{
  "results": {
    "logiqa": {
      "acc": 0.55,
      "acc_stderr": 0.11413288653790232,
      "acc_norm": 0.45,
      "acc_norm_stderr": 0.11413288653790232,
      "alias": "logiqa"
    },
    "anli_r1": {
      "acc": 0.4,
      "acc_stderr": 0.11239029738980327,
      "alias": "anli_r1"
    }
  },
  "group_subtasks": {
    "anli_r1": [],
    "logiqa": []
  },
  "configs": {
    "anli_r1": {
      "task": "anli_r1",
      "group": [
        "anli"
      ],
      "dataset_path": "anli",
      "training_split": "train_r1",
      "validation_split": "dev_r1",
      "test_split": "test_r1",
      "doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
      "doc_to_target": "{{['True', 'Neither', 'False'][label]}}",
      "doc_to_choice": [
        "True",
        "Neither",
        "False"
      ],
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "premise",
      "metadata": {
        "version": 1.0
      }
    },
    "logiqa": {
      "task": "logiqa",
      "dataset_path": "EleutherAI/logiqa",
      "dataset_name": "logiqa",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "validation_split": "validation",
      "test_split": "test",
      "doc_to_text": "def doc_to_text(doc) -> str:\n    \"\"\"\n    Passage: <passage>\n    Question: <question>\n    Choices:\n    A. <choice1>\n    B. <choice2>\n    C. <choice3>\n    D. <choice4>\n    Answer:\n    \"\"\"\n    choices = [\"a\", \"b\", \"c\", \"d\"]\n    prompt = \"Passage: \" + doc[\"context\"] + \"\\n\"\n    prompt += \"Question: \" + doc[\"question\"] + \"\\nChoices:\\n\"\n    for choice, option in zip(choices, doc[\"options\"]):\n        prompt += f\"{choice.upper()}. {option}\\n\"\n    prompt += \"Answer:\"\n    return prompt\n",
      "doc_to_target": "def doc_to_target(doc) -> int:\n    choices = [\"a\", \"b\", \"c\", \"d\"]\n    return choices.index(doc[\"label\"].strip())\n",
      "doc_to_choice": "{{options}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "{{context}}",
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "anli_r1": 1.0,
    "logiqa": 1.0
  },
  "n-shot": {
    "anli_r1": 0,
    "logiqa": 0
  },
  "higher_is_better": {
    "anli_r1": {
      "acc": true
    },
    "logiqa": {
      "acc": true,
      "acc_norm": true
    }
  },
  "n-samples": {
    "logiqa": {
      "original": 651,
      "effective": 20
    },
    "anli_r1": {
      "original": 1000,
      "effective": 20
    }
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=microsoft/Phi-3.5-mini-instruct,revision=main,dtype=bfloat16",
    "model_num_parameters": 3821079552,
    "model_dtype": "bfloat16",
    "model_revision": "main",
    "model_sha": "main",
    "batch_size": 64,
    "batch_sizes": [
      64
    ],
    "device": "cpu",
    "use_cache": null,
    "limit": 20,
    "bootstrap_iters": 100000,
    "gen_kwargs": null,
    "random_seed": 0,
    "numpy_seed": 1234,
    "torch_seed": 1234,
    "fewshot_seed": 1234,
    "model_name": "microsoft/Phi-3.5-mini-instruct"
  },
  "git_hash": "08eb026",
  "date": 1726788426.6822708,
  "pretty_env_info": "CUDA must not be initialized in the main process on Spaces with Stateless GPU environment.\nYou can look at this Stacktrace to find out which part of your code triggered a CUDA init",
  "transformers_version": "4.44.2",
  "upper_git_hash": null,
  "eot_token_id": 32000,
  "max_length": 131072
}