| { | |
| "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 | |
| } |