pythia-410m-seed1/step114000/EleutherAI__pythia-410m-seed1/results_2024-09-10T09-50-05.628850.json

{
  "results": {
    "winogender": {
      "acc,none": 0.5375,
      "acc_stderr,none": 0.011392911428362169,
      "alias": "winogender"
    },
    "winogender_all": {
      "acc,none": 0.5444444444444444,
      "acc_stderr,none": 0.0185730408419533,
      "alias": " - winogender_all"
    },
    "winogender_female": {
      "acc,none": 0.55,
      "acc_stderr,none": 0.032180193497809914,
      "alias": " - winogender_female"
    },
    "winogender_gotcha": {
      "acc,none": 0.5166666666666667,
      "acc_stderr,none": 0.03232433842302556,
      "alias": " - winogender_gotcha"
    },
    "winogender_gotcha_female": {
      "acc,none": 0.55,
      "acc_stderr,none": 0.04560517440787951,
      "alias": " - winogender_gotcha_female"
    },
    "winogender_gotcha_male": {
      "acc,none": 0.48333333333333334,
      "acc_stderr,none": 0.04580945392704764,
      "alias": " - winogender_gotcha_male"
    },
    "winogender_male": {
      "acc,none": 0.5416666666666666,
      "acc_stderr,none": 0.03222981603075176,
      "alias": " - winogender_male"
    },
    "winogender_neutral": {
      "acc,none": 0.5416666666666666,
      "acc_stderr,none": 0.03222981603075176,
      "alias": " - winogender_neutral"
    }
  },
  "groups": {
    "winogender": {
      "acc,none": 0.5375,
      "acc_stderr,none": 0.011392911428362169,
      "alias": "winogender"
    }
  },
  "group_subtasks": {
    "winogender": [
      "winogender_female",
      "winogender_all",
      "winogender_male",
      "winogender_gotcha",
      "winogender_gotcha_male",
      "winogender_neutral",
      "winogender_gotcha_female"
    ]
  },
  "configs": {
    "winogender_all": {
      "task": "winogender_all",
      "group": [
        "social_bias",
        "winogender"
      ],
      "dataset_path": "oskarvanderwal/winogender",
      "dataset_name": "all",
      "test_split": "test",
      "doc_to_text": "{{sentence}} ‘{{pronoun.capitalize()}}’ refers to the",
      "doc_to_target": "label",
      "doc_to_choice": "{{[occupation, participant]}}",
      "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": "sentence",
      "metadata": {
        "version": 1.0,
        "num_fewshot": 0
      }
    },
    "winogender_female": {
      "task": "winogender_female",
      "group": [
        "social_bias",
        "winogender"
      ],
      "dataset_path": "oskarvanderwal/winogender",
      "dataset_name": "all",
      "test_split": "test",
      "process_docs": "def filter_female(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"female\")\n",
      "doc_to_text": "{{sentence}} ‘{{pronoun.capitalize()}}’ refers to the",
      "doc_to_target": "label",
      "doc_to_choice": "{{[occupation, participant]}}",
      "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": "sentence",
      "metadata": {
        "version": 1.0,
        "num_fewshot": 0
      }
    },
    "winogender_gotcha": {
      "task": "winogender_gotcha",
      "group": [
        "social_bias",
        "winogender"
      ],
      "dataset_path": "oskarvanderwal/winogender",
      "dataset_name": "gotcha",
      "test_split": "test",
      "doc_to_text": "{{sentence}} ‘{{pronoun.capitalize()}}’ refers to the",
      "doc_to_target": "label",
      "doc_to_choice": "{{[occupation, participant]}}",
      "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": "sentence",
      "metadata": {
        "version": 1.0,
        "num_fewshot": 0
      }
    },
    "winogender_gotcha_female": {
      "task": "winogender_gotcha_female",
      "group": [
        "social_bias",
        "winogender"
      ],
      "dataset_path": "oskarvanderwal/winogender",
      "dataset_name": "gotcha",
      "test_split": "test",
      "process_docs": "def filter_female(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"female\")\n",
      "doc_to_text": "{{sentence}} ‘{{pronoun.capitalize()}}’ refers to the",
      "doc_to_target": "label",
      "doc_to_choice": "{{[occupation, participant]}}",
      "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": "sentence",
      "metadata": {
        "version": 1.0,
        "num_fewshot": 0
      }
    },
    "winogender_gotcha_male": {
      "task": "winogender_gotcha_male",
      "group": [
        "social_bias",
        "winogender"
      ],
      "dataset_path": "oskarvanderwal/winogender",
      "dataset_name": "gotcha",
      "test_split": "test",
      "process_docs": "def filter_male(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"male\")\n",
      "doc_to_text": "{{sentence}} ‘{{pronoun.capitalize()}}’ refers to the",
      "doc_to_target": "label",
      "doc_to_choice": "{{[occupation, participant]}}",
      "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": "sentence",
      "metadata": {
        "version": 1.0,
        "num_fewshot": 0
      }
    },
    "winogender_male": {
      "task": "winogender_male",
      "group": [
        "social_bias",
        "winogender"
      ],
      "dataset_path": "oskarvanderwal/winogender",
      "dataset_name": "all",
      "test_split": "test",
      "process_docs": "def filter_male(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"male\")\n",
      "doc_to_text": "{{sentence}} ‘{{pronoun.capitalize()}}’ refers to the",
      "doc_to_target": "label",
      "doc_to_choice": "{{[occupation, participant]}}",
      "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": "sentence",
      "metadata": {
        "version": 1.0,
        "num_fewshot": 0
      }
    },
    "winogender_neutral": {
      "task": "winogender_neutral",
      "group": [
        "social_bias",
        "winogender"
      ],
      "dataset_path": "oskarvanderwal/winogender",
      "dataset_name": "all",
      "test_split": "test",
      "process_docs": "def filter_neutral(dataset: datasets.Dataset) -> datasets.Dataset:\n    return filter_dataset(dataset, \"neutral\")\n",
      "doc_to_text": "{{sentence}} ‘{{pronoun.capitalize()}}’ refers to the",
      "doc_to_target": "label",
      "doc_to_choice": "{{[occupation, participant]}}",
      "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": "sentence",
      "metadata": {
        "version": 1.0,
        "num_fewshot": 0
      }
    }
  },
  "versions": {
    "winogender_all": 1.0,
    "winogender_female": 1.0,
    "winogender_gotcha": 1.0,
    "winogender_gotcha_female": 1.0,
    "winogender_gotcha_male": 1.0,
    "winogender_male": 1.0,
    "winogender_neutral": 1.0
  },
  "n-shot": {
    "winogender": 0,
    "winogender_all": 0,
    "winogender_female": 0,
    "winogender_gotcha": 0,
    "winogender_gotcha_female": 0,
    "winogender_gotcha_male": 0,
    "winogender_male": 0,
    "winogender_neutral": 0
  },
  "n-samples": {
    "winogender_female": {
      "original": 240,
      "effective": 240
    },
    "winogender_all": {
      "original": 720,
      "effective": 720
    },
    "winogender_male": {
      "original": 240,
      "effective": 240
    },
    "winogender_gotcha": {
      "original": 240,
      "effective": 240
    },
    "winogender_gotcha_male": {
      "original": 120,
      "effective": 120
    },
    "winogender_neutral": {
      "original": 240,
      "effective": 240
    },
    "winogender_gotcha_female": {
      "original": 120,
      "effective": 120
    }
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=EleutherAI/pythia-410m-seed1,revision=step114000",
    "model_num_parameters": 405334016,
    "model_dtype": "torch.float16",
    "model_revision": "step114000",
    "model_sha": "e3f197e747f179a121e894ff446f2b2a7d247b17",
    "batch_size": "128",
    "batch_sizes": [],
    "device": "cuda",
    "use_cache": null,
    "limit": null,
    "bootstrap_iters": 100000,
    "gen_kwargs": null,
    "random_seed": 0,
    "numpy_seed": 1234,
    "torch_seed": 1234,
    "fewshot_seed": 1234
  },
  "git_hash": "51a7ca9",
  "date": 1725986931.3830416,
  "pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: CentOS Linux release 7.9.2009 (Core) (x86_64)\nGCC version: (GCC) 12.1.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.17\n\nPython version: 3.9.0 (default, Oct  6 2020, 11:01:41)  [GCC 4.8.5 20150623 (Red Hat 4.8.5-36)] (64-bit runtime)\nPython platform: Linux-3.10.0-1160.119.1.el7.tuxcare.els2.x86_64-x86_64-with-glibc2.17\nIs CUDA available: True\nCUDA runtime version: 12.4.99\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: Tesla V100S-PCIE-32GB\nNvidia driver version: 550.90.07\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture:          x86_64\nCPU op-mode(s):        32-bit, 64-bit\nByte Order:            Little Endian\nCPU(s):                32\nOn-line CPU(s) list:   0-31\nThread(s) per core:    1\nCore(s) per socket:    32\nSocket(s):             1\nNUMA node(s):          2\nVendor ID:             AuthenticAMD\nCPU family:            23\nModel:                 49\nModel name:            AMD EPYC 7502P 32-Core Processor\nStepping:              0\nCPU MHz:               1500.000\nCPU max MHz:           2500.0000\nCPU min MHz:           1500.0000\nBogoMIPS:              4999.78\nVirtualization:        AMD-V\nL1d cache:             32K\nL1i cache:             32K\nL2 cache:              512K\nL3 cache:              16384K\nNUMA node0 CPU(s):     0-15\nNUMA node1 CPU(s):     16-31\nFlags:                 fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc art rep_good nopl nonstop_tsc extd_apicid aperfmperf eagerfpu pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_l2 cpb cat_l3 cdp_l3 hw_pstate sme ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip overflow_recov succor smca\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.4.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
  "transformers_version": "4.44.0",
  "upper_git_hash": null,
  "task_hashes": {},
  "model_source": "hf",
  "model_name": "EleutherAI/pythia-410m-seed1",
  "model_name_sanitized": "EleutherAI__pythia-410m-seed1",
  "start_time": 3107416.515024155,
  "end_time": 3107617.769735618,
  "total_evaluation_time_seconds": "201.2547114631161"
}