|
{ |
|
"results": { |
|
"cb": { |
|
"acc,none": 0.42857142857142855, |
|
"acc_stderr,none": 0.06672848092813058, |
|
"f1,none": 0.21956970232832304, |
|
"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=bigscience/bloom-7b1,dtype=bfloat16,trust_remote_code=True", |
|
"batch_size": "auto", |
|
"batch_sizes": [ |
|
16 |
|
], |
|
"device": null, |
|
"use_cache": null, |
|
"limit": null, |
|
"bootstrap_iters": 100000, |
|
"gen_kwargs": null |
|
}, |
|
"git_hash": "62513ca" |
|
} |