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{
"results": {
"ifeval": {
"prompt_level_strict_acc,none": 0.0036968576709796672,
"prompt_level_strict_acc_stderr,none": 0.0026116515685375786,
"inst_level_strict_acc,none": 0.004796163069544364,
"inst_level_strict_acc_stderr,none": "N/A",
"prompt_level_loose_acc,none": 0.0036968576709796672,
"prompt_level_loose_acc_stderr,none": 0.0026116515685375786,
"inst_level_loose_acc,none": 0.005995203836930456,
"inst_level_loose_acc_stderr,none": "N/A",
"alias": "ifeval"
}
},
"configs": {
"ifeval": {
"task": "ifeval",
"dataset_path": "wis-k/instruction-following-eval",
"test_split": "train",
"doc_to_text": "prompt",
"doc_to_target": 0,
"process_results": "def process_results(doc, results):\n eval_logger.warning(\n \"This task is meant for chat-finetuned models, and may not give meaningful results for models other than `openai` or `anthropic` if `doc_to_text` in its YAML is not wrapped in the appropriate chat template string. This warning will be removed when chat templating support is added natively to local models\"\n )\n\n inp = InputExample(\n key=doc[\"key\"],\n instruction_id_list=doc[\"instruction_id_list\"],\n prompt=doc[\"prompt\"],\n kwargs=doc[\"kwargs\"],\n )\n response = results[0]\n\n out_strict = test_instruction_following_strict(inp, response)\n out_loose = test_instruction_following_loose(inp, response)\n\n return {\n \"prompt_level_strict_acc\": out_strict.follow_all_instructions,\n \"inst_level_strict_acc\": out_strict.follow_instruction_list,\n \"prompt_level_loose_acc\": out_loose.follow_all_instructions,\n \"inst_level_loose_acc\": out_loose.follow_instruction_list,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "prompt_level_strict_acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "inst_level_strict_acc",
"aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n",
"higher_is_better": true
},
{
"metric": "prompt_level_loose_acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "inst_level_loose_acc",
"aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [],
"do_sample": false,
"temperature": 0.0,
"max_gen_toks": 1280
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 2.0
}
}
},
"versions": {
"ifeval": 2.0
},
"n-shot": {
"ifeval": 0
},
"config": {
"model": "hf",
"model_args": "pretrained=Qwen/Qwen1.5-0.5B-Chat,revision=main,dtype=bfloat16",
"batch_size": "auto",
"batch_sizes": [],
"device": null,
"use_cache": null,
"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null
},
"git_hash": "2d2e67f"
} |