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{
"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"
} |