
11 |
+
"alias": " - blimp_adjunct_island"
|
12 |
+
},
|
13 |
+
"blimp_anaphor_gender_agreement": {
|
14 |
+
"acc,none": 0.979,
|
15 |
+
"acc_stderr,none": 0.004536472151306513,
|
16 |
+
"alias": " - blimp_anaphor_gender_agreement"
|
17 |
+
},
|
18 |
+
"blimp_anaphor_number_agreement": {
|
19 |
+
"acc,none": 0.991,
|
20 |
+
"acc_stderr,none": 0.0029879638431426483,
|
21 |
+
"alias": " - blimp_anaphor_number_agreement"
|
22 |
+
},
|
23 |
+
"blimp_animate_subject_passive": {
|
24 |
+
"acc,none": 0.867,
|
25 |
+
"acc_stderr,none": 0.010743669132397327,
|
26 |
+
"alias": " - blimp_animate_subject_passive"
|
27 |
+
},
|
28 |
+
"blimp_animate_subject_trans": {
|
29 |
+
"acc,none": 0.924,
|
30 |
+
"acc_stderr,none": 0.008384169266796393,
|
31 |
+
"alias": " - blimp_animate_subject_trans"
|
32 |
+
},
|
33 |
+
"blimp_causative": {
|
34 |
+
"acc,none": 0.798,
|
35 |
+
"acc_stderr,none": 0.01270265158765512,
|
36 |
+
"alias": " - blimp_causative"
|
37 |
+
},
|
38 |
+
"blimp_complex_NP_island": {
|
39 |
+
"acc,none": 0.624,
|
40 |
+
"acc_stderr,none": 0.015325105508898129,
|
41 |
+
"alias": " - blimp_complex_NP_island"
|
42 |
+
},
|
43 |
+
"blimp_coordinate_structure_constraint_complex_left_branch": {
|
44 |
+
"acc,none": 0.901,
|
45 |
+
"acc_stderr,none": 0.009449248027662737,
|
46 |
+
"alias": " - blimp_coordinate_structure_constraint_complex_left_branch"
|
47 |
+
},
|
48 |
+
"blimp_coordinate_structure_constraint_object_extraction": {
|
49 |
+
"acc,none": 0.891,
|
50 |
+
"acc_stderr,none": 0.009859828407037193,
|
51 |
+
"alias": " - blimp_coordinate_structure_constraint_object_extraction"
|
52 |
+
},
|
53 |
+
"blimp_determiner_noun_agreement_1": {
|
54 |
+
"acc,none": 0.997,
|
55 |
+
"acc_stderr,none": 0.0017303161543469362,
|
56 |
+
"alias": " - blimp_determiner_noun_agreement_1"
|
57 |
+
},
|
58 |
+
"blimp_determiner_noun_agreement_2": {
|
59 |
+
"acc,none": 0.991,
|
60 |
+
"acc_stderr,none": 0.002987963843142671,
|
61 |
+
"alias": " - blimp_determiner_noun_agreement_2"
|
62 |
+
},
|
63 |
+
"blimp_determiner_noun_agreement_irregular_1": {
|
64 |
+
"acc,none": 0.943,
|
65 |
+
"acc_stderr,none": 0.007335175853706846,
|
66 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_1"
|
67 |
+
},
|
68 |
+
"blimp_determiner_noun_agreement_irregular_2": {
|
69 |
+
"acc,none": 0.962,
|
70 |
+
"acc_stderr,none": 0.006049181150584941,
|
71 |
+
"alias": " - blimp_determiner_noun_agreement_irregular_2"
|
72 |
+
},
|
73 |
+
"blimp_determiner_noun_agreement_with_adj_2": {
|
74 |
+
"acc,none": 0.958,
|
75 |
+
"acc_stderr,none": 0.006346359293033856,
|
76 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_2"
|
77 |
+
},
|
78 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_1": {
|
79 |
+
"acc,none": 0.907,
|
80 |
+
"acc_stderr,none": 0.009188875634996662,
|
81 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_1"
|
82 |
+
},
|
83 |
+
"blimp_determiner_noun_agreement_with_adj_irregular_2": {
|
84 |
+
"acc,none": 0.937,
|
85 |
+
"acc_stderr,none": 0.0076870078762864376,
|
86 |
+
"alias": " - blimp_determiner_noun_agreement_with_adj_irregular_2"
|
87 |
+
},
|
88 |
+
"blimp_determiner_noun_agreement_with_adjective_1": {
|
89 |
+
"acc,none": 0.98,
|
90 |
+
"acc_stderr,none": 0.004429403980178323,
|
91 |
+
"alias": " - blimp_determiner_noun_agreement_with_adjective_1"
|
92 |
+
},
|
93 |
+
"blimp_distractor_agreement_relational_noun": {
|
94 |
+
"acc,none": 0.909,
|
95 |
+
"acc_stderr,none": 0.009099549538400234,
|
96 |
+
"alias": " - blimp_distractor_agreement_relational_noun"
|
97 |
+
},
|
98 |
+
"blimp_distractor_agreement_relative_clause": {
|
99 |
+
"acc,none": 0.722,
|
100 |
+
"acc_stderr,none": 0.01417451646148526,
|
101 |
+
"alias": " - blimp_distractor_agreement_relative_clause"
|
102 |
+
},
|
103 |
+
"blimp_drop_argument": {
|
104 |
+
"acc,none": 0.692,
|
105 |
+
"acc_stderr,none": 0.01460648312734276,
|
106 |
+
"alias": " - blimp_drop_argument"
|
107 |
+
},
|
108 |
+
"blimp_ellipsis_n_bar_1": {
|
109 |
+
"acc,none": 0.71,
|
110 |
+
"acc_stderr,none": 0.01435639599990569,
|
111 |
+
"alias": " - blimp_ellipsis_n_bar_1"
|
112 |
+
},
|
113 |
+
"blimp_ellipsis_n_bar_2": {
|
114 |
+
"acc,none": 0.954,
|
115 |
+
"acc_stderr,none": 0.006627814717380705,
|
116 |
+
"alias": " - blimp_ellipsis_n_bar_2"
|
117 |
+
},
|
118 |
+
"blimp_existential_there_object_raising": {
|
119 |
+
"acc,none": 0.889,
|
120 |
+
"acc_stderr,none": 0.009938701010583726,
|
121 |
+
"alias": " - blimp_existential_there_object_raising"
|
122 |
+
},
|
123 |
+
"blimp_existential_there_quantifiers_1": {
|
124 |
+
"acc,none": 0.978,
|
125 |
+
"acc_stderr,none": 0.004640855259274702,
|
126 |
+
"alias": " - blimp_existential_there_quantifiers_1"
|
127 |
+
},
|
128 |
+
"blimp_existential_there_quantifiers_2": {
|
129 |
+
"acc,none": 0.385,
|
130 |
+
"acc_stderr,none": 0.01539519444541081,
|
131 |
+
"alias": " - blimp_existential_there_quantifiers_2"
|
132 |
+
},
|
133 |
+
"blimp_existential_there_subject_raising": {
|
134 |
+
"acc,none": 0.911,
|
135 |
+
"acc_stderr,none": 0.00900889339265152,
|
136 |
+
"alias": " - blimp_existential_there_subject_raising"
|
137 |
+
},
|
138 |
+
"blimp_expletive_it_object_raising": {
|
139 |
+
"acc,none": 0.821,
|
140 |
+
"acc_stderr,none": 0.012128730605719123,
|
141 |
+
"alias": " - blimp_expletive_it_object_raising"
|
142 |
+
},
|
143 |
+
"blimp_inchoative": {
|
144 |
+
"acc,none": 0.574,
|
145 |
+
"acc_stderr,none": 0.01564508768811381,
|
146 |
+
"alias": " - blimp_inchoative"
|
147 |
+
},
|
148 |
+
"blimp_intransitive": {
|
149 |
+
"acc,none": 0.719,
|
150 |
+
"acc_stderr,none": 0.014221154708434935,
|
151 |
+
"alias": " - blimp_intransitive"
|
152 |
+
},
|
153 |
+
"blimp_irregular_past_participle_adjectives": {
|
154 |
+
"acc,none": 0.9,
|
155 |
+
"acc_stderr,none": 0.009491579957525045,
|
156 |
+
"alias": " - blimp_irregular_past_participle_adjectives"
|
157 |
+
},
|
158 |
+
"blimp_irregular_past_participle_verbs": {
|
159 |
+
"acc,none": 0.888,
|
160 |
+
"acc_stderr,none": 0.009977753031397226,
|
161 |
+
"alias": " - blimp_irregular_past_participle_verbs"
|
162 |
+
},
|
163 |
+
"blimp_irregular_plural_subject_verb_agreement_1": {
|
164 |
+
"acc,none": 0.929,
|
165 |
+
"acc_stderr,none": 0.008125578442487905,
|
166 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_1"
|
167 |
+
},
|
168 |
+
"blimp_irregular_plural_subject_verb_agreement_2": {
|
169 |
+
"acc,none": 0.913,
|
170 |
+
"acc_stderr,none": 0.008916866630745885,
|
171 |
+
"alias": " - blimp_irregular_plural_subject_verb_agreement_2"
|
172 |
+
},
|
173 |
+
"blimp_left_branch_island_echo_question": {
|
174 |
+
"acc,none": 0.638,
|
175 |
+
"acc_stderr,none": 0.015204840912919498,
|
176 |
+
"alias": " - blimp_left_branch_island_echo_question"
|
177 |
+
},
|
178 |
+
"blimp_left_branch_island_simple_question": {
|
179 |
+
"acc,none": 0.928,
|
180 |
+
"acc_stderr,none": 0.008178195576218681,
|
181 |
+
"alias": " - blimp_left_branch_island_simple_question"
|
182 |
+
},
|
183 |
+
"blimp_matrix_question_npi_licensor_present": {
|
184 |
+
"acc,none": 0.442,
|
185 |
+
"acc_stderr,none": 0.015712507211864207,
|
186 |
+
"alias": " - blimp_matrix_question_npi_licensor_present"
|
187 |
+
},
|
188 |
+
"blimp_npi_present_1": {
|
189 |
+
"acc,none": 0.607,
|
190 |
+
"acc_stderr,none": 0.015452824654081496,
|
191 |
+
"alias": " - blimp_npi_present_1"
|
192 |
+
},
|
193 |
+
"blimp_npi_present_2": {
|
194 |
+
"acc,none": 0.542,
|
195 |
+
"acc_stderr,none": 0.015763390640483706,
|
196 |
+
"alias": " - blimp_npi_present_2"
|
197 |
+
},
|
198 |
+
"blimp_only_npi_licensor_present": {
|
199 |
+
"acc,none": 0.613,
|
200 |
+
"acc_stderr,none": 0.01541001195549393,
|
201 |
+
"alias": " - blimp_only_npi_licensor_present"
|
202 |
+
},
|
203 |
+
"blimp_only_npi_scope": {
|
204 |
+
"acc,none": 0.503,
|
205 |
+
"acc_stderr,none": 0.015819015179246724,
|
206 |
+
"alias": " - blimp_only_npi_scope"
|
207 |
+
},
|
208 |
+
"blimp_passive_1": {
|
209 |
+
"acc,none": 0.889,
|
210 |
+
"acc_stderr,none": 0.009938701010583726,
|
211 |
+
"alias": " - blimp_passive_1"
|
212 |
+
},
|
213 |
+
"blimp_passive_2": {
|
214 |
+
"acc,none": 0.907,
|
215 |
+
"acc_stderr,none": 0.009188875634996695,
|
216 |
+
"alias": " - blimp_passive_2"
|
217 |
+
},
|
218 |
+
"blimp_principle_A_c_command": {
|
219 |
+
"acc,none": 0.763,
|
220 |
+
"acc_stderr,none": 0.013454070462577945,
|
221 |
+
"alias": " - blimp_principle_A_c_command"
|
222 |
+
},
|
223 |
+
"blimp_principle_A_case_1": {
|
224 |
+
"acc,none": 1.0,
|
225 |
+
"acc_stderr,none": 0.0,
|
226 |
+
"alias": " - blimp_principle_A_case_1"
|
227 |
+
},
|
228 |
+
"blimp_principle_A_case_2": {
|
229 |
+
"acc,none": 0.935,
|
230 |
+
"acc_stderr,none": 0.007799733061832009,
|
231 |
+
"alias": " - blimp_principle_A_case_2"
|
232 |
+
},
|
233 |
+
"blimp_principle_A_domain_1": {
|
234 |
+
"acc,none": 0.989,
|
235 |
+
"acc_stderr,none": 0.0032999833166078166,
|
236 |
+
"alias": " - blimp_principle_A_domain_1"
|
237 |
+
},
|
238 |
+
"blimp_principle_A_domain_2": {
|
239 |
+
"acc,none": 0.91,
|
240 |
+
"acc_stderr,none": 0.009054390204866437,
|
241 |
+
"alias": " - blimp_principle_A_domain_2"
|
242 |
+
},
|
243 |
+
"blimp_principle_A_domain_3": {
|
244 |
+
"acc,none": 0.83,
|
245 |
+
"acc_stderr,none": 0.01188449583454166,
|
246 |
+
"alias": " - blimp_principle_A_domain_3"
|
247 |
+
},
|
248 |
+
"blimp_principle_A_reconstruction": {
|
249 |
+
"acc,none": 0.605,
|
250 |
+
"acc_stderr,none": 0.015466551464829347,
|
251 |
+
"alias": " - blimp_principle_A_reconstruction"
|
252 |
+
},
|
253 |
+
"blimp_regular_plural_subject_verb_agreement_1": {
|
254 |
+
"acc,none": 0.951,
|
255 |
+
"acc_stderr,none": 0.006829761756140911,
|
256 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_1"
|
257 |
+
},
|
258 |
+
"blimp_regular_plural_subject_verb_agreement_2": {
|
259 |
+
"acc,none": 0.876,
|
260 |
+
"acc_stderr,none": 0.010427498872343972,
|
261 |
+
"alias": " - blimp_regular_plural_subject_verb_agreement_2"
|
262 |
+
},
|
263 |
+
"blimp_sentential_negation_npi_licensor_present": {
|
264 |
+
"acc,none": 0.985,
|
265 |
+
"acc_stderr,none": 0.0038457495745030036,
|
266 |
+
"alias": " - blimp_sentential_negation_npi_licensor_present"
|
267 |
+
},
|
268 |
+
"blimp_sentential_negation_npi_scope": {
|
269 |
+
"acc,none": 0.696,
|
270 |
+
"acc_stderr,none": 0.01455320568795043,
|
271 |
+
"alias": " - blimp_sentential_negation_npi_scope"
|
272 |
+
},
|
273 |
+
"blimp_sentential_subject_island": {
|
274 |
+
"acc,none": 0.466,
|
275 |
+
"acc_stderr,none": 0.01578268332993762,
|
276 |
+
"alias": " - blimp_sentential_subject_island"
|
277 |
+
},
|
278 |
+
"blimp_superlative_quantifiers_1": {
|
279 |
+
"acc,none": 0.893,
|
280 |
+
"acc_stderr,none": 0.009779910359847169,
|
281 |
+
"alias": " - blimp_superlative_quantifiers_1"
|
282 |
+
},
|
283 |
+
"blimp_superlative_quantifiers_2": {
|
284 |
+
"acc,none": 0.939,
|
285 |
+
"acc_stderr,none": 0.007572076091557423,
|
286 |
+
"alias": " - blimp_superlative_quantifiers_2"
|
287 |
+
},
|
288 |
+
"blimp_tough_vs_raising_1": {
|
289 |
+
"acc,none": 0.618,
|
290 |
+
"acc_stderr,none": 0.015372453034968531,
|
291 |
+
"alias": " - blimp_tough_vs_raising_1"
|
292 |
+
},
|
293 |
+
"blimp_tough_vs_raising_2": {
|
294 |
+
"acc,none": 0.856,
|
295 |
+
"acc_stderr,none": 0.01110798754893915,
|
296 |
+
"alias": " - blimp_tough_vs_raising_2"
|
297 |
+
},
|
298 |
+
"blimp_transitive": {
|
299 |
+
"acc,none": 0.897,
|
300 |
+
"acc_stderr,none": 0.009616833339695794,
|
301 |
+
"alias": " - blimp_transitive"
|
302 |
+
},
|
303 |
+
"blimp_wh_island": {
|
304 |
+
"acc,none": 0.568,
|
305 |
+
"acc_stderr,none": 0.015672320237336206,
|
306 |
+
"alias": " - blimp_wh_island"
|
307 |
+
},
|
308 |
+
"blimp_wh_questions_object_gap": {
|
309 |
+
"acc,none": 0.85,
|
310 |
+
"acc_stderr,none": 0.011297239823409293,
|
311 |
+
"alias": " - blimp_wh_questions_object_gap"
|
312 |
+
},
|
313 |
+
"blimp_wh_questions_subject_gap": {
|
314 |
+
"acc,none": 0.949,
|
315 |
+
"acc_stderr,none": 0.006960420062571408,
|
316 |
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/logiqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
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@@ -0,0 +1,66 @@
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|
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"doc_to_target": "def doc_to_target(doc) -> int:\n choices = [\"a\", \"b\", \"c\", \"d\"]\n return choices.index(doc[\"label\"].strip())\n",
|
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size 38145
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/mmlu/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
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|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"mmlu": {
|
4 |
+
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|
5 |
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|
6 |
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|
7 |
+
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|
8 |
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|
9 |
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|
10 |
+
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|
11 |
+
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|
12 |
+
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|
13 |
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|
14 |
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|
15 |
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|
16 |
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|
17 |
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|
18 |
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|
19 |
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|
20 |
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|
21 |
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|
22 |
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|
23 |
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|
24 |
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|
25 |
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|
26 |
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|
27 |
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|
28 |
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|
29 |
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|
30 |
+
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|
31 |
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|
32 |
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|
33 |
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|
34 |
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|
35 |
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|
36 |
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|
37 |
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|
38 |
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|
39 |
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|
40 |
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|
41 |
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|
42 |
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|
43 |
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|
44 |
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|
45 |
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|
46 |
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|
47 |
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|
48 |
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|
49 |
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|
50 |
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|
51 |
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|
52 |
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|
53 |
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|
54 |
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|
55 |
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|
56 |
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|
57 |
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|
58 |
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|
59 |
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|
60 |
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61 |
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62 |
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63 |
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65 |
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66 |
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67 |
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|
70 |
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71 |
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72 |
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73 |
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|
75 |
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76 |
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77 |
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78 |
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|
79 |
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|
80 |
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81 |
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82 |
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|
83 |
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|
84 |
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|
85 |
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|
86 |
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|
87 |
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|
88 |
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|
89 |
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|
90 |
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|
91 |
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|
92 |
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|
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|
94 |
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|
95 |
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|
96 |
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97 |
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98 |
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|
99 |
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|
100 |
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|
101 |
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102 |
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103 |
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|
104 |
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|
105 |
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|
106 |
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107 |
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108 |
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109 |
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|
110 |
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|
111 |
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|
112 |
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|
113 |
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|
114 |
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|
115 |
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|
116 |
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|
117 |
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|
118 |
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|
119 |
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|
120 |
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|
121 |
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|
122 |
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|
123 |
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|
124 |
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|
125 |
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|
126 |
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|
127 |
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|
128 |
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|
129 |
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|
130 |
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|
131 |
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|
132 |
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133 |
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|
134 |
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|
135 |
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|
136 |
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|
137 |
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138 |
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|
140 |
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|
141 |
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142 |
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143 |
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|
144 |
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|
145 |
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|
146 |
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|
147 |
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148 |
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|
149 |
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|
150 |
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|
151 |
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|
152 |
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|
153 |
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|
154 |
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|
155 |
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|
156 |
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|
157 |
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|
158 |
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|
159 |
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|
160 |
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|
161 |
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162 |
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|
163 |
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|
164 |
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|
165 |
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|
166 |
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167 |
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|
168 |
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|
169 |
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|
170 |
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|
171 |
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172 |
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173 |
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174 |
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|
175 |
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|
176 |
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177 |
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178 |
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179 |
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|
180 |
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181 |
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182 |
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183 |
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184 |
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|
185 |
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186 |
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187 |
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188 |
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|
190 |
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191 |
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192 |
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193 |
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194 |
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195 |
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|
196 |
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197 |
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|
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|
200 |
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201 |
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202 |
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203 |
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|
204 |
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205 |
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206 |
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207 |
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|
209 |
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|
210 |
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|
211 |
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212 |
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213 |
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|
214 |
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|
215 |
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216 |
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217 |
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218 |
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|
219 |
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|
220 |
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|
221 |
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222 |
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223 |
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|
224 |
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|
225 |
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|
226 |
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|
227 |
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|
228 |
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|
229 |
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|
230 |
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|
231 |
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|
232 |
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|
233 |
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|
234 |
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|
235 |
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"acc,none": 0.3958333333333333,
|
236 |
+
"acc_stderr,none": 0.04089465449325583
|
237 |
+
},
|
238 |
+
"mmlu_college_chemistry": {
|
239 |
+
"alias": " - college_chemistry",
|
240 |
+
"acc,none": 0.3,
|
241 |
+
"acc_stderr,none": 0.046056618647183814
|
242 |
+
},
|
243 |
+
"mmlu_college_computer_science": {
|
244 |
+
"alias": " - college_computer_science",
|
245 |
+
"acc,none": 0.28,
|
246 |
+
"acc_stderr,none": 0.04512608598542126
|
247 |
+
},
|
248 |
+
"mmlu_college_mathematics": {
|
249 |
+
"alias": " - college_mathematics",
|
250 |
+
"acc,none": 0.24,
|
251 |
+
"acc_stderr,none": 0.04292346959909282
|
252 |
+
},
|
253 |
+
"mmlu_college_physics": {
|
254 |
+
"alias": " - college_physics",
|
255 |
+
"acc,none": 0.2549019607843137,
|
256 |
+
"acc_stderr,none": 0.043364327079931785
|
257 |
+
},
|
258 |
+
"mmlu_computer_security": {
|
259 |
+
"alias": " - computer_security",
|
260 |
+
"acc,none": 0.4,
|
261 |
+
"acc_stderr,none": 0.04923659639173309
|
262 |
+
},
|
263 |
+
"mmlu_conceptual_physics": {
|
264 |
+
"alias": " - conceptual_physics",
|
265 |
+
"acc,none": 0.4085106382978723,
|
266 |
+
"acc_stderr,none": 0.03213418026701576
|
267 |
+
},
|
268 |
+
"mmlu_electrical_engineering": {
|
269 |
+
"alias": " - electrical_engineering",
|
270 |
+
"acc,none": 0.3103448275862069,
|
271 |
+
"acc_stderr,none": 0.03855289616378949
|
272 |
+
},
|
273 |
+
"mmlu_elementary_mathematics": {
|
274 |
+
"alias": " - elementary_mathematics",
|
275 |
+
"acc,none": 0.2724867724867725,
|
276 |
+
"acc_stderr,none": 0.022930973071633356
|
277 |
+
},
|
278 |
+
"mmlu_high_school_biology": {
|
279 |
+
"alias": " - high_school_biology",
|
280 |
+
"acc,none": 0.432258064516129,
|
281 |
+
"acc_stderr,none": 0.028181739720019416
|
282 |
+
},
|
283 |
+
"mmlu_high_school_chemistry": {
|
284 |
+
"alias": " - high_school_chemistry",
|
285 |
+
"acc,none": 0.2561576354679803,
|
286 |
+
"acc_stderr,none": 0.030712730070982592
|
287 |
+
},
|
288 |
+
"mmlu_high_school_computer_science": {
|
289 |
+
"alias": " - high_school_computer_science",
|
290 |
+
"acc,none": 0.32,
|
291 |
+
"acc_stderr,none": 0.04688261722621504
|
292 |
+
},
|
293 |
+
"mmlu_high_school_mathematics": {
|
294 |
+
"alias": " - high_school_mathematics",
|
295 |
+
"acc,none": 0.26296296296296295,
|
296 |
+
"acc_stderr,none": 0.02684205787383371
|
297 |
+
},
|
298 |
+
"mmlu_high_school_physics": {
|
299 |
+
"alias": " - high_school_physics",
|
300 |
+
"acc,none": 0.2847682119205298,
|
301 |
+
"acc_stderr,none": 0.03684881521389024
|
302 |
+
},
|
303 |
+
"mmlu_high_school_statistics": {
|
304 |
+
"alias": " - high_school_statistics",
|
305 |
+
"acc,none": 0.20833333333333334,
|
306 |
+
"acc_stderr,none": 0.027696910713093933
|
307 |
+
},
|
308 |
+
"mmlu_machine_learning": {
|
309 |
+
"alias": " - machine_learning",
|
310 |
+
"acc,none": 0.32142857142857145,
|
311 |
+
"acc_stderr,none": 0.04432804055291519
|
312 |
+
}
|
313 |
+
},
|
314 |
+
"groups": {
|
315 |
+
"mmlu": {
|
316 |
+
"acc,none": 0.36597350804728673,
|
317 |
+
"acc_stderr,none": 0.07954926761608928,
|
318 |
+
"alias": "mmlu"
|
319 |
+
},
|
320 |
+
"mmlu_humanities": {
|
321 |
+
"alias": " - humanities",
|
322 |
+
"acc,none": 0.3523910733262487,
|
323 |
+
"acc_stderr,none": 0.0767171024538961
|
324 |
+
},
|
325 |
+
"mmlu_other": {
|
326 |
+
"alias": " - other",
|
327 |
+
"acc,none": 0.4235597038944319,
|
328 |
+
"acc_stderr,none": 0.07911482455386332
|
329 |
+
},
|
330 |
+
"mmlu_social_sciences": {
|
331 |
+
"alias": " - social_sciences",
|
332 |
+
"acc,none": 0.38446538836529087,
|
333 |
+
"acc_stderr,none": 0.0663097696603997
|
334 |
+
},
|
335 |
+
"mmlu_stem": {
|
336 |
+
"alias": " - stem",
|
337 |
+
"acc,none": 0.3114494132572153,
|
338 |
+
"acc_stderr,none": 0.07202961117104478
|
339 |
+
}
|
340 |
+
},
|
341 |
+
"configs": {
|
342 |
+
"mmlu_abstract_algebra": {
|
343 |
+
"task": "mmlu_abstract_algebra",
|
344 |
+
"task_alias": "abstract_algebra",
|
345 |
+
"group": "mmlu_stem",
|
346 |
+
"group_alias": "stem",
|
347 |
+
"dataset_path": "hails/mmlu_no_train",
|
348 |
+
"dataset_name": "abstract_algebra",
|
349 |
+
"test_split": "test",
|
350 |
+
"fewshot_split": "dev",
|
351 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
352 |
+
"doc_to_target": "answer",
|
353 |
+
"doc_to_choice": [
|
354 |
+
"A",
|
355 |
+
"B",
|
356 |
+
"C",
|
357 |
+
"D"
|
358 |
+
],
|
359 |
+
"description": "The following are multiple choice questions (with answers) about abstract algebra.\n\n",
|
360 |
+
"target_delimiter": " ",
|
361 |
+
"fewshot_delimiter": "\n\n",
|
362 |
+
"fewshot_config": {
|
363 |
+
"sampler": "first_n"
|
364 |
+
},
|
365 |
+
"metric_list": [
|
366 |
+
{
|
367 |
+
"metric": "acc",
|
368 |
+
"aggregation": "mean",
|
369 |
+
"higher_is_better": true
|
370 |
+
}
|
371 |
+
],
|
372 |
+
"output_type": "multiple_choice",
|
373 |
+
"repeats": 1,
|
374 |
+
"should_decontaminate": false,
|
375 |
+
"metadata": {
|
376 |
+
"version": 0.0
|
377 |
+
}
|
378 |
+
},
|
379 |
+
"mmlu_anatomy": {
|
380 |
+
"task": "mmlu_anatomy",
|
381 |
+
"task_alias": "anatomy",
|
382 |
+
"group": "mmlu_stem",
|
383 |
+
"group_alias": "stem",
|
384 |
+
"dataset_path": "hails/mmlu_no_train",
|
385 |
+
"dataset_name": "anatomy",
|
386 |
+
"test_split": "test",
|
387 |
+
"fewshot_split": "dev",
|
388 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
389 |
+
"doc_to_target": "answer",
|
390 |
+
"doc_to_choice": [
|
391 |
+
"A",
|
392 |
+
"B",
|
393 |
+
"C",
|
394 |
+
"D"
|
395 |
+
],
|
396 |
+
"description": "The following are multiple choice questions (with answers) about anatomy.\n\n",
|
397 |
+
"target_delimiter": " ",
|
398 |
+
"fewshot_delimiter": "\n\n",
|
399 |
+
"fewshot_config": {
|
400 |
+
"sampler": "first_n"
|
401 |
+
},
|
402 |
+
"metric_list": [
|
403 |
+
{
|
404 |
+
"metric": "acc",
|
405 |
+
"aggregation": "mean",
|
406 |
+
"higher_is_better": true
|
407 |
+
}
|
408 |
+
],
|
409 |
+
"output_type": "multiple_choice",
|
410 |
+
"repeats": 1,
|
411 |
+
"should_decontaminate": false,
|
412 |
+
"metadata": {
|
413 |
+
"version": 0.0
|
414 |
+
}
|
415 |
+
},
|
416 |
+
"mmlu_astronomy": {
|
417 |
+
"task": "mmlu_astronomy",
|
418 |
+
"task_alias": "astronomy",
|
419 |
+
"group": "mmlu_stem",
|
420 |
+
"group_alias": "stem",
|
421 |
+
"dataset_path": "hails/mmlu_no_train",
|
422 |
+
"dataset_name": "astronomy",
|
423 |
+
"test_split": "test",
|
424 |
+
"fewshot_split": "dev",
|
425 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
426 |
+
"doc_to_target": "answer",
|
427 |
+
"doc_to_choice": [
|
428 |
+
"A",
|
429 |
+
"B",
|
430 |
+
"C",
|
431 |
+
"D"
|
432 |
+
],
|
433 |
+
"description": "The following are multiple choice questions (with answers) about astronomy.\n\n",
|
434 |
+
"target_delimiter": " ",
|
435 |
+
"fewshot_delimiter": "\n\n",
|
436 |
+
"fewshot_config": {
|
437 |
+
"sampler": "first_n"
|
438 |
+
},
|
439 |
+
"metric_list": [
|
440 |
+
{
|
441 |
+
"metric": "acc",
|
442 |
+
"aggregation": "mean",
|
443 |
+
"higher_is_better": true
|
444 |
+
}
|
445 |
+
],
|
446 |
+
"output_type": "multiple_choice",
|
447 |
+
"repeats": 1,
|
448 |
+
"should_decontaminate": false,
|
449 |
+
"metadata": {
|
450 |
+
"version": 0.0
|
451 |
+
}
|
452 |
+
},
|
453 |
+
"mmlu_business_ethics": {
|
454 |
+
"task": "mmlu_business_ethics",
|
455 |
+
"task_alias": "business_ethics",
|
456 |
+
"group": "mmlu_other",
|
457 |
+
"group_alias": "other",
|
458 |
+
"dataset_path": "hails/mmlu_no_train",
|
459 |
+
"dataset_name": "business_ethics",
|
460 |
+
"test_split": "test",
|
461 |
+
"fewshot_split": "dev",
|
462 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
463 |
+
"doc_to_target": "answer",
|
464 |
+
"doc_to_choice": [
|
465 |
+
"A",
|
466 |
+
"B",
|
467 |
+
"C",
|
468 |
+
"D"
|
469 |
+
],
|
470 |
+
"description": "The following are multiple choice questions (with answers) about business ethics.\n\n",
|
471 |
+
"target_delimiter": " ",
|
472 |
+
"fewshot_delimiter": "\n\n",
|
473 |
+
"fewshot_config": {
|
474 |
+
"sampler": "first_n"
|
475 |
+
},
|
476 |
+
"metric_list": [
|
477 |
+
{
|
478 |
+
"metric": "acc",
|
479 |
+
"aggregation": "mean",
|
480 |
+
"higher_is_better": true
|
481 |
+
}
|
482 |
+
],
|
483 |
+
"output_type": "multiple_choice",
|
484 |
+
"repeats": 1,
|
485 |
+
"should_decontaminate": false,
|
486 |
+
"metadata": {
|
487 |
+
"version": 0.0
|
488 |
+
}
|
489 |
+
},
|
490 |
+
"mmlu_clinical_knowledge": {
|
491 |
+
"task": "mmlu_clinical_knowledge",
|
492 |
+
"task_alias": "clinical_knowledge",
|
493 |
+
"group": "mmlu_other",
|
494 |
+
"group_alias": "other",
|
495 |
+
"dataset_path": "hails/mmlu_no_train",
|
496 |
+
"dataset_name": "clinical_knowledge",
|
497 |
+
"test_split": "test",
|
498 |
+
"fewshot_split": "dev",
|
499 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
500 |
+
"doc_to_target": "answer",
|
501 |
+
"doc_to_choice": [
|
502 |
+
"A",
|
503 |
+
"B",
|
504 |
+
"C",
|
505 |
+
"D"
|
506 |
+
],
|
507 |
+
"description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n",
|
508 |
+
"target_delimiter": " ",
|
509 |
+
"fewshot_delimiter": "\n\n",
|
510 |
+
"fewshot_config": {
|
511 |
+
"sampler": "first_n"
|
512 |
+
},
|
513 |
+
"metric_list": [
|
514 |
+
{
|
515 |
+
"metric": "acc",
|
516 |
+
"aggregation": "mean",
|
517 |
+
"higher_is_better": true
|
518 |
+
}
|
519 |
+
],
|
520 |
+
"output_type": "multiple_choice",
|
521 |
+
"repeats": 1,
|
522 |
+
"should_decontaminate": false,
|
523 |
+
"metadata": {
|
524 |
+
"version": 0.0
|
525 |
+
}
|
526 |
+
},
|
527 |
+
"mmlu_college_biology": {
|
528 |
+
"task": "mmlu_college_biology",
|
529 |
+
"task_alias": "college_biology",
|
530 |
+
"group": "mmlu_stem",
|
531 |
+
"group_alias": "stem",
|
532 |
+
"dataset_path": "hails/mmlu_no_train",
|
533 |
+
"dataset_name": "college_biology",
|
534 |
+
"test_split": "test",
|
535 |
+
"fewshot_split": "dev",
|
536 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
537 |
+
"doc_to_target": "answer",
|
538 |
+
"doc_to_choice": [
|
539 |
+
"A",
|
540 |
+
"B",
|
541 |
+
"C",
|
542 |
+
"D"
|
543 |
+
],
|
544 |
+
"description": "The following are multiple choice questions (with answers) about college biology.\n\n",
|
545 |
+
"target_delimiter": " ",
|
546 |
+
"fewshot_delimiter": "\n\n",
|
547 |
+
"fewshot_config": {
|
548 |
+
"sampler": "first_n"
|
549 |
+
},
|
550 |
+
"metric_list": [
|
551 |
+
{
|
552 |
+
"metric": "acc",
|
553 |
+
"aggregation": "mean",
|
554 |
+
"higher_is_better": true
|
555 |
+
}
|
556 |
+
],
|
557 |
+
"output_type": "multiple_choice",
|
558 |
+
"repeats": 1,
|
559 |
+
"should_decontaminate": false,
|
560 |
+
"metadata": {
|
561 |
+
"version": 0.0
|
562 |
+
}
|
563 |
+
},
|
564 |
+
"mmlu_college_chemistry": {
|
565 |
+
"task": "mmlu_college_chemistry",
|
566 |
+
"task_alias": "college_chemistry",
|
567 |
+
"group": "mmlu_stem",
|
568 |
+
"group_alias": "stem",
|
569 |
+
"dataset_path": "hails/mmlu_no_train",
|
570 |
+
"dataset_name": "college_chemistry",
|
571 |
+
"test_split": "test",
|
572 |
+
"fewshot_split": "dev",
|
573 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
574 |
+
"doc_to_target": "answer",
|
575 |
+
"doc_to_choice": [
|
576 |
+
"A",
|
577 |
+
"B",
|
578 |
+
"C",
|
579 |
+
"D"
|
580 |
+
],
|
581 |
+
"description": "The following are multiple choice questions (with answers) about college chemistry.\n\n",
|
582 |
+
"target_delimiter": " ",
|
583 |
+
"fewshot_delimiter": "\n\n",
|
584 |
+
"fewshot_config": {
|
585 |
+
"sampler": "first_n"
|
586 |
+
},
|
587 |
+
"metric_list": [
|
588 |
+
{
|
589 |
+
"metric": "acc",
|
590 |
+
"aggregation": "mean",
|
591 |
+
"higher_is_better": true
|
592 |
+
}
|
593 |
+
],
|
594 |
+
"output_type": "multiple_choice",
|
595 |
+
"repeats": 1,
|
596 |
+
"should_decontaminate": false,
|
597 |
+
"metadata": {
|
598 |
+
"version": 0.0
|
599 |
+
}
|
600 |
+
},
|
601 |
+
"mmlu_college_computer_science": {
|
602 |
+
"task": "mmlu_college_computer_science",
|
603 |
+
"task_alias": "college_computer_science",
|
604 |
+
"group": "mmlu_stem",
|
605 |
+
"group_alias": "stem",
|
606 |
+
"dataset_path": "hails/mmlu_no_train",
|
607 |
+
"dataset_name": "college_computer_science",
|
608 |
+
"test_split": "test",
|
609 |
+
"fewshot_split": "dev",
|
610 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
611 |
+
"doc_to_target": "answer",
|
612 |
+
"doc_to_choice": [
|
613 |
+
"A",
|
614 |
+
"B",
|
615 |
+
"C",
|
616 |
+
"D"
|
617 |
+
],
|
618 |
+
"description": "The following are multiple choice questions (with answers) about college computer science.\n\n",
|
619 |
+
"target_delimiter": " ",
|
620 |
+
"fewshot_delimiter": "\n\n",
|
621 |
+
"fewshot_config": {
|
622 |
+
"sampler": "first_n"
|
623 |
+
},
|
624 |
+
"metric_list": [
|
625 |
+
{
|
626 |
+
"metric": "acc",
|
627 |
+
"aggregation": "mean",
|
628 |
+
"higher_is_better": true
|
629 |
+
}
|
630 |
+
],
|
631 |
+
"output_type": "multiple_choice",
|
632 |
+
"repeats": 1,
|
633 |
+
"should_decontaminate": false,
|
634 |
+
"metadata": {
|
635 |
+
"version": 0.0
|
636 |
+
}
|
637 |
+
},
|
638 |
+
"mmlu_college_mathematics": {
|
639 |
+
"task": "mmlu_college_mathematics",
|
640 |
+
"task_alias": "college_mathematics",
|
641 |
+
"group": "mmlu_stem",
|
642 |
+
"group_alias": "stem",
|
643 |
+
"dataset_path": "hails/mmlu_no_train",
|
644 |
+
"dataset_name": "college_mathematics",
|
645 |
+
"test_split": "test",
|
646 |
+
"fewshot_split": "dev",
|
647 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
648 |
+
"doc_to_target": "answer",
|
649 |
+
"doc_to_choice": [
|
650 |
+
"A",
|
651 |
+
"B",
|
652 |
+
"C",
|
653 |
+
"D"
|
654 |
+
],
|
655 |
+
"description": "The following are multiple choice questions (with answers) about college mathematics.\n\n",
|
656 |
+
"target_delimiter": " ",
|
657 |
+
"fewshot_delimiter": "\n\n",
|
658 |
+
"fewshot_config": {
|
659 |
+
"sampler": "first_n"
|
660 |
+
},
|
661 |
+
"metric_list": [
|
662 |
+
{
|
663 |
+
"metric": "acc",
|
664 |
+
"aggregation": "mean",
|
665 |
+
"higher_is_better": true
|
666 |
+
}
|
667 |
+
],
|
668 |
+
"output_type": "multiple_choice",
|
669 |
+
"repeats": 1,
|
670 |
+
"should_decontaminate": false,
|
671 |
+
"metadata": {
|
672 |
+
"version": 0.0
|
673 |
+
}
|
674 |
+
},
|
675 |
+
"mmlu_college_medicine": {
|
676 |
+
"task": "mmlu_college_medicine",
|
677 |
+
"task_alias": "college_medicine",
|
678 |
+
"group": "mmlu_other",
|
679 |
+
"group_alias": "other",
|
680 |
+
"dataset_path": "hails/mmlu_no_train",
|
681 |
+
"dataset_name": "college_medicine",
|
682 |
+
"test_split": "test",
|
683 |
+
"fewshot_split": "dev",
|
684 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
685 |
+
"doc_to_target": "answer",
|
686 |
+
"doc_to_choice": [
|
687 |
+
"A",
|
688 |
+
"B",
|
689 |
+
"C",
|
690 |
+
"D"
|
691 |
+
],
|
692 |
+
"description": "The following are multiple choice questions (with answers) about college medicine.\n\n",
|
693 |
+
"target_delimiter": " ",
|
694 |
+
"fewshot_delimiter": "\n\n",
|
695 |
+
"fewshot_config": {
|
696 |
+
"sampler": "first_n"
|
697 |
+
},
|
698 |
+
"metric_list": [
|
699 |
+
{
|
700 |
+
"metric": "acc",
|
701 |
+
"aggregation": "mean",
|
702 |
+
"higher_is_better": true
|
703 |
+
}
|
704 |
+
],
|
705 |
+
"output_type": "multiple_choice",
|
706 |
+
"repeats": 1,
|
707 |
+
"should_decontaminate": false,
|
708 |
+
"metadata": {
|
709 |
+
"version": 0.0
|
710 |
+
}
|
711 |
+
},
|
712 |
+
"mmlu_college_physics": {
|
713 |
+
"task": "mmlu_college_physics",
|
714 |
+
"task_alias": "college_physics",
|
715 |
+
"group": "mmlu_stem",
|
716 |
+
"group_alias": "stem",
|
717 |
+
"dataset_path": "hails/mmlu_no_train",
|
718 |
+
"dataset_name": "college_physics",
|
719 |
+
"test_split": "test",
|
720 |
+
"fewshot_split": "dev",
|
721 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
722 |
+
"doc_to_target": "answer",
|
723 |
+
"doc_to_choice": [
|
724 |
+
"A",
|
725 |
+
"B",
|
726 |
+
"C",
|
727 |
+
"D"
|
728 |
+
],
|
729 |
+
"description": "The following are multiple choice questions (with answers) about college physics.\n\n",
|
730 |
+
"target_delimiter": " ",
|
731 |
+
"fewshot_delimiter": "\n\n",
|
732 |
+
"fewshot_config": {
|
733 |
+
"sampler": "first_n"
|
734 |
+
},
|
735 |
+
"metric_list": [
|
736 |
+
{
|
737 |
+
"metric": "acc",
|
738 |
+
"aggregation": "mean",
|
739 |
+
"higher_is_better": true
|
740 |
+
}
|
741 |
+
],
|
742 |
+
"output_type": "multiple_choice",
|
743 |
+
"repeats": 1,
|
744 |
+
"should_decontaminate": false,
|
745 |
+
"metadata": {
|
746 |
+
"version": 0.0
|
747 |
+
}
|
748 |
+
},
|
749 |
+
"mmlu_computer_security": {
|
750 |
+
"task": "mmlu_computer_security",
|
751 |
+
"task_alias": "computer_security",
|
752 |
+
"group": "mmlu_stem",
|
753 |
+
"group_alias": "stem",
|
754 |
+
"dataset_path": "hails/mmlu_no_train",
|
755 |
+
"dataset_name": "computer_security",
|
756 |
+
"test_split": "test",
|
757 |
+
"fewshot_split": "dev",
|
758 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
759 |
+
"doc_to_target": "answer",
|
760 |
+
"doc_to_choice": [
|
761 |
+
"A",
|
762 |
+
"B",
|
763 |
+
"C",
|
764 |
+
"D"
|
765 |
+
],
|
766 |
+
"description": "The following are multiple choice questions (with answers) about computer security.\n\n",
|
767 |
+
"target_delimiter": " ",
|
768 |
+
"fewshot_delimiter": "\n\n",
|
769 |
+
"fewshot_config": {
|
770 |
+
"sampler": "first_n"
|
771 |
+
},
|
772 |
+
"metric_list": [
|
773 |
+
{
|
774 |
+
"metric": "acc",
|
775 |
+
"aggregation": "mean",
|
776 |
+
"higher_is_better": true
|
777 |
+
}
|
778 |
+
],
|
779 |
+
"output_type": "multiple_choice",
|
780 |
+
"repeats": 1,
|
781 |
+
"should_decontaminate": false,
|
782 |
+
"metadata": {
|
783 |
+
"version": 0.0
|
784 |
+
}
|
785 |
+
},
|
786 |
+
"mmlu_conceptual_physics": {
|
787 |
+
"task": "mmlu_conceptual_physics",
|
788 |
+
"task_alias": "conceptual_physics",
|
789 |
+
"group": "mmlu_stem",
|
790 |
+
"group_alias": "stem",
|
791 |
+
"dataset_path": "hails/mmlu_no_train",
|
792 |
+
"dataset_name": "conceptual_physics",
|
793 |
+
"test_split": "test",
|
794 |
+
"fewshot_split": "dev",
|
795 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
796 |
+
"doc_to_target": "answer",
|
797 |
+
"doc_to_choice": [
|
798 |
+
"A",
|
799 |
+
"B",
|
800 |
+
"C",
|
801 |
+
"D"
|
802 |
+
],
|
803 |
+
"description": "The following are multiple choice questions (with answers) about conceptual physics.\n\n",
|
804 |
+
"target_delimiter": " ",
|
805 |
+
"fewshot_delimiter": "\n\n",
|
806 |
+
"fewshot_config": {
|
807 |
+
"sampler": "first_n"
|
808 |
+
},
|
809 |
+
"metric_list": [
|
810 |
+
{
|
811 |
+
"metric": "acc",
|
812 |
+
"aggregation": "mean",
|
813 |
+
"higher_is_better": true
|
814 |
+
}
|
815 |
+
],
|
816 |
+
"output_type": "multiple_choice",
|
817 |
+
"repeats": 1,
|
818 |
+
"should_decontaminate": false,
|
819 |
+
"metadata": {
|
820 |
+
"version": 0.0
|
821 |
+
}
|
822 |
+
},
|
823 |
+
"mmlu_econometrics": {
|
824 |
+
"task": "mmlu_econometrics",
|
825 |
+
"task_alias": "econometrics",
|
826 |
+
"group": "mmlu_social_sciences",
|
827 |
+
"group_alias": "social_sciences",
|
828 |
+
"dataset_path": "hails/mmlu_no_train",
|
829 |
+
"dataset_name": "econometrics",
|
830 |
+
"test_split": "test",
|
831 |
+
"fewshot_split": "dev",
|
832 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
833 |
+
"doc_to_target": "answer",
|
834 |
+
"doc_to_choice": [
|
835 |
+
"A",
|
836 |
+
"B",
|
837 |
+
"C",
|
838 |
+
"D"
|
839 |
+
],
|
840 |
+
"description": "The following are multiple choice questions (with answers) about econometrics.\n\n",
|
841 |
+
"target_delimiter": " ",
|
842 |
+
"fewshot_delimiter": "\n\n",
|
843 |
+
"fewshot_config": {
|
844 |
+
"sampler": "first_n"
|
845 |
+
},
|
846 |
+
"metric_list": [
|
847 |
+
{
|
848 |
+
"metric": "acc",
|
849 |
+
"aggregation": "mean",
|
850 |
+
"higher_is_better": true
|
851 |
+
}
|
852 |
+
],
|
853 |
+
"output_type": "multiple_choice",
|
854 |
+
"repeats": 1,
|
855 |
+
"should_decontaminate": false,
|
856 |
+
"metadata": {
|
857 |
+
"version": 0.0
|
858 |
+
}
|
859 |
+
},
|
860 |
+
"mmlu_electrical_engineering": {
|
861 |
+
"task": "mmlu_electrical_engineering",
|
862 |
+
"task_alias": "electrical_engineering",
|
863 |
+
"group": "mmlu_stem",
|
864 |
+
"group_alias": "stem",
|
865 |
+
"dataset_path": "hails/mmlu_no_train",
|
866 |
+
"dataset_name": "electrical_engineering",
|
867 |
+
"test_split": "test",
|
868 |
+
"fewshot_split": "dev",
|
869 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
870 |
+
"doc_to_target": "answer",
|
871 |
+
"doc_to_choice": [
|
872 |
+
"A",
|
873 |
+
"B",
|
874 |
+
"C",
|
875 |
+
"D"
|
876 |
+
],
|
877 |
+
"description": "The following are multiple choice questions (with answers) about electrical engineering.\n\n",
|
878 |
+
"target_delimiter": " ",
|
879 |
+
"fewshot_delimiter": "\n\n",
|
880 |
+
"fewshot_config": {
|
881 |
+
"sampler": "first_n"
|
882 |
+
},
|
883 |
+
"metric_list": [
|
884 |
+
{
|
885 |
+
"metric": "acc",
|
886 |
+
"aggregation": "mean",
|
887 |
+
"higher_is_better": true
|
888 |
+
}
|
889 |
+
],
|
890 |
+
"output_type": "multiple_choice",
|
891 |
+
"repeats": 1,
|
892 |
+
"should_decontaminate": false,
|
893 |
+
"metadata": {
|
894 |
+
"version": 0.0
|
895 |
+
}
|
896 |
+
},
|
897 |
+
"mmlu_elementary_mathematics": {
|
898 |
+
"task": "mmlu_elementary_mathematics",
|
899 |
+
"task_alias": "elementary_mathematics",
|
900 |
+
"group": "mmlu_stem",
|
901 |
+
"group_alias": "stem",
|
902 |
+
"dataset_path": "hails/mmlu_no_train",
|
903 |
+
"dataset_name": "elementary_mathematics",
|
904 |
+
"test_split": "test",
|
905 |
+
"fewshot_split": "dev",
|
906 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
907 |
+
"doc_to_target": "answer",
|
908 |
+
"doc_to_choice": [
|
909 |
+
"A",
|
910 |
+
"B",
|
911 |
+
"C",
|
912 |
+
"D"
|
913 |
+
],
|
914 |
+
"description": "The following are multiple choice questions (with answers) about elementary mathematics.\n\n",
|
915 |
+
"target_delimiter": " ",
|
916 |
+
"fewshot_delimiter": "\n\n",
|
917 |
+
"fewshot_config": {
|
918 |
+
"sampler": "first_n"
|
919 |
+
},
|
920 |
+
"metric_list": [
|
921 |
+
{
|
922 |
+
"metric": "acc",
|
923 |
+
"aggregation": "mean",
|
924 |
+
"higher_is_better": true
|
925 |
+
}
|
926 |
+
],
|
927 |
+
"output_type": "multiple_choice",
|
928 |
+
"repeats": 1,
|
929 |
+
"should_decontaminate": false,
|
930 |
+
"metadata": {
|
931 |
+
"version": 0.0
|
932 |
+
}
|
933 |
+
},
|
934 |
+
"mmlu_formal_logic": {
|
935 |
+
"task": "mmlu_formal_logic",
|
936 |
+
"task_alias": "formal_logic",
|
937 |
+
"group": "mmlu_humanities",
|
938 |
+
"group_alias": "humanities",
|
939 |
+
"dataset_path": "hails/mmlu_no_train",
|
940 |
+
"dataset_name": "formal_logic",
|
941 |
+
"test_split": "test",
|
942 |
+
"fewshot_split": "dev",
|
943 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
944 |
+
"doc_to_target": "answer",
|
945 |
+
"doc_to_choice": [
|
946 |
+
"A",
|
947 |
+
"B",
|
948 |
+
"C",
|
949 |
+
"D"
|
950 |
+
],
|
951 |
+
"description": "The following are multiple choice questions (with answers) about formal logic.\n\n",
|
952 |
+
"target_delimiter": " ",
|
953 |
+
"fewshot_delimiter": "\n\n",
|
954 |
+
"fewshot_config": {
|
955 |
+
"sampler": "first_n"
|
956 |
+
},
|
957 |
+
"metric_list": [
|
958 |
+
{
|
959 |
+
"metric": "acc",
|
960 |
+
"aggregation": "mean",
|
961 |
+
"higher_is_better": true
|
962 |
+
}
|
963 |
+
],
|
964 |
+
"output_type": "multiple_choice",
|
965 |
+
"repeats": 1,
|
966 |
+
"should_decontaminate": false,
|
967 |
+
"metadata": {
|
968 |
+
"version": 0.0
|
969 |
+
}
|
970 |
+
},
|
971 |
+
"mmlu_global_facts": {
|
972 |
+
"task": "mmlu_global_facts",
|
973 |
+
"task_alias": "global_facts",
|
974 |
+
"group": "mmlu_other",
|
975 |
+
"group_alias": "other",
|
976 |
+
"dataset_path": "hails/mmlu_no_train",
|
977 |
+
"dataset_name": "global_facts",
|
978 |
+
"test_split": "test",
|
979 |
+
"fewshot_split": "dev",
|
980 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
981 |
+
"doc_to_target": "answer",
|
982 |
+
"doc_to_choice": [
|
983 |
+
"A",
|
984 |
+
"B",
|
985 |
+
"C",
|
986 |
+
"D"
|
987 |
+
],
|
988 |
+
"description": "The following are multiple choice questions (with answers) about global facts.\n\n",
|
989 |
+
"target_delimiter": " ",
|
990 |
+
"fewshot_delimiter": "\n\n",
|
991 |
+
"fewshot_config": {
|
992 |
+
"sampler": "first_n"
|
993 |
+
},
|
994 |
+
"metric_list": [
|
995 |
+
{
|
996 |
+
"metric": "acc",
|
997 |
+
"aggregation": "mean",
|
998 |
+
"higher_is_better": true
|
999 |
+
}
|
1000 |
+
],
|
1001 |
+
"output_type": "multiple_choice",
|
1002 |
+
"repeats": 1,
|
1003 |
+
"should_decontaminate": false,
|
1004 |
+
"metadata": {
|
1005 |
+
"version": 0.0
|
1006 |
+
}
|
1007 |
+
},
|
1008 |
+
"mmlu_high_school_biology": {
|
1009 |
+
"task": "mmlu_high_school_biology",
|
1010 |
+
"task_alias": "high_school_biology",
|
1011 |
+
"group": "mmlu_stem",
|
1012 |
+
"group_alias": "stem",
|
1013 |
+
"dataset_path": "hails/mmlu_no_train",
|
1014 |
+
"dataset_name": "high_school_biology",
|
1015 |
+
"test_split": "test",
|
1016 |
+
"fewshot_split": "dev",
|
1017 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1018 |
+
"doc_to_target": "answer",
|
1019 |
+
"doc_to_choice": [
|
1020 |
+
"A",
|
1021 |
+
"B",
|
1022 |
+
"C",
|
1023 |
+
"D"
|
1024 |
+
],
|
1025 |
+
"description": "The following are multiple choice questions (with answers) about high school biology.\n\n",
|
1026 |
+
"target_delimiter": " ",
|
1027 |
+
"fewshot_delimiter": "\n\n",
|
1028 |
+
"fewshot_config": {
|
1029 |
+
"sampler": "first_n"
|
1030 |
+
},
|
1031 |
+
"metric_list": [
|
1032 |
+
{
|
1033 |
+
"metric": "acc",
|
1034 |
+
"aggregation": "mean",
|
1035 |
+
"higher_is_better": true
|
1036 |
+
}
|
1037 |
+
],
|
1038 |
+
"output_type": "multiple_choice",
|
1039 |
+
"repeats": 1,
|
1040 |
+
"should_decontaminate": false,
|
1041 |
+
"metadata": {
|
1042 |
+
"version": 0.0
|
1043 |
+
}
|
1044 |
+
},
|
1045 |
+
"mmlu_high_school_chemistry": {
|
1046 |
+
"task": "mmlu_high_school_chemistry",
|
1047 |
+
"task_alias": "high_school_chemistry",
|
1048 |
+
"group": "mmlu_stem",
|
1049 |
+
"group_alias": "stem",
|
1050 |
+
"dataset_path": "hails/mmlu_no_train",
|
1051 |
+
"dataset_name": "high_school_chemistry",
|
1052 |
+
"test_split": "test",
|
1053 |
+
"fewshot_split": "dev",
|
1054 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1055 |
+
"doc_to_target": "answer",
|
1056 |
+
"doc_to_choice": [
|
1057 |
+
"A",
|
1058 |
+
"B",
|
1059 |
+
"C",
|
1060 |
+
"D"
|
1061 |
+
],
|
1062 |
+
"description": "The following are multiple choice questions (with answers) about high school chemistry.\n\n",
|
1063 |
+
"target_delimiter": " ",
|
1064 |
+
"fewshot_delimiter": "\n\n",
|
1065 |
+
"fewshot_config": {
|
1066 |
+
"sampler": "first_n"
|
1067 |
+
},
|
1068 |
+
"metric_list": [
|
1069 |
+
{
|
1070 |
+
"metric": "acc",
|
1071 |
+
"aggregation": "mean",
|
1072 |
+
"higher_is_better": true
|
1073 |
+
}
|
1074 |
+
],
|
1075 |
+
"output_type": "multiple_choice",
|
1076 |
+
"repeats": 1,
|
1077 |
+
"should_decontaminate": false,
|
1078 |
+
"metadata": {
|
1079 |
+
"version": 0.0
|
1080 |
+
}
|
1081 |
+
},
|
1082 |
+
"mmlu_high_school_computer_science": {
|
1083 |
+
"task": "mmlu_high_school_computer_science",
|
1084 |
+
"task_alias": "high_school_computer_science",
|
1085 |
+
"group": "mmlu_stem",
|
1086 |
+
"group_alias": "stem",
|
1087 |
+
"dataset_path": "hails/mmlu_no_train",
|
1088 |
+
"dataset_name": "high_school_computer_science",
|
1089 |
+
"test_split": "test",
|
1090 |
+
"fewshot_split": "dev",
|
1091 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1092 |
+
"doc_to_target": "answer",
|
1093 |
+
"doc_to_choice": [
|
1094 |
+
"A",
|
1095 |
+
"B",
|
1096 |
+
"C",
|
1097 |
+
"D"
|
1098 |
+
],
|
1099 |
+
"description": "The following are multiple choice questions (with answers) about high school computer science.\n\n",
|
1100 |
+
"target_delimiter": " ",
|
1101 |
+
"fewshot_delimiter": "\n\n",
|
1102 |
+
"fewshot_config": {
|
1103 |
+
"sampler": "first_n"
|
1104 |
+
},
|
1105 |
+
"metric_list": [
|
1106 |
+
{
|
1107 |
+
"metric": "acc",
|
1108 |
+
"aggregation": "mean",
|
1109 |
+
"higher_is_better": true
|
1110 |
+
}
|
1111 |
+
],
|
1112 |
+
"output_type": "multiple_choice",
|
1113 |
+
"repeats": 1,
|
1114 |
+
"should_decontaminate": false,
|
1115 |
+
"metadata": {
|
1116 |
+
"version": 0.0
|
1117 |
+
}
|
1118 |
+
},
|
1119 |
+
"mmlu_high_school_european_history": {
|
1120 |
+
"task": "mmlu_high_school_european_history",
|
1121 |
+
"task_alias": "high_school_european_history",
|
1122 |
+
"group": "mmlu_humanities",
|
1123 |
+
"group_alias": "humanities",
|
1124 |
+
"dataset_path": "hails/mmlu_no_train",
|
1125 |
+
"dataset_name": "high_school_european_history",
|
1126 |
+
"test_split": "test",
|
1127 |
+
"fewshot_split": "dev",
|
1128 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1129 |
+
"doc_to_target": "answer",
|
1130 |
+
"doc_to_choice": [
|
1131 |
+
"A",
|
1132 |
+
"B",
|
1133 |
+
"C",
|
1134 |
+
"D"
|
1135 |
+
],
|
1136 |
+
"description": "The following are multiple choice questions (with answers) about high school european history.\n\n",
|
1137 |
+
"target_delimiter": " ",
|
1138 |
+
"fewshot_delimiter": "\n\n",
|
1139 |
+
"fewshot_config": {
|
1140 |
+
"sampler": "first_n"
|
1141 |
+
},
|
1142 |
+
"metric_list": [
|
1143 |
+
{
|
1144 |
+
"metric": "acc",
|
1145 |
+
"aggregation": "mean",
|
1146 |
+
"higher_is_better": true
|
1147 |
+
}
|
1148 |
+
],
|
1149 |
+
"output_type": "multiple_choice",
|
1150 |
+
"repeats": 1,
|
1151 |
+
"should_decontaminate": false,
|
1152 |
+
"metadata": {
|
1153 |
+
"version": 0.0
|
1154 |
+
}
|
1155 |
+
},
|
1156 |
+
"mmlu_high_school_geography": {
|
1157 |
+
"task": "mmlu_high_school_geography",
|
1158 |
+
"task_alias": "high_school_geography",
|
1159 |
+
"group": "mmlu_social_sciences",
|
1160 |
+
"group_alias": "social_sciences",
|
1161 |
+
"dataset_path": "hails/mmlu_no_train",
|
1162 |
+
"dataset_name": "high_school_geography",
|
1163 |
+
"test_split": "test",
|
1164 |
+
"fewshot_split": "dev",
|
1165 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1166 |
+
"doc_to_target": "answer",
|
1167 |
+
"doc_to_choice": [
|
1168 |
+
"A",
|
1169 |
+
"B",
|
1170 |
+
"C",
|
1171 |
+
"D"
|
1172 |
+
],
|
1173 |
+
"description": "The following are multiple choice questions (with answers) about high school geography.\n\n",
|
1174 |
+
"target_delimiter": " ",
|
1175 |
+
"fewshot_delimiter": "\n\n",
|
1176 |
+
"fewshot_config": {
|
1177 |
+
"sampler": "first_n"
|
1178 |
+
},
|
1179 |
+
"metric_list": [
|
1180 |
+
{
|
1181 |
+
"metric": "acc",
|
1182 |
+
"aggregation": "mean",
|
1183 |
+
"higher_is_better": true
|
1184 |
+
}
|
1185 |
+
],
|
1186 |
+
"output_type": "multiple_choice",
|
1187 |
+
"repeats": 1,
|
1188 |
+
"should_decontaminate": false,
|
1189 |
+
"metadata": {
|
1190 |
+
"version": 0.0
|
1191 |
+
}
|
1192 |
+
},
|
1193 |
+
"mmlu_high_school_government_and_politics": {
|
1194 |
+
"task": "mmlu_high_school_government_and_politics",
|
1195 |
+
"task_alias": "high_school_government_and_politics",
|
1196 |
+
"group": "mmlu_social_sciences",
|
1197 |
+
"group_alias": "social_sciences",
|
1198 |
+
"dataset_path": "hails/mmlu_no_train",
|
1199 |
+
"dataset_name": "high_school_government_and_politics",
|
1200 |
+
"test_split": "test",
|
1201 |
+
"fewshot_split": "dev",
|
1202 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1203 |
+
"doc_to_target": "answer",
|
1204 |
+
"doc_to_choice": [
|
1205 |
+
"A",
|
1206 |
+
"B",
|
1207 |
+
"C",
|
1208 |
+
"D"
|
1209 |
+
],
|
1210 |
+
"description": "The following are multiple choice questions (with answers) about high school government and politics.\n\n",
|
1211 |
+
"target_delimiter": " ",
|
1212 |
+
"fewshot_delimiter": "\n\n",
|
1213 |
+
"fewshot_config": {
|
1214 |
+
"sampler": "first_n"
|
1215 |
+
},
|
1216 |
+
"metric_list": [
|
1217 |
+
{
|
1218 |
+
"metric": "acc",
|
1219 |
+
"aggregation": "mean",
|
1220 |
+
"higher_is_better": true
|
1221 |
+
}
|
1222 |
+
],
|
1223 |
+
"output_type": "multiple_choice",
|
1224 |
+
"repeats": 1,
|
1225 |
+
"should_decontaminate": false,
|
1226 |
+
"metadata": {
|
1227 |
+
"version": 0.0
|
1228 |
+
}
|
1229 |
+
},
|
1230 |
+
"mmlu_high_school_macroeconomics": {
|
1231 |
+
"task": "mmlu_high_school_macroeconomics",
|
1232 |
+
"task_alias": "high_school_macroeconomics",
|
1233 |
+
"group": "mmlu_social_sciences",
|
1234 |
+
"group_alias": "social_sciences",
|
1235 |
+
"dataset_path": "hails/mmlu_no_train",
|
1236 |
+
"dataset_name": "high_school_macroeconomics",
|
1237 |
+
"test_split": "test",
|
1238 |
+
"fewshot_split": "dev",
|
1239 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1240 |
+
"doc_to_target": "answer",
|
1241 |
+
"doc_to_choice": [
|
1242 |
+
"A",
|
1243 |
+
"B",
|
1244 |
+
"C",
|
1245 |
+
"D"
|
1246 |
+
],
|
1247 |
+
"description": "The following are multiple choice questions (with answers) about high school macroeconomics.\n\n",
|
1248 |
+
"target_delimiter": " ",
|
1249 |
+
"fewshot_delimiter": "\n\n",
|
1250 |
+
"fewshot_config": {
|
1251 |
+
"sampler": "first_n"
|
1252 |
+
},
|
1253 |
+
"metric_list": [
|
1254 |
+
{
|
1255 |
+
"metric": "acc",
|
1256 |
+
"aggregation": "mean",
|
1257 |
+
"higher_is_better": true
|
1258 |
+
}
|
1259 |
+
],
|
1260 |
+
"output_type": "multiple_choice",
|
1261 |
+
"repeats": 1,
|
1262 |
+
"should_decontaminate": false,
|
1263 |
+
"metadata": {
|
1264 |
+
"version": 0.0
|
1265 |
+
}
|
1266 |
+
},
|
1267 |
+
"mmlu_high_school_mathematics": {
|
1268 |
+
"task": "mmlu_high_school_mathematics",
|
1269 |
+
"task_alias": "high_school_mathematics",
|
1270 |
+
"group": "mmlu_stem",
|
1271 |
+
"group_alias": "stem",
|
1272 |
+
"dataset_path": "hails/mmlu_no_train",
|
1273 |
+
"dataset_name": "high_school_mathematics",
|
1274 |
+
"test_split": "test",
|
1275 |
+
"fewshot_split": "dev",
|
1276 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1277 |
+
"doc_to_target": "answer",
|
1278 |
+
"doc_to_choice": [
|
1279 |
+
"A",
|
1280 |
+
"B",
|
1281 |
+
"C",
|
1282 |
+
"D"
|
1283 |
+
],
|
1284 |
+
"description": "The following are multiple choice questions (with answers) about high school mathematics.\n\n",
|
1285 |
+
"target_delimiter": " ",
|
1286 |
+
"fewshot_delimiter": "\n\n",
|
1287 |
+
"fewshot_config": {
|
1288 |
+
"sampler": "first_n"
|
1289 |
+
},
|
1290 |
+
"metric_list": [
|
1291 |
+
{
|
1292 |
+
"metric": "acc",
|
1293 |
+
"aggregation": "mean",
|
1294 |
+
"higher_is_better": true
|
1295 |
+
}
|
1296 |
+
],
|
1297 |
+
"output_type": "multiple_choice",
|
1298 |
+
"repeats": 1,
|
1299 |
+
"should_decontaminate": false,
|
1300 |
+
"metadata": {
|
1301 |
+
"version": 0.0
|
1302 |
+
}
|
1303 |
+
},
|
1304 |
+
"mmlu_high_school_microeconomics": {
|
1305 |
+
"task": "mmlu_high_school_microeconomics",
|
1306 |
+
"task_alias": "high_school_microeconomics",
|
1307 |
+
"group": "mmlu_social_sciences",
|
1308 |
+
"group_alias": "social_sciences",
|
1309 |
+
"dataset_path": "hails/mmlu_no_train",
|
1310 |
+
"dataset_name": "high_school_microeconomics",
|
1311 |
+
"test_split": "test",
|
1312 |
+
"fewshot_split": "dev",
|
1313 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1314 |
+
"doc_to_target": "answer",
|
1315 |
+
"doc_to_choice": [
|
1316 |
+
"A",
|
1317 |
+
"B",
|
1318 |
+
"C",
|
1319 |
+
"D"
|
1320 |
+
],
|
1321 |
+
"description": "The following are multiple choice questions (with answers) about high school microeconomics.\n\n",
|
1322 |
+
"target_delimiter": " ",
|
1323 |
+
"fewshot_delimiter": "\n\n",
|
1324 |
+
"fewshot_config": {
|
1325 |
+
"sampler": "first_n"
|
1326 |
+
},
|
1327 |
+
"metric_list": [
|
1328 |
+
{
|
1329 |
+
"metric": "acc",
|
1330 |
+
"aggregation": "mean",
|
1331 |
+
"higher_is_better": true
|
1332 |
+
}
|
1333 |
+
],
|
1334 |
+
"output_type": "multiple_choice",
|
1335 |
+
"repeats": 1,
|
1336 |
+
"should_decontaminate": false,
|
1337 |
+
"metadata": {
|
1338 |
+
"version": 0.0
|
1339 |
+
}
|
1340 |
+
},
|
1341 |
+
"mmlu_high_school_physics": {
|
1342 |
+
"task": "mmlu_high_school_physics",
|
1343 |
+
"task_alias": "high_school_physics",
|
1344 |
+
"group": "mmlu_stem",
|
1345 |
+
"group_alias": "stem",
|
1346 |
+
"dataset_path": "hails/mmlu_no_train",
|
1347 |
+
"dataset_name": "high_school_physics",
|
1348 |
+
"test_split": "test",
|
1349 |
+
"fewshot_split": "dev",
|
1350 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1351 |
+
"doc_to_target": "answer",
|
1352 |
+
"doc_to_choice": [
|
1353 |
+
"A",
|
1354 |
+
"B",
|
1355 |
+
"C",
|
1356 |
+
"D"
|
1357 |
+
],
|
1358 |
+
"description": "The following are multiple choice questions (with answers) about high school physics.\n\n",
|
1359 |
+
"target_delimiter": " ",
|
1360 |
+
"fewshot_delimiter": "\n\n",
|
1361 |
+
"fewshot_config": {
|
1362 |
+
"sampler": "first_n"
|
1363 |
+
},
|
1364 |
+
"metric_list": [
|
1365 |
+
{
|
1366 |
+
"metric": "acc",
|
1367 |
+
"aggregation": "mean",
|
1368 |
+
"higher_is_better": true
|
1369 |
+
}
|
1370 |
+
],
|
1371 |
+
"output_type": "multiple_choice",
|
1372 |
+
"repeats": 1,
|
1373 |
+
"should_decontaminate": false,
|
1374 |
+
"metadata": {
|
1375 |
+
"version": 0.0
|
1376 |
+
}
|
1377 |
+
},
|
1378 |
+
"mmlu_high_school_psychology": {
|
1379 |
+
"task": "mmlu_high_school_psychology",
|
1380 |
+
"task_alias": "high_school_psychology",
|
1381 |
+
"group": "mmlu_social_sciences",
|
1382 |
+
"group_alias": "social_sciences",
|
1383 |
+
"dataset_path": "hails/mmlu_no_train",
|
1384 |
+
"dataset_name": "high_school_psychology",
|
1385 |
+
"test_split": "test",
|
1386 |
+
"fewshot_split": "dev",
|
1387 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1388 |
+
"doc_to_target": "answer",
|
1389 |
+
"doc_to_choice": [
|
1390 |
+
"A",
|
1391 |
+
"B",
|
1392 |
+
"C",
|
1393 |
+
"D"
|
1394 |
+
],
|
1395 |
+
"description": "The following are multiple choice questions (with answers) about high school psychology.\n\n",
|
1396 |
+
"target_delimiter": " ",
|
1397 |
+
"fewshot_delimiter": "\n\n",
|
1398 |
+
"fewshot_config": {
|
1399 |
+
"sampler": "first_n"
|
1400 |
+
},
|
1401 |
+
"metric_list": [
|
1402 |
+
{
|
1403 |
+
"metric": "acc",
|
1404 |
+
"aggregation": "mean",
|
1405 |
+
"higher_is_better": true
|
1406 |
+
}
|
1407 |
+
],
|
1408 |
+
"output_type": "multiple_choice",
|
1409 |
+
"repeats": 1,
|
1410 |
+
"should_decontaminate": false,
|
1411 |
+
"metadata": {
|
1412 |
+
"version": 0.0
|
1413 |
+
}
|
1414 |
+
},
|
1415 |
+
"mmlu_high_school_statistics": {
|
1416 |
+
"task": "mmlu_high_school_statistics",
|
1417 |
+
"task_alias": "high_school_statistics",
|
1418 |
+
"group": "mmlu_stem",
|
1419 |
+
"group_alias": "stem",
|
1420 |
+
"dataset_path": "hails/mmlu_no_train",
|
1421 |
+
"dataset_name": "high_school_statistics",
|
1422 |
+
"test_split": "test",
|
1423 |
+
"fewshot_split": "dev",
|
1424 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1425 |
+
"doc_to_target": "answer",
|
1426 |
+
"doc_to_choice": [
|
1427 |
+
"A",
|
1428 |
+
"B",
|
1429 |
+
"C",
|
1430 |
+
"D"
|
1431 |
+
],
|
1432 |
+
"description": "The following are multiple choice questions (with answers) about high school statistics.\n\n",
|
1433 |
+
"target_delimiter": " ",
|
1434 |
+
"fewshot_delimiter": "\n\n",
|
1435 |
+
"fewshot_config": {
|
1436 |
+
"sampler": "first_n"
|
1437 |
+
},
|
1438 |
+
"metric_list": [
|
1439 |
+
{
|
1440 |
+
"metric": "acc",
|
1441 |
+
"aggregation": "mean",
|
1442 |
+
"higher_is_better": true
|
1443 |
+
}
|
1444 |
+
],
|
1445 |
+
"output_type": "multiple_choice",
|
1446 |
+
"repeats": 1,
|
1447 |
+
"should_decontaminate": false,
|
1448 |
+
"metadata": {
|
1449 |
+
"version": 0.0
|
1450 |
+
}
|
1451 |
+
},
|
1452 |
+
"mmlu_high_school_us_history": {
|
1453 |
+
"task": "mmlu_high_school_us_history",
|
1454 |
+
"task_alias": "high_school_us_history",
|
1455 |
+
"group": "mmlu_humanities",
|
1456 |
+
"group_alias": "humanities",
|
1457 |
+
"dataset_path": "hails/mmlu_no_train",
|
1458 |
+
"dataset_name": "high_school_us_history",
|
1459 |
+
"test_split": "test",
|
1460 |
+
"fewshot_split": "dev",
|
1461 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1462 |
+
"doc_to_target": "answer",
|
1463 |
+
"doc_to_choice": [
|
1464 |
+
"A",
|
1465 |
+
"B",
|
1466 |
+
"C",
|
1467 |
+
"D"
|
1468 |
+
],
|
1469 |
+
"description": "The following are multiple choice questions (with answers) about high school us history.\n\n",
|
1470 |
+
"target_delimiter": " ",
|
1471 |
+
"fewshot_delimiter": "\n\n",
|
1472 |
+
"fewshot_config": {
|
1473 |
+
"sampler": "first_n"
|
1474 |
+
},
|
1475 |
+
"metric_list": [
|
1476 |
+
{
|
1477 |
+
"metric": "acc",
|
1478 |
+
"aggregation": "mean",
|
1479 |
+
"higher_is_better": true
|
1480 |
+
}
|
1481 |
+
],
|
1482 |
+
"output_type": "multiple_choice",
|
1483 |
+
"repeats": 1,
|
1484 |
+
"should_decontaminate": false,
|
1485 |
+
"metadata": {
|
1486 |
+
"version": 0.0
|
1487 |
+
}
|
1488 |
+
},
|
1489 |
+
"mmlu_high_school_world_history": {
|
1490 |
+
"task": "mmlu_high_school_world_history",
|
1491 |
+
"task_alias": "high_school_world_history",
|
1492 |
+
"group": "mmlu_humanities",
|
1493 |
+
"group_alias": "humanities",
|
1494 |
+
"dataset_path": "hails/mmlu_no_train",
|
1495 |
+
"dataset_name": "high_school_world_history",
|
1496 |
+
"test_split": "test",
|
1497 |
+
"fewshot_split": "dev",
|
1498 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1499 |
+
"doc_to_target": "answer",
|
1500 |
+
"doc_to_choice": [
|
1501 |
+
"A",
|
1502 |
+
"B",
|
1503 |
+
"C",
|
1504 |
+
"D"
|
1505 |
+
],
|
1506 |
+
"description": "The following are multiple choice questions (with answers) about high school world history.\n\n",
|
1507 |
+
"target_delimiter": " ",
|
1508 |
+
"fewshot_delimiter": "\n\n",
|
1509 |
+
"fewshot_config": {
|
1510 |
+
"sampler": "first_n"
|
1511 |
+
},
|
1512 |
+
"metric_list": [
|
1513 |
+
{
|
1514 |
+
"metric": "acc",
|
1515 |
+
"aggregation": "mean",
|
1516 |
+
"higher_is_better": true
|
1517 |
+
}
|
1518 |
+
],
|
1519 |
+
"output_type": "multiple_choice",
|
1520 |
+
"repeats": 1,
|
1521 |
+
"should_decontaminate": false,
|
1522 |
+
"metadata": {
|
1523 |
+
"version": 0.0
|
1524 |
+
}
|
1525 |
+
},
|
1526 |
+
"mmlu_human_aging": {
|
1527 |
+
"task": "mmlu_human_aging",
|
1528 |
+
"task_alias": "human_aging",
|
1529 |
+
"group": "mmlu_other",
|
1530 |
+
"group_alias": "other",
|
1531 |
+
"dataset_path": "hails/mmlu_no_train",
|
1532 |
+
"dataset_name": "human_aging",
|
1533 |
+
"test_split": "test",
|
1534 |
+
"fewshot_split": "dev",
|
1535 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1536 |
+
"doc_to_target": "answer",
|
1537 |
+
"doc_to_choice": [
|
1538 |
+
"A",
|
1539 |
+
"B",
|
1540 |
+
"C",
|
1541 |
+
"D"
|
1542 |
+
],
|
1543 |
+
"description": "The following are multiple choice questions (with answers) about human aging.\n\n",
|
1544 |
+
"target_delimiter": " ",
|
1545 |
+
"fewshot_delimiter": "\n\n",
|
1546 |
+
"fewshot_config": {
|
1547 |
+
"sampler": "first_n"
|
1548 |
+
},
|
1549 |
+
"metric_list": [
|
1550 |
+
{
|
1551 |
+
"metric": "acc",
|
1552 |
+
"aggregation": "mean",
|
1553 |
+
"higher_is_better": true
|
1554 |
+
}
|
1555 |
+
],
|
1556 |
+
"output_type": "multiple_choice",
|
1557 |
+
"repeats": 1,
|
1558 |
+
"should_decontaminate": false,
|
1559 |
+
"metadata": {
|
1560 |
+
"version": 0.0
|
1561 |
+
}
|
1562 |
+
},
|
1563 |
+
"mmlu_human_sexuality": {
|
1564 |
+
"task": "mmlu_human_sexuality",
|
1565 |
+
"task_alias": "human_sexuality",
|
1566 |
+
"group": "mmlu_social_sciences",
|
1567 |
+
"group_alias": "social_sciences",
|
1568 |
+
"dataset_path": "hails/mmlu_no_train",
|
1569 |
+
"dataset_name": "human_sexuality",
|
1570 |
+
"test_split": "test",
|
1571 |
+
"fewshot_split": "dev",
|
1572 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1573 |
+
"doc_to_target": "answer",
|
1574 |
+
"doc_to_choice": [
|
1575 |
+
"A",
|
1576 |
+
"B",
|
1577 |
+
"C",
|
1578 |
+
"D"
|
1579 |
+
],
|
1580 |
+
"description": "The following are multiple choice questions (with answers) about human sexuality.\n\n",
|
1581 |
+
"target_delimiter": " ",
|
1582 |
+
"fewshot_delimiter": "\n\n",
|
1583 |
+
"fewshot_config": {
|
1584 |
+
"sampler": "first_n"
|
1585 |
+
},
|
1586 |
+
"metric_list": [
|
1587 |
+
{
|
1588 |
+
"metric": "acc",
|
1589 |
+
"aggregation": "mean",
|
1590 |
+
"higher_is_better": true
|
1591 |
+
}
|
1592 |
+
],
|
1593 |
+
"output_type": "multiple_choice",
|
1594 |
+
"repeats": 1,
|
1595 |
+
"should_decontaminate": false,
|
1596 |
+
"metadata": {
|
1597 |
+
"version": 0.0
|
1598 |
+
}
|
1599 |
+
},
|
1600 |
+
"mmlu_international_law": {
|
1601 |
+
"task": "mmlu_international_law",
|
1602 |
+
"task_alias": "international_law",
|
1603 |
+
"group": "mmlu_humanities",
|
1604 |
+
"group_alias": "humanities",
|
1605 |
+
"dataset_path": "hails/mmlu_no_train",
|
1606 |
+
"dataset_name": "international_law",
|
1607 |
+
"test_split": "test",
|
1608 |
+
"fewshot_split": "dev",
|
1609 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1610 |
+
"doc_to_target": "answer",
|
1611 |
+
"doc_to_choice": [
|
1612 |
+
"A",
|
1613 |
+
"B",
|
1614 |
+
"C",
|
1615 |
+
"D"
|
1616 |
+
],
|
1617 |
+
"description": "The following are multiple choice questions (with answers) about international law.\n\n",
|
1618 |
+
"target_delimiter": " ",
|
1619 |
+
"fewshot_delimiter": "\n\n",
|
1620 |
+
"fewshot_config": {
|
1621 |
+
"sampler": "first_n"
|
1622 |
+
},
|
1623 |
+
"metric_list": [
|
1624 |
+
{
|
1625 |
+
"metric": "acc",
|
1626 |
+
"aggregation": "mean",
|
1627 |
+
"higher_is_better": true
|
1628 |
+
}
|
1629 |
+
],
|
1630 |
+
"output_type": "multiple_choice",
|
1631 |
+
"repeats": 1,
|
1632 |
+
"should_decontaminate": false,
|
1633 |
+
"metadata": {
|
1634 |
+
"version": 0.0
|
1635 |
+
}
|
1636 |
+
},
|
1637 |
+
"mmlu_jurisprudence": {
|
1638 |
+
"task": "mmlu_jurisprudence",
|
1639 |
+
"task_alias": "jurisprudence",
|
1640 |
+
"group": "mmlu_humanities",
|
1641 |
+
"group_alias": "humanities",
|
1642 |
+
"dataset_path": "hails/mmlu_no_train",
|
1643 |
+
"dataset_name": "jurisprudence",
|
1644 |
+
"test_split": "test",
|
1645 |
+
"fewshot_split": "dev",
|
1646 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1647 |
+
"doc_to_target": "answer",
|
1648 |
+
"doc_to_choice": [
|
1649 |
+
"A",
|
1650 |
+
"B",
|
1651 |
+
"C",
|
1652 |
+
"D"
|
1653 |
+
],
|
1654 |
+
"description": "The following are multiple choice questions (with answers) about jurisprudence.\n\n",
|
1655 |
+
"target_delimiter": " ",
|
1656 |
+
"fewshot_delimiter": "\n\n",
|
1657 |
+
"fewshot_config": {
|
1658 |
+
"sampler": "first_n"
|
1659 |
+
},
|
1660 |
+
"metric_list": [
|
1661 |
+
{
|
1662 |
+
"metric": "acc",
|
1663 |
+
"aggregation": "mean",
|
1664 |
+
"higher_is_better": true
|
1665 |
+
}
|
1666 |
+
],
|
1667 |
+
"output_type": "multiple_choice",
|
1668 |
+
"repeats": 1,
|
1669 |
+
"should_decontaminate": false,
|
1670 |
+
"metadata": {
|
1671 |
+
"version": 0.0
|
1672 |
+
}
|
1673 |
+
},
|
1674 |
+
"mmlu_logical_fallacies": {
|
1675 |
+
"task": "mmlu_logical_fallacies",
|
1676 |
+
"task_alias": "logical_fallacies",
|
1677 |
+
"group": "mmlu_humanities",
|
1678 |
+
"group_alias": "humanities",
|
1679 |
+
"dataset_path": "hails/mmlu_no_train",
|
1680 |
+
"dataset_name": "logical_fallacies",
|
1681 |
+
"test_split": "test",
|
1682 |
+
"fewshot_split": "dev",
|
1683 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1684 |
+
"doc_to_target": "answer",
|
1685 |
+
"doc_to_choice": [
|
1686 |
+
"A",
|
1687 |
+
"B",
|
1688 |
+
"C",
|
1689 |
+
"D"
|
1690 |
+
],
|
1691 |
+
"description": "The following are multiple choice questions (with answers) about logical fallacies.\n\n",
|
1692 |
+
"target_delimiter": " ",
|
1693 |
+
"fewshot_delimiter": "\n\n",
|
1694 |
+
"fewshot_config": {
|
1695 |
+
"sampler": "first_n"
|
1696 |
+
},
|
1697 |
+
"metric_list": [
|
1698 |
+
{
|
1699 |
+
"metric": "acc",
|
1700 |
+
"aggregation": "mean",
|
1701 |
+
"higher_is_better": true
|
1702 |
+
}
|
1703 |
+
],
|
1704 |
+
"output_type": "multiple_choice",
|
1705 |
+
"repeats": 1,
|
1706 |
+
"should_decontaminate": false,
|
1707 |
+
"metadata": {
|
1708 |
+
"version": 0.0
|
1709 |
+
}
|
1710 |
+
},
|
1711 |
+
"mmlu_machine_learning": {
|
1712 |
+
"task": "mmlu_machine_learning",
|
1713 |
+
"task_alias": "machine_learning",
|
1714 |
+
"group": "mmlu_stem",
|
1715 |
+
"group_alias": "stem",
|
1716 |
+
"dataset_path": "hails/mmlu_no_train",
|
1717 |
+
"dataset_name": "machine_learning",
|
1718 |
+
"test_split": "test",
|
1719 |
+
"fewshot_split": "dev",
|
1720 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1721 |
+
"doc_to_target": "answer",
|
1722 |
+
"doc_to_choice": [
|
1723 |
+
"A",
|
1724 |
+
"B",
|
1725 |
+
"C",
|
1726 |
+
"D"
|
1727 |
+
],
|
1728 |
+
"description": "The following are multiple choice questions (with answers) about machine learning.\n\n",
|
1729 |
+
"target_delimiter": " ",
|
1730 |
+
"fewshot_delimiter": "\n\n",
|
1731 |
+
"fewshot_config": {
|
1732 |
+
"sampler": "first_n"
|
1733 |
+
},
|
1734 |
+
"metric_list": [
|
1735 |
+
{
|
1736 |
+
"metric": "acc",
|
1737 |
+
"aggregation": "mean",
|
1738 |
+
"higher_is_better": true
|
1739 |
+
}
|
1740 |
+
],
|
1741 |
+
"output_type": "multiple_choice",
|
1742 |
+
"repeats": 1,
|
1743 |
+
"should_decontaminate": false,
|
1744 |
+
"metadata": {
|
1745 |
+
"version": 0.0
|
1746 |
+
}
|
1747 |
+
},
|
1748 |
+
"mmlu_management": {
|
1749 |
+
"task": "mmlu_management",
|
1750 |
+
"task_alias": "management",
|
1751 |
+
"group": "mmlu_other",
|
1752 |
+
"group_alias": "other",
|
1753 |
+
"dataset_path": "hails/mmlu_no_train",
|
1754 |
+
"dataset_name": "management",
|
1755 |
+
"test_split": "test",
|
1756 |
+
"fewshot_split": "dev",
|
1757 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1758 |
+
"doc_to_target": "answer",
|
1759 |
+
"doc_to_choice": [
|
1760 |
+
"A",
|
1761 |
+
"B",
|
1762 |
+
"C",
|
1763 |
+
"D"
|
1764 |
+
],
|
1765 |
+
"description": "The following are multiple choice questions (with answers) about management.\n\n",
|
1766 |
+
"target_delimiter": " ",
|
1767 |
+
"fewshot_delimiter": "\n\n",
|
1768 |
+
"fewshot_config": {
|
1769 |
+
"sampler": "first_n"
|
1770 |
+
},
|
1771 |
+
"metric_list": [
|
1772 |
+
{
|
1773 |
+
"metric": "acc",
|
1774 |
+
"aggregation": "mean",
|
1775 |
+
"higher_is_better": true
|
1776 |
+
}
|
1777 |
+
],
|
1778 |
+
"output_type": "multiple_choice",
|
1779 |
+
"repeats": 1,
|
1780 |
+
"should_decontaminate": false,
|
1781 |
+
"metadata": {
|
1782 |
+
"version": 0.0
|
1783 |
+
}
|
1784 |
+
},
|
1785 |
+
"mmlu_marketing": {
|
1786 |
+
"task": "mmlu_marketing",
|
1787 |
+
"task_alias": "marketing",
|
1788 |
+
"group": "mmlu_other",
|
1789 |
+
"group_alias": "other",
|
1790 |
+
"dataset_path": "hails/mmlu_no_train",
|
1791 |
+
"dataset_name": "marketing",
|
1792 |
+
"test_split": "test",
|
1793 |
+
"fewshot_split": "dev",
|
1794 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1795 |
+
"doc_to_target": "answer",
|
1796 |
+
"doc_to_choice": [
|
1797 |
+
"A",
|
1798 |
+
"B",
|
1799 |
+
"C",
|
1800 |
+
"D"
|
1801 |
+
],
|
1802 |
+
"description": "The following are multiple choice questions (with answers) about marketing.\n\n",
|
1803 |
+
"target_delimiter": " ",
|
1804 |
+
"fewshot_delimiter": "\n\n",
|
1805 |
+
"fewshot_config": {
|
1806 |
+
"sampler": "first_n"
|
1807 |
+
},
|
1808 |
+
"metric_list": [
|
1809 |
+
{
|
1810 |
+
"metric": "acc",
|
1811 |
+
"aggregation": "mean",
|
1812 |
+
"higher_is_better": true
|
1813 |
+
}
|
1814 |
+
],
|
1815 |
+
"output_type": "multiple_choice",
|
1816 |
+
"repeats": 1,
|
1817 |
+
"should_decontaminate": false,
|
1818 |
+
"metadata": {
|
1819 |
+
"version": 0.0
|
1820 |
+
}
|
1821 |
+
},
|
1822 |
+
"mmlu_medical_genetics": {
|
1823 |
+
"task": "mmlu_medical_genetics",
|
1824 |
+
"task_alias": "medical_genetics",
|
1825 |
+
"group": "mmlu_other",
|
1826 |
+
"group_alias": "other",
|
1827 |
+
"dataset_path": "hails/mmlu_no_train",
|
1828 |
+
"dataset_name": "medical_genetics",
|
1829 |
+
"test_split": "test",
|
1830 |
+
"fewshot_split": "dev",
|
1831 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1832 |
+
"doc_to_target": "answer",
|
1833 |
+
"doc_to_choice": [
|
1834 |
+
"A",
|
1835 |
+
"B",
|
1836 |
+
"C",
|
1837 |
+
"D"
|
1838 |
+
],
|
1839 |
+
"description": "The following are multiple choice questions (with answers) about medical genetics.\n\n",
|
1840 |
+
"target_delimiter": " ",
|
1841 |
+
"fewshot_delimiter": "\n\n",
|
1842 |
+
"fewshot_config": {
|
1843 |
+
"sampler": "first_n"
|
1844 |
+
},
|
1845 |
+
"metric_list": [
|
1846 |
+
{
|
1847 |
+
"metric": "acc",
|
1848 |
+
"aggregation": "mean",
|
1849 |
+
"higher_is_better": true
|
1850 |
+
}
|
1851 |
+
],
|
1852 |
+
"output_type": "multiple_choice",
|
1853 |
+
"repeats": 1,
|
1854 |
+
"should_decontaminate": false,
|
1855 |
+
"metadata": {
|
1856 |
+
"version": 0.0
|
1857 |
+
}
|
1858 |
+
},
|
1859 |
+
"mmlu_miscellaneous": {
|
1860 |
+
"task": "mmlu_miscellaneous",
|
1861 |
+
"task_alias": "miscellaneous",
|
1862 |
+
"group": "mmlu_other",
|
1863 |
+
"group_alias": "other",
|
1864 |
+
"dataset_path": "hails/mmlu_no_train",
|
1865 |
+
"dataset_name": "miscellaneous",
|
1866 |
+
"test_split": "test",
|
1867 |
+
"fewshot_split": "dev",
|
1868 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1869 |
+
"doc_to_target": "answer",
|
1870 |
+
"doc_to_choice": [
|
1871 |
+
"A",
|
1872 |
+
"B",
|
1873 |
+
"C",
|
1874 |
+
"D"
|
1875 |
+
],
|
1876 |
+
"description": "The following are multiple choice questions (with answers) about miscellaneous.\n\n",
|
1877 |
+
"target_delimiter": " ",
|
1878 |
+
"fewshot_delimiter": "\n\n",
|
1879 |
+
"fewshot_config": {
|
1880 |
+
"sampler": "first_n"
|
1881 |
+
},
|
1882 |
+
"metric_list": [
|
1883 |
+
{
|
1884 |
+
"metric": "acc",
|
1885 |
+
"aggregation": "mean",
|
1886 |
+
"higher_is_better": true
|
1887 |
+
}
|
1888 |
+
],
|
1889 |
+
"output_type": "multiple_choice",
|
1890 |
+
"repeats": 1,
|
1891 |
+
"should_decontaminate": false,
|
1892 |
+
"metadata": {
|
1893 |
+
"version": 0.0
|
1894 |
+
}
|
1895 |
+
},
|
1896 |
+
"mmlu_moral_disputes": {
|
1897 |
+
"task": "mmlu_moral_disputes",
|
1898 |
+
"task_alias": "moral_disputes",
|
1899 |
+
"group": "mmlu_humanities",
|
1900 |
+
"group_alias": "humanities",
|
1901 |
+
"dataset_path": "hails/mmlu_no_train",
|
1902 |
+
"dataset_name": "moral_disputes",
|
1903 |
+
"test_split": "test",
|
1904 |
+
"fewshot_split": "dev",
|
1905 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1906 |
+
"doc_to_target": "answer",
|
1907 |
+
"doc_to_choice": [
|
1908 |
+
"A",
|
1909 |
+
"B",
|
1910 |
+
"C",
|
1911 |
+
"D"
|
1912 |
+
],
|
1913 |
+
"description": "The following are multiple choice questions (with answers) about moral disputes.\n\n",
|
1914 |
+
"target_delimiter": " ",
|
1915 |
+
"fewshot_delimiter": "\n\n",
|
1916 |
+
"fewshot_config": {
|
1917 |
+
"sampler": "first_n"
|
1918 |
+
},
|
1919 |
+
"metric_list": [
|
1920 |
+
{
|
1921 |
+
"metric": "acc",
|
1922 |
+
"aggregation": "mean",
|
1923 |
+
"higher_is_better": true
|
1924 |
+
}
|
1925 |
+
],
|
1926 |
+
"output_type": "multiple_choice",
|
1927 |
+
"repeats": 1,
|
1928 |
+
"should_decontaminate": false,
|
1929 |
+
"metadata": {
|
1930 |
+
"version": 0.0
|
1931 |
+
}
|
1932 |
+
},
|
1933 |
+
"mmlu_moral_scenarios": {
|
1934 |
+
"task": "mmlu_moral_scenarios",
|
1935 |
+
"task_alias": "moral_scenarios",
|
1936 |
+
"group": "mmlu_humanities",
|
1937 |
+
"group_alias": "humanities",
|
1938 |
+
"dataset_path": "hails/mmlu_no_train",
|
1939 |
+
"dataset_name": "moral_scenarios",
|
1940 |
+
"test_split": "test",
|
1941 |
+
"fewshot_split": "dev",
|
1942 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1943 |
+
"doc_to_target": "answer",
|
1944 |
+
"doc_to_choice": [
|
1945 |
+
"A",
|
1946 |
+
"B",
|
1947 |
+
"C",
|
1948 |
+
"D"
|
1949 |
+
],
|
1950 |
+
"description": "The following are multiple choice questions (with answers) about moral scenarios.\n\n",
|
1951 |
+
"target_delimiter": " ",
|
1952 |
+
"fewshot_delimiter": "\n\n",
|
1953 |
+
"fewshot_config": {
|
1954 |
+
"sampler": "first_n"
|
1955 |
+
},
|
1956 |
+
"metric_list": [
|
1957 |
+
{
|
1958 |
+
"metric": "acc",
|
1959 |
+
"aggregation": "mean",
|
1960 |
+
"higher_is_better": true
|
1961 |
+
}
|
1962 |
+
],
|
1963 |
+
"output_type": "multiple_choice",
|
1964 |
+
"repeats": 1,
|
1965 |
+
"should_decontaminate": false,
|
1966 |
+
"metadata": {
|
1967 |
+
"version": 0.0
|
1968 |
+
}
|
1969 |
+
},
|
1970 |
+
"mmlu_nutrition": {
|
1971 |
+
"task": "mmlu_nutrition",
|
1972 |
+
"task_alias": "nutrition",
|
1973 |
+
"group": "mmlu_other",
|
1974 |
+
"group_alias": "other",
|
1975 |
+
"dataset_path": "hails/mmlu_no_train",
|
1976 |
+
"dataset_name": "nutrition",
|
1977 |
+
"test_split": "test",
|
1978 |
+
"fewshot_split": "dev",
|
1979 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
1980 |
+
"doc_to_target": "answer",
|
1981 |
+
"doc_to_choice": [
|
1982 |
+
"A",
|
1983 |
+
"B",
|
1984 |
+
"C",
|
1985 |
+
"D"
|
1986 |
+
],
|
1987 |
+
"description": "The following are multiple choice questions (with answers) about nutrition.\n\n",
|
1988 |
+
"target_delimiter": " ",
|
1989 |
+
"fewshot_delimiter": "\n\n",
|
1990 |
+
"fewshot_config": {
|
1991 |
+
"sampler": "first_n"
|
1992 |
+
},
|
1993 |
+
"metric_list": [
|
1994 |
+
{
|
1995 |
+
"metric": "acc",
|
1996 |
+
"aggregation": "mean",
|
1997 |
+
"higher_is_better": true
|
1998 |
+
}
|
1999 |
+
],
|
2000 |
+
"output_type": "multiple_choice",
|
2001 |
+
"repeats": 1,
|
2002 |
+
"should_decontaminate": false,
|
2003 |
+
"metadata": {
|
2004 |
+
"version": 0.0
|
2005 |
+
}
|
2006 |
+
},
|
2007 |
+
"mmlu_philosophy": {
|
2008 |
+
"task": "mmlu_philosophy",
|
2009 |
+
"task_alias": "philosophy",
|
2010 |
+
"group": "mmlu_humanities",
|
2011 |
+
"group_alias": "humanities",
|
2012 |
+
"dataset_path": "hails/mmlu_no_train",
|
2013 |
+
"dataset_name": "philosophy",
|
2014 |
+
"test_split": "test",
|
2015 |
+
"fewshot_split": "dev",
|
2016 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2017 |
+
"doc_to_target": "answer",
|
2018 |
+
"doc_to_choice": [
|
2019 |
+
"A",
|
2020 |
+
"B",
|
2021 |
+
"C",
|
2022 |
+
"D"
|
2023 |
+
],
|
2024 |
+
"description": "The following are multiple choice questions (with answers) about philosophy.\n\n",
|
2025 |
+
"target_delimiter": " ",
|
2026 |
+
"fewshot_delimiter": "\n\n",
|
2027 |
+
"fewshot_config": {
|
2028 |
+
"sampler": "first_n"
|
2029 |
+
},
|
2030 |
+
"metric_list": [
|
2031 |
+
{
|
2032 |
+
"metric": "acc",
|
2033 |
+
"aggregation": "mean",
|
2034 |
+
"higher_is_better": true
|
2035 |
+
}
|
2036 |
+
],
|
2037 |
+
"output_type": "multiple_choice",
|
2038 |
+
"repeats": 1,
|
2039 |
+
"should_decontaminate": false,
|
2040 |
+
"metadata": {
|
2041 |
+
"version": 0.0
|
2042 |
+
}
|
2043 |
+
},
|
2044 |
+
"mmlu_prehistory": {
|
2045 |
+
"task": "mmlu_prehistory",
|
2046 |
+
"task_alias": "prehistory",
|
2047 |
+
"group": "mmlu_humanities",
|
2048 |
+
"group_alias": "humanities",
|
2049 |
+
"dataset_path": "hails/mmlu_no_train",
|
2050 |
+
"dataset_name": "prehistory",
|
2051 |
+
"test_split": "test",
|
2052 |
+
"fewshot_split": "dev",
|
2053 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2054 |
+
"doc_to_target": "answer",
|
2055 |
+
"doc_to_choice": [
|
2056 |
+
"A",
|
2057 |
+
"B",
|
2058 |
+
"C",
|
2059 |
+
"D"
|
2060 |
+
],
|
2061 |
+
"description": "The following are multiple choice questions (with answers) about prehistory.\n\n",
|
2062 |
+
"target_delimiter": " ",
|
2063 |
+
"fewshot_delimiter": "\n\n",
|
2064 |
+
"fewshot_config": {
|
2065 |
+
"sampler": "first_n"
|
2066 |
+
},
|
2067 |
+
"metric_list": [
|
2068 |
+
{
|
2069 |
+
"metric": "acc",
|
2070 |
+
"aggregation": "mean",
|
2071 |
+
"higher_is_better": true
|
2072 |
+
}
|
2073 |
+
],
|
2074 |
+
"output_type": "multiple_choice",
|
2075 |
+
"repeats": 1,
|
2076 |
+
"should_decontaminate": false,
|
2077 |
+
"metadata": {
|
2078 |
+
"version": 0.0
|
2079 |
+
}
|
2080 |
+
},
|
2081 |
+
"mmlu_professional_accounting": {
|
2082 |
+
"task": "mmlu_professional_accounting",
|
2083 |
+
"task_alias": "professional_accounting",
|
2084 |
+
"group": "mmlu_other",
|
2085 |
+
"group_alias": "other",
|
2086 |
+
"dataset_path": "hails/mmlu_no_train",
|
2087 |
+
"dataset_name": "professional_accounting",
|
2088 |
+
"test_split": "test",
|
2089 |
+
"fewshot_split": "dev",
|
2090 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2091 |
+
"doc_to_target": "answer",
|
2092 |
+
"doc_to_choice": [
|
2093 |
+
"A",
|
2094 |
+
"B",
|
2095 |
+
"C",
|
2096 |
+
"D"
|
2097 |
+
],
|
2098 |
+
"description": "The following are multiple choice questions (with answers) about professional accounting.\n\n",
|
2099 |
+
"target_delimiter": " ",
|
2100 |
+
"fewshot_delimiter": "\n\n",
|
2101 |
+
"fewshot_config": {
|
2102 |
+
"sampler": "first_n"
|
2103 |
+
},
|
2104 |
+
"metric_list": [
|
2105 |
+
{
|
2106 |
+
"metric": "acc",
|
2107 |
+
"aggregation": "mean",
|
2108 |
+
"higher_is_better": true
|
2109 |
+
}
|
2110 |
+
],
|
2111 |
+
"output_type": "multiple_choice",
|
2112 |
+
"repeats": 1,
|
2113 |
+
"should_decontaminate": false,
|
2114 |
+
"metadata": {
|
2115 |
+
"version": 0.0
|
2116 |
+
}
|
2117 |
+
},
|
2118 |
+
"mmlu_professional_law": {
|
2119 |
+
"task": "mmlu_professional_law",
|
2120 |
+
"task_alias": "professional_law",
|
2121 |
+
"group": "mmlu_humanities",
|
2122 |
+
"group_alias": "humanities",
|
2123 |
+
"dataset_path": "hails/mmlu_no_train",
|
2124 |
+
"dataset_name": "professional_law",
|
2125 |
+
"test_split": "test",
|
2126 |
+
"fewshot_split": "dev",
|
2127 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2128 |
+
"doc_to_target": "answer",
|
2129 |
+
"doc_to_choice": [
|
2130 |
+
"A",
|
2131 |
+
"B",
|
2132 |
+
"C",
|
2133 |
+
"D"
|
2134 |
+
],
|
2135 |
+
"description": "The following are multiple choice questions (with answers) about professional law.\n\n",
|
2136 |
+
"target_delimiter": " ",
|
2137 |
+
"fewshot_delimiter": "\n\n",
|
2138 |
+
"fewshot_config": {
|
2139 |
+
"sampler": "first_n"
|
2140 |
+
},
|
2141 |
+
"metric_list": [
|
2142 |
+
{
|
2143 |
+
"metric": "acc",
|
2144 |
+
"aggregation": "mean",
|
2145 |
+
"higher_is_better": true
|
2146 |
+
}
|
2147 |
+
],
|
2148 |
+
"output_type": "multiple_choice",
|
2149 |
+
"repeats": 1,
|
2150 |
+
"should_decontaminate": false,
|
2151 |
+
"metadata": {
|
2152 |
+
"version": 0.0
|
2153 |
+
}
|
2154 |
+
},
|
2155 |
+
"mmlu_professional_medicine": {
|
2156 |
+
"task": "mmlu_professional_medicine",
|
2157 |
+
"task_alias": "professional_medicine",
|
2158 |
+
"group": "mmlu_other",
|
2159 |
+
"group_alias": "other",
|
2160 |
+
"dataset_path": "hails/mmlu_no_train",
|
2161 |
+
"dataset_name": "professional_medicine",
|
2162 |
+
"test_split": "test",
|
2163 |
+
"fewshot_split": "dev",
|
2164 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2165 |
+
"doc_to_target": "answer",
|
2166 |
+
"doc_to_choice": [
|
2167 |
+
"A",
|
2168 |
+
"B",
|
2169 |
+
"C",
|
2170 |
+
"D"
|
2171 |
+
],
|
2172 |
+
"description": "The following are multiple choice questions (with answers) about professional medicine.\n\n",
|
2173 |
+
"target_delimiter": " ",
|
2174 |
+
"fewshot_delimiter": "\n\n",
|
2175 |
+
"fewshot_config": {
|
2176 |
+
"sampler": "first_n"
|
2177 |
+
},
|
2178 |
+
"metric_list": [
|
2179 |
+
{
|
2180 |
+
"metric": "acc",
|
2181 |
+
"aggregation": "mean",
|
2182 |
+
"higher_is_better": true
|
2183 |
+
}
|
2184 |
+
],
|
2185 |
+
"output_type": "multiple_choice",
|
2186 |
+
"repeats": 1,
|
2187 |
+
"should_decontaminate": false,
|
2188 |
+
"metadata": {
|
2189 |
+
"version": 0.0
|
2190 |
+
}
|
2191 |
+
},
|
2192 |
+
"mmlu_professional_psychology": {
|
2193 |
+
"task": "mmlu_professional_psychology",
|
2194 |
+
"task_alias": "professional_psychology",
|
2195 |
+
"group": "mmlu_social_sciences",
|
2196 |
+
"group_alias": "social_sciences",
|
2197 |
+
"dataset_path": "hails/mmlu_no_train",
|
2198 |
+
"dataset_name": "professional_psychology",
|
2199 |
+
"test_split": "test",
|
2200 |
+
"fewshot_split": "dev",
|
2201 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2202 |
+
"doc_to_target": "answer",
|
2203 |
+
"doc_to_choice": [
|
2204 |
+
"A",
|
2205 |
+
"B",
|
2206 |
+
"C",
|
2207 |
+
"D"
|
2208 |
+
],
|
2209 |
+
"description": "The following are multiple choice questions (with answers) about professional psychology.\n\n",
|
2210 |
+
"target_delimiter": " ",
|
2211 |
+
"fewshot_delimiter": "\n\n",
|
2212 |
+
"fewshot_config": {
|
2213 |
+
"sampler": "first_n"
|
2214 |
+
},
|
2215 |
+
"metric_list": [
|
2216 |
+
{
|
2217 |
+
"metric": "acc",
|
2218 |
+
"aggregation": "mean",
|
2219 |
+
"higher_is_better": true
|
2220 |
+
}
|
2221 |
+
],
|
2222 |
+
"output_type": "multiple_choice",
|
2223 |
+
"repeats": 1,
|
2224 |
+
"should_decontaminate": false,
|
2225 |
+
"metadata": {
|
2226 |
+
"version": 0.0
|
2227 |
+
}
|
2228 |
+
},
|
2229 |
+
"mmlu_public_relations": {
|
2230 |
+
"task": "mmlu_public_relations",
|
2231 |
+
"task_alias": "public_relations",
|
2232 |
+
"group": "mmlu_social_sciences",
|
2233 |
+
"group_alias": "social_sciences",
|
2234 |
+
"dataset_path": "hails/mmlu_no_train",
|
2235 |
+
"dataset_name": "public_relations",
|
2236 |
+
"test_split": "test",
|
2237 |
+
"fewshot_split": "dev",
|
2238 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2239 |
+
"doc_to_target": "answer",
|
2240 |
+
"doc_to_choice": [
|
2241 |
+
"A",
|
2242 |
+
"B",
|
2243 |
+
"C",
|
2244 |
+
"D"
|
2245 |
+
],
|
2246 |
+
"description": "The following are multiple choice questions (with answers) about public relations.\n\n",
|
2247 |
+
"target_delimiter": " ",
|
2248 |
+
"fewshot_delimiter": "\n\n",
|
2249 |
+
"fewshot_config": {
|
2250 |
+
"sampler": "first_n"
|
2251 |
+
},
|
2252 |
+
"metric_list": [
|
2253 |
+
{
|
2254 |
+
"metric": "acc",
|
2255 |
+
"aggregation": "mean",
|
2256 |
+
"higher_is_better": true
|
2257 |
+
}
|
2258 |
+
],
|
2259 |
+
"output_type": "multiple_choice",
|
2260 |
+
"repeats": 1,
|
2261 |
+
"should_decontaminate": false,
|
2262 |
+
"metadata": {
|
2263 |
+
"version": 0.0
|
2264 |
+
}
|
2265 |
+
},
|
2266 |
+
"mmlu_security_studies": {
|
2267 |
+
"task": "mmlu_security_studies",
|
2268 |
+
"task_alias": "security_studies",
|
2269 |
+
"group": "mmlu_social_sciences",
|
2270 |
+
"group_alias": "social_sciences",
|
2271 |
+
"dataset_path": "hails/mmlu_no_train",
|
2272 |
+
"dataset_name": "security_studies",
|
2273 |
+
"test_split": "test",
|
2274 |
+
"fewshot_split": "dev",
|
2275 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2276 |
+
"doc_to_target": "answer",
|
2277 |
+
"doc_to_choice": [
|
2278 |
+
"A",
|
2279 |
+
"B",
|
2280 |
+
"C",
|
2281 |
+
"D"
|
2282 |
+
],
|
2283 |
+
"description": "The following are multiple choice questions (with answers) about security studies.\n\n",
|
2284 |
+
"target_delimiter": " ",
|
2285 |
+
"fewshot_delimiter": "\n\n",
|
2286 |
+
"fewshot_config": {
|
2287 |
+
"sampler": "first_n"
|
2288 |
+
},
|
2289 |
+
"metric_list": [
|
2290 |
+
{
|
2291 |
+
"metric": "acc",
|
2292 |
+
"aggregation": "mean",
|
2293 |
+
"higher_is_better": true
|
2294 |
+
}
|
2295 |
+
],
|
2296 |
+
"output_type": "multiple_choice",
|
2297 |
+
"repeats": 1,
|
2298 |
+
"should_decontaminate": false,
|
2299 |
+
"metadata": {
|
2300 |
+
"version": 0.0
|
2301 |
+
}
|
2302 |
+
},
|
2303 |
+
"mmlu_sociology": {
|
2304 |
+
"task": "mmlu_sociology",
|
2305 |
+
"task_alias": "sociology",
|
2306 |
+
"group": "mmlu_social_sciences",
|
2307 |
+
"group_alias": "social_sciences",
|
2308 |
+
"dataset_path": "hails/mmlu_no_train",
|
2309 |
+
"dataset_name": "sociology",
|
2310 |
+
"test_split": "test",
|
2311 |
+
"fewshot_split": "dev",
|
2312 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2313 |
+
"doc_to_target": "answer",
|
2314 |
+
"doc_to_choice": [
|
2315 |
+
"A",
|
2316 |
+
"B",
|
2317 |
+
"C",
|
2318 |
+
"D"
|
2319 |
+
],
|
2320 |
+
"description": "The following are multiple choice questions (with answers) about sociology.\n\n",
|
2321 |
+
"target_delimiter": " ",
|
2322 |
+
"fewshot_delimiter": "\n\n",
|
2323 |
+
"fewshot_config": {
|
2324 |
+
"sampler": "first_n"
|
2325 |
+
},
|
2326 |
+
"metric_list": [
|
2327 |
+
{
|
2328 |
+
"metric": "acc",
|
2329 |
+
"aggregation": "mean",
|
2330 |
+
"higher_is_better": true
|
2331 |
+
}
|
2332 |
+
],
|
2333 |
+
"output_type": "multiple_choice",
|
2334 |
+
"repeats": 1,
|
2335 |
+
"should_decontaminate": false,
|
2336 |
+
"metadata": {
|
2337 |
+
"version": 0.0
|
2338 |
+
}
|
2339 |
+
},
|
2340 |
+
"mmlu_us_foreign_policy": {
|
2341 |
+
"task": "mmlu_us_foreign_policy",
|
2342 |
+
"task_alias": "us_foreign_policy",
|
2343 |
+
"group": "mmlu_social_sciences",
|
2344 |
+
"group_alias": "social_sciences",
|
2345 |
+
"dataset_path": "hails/mmlu_no_train",
|
2346 |
+
"dataset_name": "us_foreign_policy",
|
2347 |
+
"test_split": "test",
|
2348 |
+
"fewshot_split": "dev",
|
2349 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2350 |
+
"doc_to_target": "answer",
|
2351 |
+
"doc_to_choice": [
|
2352 |
+
"A",
|
2353 |
+
"B",
|
2354 |
+
"C",
|
2355 |
+
"D"
|
2356 |
+
],
|
2357 |
+
"description": "The following are multiple choice questions (with answers) about us foreign policy.\n\n",
|
2358 |
+
"target_delimiter": " ",
|
2359 |
+
"fewshot_delimiter": "\n\n",
|
2360 |
+
"fewshot_config": {
|
2361 |
+
"sampler": "first_n"
|
2362 |
+
},
|
2363 |
+
"metric_list": [
|
2364 |
+
{
|
2365 |
+
"metric": "acc",
|
2366 |
+
"aggregation": "mean",
|
2367 |
+
"higher_is_better": true
|
2368 |
+
}
|
2369 |
+
],
|
2370 |
+
"output_type": "multiple_choice",
|
2371 |
+
"repeats": 1,
|
2372 |
+
"should_decontaminate": false,
|
2373 |
+
"metadata": {
|
2374 |
+
"version": 0.0
|
2375 |
+
}
|
2376 |
+
},
|
2377 |
+
"mmlu_virology": {
|
2378 |
+
"task": "mmlu_virology",
|
2379 |
+
"task_alias": "virology",
|
2380 |
+
"group": "mmlu_other",
|
2381 |
+
"group_alias": "other",
|
2382 |
+
"dataset_path": "hails/mmlu_no_train",
|
2383 |
+
"dataset_name": "virology",
|
2384 |
+
"test_split": "test",
|
2385 |
+
"fewshot_split": "dev",
|
2386 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2387 |
+
"doc_to_target": "answer",
|
2388 |
+
"doc_to_choice": [
|
2389 |
+
"A",
|
2390 |
+
"B",
|
2391 |
+
"C",
|
2392 |
+
"D"
|
2393 |
+
],
|
2394 |
+
"description": "The following are multiple choice questions (with answers) about virology.\n\n",
|
2395 |
+
"target_delimiter": " ",
|
2396 |
+
"fewshot_delimiter": "\n\n",
|
2397 |
+
"fewshot_config": {
|
2398 |
+
"sampler": "first_n"
|
2399 |
+
},
|
2400 |
+
"metric_list": [
|
2401 |
+
{
|
2402 |
+
"metric": "acc",
|
2403 |
+
"aggregation": "mean",
|
2404 |
+
"higher_is_better": true
|
2405 |
+
}
|
2406 |
+
],
|
2407 |
+
"output_type": "multiple_choice",
|
2408 |
+
"repeats": 1,
|
2409 |
+
"should_decontaminate": false,
|
2410 |
+
"metadata": {
|
2411 |
+
"version": 0.0
|
2412 |
+
}
|
2413 |
+
},
|
2414 |
+
"mmlu_world_religions": {
|
2415 |
+
"task": "mmlu_world_religions",
|
2416 |
+
"task_alias": "world_religions",
|
2417 |
+
"group": "mmlu_humanities",
|
2418 |
+
"group_alias": "humanities",
|
2419 |
+
"dataset_path": "hails/mmlu_no_train",
|
2420 |
+
"dataset_name": "world_religions",
|
2421 |
+
"test_split": "test",
|
2422 |
+
"fewshot_split": "dev",
|
2423 |
+
"doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
|
2424 |
+
"doc_to_target": "answer",
|
2425 |
+
"doc_to_choice": [
|
2426 |
+
"A",
|
2427 |
+
"B",
|
2428 |
+
"C",
|
2429 |
+
"D"
|
2430 |
+
],
|
2431 |
+
"description": "The following are multiple choice questions (with answers) about world religions.\n\n",
|
2432 |
+
"target_delimiter": " ",
|
2433 |
+
"fewshot_delimiter": "\n\n",
|
2434 |
+
"fewshot_config": {
|
2435 |
+
"sampler": "first_n"
|
2436 |
+
},
|
2437 |
+
"metric_list": [
|
2438 |
+
{
|
2439 |
+
"metric": "acc",
|
2440 |
+
"aggregation": "mean",
|
2441 |
+
"higher_is_better": true
|
2442 |
+
}
|
2443 |
+
],
|
2444 |
+
"output_type": "multiple_choice",
|
2445 |
+
"repeats": 1,
|
2446 |
+
"should_decontaminate": false,
|
2447 |
+
"metadata": {
|
2448 |
+
"version": 0.0
|
2449 |
+
}
|
2450 |
+
}
|
2451 |
+
},
|
2452 |
+
"versions": {
|
2453 |
+
"mmlu": "N/A",
|
2454 |
+
"mmlu_abstract_algebra": 0.0,
|
2455 |
+
"mmlu_anatomy": 0.0,
|
2456 |
+
"mmlu_astronomy": 0.0,
|
2457 |
+
"mmlu_business_ethics": 0.0,
|
2458 |
+
"mmlu_clinical_knowledge": 0.0,
|
2459 |
+
"mmlu_college_biology": 0.0,
|
2460 |
+
"mmlu_college_chemistry": 0.0,
|
2461 |
+
"mmlu_college_computer_science": 0.0,
|
2462 |
+
"mmlu_college_mathematics": 0.0,
|
2463 |
+
"mmlu_college_medicine": 0.0,
|
2464 |
+
"mmlu_college_physics": 0.0,
|
2465 |
+
"mmlu_computer_security": 0.0,
|
2466 |
+
"mmlu_conceptual_physics": 0.0,
|
2467 |
+
"mmlu_econometrics": 0.0,
|
2468 |
+
"mmlu_electrical_engineering": 0.0,
|
2469 |
+
"mmlu_elementary_mathematics": 0.0,
|
2470 |
+
"mmlu_formal_logic": 0.0,
|
2471 |
+
"mmlu_global_facts": 0.0,
|
2472 |
+
"mmlu_high_school_biology": 0.0,
|
2473 |
+
"mmlu_high_school_chemistry": 0.0,
|
2474 |
+
"mmlu_high_school_computer_science": 0.0,
|
2475 |
+
"mmlu_high_school_european_history": 0.0,
|
2476 |
+
"mmlu_high_school_geography": 0.0,
|
2477 |
+
"mmlu_high_school_government_and_politics": 0.0,
|
2478 |
+
"mmlu_high_school_macroeconomics": 0.0,
|
2479 |
+
"mmlu_high_school_mathematics": 0.0,
|
2480 |
+
"mmlu_high_school_microeconomics": 0.0,
|
2481 |
+
"mmlu_high_school_physics": 0.0,
|
2482 |
+
"mmlu_high_school_psychology": 0.0,
|
2483 |
+
"mmlu_high_school_statistics": 0.0,
|
2484 |
+
"mmlu_high_school_us_history": 0.0,
|
2485 |
+
"mmlu_high_school_world_history": 0.0,
|
2486 |
+
"mmlu_human_aging": 0.0,
|
2487 |
+
"mmlu_human_sexuality": 0.0,
|
2488 |
+
"mmlu_humanities": "N/A",
|
2489 |
+
"mmlu_international_law": 0.0,
|
2490 |
+
"mmlu_jurisprudence": 0.0,
|
2491 |
+
"mmlu_logical_fallacies": 0.0,
|
2492 |
+
"mmlu_machine_learning": 0.0,
|
2493 |
+
"mmlu_management": 0.0,
|
2494 |
+
"mmlu_marketing": 0.0,
|
2495 |
+
"mmlu_medical_genetics": 0.0,
|
2496 |
+
"mmlu_miscellaneous": 0.0,
|
2497 |
+
"mmlu_moral_disputes": 0.0,
|
2498 |
+
"mmlu_moral_scenarios": 0.0,
|
2499 |
+
"mmlu_nutrition": 0.0,
|
2500 |
+
"mmlu_other": "N/A",
|
2501 |
+
"mmlu_philosophy": 0.0,
|
2502 |
+
"mmlu_prehistory": 0.0,
|
2503 |
+
"mmlu_professional_accounting": 0.0,
|
2504 |
+
"mmlu_professional_law": 0.0,
|
2505 |
+
"mmlu_professional_medicine": 0.0,
|
2506 |
+
"mmlu_professional_psychology": 0.0,
|
2507 |
+
"mmlu_public_relations": 0.0,
|
2508 |
+
"mmlu_security_studies": 0.0,
|
2509 |
+
"mmlu_social_sciences": "N/A",
|
2510 |
+
"mmlu_sociology": 0.0,
|
2511 |
+
"mmlu_stem": "N/A",
|
2512 |
+
"mmlu_us_foreign_policy": 0.0,
|
2513 |
+
"mmlu_virology": 0.0,
|
2514 |
+
"mmlu_world_religions": 0.0
|
2515 |
+
},
|
2516 |
+
"n-shot": {
|
2517 |
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/record/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
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@@ -0,0 +1,3 @@
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size 99548
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/sciq/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
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size 43486
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"rouge1_diff_stderr,none": 0.9253507541541494,
|
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+
"rouge2_max,none": 36.33254780289301,
|
87 |
+
"rouge2_max_stderr,none": 1.0256135646393412,
|
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+
"rouge2_acc,none": 0.26438188494492043,
|
89 |
+
"rouge2_acc_stderr,none": 0.015438211119522495,
|
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+
"rouge2_diff,none": -10.04732723165974,
|
91 |
+
"rouge2_diff_stderr,none": 1.1316617230538066,
|
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"rougeL_max,none": 49.387256832280386,
|
93 |
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"rougeL_max_stderr,none": 0.890587396141587,
|
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+
"rougeL_acc,none": 0.29253365973072215,
|
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"rougeL_acc_stderr,none": 0.015925597445286165,
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"rougeL_diff,none": -8.707625480067987,
|
97 |
+
"rougeL_diff_stderr,none": 0.9450801901077751,
|
98 |
+
"alias": "truthfulqa"
|
99 |
+
}
|
100 |
+
},
|
101 |
+
"configs": {
|
102 |
+
"truthfulqa_gen": {
|
103 |
+
"task": "truthfulqa_gen",
|
104 |
+
"group": [
|
105 |
+
"truthfulqa"
|
106 |
+
],
|
107 |
+
"dataset_path": "truthful_qa",
|
108 |
+
"dataset_name": "generation",
|
109 |
+
"validation_split": "validation",
|
110 |
+
"process_docs": "def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:\n return dataset.map(preprocess_function)\n",
|
111 |
+
"doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question}}",
|
112 |
+
"doc_to_target": " ",
|
113 |
+
"process_results": "def process_results_gen(doc, results):\n completion = results[0]\n true_refs, false_refs = doc[\"correct_answers\"], doc[\"incorrect_answers\"]\n all_refs = true_refs + false_refs\n\n # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.\n\n # # BLEURT\n # bleurt_scores_true = self.bleurt.compute(\n # predictions=[completion] * len(true_refs), references=true_refs\n # )[\"scores\"]\n # bleurt_scores_false = self.bleurt.compute(\n # predictions=[completion] * len(false_refs), references=false_refs\n # )[\"scores\"]\n # bleurt_correct = max(bleurt_scores_true)\n # bleurt_incorrect = max(bleurt_scores_false)\n # bleurt_max = bleurt_correct\n # bleurt_diff = bleurt_correct - bleurt_incorrect\n # bleurt_acc = int(bleurt_correct > bleurt_incorrect)\n\n # BLEU\n bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]\n bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])\n bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])\n bleu_max = bleu_correct\n bleu_diff = bleu_correct - bleu_incorrect\n bleu_acc = int(bleu_correct > bleu_incorrect)\n\n # ROUGE-N\n rouge_scores = [rouge([ref], [completion]) for ref in all_refs]\n # ROUGE-1\n rouge1_scores = [score[\"rouge1\"] for score in rouge_scores]\n rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])\n rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])\n rouge1_max = rouge1_correct\n rouge1_diff = rouge1_correct - rouge1_incorrect\n rouge1_acc = int(rouge1_correct > rouge1_incorrect)\n # ROUGE-2\n rouge2_scores = [score[\"rouge2\"] for score in rouge_scores]\n rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])\n rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])\n rouge2_max = rouge2_correct\n rouge2_diff = rouge2_correct - rouge2_incorrect\n rouge2_acc = int(rouge2_correct > rouge2_incorrect)\n # ROUGE-L\n rougeL_scores = [score[\"rougeLsum\"] for score in rouge_scores]\n rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])\n rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])\n rougeL_max = rougeL_correct\n rougeL_diff = rougeL_correct - rougeL_incorrect\n rougeL_acc = int(rougeL_correct > rougeL_incorrect)\n\n return {\n # \"bleurt_max\": bleurt_max,\n # \"bleurt_acc\": bleurt_acc,\n # \"bleurt_diff\": bleurt_diff,\n \"bleu_max\": bleu_max,\n \"bleu_acc\": bleu_acc,\n \"bleu_diff\": bleu_diff,\n \"rouge1_max\": rouge1_max,\n \"rouge1_acc\": rouge1_acc,\n \"rouge1_diff\": rouge1_diff,\n \"rouge2_max\": rouge2_max,\n \"rouge2_acc\": rouge2_acc,\n \"rouge2_diff\": rouge2_diff,\n \"rougeL_max\": rougeL_max,\n \"rougeL_acc\": rougeL_acc,\n \"rougeL_diff\": rougeL_diff,\n }\n",
|
114 |
+
"description": "",
|
115 |
+
"target_delimiter": " ",
|
116 |
+
"fewshot_delimiter": "\n\n",
|
117 |
+
"num_fewshot": 0,
|
118 |
+
"metric_list": [
|
119 |
+
{
|
120 |
+
"metric": "bleu_max",
|
121 |
+
"aggregation": "mean",
|
122 |
+
"higher_is_better": true
|
123 |
+
},
|
124 |
+
{
|
125 |
+
"metric": "bleu_acc",
|
126 |
+
"aggregation": "mean",
|
127 |
+
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|
128 |
+
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|
129 |
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{
|
130 |
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"metric": "bleu_diff",
|
131 |
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|
132 |
+
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|
133 |
+
},
|
134 |
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|
135 |
+
"metric": "rouge1_max",
|
136 |
+
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|
137 |
+
"higher_is_better": true
|
138 |
+
},
|
139 |
+
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|
140 |
+
"metric": "rouge1_acc",
|
141 |
+
"aggregation": "mean",
|
142 |
+
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|
143 |
+
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|
144 |
+
{
|
145 |
+
"metric": "rouge1_diff",
|
146 |
+
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|
147 |
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|
148 |
+
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|
149 |
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|
150 |
+
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|
151 |
+
"aggregation": "mean",
|
152 |
+
"higher_is_better": true
|
153 |
+
},
|
154 |
+
{
|
155 |
+
"metric": "rouge2_acc",
|
156 |
+
"aggregation": "mean",
|
157 |
+
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|
158 |
+
},
|
159 |
+
{
|
160 |
+
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|
161 |
+
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|
162 |
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|
163 |
+
},
|
164 |
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{
|
165 |
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"metric": "rougeL_max",
|
166 |
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"aggregation": "mean",
|
167 |
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|
168 |
+
},
|
169 |
+
{
|
170 |
+
"metric": "rougeL_acc",
|
171 |
+
"aggregation": "mean",
|
172 |
+
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|
173 |
+
},
|
174 |
+
{
|
175 |
+
"metric": "rougeL_diff",
|
176 |
+
"aggregation": "mean",
|
177 |
+
"higher_is_better": true
|
178 |
+
}
|
179 |
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],
|
180 |
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"output_type": "generate_until",
|
181 |
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|
182 |
+
"until": [
|
183 |
+
"\n\n"
|
184 |
+
],
|
185 |
+
"do_sample": false
|
186 |
+
},
|
187 |
+
"repeats": 1,
|
188 |
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"should_decontaminate": true,
|
189 |
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"doc_to_decontamination_query": "question",
|
190 |
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"metadata": {
|
191 |
+
"version": 3.0
|
192 |
+
}
|
193 |
+
},
|
194 |
+
"truthfulqa_mc1": {
|
195 |
+
"task": "truthfulqa_mc1",
|
196 |
+
"group": [
|
197 |
+
"truthfulqa"
|
198 |
+
],
|
199 |
+
"dataset_path": "truthful_qa",
|
200 |
+
"dataset_name": "multiple_choice",
|
201 |
+
"validation_split": "validation",
|
202 |
+
"doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
|
203 |
+
"doc_to_target": 0,
|
204 |
+
"doc_to_choice": "{{mc1_targets.choices}}",
|
205 |
+
"description": "",
|
206 |
+
"target_delimiter": " ",
|
207 |
+
"fewshot_delimiter": "\n\n",
|
208 |
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"num_fewshot": 0,
|
209 |
+
"metric_list": [
|
210 |
+
{
|
211 |
+
"metric": "acc",
|
212 |
+
"aggregation": "mean",
|
213 |
+
"higher_is_better": true
|
214 |
+
}
|
215 |
+
],
|
216 |
+
"output_type": "multiple_choice",
|
217 |
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"repeats": 1,
|
218 |
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"should_decontaminate": true,
|
219 |
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"doc_to_decontamination_query": "question",
|
220 |
+
"metadata": {
|
221 |
+
"version": 2.0
|
222 |
+
}
|
223 |
+
},
|
224 |
+
"truthfulqa_mc2": {
|
225 |
+
"task": "truthfulqa_mc2",
|
226 |
+
"group": [
|
227 |
+
"truthfulqa"
|
228 |
+
],
|
229 |
+
"dataset_path": "truthful_qa",
|
230 |
+
"dataset_name": "multiple_choice",
|
231 |
+
"validation_split": "validation",
|
232 |
+
"doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
|
233 |
+
"doc_to_target": 0,
|
234 |
+
"doc_to_choice": "{{mc2_targets.choices}}",
|
235 |
+
"process_results": "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\n # Split on the first `0` as everything before it is true (`1`).\n split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n",
|
236 |
+
"description": "",
|
237 |
+
"target_delimiter": " ",
|
238 |
+
"fewshot_delimiter": "\n\n",
|
239 |
+
"num_fewshot": 0,
|
240 |
+
"metric_list": [
|
241 |
+
{
|
242 |
+
"metric": "acc",
|
243 |
+
"aggregation": "mean",
|
244 |
+
"higher_is_better": true
|
245 |
+
}
|
246 |
+
],
|
247 |
+
"output_type": "multiple_choice",
|
248 |
+
"repeats": 1,
|
249 |
+
"should_decontaminate": true,
|
250 |
+
"doc_to_decontamination_query": "question",
|
251 |
+
"metadata": {
|
252 |
+
"version": 2.0
|
253 |
+
}
|
254 |
+
}
|
255 |
+
},
|
256 |
+
"versions": {
|
257 |
+
"truthfulqa": "N/A",
|
258 |
+
"truthfulqa_gen": 3.0,
|
259 |
+
"truthfulqa_mc1": 2.0,
|
260 |
+
"truthfulqa_mc2": 2.0
|
261 |
+
},
|
262 |
+
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|
263 |
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|
264 |
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"truthfulqa_gen": 0,
|
265 |
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"truthfulqa_mc1": 0,
|
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|
267 |
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|
268 |
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"config": {
|
269 |
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"model": "hf",
|
270 |
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"model_args": "pretrained=./rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30_pth,dtype=bfloat16,trust_remote_code=True",
|
271 |
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|
272 |
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|
274 |
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|
275 |
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|
276 |
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|
277 |
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|
278 |
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|
279 |
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|
280 |
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},
|
281 |
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"git_hash": "173e67b"
|
282 |
+
}
|
lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/truthfulqa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
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1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:6d108374520e81f782c994565f095689a087cbb92240b226811dc893e82b49ba
|
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size 602095
|
lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,58 @@
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{
|
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|
3 |
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|
4 |
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|
5 |
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|
6 |
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"alias": "winogrande"
|
7 |
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}
|
8 |
+
},
|
9 |
+
"configs": {
|
10 |
+
"winogrande": {
|
11 |
+
"task": "winogrande",
|
12 |
+
"dataset_path": "winogrande",
|
13 |
+
"dataset_name": "winogrande_xl",
|
14 |
+
"training_split": "train",
|
15 |
+
"validation_split": "validation",
|
16 |
+
"doc_to_text": "def doc_to_text(doc):\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
17 |
+
"doc_to_target": "def doc_to_target(doc):\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
18 |
+
"doc_to_choice": "def doc_to_choice(doc):\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
19 |
+
"description": "",
|
20 |
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"target_delimiter": " ",
|
21 |
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"fewshot_delimiter": "\n\n",
|
22 |
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"metric_list": [
|
23 |
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{
|
24 |
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"metric": "acc",
|
25 |
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"aggregation": "mean",
|
26 |
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"higher_is_better": true
|
27 |
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}
|
28 |
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],
|
29 |
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"output_type": "multiple_choice",
|
30 |
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"repeats": 1,
|
31 |
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"should_decontaminate": true,
|
32 |
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"doc_to_decontamination_query": "sentence",
|
33 |
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"metadata": {
|
34 |
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"version": 1.0
|
35 |
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}
|
36 |
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}
|
37 |
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},
|
38 |
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"versions": {
|
39 |
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"winogrande": 1.0
|
40 |
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},
|
41 |
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|
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|
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|
44 |
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|
45 |
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|
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|
47 |
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|
48 |
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|
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|
50 |
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|
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|
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|
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|
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|
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|
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|
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"git_hash": "173e67b"
|
58 |
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}
|
lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/winogrande/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
ADDED
@@ -0,0 +1,3 @@
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1 |
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version https://git-lfs.github.com/spec/v1
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size 43220
|
lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
ADDED
@@ -0,0 +1,390 @@
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|
1 |
+
{
|
2 |
+
"results": {
|
3 |
+
"xcopa": {
|
4 |
+
"acc,none": 0.6247272727272727,
|
5 |
+
"acc_stderr,none": 0.06961187297007985,
|
6 |
+
"alias": "xcopa"
|
7 |
+
},
|
8 |
+
"xcopa_et": {
|
9 |
+
"acc,none": 0.608,
|
10 |
+
"acc_stderr,none": 0.021854684955611256,
|
11 |
+
"alias": " - xcopa_et"
|
12 |
+
},
|
13 |
+
"xcopa_ht": {
|
14 |
+
"acc,none": 0.526,
|
15 |
+
"acc_stderr,none": 0.022352791650914163,
|
16 |
+
"alias": " - xcopa_ht"
|
17 |
+
},
|
18 |
+
"xcopa_id": {
|
19 |
+
"acc,none": 0.72,
|
20 |
+
"acc_stderr,none": 0.02009995064750324,
|
21 |
+
"alias": " - xcopa_id"
|
22 |
+
},
|
23 |
+
"xcopa_it": {
|
24 |
+
"acc,none": 0.746,
|
25 |
+
"acc_stderr,none": 0.019486596801643382,
|
26 |
+
"alias": " - xcopa_it"
|
27 |
+
},
|
28 |
+
"xcopa_qu": {
|
29 |
+
"acc,none": 0.514,
|
30 |
+
"acc_stderr,none": 0.022374298166353185,
|
31 |
+
"alias": " - xcopa_qu"
|
32 |
+
},
|
33 |
+
"xcopa_sw": {
|
34 |
+
"acc,none": 0.556,
|
35 |
+
"acc_stderr,none": 0.02224224437573102,
|
36 |
+
"alias": " - xcopa_sw"
|
37 |
+
},
|
38 |
+
"xcopa_ta": {
|
39 |
+
"acc,none": 0.572,
|
40 |
+
"acc_stderr,none": 0.022149790663861923,
|
41 |
+
"alias": " - xcopa_ta"
|
42 |
+
},
|
43 |
+
"xcopa_th": {
|
44 |
+
"acc,none": 0.592,
|
45 |
+
"acc_stderr,none": 0.02200091089387719,
|
46 |
+
"alias": " - xcopa_th"
|
47 |
+
},
|
48 |
+
"xcopa_tr": {
|
49 |
+
"acc,none": 0.632,
|
50 |
+
"acc_stderr,none": 0.02158898256835354,
|
51 |
+
"alias": " - xcopa_tr"
|
52 |
+
},
|
53 |
+
"xcopa_vi": {
|
54 |
+
"acc,none": 0.712,
|
55 |
+
"acc_stderr,none": 0.02027150383507522,
|
56 |
+
"alias": " - xcopa_vi"
|
57 |
+
},
|
58 |
+
"xcopa_zh": {
|
59 |
+
"acc,none": 0.694,
|
60 |
+
"acc_stderr,none": 0.020629569998345396,
|
61 |
+
"alias": " - xcopa_zh"
|
62 |
+
}
|
63 |
+
},
|
64 |
+
"groups": {
|
65 |
+
"xcopa": {
|
66 |
+
"acc,none": 0.6247272727272727,
|
67 |
+
"acc_stderr,none": 0.06961187297007985,
|
68 |
+
"alias": "xcopa"
|
69 |
+
}
|
70 |
+
},
|
71 |
+
"configs": {
|
72 |
+
"xcopa_et": {
|
73 |
+
"task": "xcopa_et",
|
74 |
+
"group": "xcopa",
|
75 |
+
"dataset_path": "xcopa",
|
76 |
+
"dataset_name": "et",
|
77 |
+
"validation_split": "validation",
|
78 |
+
"test_split": "test",
|
79 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028eb3b2e0>, connector={'cause': 'sest', 'effect': 'seetõttu'})",
|
80 |
+
"doc_to_target": "label",
|
81 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
82 |
+
"description": "",
|
83 |
+
"target_delimiter": " ",
|
84 |
+
"fewshot_delimiter": "\n\n",
|
85 |
+
"metric_list": [
|
86 |
+
{
|
87 |
+
"metric": "acc"
|
88 |
+
}
|
89 |
+
],
|
90 |
+
"output_type": "multiple_choice",
|
91 |
+
"repeats": 1,
|
92 |
+
"should_decontaminate": false,
|
93 |
+
"metadata": {
|
94 |
+
"version": 1.0
|
95 |
+
}
|
96 |
+
},
|
97 |
+
"xcopa_ht": {
|
98 |
+
"task": "xcopa_ht",
|
99 |
+
"group": "xcopa",
|
100 |
+
"dataset_path": "xcopa",
|
101 |
+
"dataset_name": "ht",
|
102 |
+
"validation_split": "validation",
|
103 |
+
"test_split": "test",
|
104 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028eb58860>, connector={'cause': 'poukisa', 'effect': 'donk sa'})",
|
105 |
+
"doc_to_target": "label",
|
106 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
107 |
+
"description": "",
|
108 |
+
"target_delimiter": " ",
|
109 |
+
"fewshot_delimiter": "\n\n",
|
110 |
+
"metric_list": [
|
111 |
+
{
|
112 |
+
"metric": "acc"
|
113 |
+
}
|
114 |
+
],
|
115 |
+
"output_type": "multiple_choice",
|
116 |
+
"repeats": 1,
|
117 |
+
"should_decontaminate": false,
|
118 |
+
"metadata": {
|
119 |
+
"version": 1.0
|
120 |
+
}
|
121 |
+
},
|
122 |
+
"xcopa_id": {
|
123 |
+
"task": "xcopa_id",
|
124 |
+
"group": "xcopa",
|
125 |
+
"dataset_path": "xcopa",
|
126 |
+
"dataset_name": "id",
|
127 |
+
"validation_split": "validation",
|
128 |
+
"test_split": "test",
|
129 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028ea53600>, connector={'cause': 'karena', 'effect': 'maka'})",
|
130 |
+
"doc_to_target": "label",
|
131 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
132 |
+
"description": "",
|
133 |
+
"target_delimiter": " ",
|
134 |
+
"fewshot_delimiter": "\n\n",
|
135 |
+
"metric_list": [
|
136 |
+
{
|
137 |
+
"metric": "acc"
|
138 |
+
}
|
139 |
+
],
|
140 |
+
"output_type": "multiple_choice",
|
141 |
+
"repeats": 1,
|
142 |
+
"should_decontaminate": false,
|
143 |
+
"metadata": {
|
144 |
+
"version": 1.0
|
145 |
+
}
|
146 |
+
},
|
147 |
+
"xcopa_it": {
|
148 |
+
"task": "xcopa_it",
|
149 |
+
"group": "xcopa",
|
150 |
+
"dataset_path": "xcopa",
|
151 |
+
"dataset_name": "it",
|
152 |
+
"validation_split": "validation",
|
153 |
+
"test_split": "test",
|
154 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028eca4220>, connector={'cause': 'perché', 'effect': 'quindi'})",
|
155 |
+
"doc_to_target": "label",
|
156 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
157 |
+
"description": "",
|
158 |
+
"target_delimiter": " ",
|
159 |
+
"fewshot_delimiter": "\n\n",
|
160 |
+
"metric_list": [
|
161 |
+
{
|
162 |
+
"metric": "acc"
|
163 |
+
}
|
164 |
+
],
|
165 |
+
"output_type": "multiple_choice",
|
166 |
+
"repeats": 1,
|
167 |
+
"should_decontaminate": false,
|
168 |
+
"metadata": {
|
169 |
+
"version": 1.0
|
170 |
+
}
|
171 |
+
},
|
172 |
+
"xcopa_qu": {
|
173 |
+
"task": "xcopa_qu",
|
174 |
+
"group": "xcopa",
|
175 |
+
"dataset_path": "xcopa",
|
176 |
+
"dataset_name": "qu",
|
177 |
+
"validation_split": "validation",
|
178 |
+
"test_split": "test",
|
179 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028eca54e0>, connector={'cause': 'imataq', 'effect': 'chaymi'})",
|
180 |
+
"doc_to_target": "label",
|
181 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
182 |
+
"description": "",
|
183 |
+
"target_delimiter": " ",
|
184 |
+
"fewshot_delimiter": "\n\n",
|
185 |
+
"metric_list": [
|
186 |
+
{
|
187 |
+
"metric": "acc"
|
188 |
+
}
|
189 |
+
],
|
190 |
+
"output_type": "multiple_choice",
|
191 |
+
"repeats": 1,
|
192 |
+
"should_decontaminate": false,
|
193 |
+
"metadata": {
|
194 |
+
"version": 1.0
|
195 |
+
}
|
196 |
+
},
|
197 |
+
"xcopa_sw": {
|
198 |
+
"task": "xcopa_sw",
|
199 |
+
"group": "xcopa",
|
200 |
+
"dataset_path": "xcopa",
|
201 |
+
"dataset_name": "sw",
|
202 |
+
"validation_split": "validation",
|
203 |
+
"test_split": "test",
|
204 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028eca45e0>, connector={'cause': 'kwa sababu', 'effect': 'kwa hiyo'})",
|
205 |
+
"doc_to_target": "label",
|
206 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
207 |
+
"description": "",
|
208 |
+
"target_delimiter": " ",
|
209 |
+
"fewshot_delimiter": "\n\n",
|
210 |
+
"metric_list": [
|
211 |
+
{
|
212 |
+
"metric": "acc"
|
213 |
+
}
|
214 |
+
],
|
215 |
+
"output_type": "multiple_choice",
|
216 |
+
"repeats": 1,
|
217 |
+
"should_decontaminate": false,
|
218 |
+
"metadata": {
|
219 |
+
"version": 1.0
|
220 |
+
}
|
221 |
+
},
|
222 |
+
"xcopa_ta": {
|
223 |
+
"task": "xcopa_ta",
|
224 |
+
"group": "xcopa",
|
225 |
+
"dataset_path": "xcopa",
|
226 |
+
"dataset_name": "ta",
|
227 |
+
"validation_split": "validation",
|
228 |
+
"test_split": "test",
|
229 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028eca6520>, connector={'cause': 'காரணமாக', 'effect': 'எனவே'})",
|
230 |
+
"doc_to_target": "label",
|
231 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
232 |
+
"description": "",
|
233 |
+
"target_delimiter": " ",
|
234 |
+
"fewshot_delimiter": "\n\n",
|
235 |
+
"metric_list": [
|
236 |
+
{
|
237 |
+
"metric": "acc"
|
238 |
+
}
|
239 |
+
],
|
240 |
+
"output_type": "multiple_choice",
|
241 |
+
"repeats": 1,
|
242 |
+
"should_decontaminate": false,
|
243 |
+
"metadata": {
|
244 |
+
"version": 1.0
|
245 |
+
}
|
246 |
+
},
|
247 |
+
"xcopa_th": {
|
248 |
+
"task": "xcopa_th",
|
249 |
+
"group": "xcopa",
|
250 |
+
"dataset_path": "xcopa",
|
251 |
+
"dataset_name": "th",
|
252 |
+
"validation_split": "validation",
|
253 |
+
"test_split": "test",
|
254 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028e9b8ae0>, connector={'cause': 'เพราะ', 'effect': 'ดังนั้น'})",
|
255 |
+
"doc_to_target": "label",
|
256 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
257 |
+
"description": "",
|
258 |
+
"target_delimiter": " ",
|
259 |
+
"fewshot_delimiter": "\n\n",
|
260 |
+
"metric_list": [
|
261 |
+
{
|
262 |
+
"metric": "acc"
|
263 |
+
}
|
264 |
+
],
|
265 |
+
"output_type": "multiple_choice",
|
266 |
+
"repeats": 1,
|
267 |
+
"should_decontaminate": false,
|
268 |
+
"metadata": {
|
269 |
+
"version": 1.0
|
270 |
+
}
|
271 |
+
},
|
272 |
+
"xcopa_tr": {
|
273 |
+
"task": "xcopa_tr",
|
274 |
+
"group": "xcopa",
|
275 |
+
"dataset_path": "xcopa",
|
276 |
+
"dataset_name": "tr",
|
277 |
+
"validation_split": "validation",
|
278 |
+
"test_split": "test",
|
279 |
+
"doc_to_text": "functools.partial(<function doc_to_text at 0x7f028e9b9800>, connector={'cause': 'çünkü', 'effect': 'bu yüzden'})",
|
280 |
+
"doc_to_target": "label",
|
281 |
+
"doc_to_choice": "def doc_to_choice(doc):\n return [convert_choice(doc[\"choice1\"]), convert_choice(doc[\"choice2\"])]\n",
|
282 |
+
"description": "",
|
283 |
+
"target_delimiter": " ",
|
284 |
+
"fewshot_delimiter": "\n\n",
|
285 |
+
"metric_list": [
|
286 |
+
{
|
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
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1 |
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{
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"results": {
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},
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106 |
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lm-eval-output/rwkv-x-dev/1_3-C0-PREPRERUN-rwkv-30/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json
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93 |
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{
|
94 |
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95 |
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96 |
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97 |
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98 |
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99 |
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100 |
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101 |
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102 |
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103 |
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104 |
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105 |
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}
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106 |
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107 |
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108 |
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112 |
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117 |
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118 |
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119 |
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121 |
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122 |
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64
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124 |
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],
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130 |
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132 |
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}
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