| { | |
| "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" | |
| } |