{
  "results": {
    "cb": {
      "acc,none": 0.25,
      "acc_stderr,none": 0.058387420812114225,
      "f1,none": 0.2407230196703881,
      "f1_stderr,none": "N/A",
      "alias": "cb"
    }
  },
  "configs": {
    "cb": {
      "task": "cb",
      "group": [
        "super-glue-lm-eval-v1"
      ],
      "dataset_path": "super_glue",
      "dataset_name": "cb",
      "training_split": "train",
      "validation_split": "validation",
      "doc_to_text": "{{premise}}\nQuestion: {{hypothesis}}. True, False, or Neither?\nAnswer:",
      "doc_to_target": "label",
      "doc_to_choice": [
        "True",
        "False",
        "Neither"
      ],
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "metric_list": [
        {
          "metric": "acc"
        },
        {
          "metric": "f1",
          "aggregation": "def cb_multi_fi(items):\n    preds, golds = zip(*items)\n    preds = np.array(preds)\n    golds = np.array(golds)\n    f11 = sklearn.metrics.f1_score(y_true=golds == 0, y_pred=preds == 0)\n    f12 = sklearn.metrics.f1_score(y_true=golds == 1, y_pred=preds == 1)\n    f13 = sklearn.metrics.f1_score(y_true=golds == 2, y_pred=preds == 2)\n    avg_f1 = np.mean([f11, f12, f13])\n    return avg_f1\n"
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "cb": 1.0
  },
  "n-shot": {
    "cb": 0
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=TinyLlama/TinyLlama-1.1B-Chat-v1.0,dtype=bfloat16,trust_remote_code=True",
    "batch_size": "auto",
    "batch_sizes": [
      64
    ],
    "device": null,
    "use_cache": null,
    "limit": null,
    "bootstrap_iters": 100000,
    "gen_kwargs": null
  },
  "git_hash": "62513ca"
}