Adding samples results for afrimgsm_direct_xho to CohereForAI/aya-23-8B

CohereForAI__aya-23-8B/results_2024-10-01T03-01-55.801880.json

@@ -0,0 +1,536 @@
+{
+  "results": {
+    "afrimgsm_direct_xho": {
+      "alias": "afrimgsm_direct_xho",
+      "exact_match,remove_whitespace": 0.0,
+      "exact_match_stderr,remove_whitespace": 0.0,
+      "exact_match,flexible-extract": 0.008,
+      "exact_match_stderr,flexible-extract": 0.005645483676690173
+    },
+    "afrimgsm_direct_zul": {
+      "alias": "afrimgsm_direct_zul",
+      "exact_match,remove_whitespace": 0.0,
+      "exact_match_stderr,remove_whitespace": 0.0,
+      "exact_match,flexible-extract": 0.02,
+      "exact_match_stderr,flexible-extract": 0.008872139507342683
+    },
+    "afrimmlu_direct_xho": {
+      "alias": "afrimmlu_direct_xho",
+      "acc,none": 0.228,
+      "acc_stderr,none": 0.018781306529363204,
+      "f1,none": 0.22499762903231807,
+      "f1_stderr,none": "N/A"
+    },
+    "afrimmlu_direct_zul": {
+      "alias": "afrimmlu_direct_zul",
+      "acc,none": 0.25,
+      "acc_stderr,none": 0.019384310743640384,
+      "f1,none": 0.2522065219763795,
+      "f1_stderr,none": "N/A"
+    },
+    "afrixnli_en_direct_xho": {
+      "alias": "afrixnli_en_direct_xho",
+      "acc,none": 0.335,
+      "acc_stderr,none": 0.019285007627653686,
+      "f1,none": 0.27778903523584375,
+      "f1_stderr,none": "N/A"
+    },
+    "afrixnli_en_direct_zul": {
+      "alias": "afrixnli_en_direct_zul",
+      "acc,none": 0.33166666666666667,
+      "acc_stderr,none": 0.019236854622688142,
+      "f1,none": 0.26541415513303723,
+      "f1_stderr,none": "N/A"
+    }
+  },
+  "group_subtasks": {
+    "afrimgsm_direct_xho": [],
+    "afrimgsm_direct_zul": [],
+    "afrimmlu_direct_xho": [],
+    "afrimmlu_direct_zul": [],
+    "afrixnli_en_direct_xho": [],
+    "afrixnli_en_direct_zul": []
+  },
+  "configs": {
+    "afrimgsm_direct_xho": {
+      "task": "afrimgsm_direct_xho",
+      "tag": [
+        "afrimgsm",
+        "afrimgsm_direct"
+      ],
+      "group": [
+        "afrimgsm",
+        "afrimgsm_direct"
+      ],
+      "dataset_path": "masakhane/afrimgsm",
+      "dataset_name": "xho",
+      "test_split": "test",
+      "doc_to_text": "{% if answer is not none %}{{question+\"\\nAnswer:\"}}{% else %}{{\"Question: \"+question+\"\\nAnswer:\"}}{% endif %}",
+      "doc_to_target": "{% if answer is not none %}{{answer[21:]}}{% else %}{{answer_number|string}}{% endif %}",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "exact_match",
+          "aggregation": "mean",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true
+        }
+      ],
+      "output_type": "generate_until",
+      "generation_kwargs": {
+        "do_sample": false,
+        "until": [
+          "Question:",
+          "</s>",
+          "<|im_end|>"
+        ]
+      },
+      "repeats": 1,
+      "filter_list": [
+        {
+          "name": "remove_whitespace",
+          "filter": [
+            {
+              "function": "remove_whitespace"
+            },
+            {
+              "function": "take_first"
+            }
+          ]
+        },
+        {
+          "filter": [
+            {
+              "function": "regex",
+              "group_select": -1,
+              "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)"
+            },
+            {
+              "function": "take_first"
+            }
+          ],
+          "name": "flexible-extract"
+        }
+      ],
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 2.0
+      }
+    },
+    "afrimgsm_direct_zul": {
+      "task": "afrimgsm_direct_zul",
+      "tag": [
+        "afrimgsm",
+        "afrimgsm_direct"
+      ],
+      "group": [
+        "afrimgsm",
+        "afrimgsm_direct"
+      ],
+      "dataset_path": "masakhane/afrimgsm",
+      "dataset_name": "zul",
+      "test_split": "test",
+      "doc_to_text": "{% if answer is not none %}{{question+\"\\nAnswer:\"}}{% else %}{{\"Question: \"+question+\"\\nAnswer:\"}}{% endif %}",
+      "doc_to_target": "{% if answer is not none %}{{answer[21:]}}{% else %}{{answer_number|string}}{% endif %}",
+      "description": "",
+      "target_delimiter": "",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "exact_match",
+          "aggregation": "mean",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true
+        }
+      ],
+      "output_type": "generate_until",
+      "generation_kwargs": {
+        "do_sample": false,
+        "until": [
+          "Question:",
+          "</s>",
+          "<|im_end|>"
+        ]
+      },
+      "repeats": 1,
+      "filter_list": [
+        {
+          "name": "remove_whitespace",
+          "filter": [
+            {
+              "function": "remove_whitespace"
+            },
+            {
+              "function": "take_first"
+            }
+          ]
+        },
+        {
+          "filter": [
+            {
+              "function": "regex",
+              "group_select": -1,
+              "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)"
+            },
+            {
+              "function": "take_first"
+            }
+          ],
+          "name": "flexible-extract"
+        }
+      ],
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 2.0
+      }
+    },
+    "afrimmlu_direct_xho": {
+      "task": "afrimmlu_direct_xho",
+      "tag": [
+        "afrimmlu",
+        "afrimmlu_direct"
+      ],
+      "group": [
+        "afrimmlu",
+        "afrimmlu_direct"
+      ],
+      "dataset_path": "masakhane/afrimmlu",
+      "dataset_name": "xho",
+      "validation_split": "validation",
+      "test_split": "test",
+      "fewshot_split": "validation",
+      "doc_to_text": "def doc_to_text(doc):\n    output = \"\"\"You are a highly knowledgeable and intelligent artificial intelligence\n                model answers multiple-choice questions about {subject}\n\n                Question: {question}\n\n                Choices:\n                        A: {choice1}\n                        B: {choice2}\n                        C: {choice3}\n                        D: {choice4}\n\n                Answer:  \"\"\"\n\n    choices = eval(doc[\"choices\"])\n    text = output.format(\n        subject=doc[\"subject\"],\n        question=doc[\"question\"],\n        choice1=choices[0],\n        choice2=choices[1],\n        choice3=choices[2],\n        choice4=choices[3],\n    )\n    return text\n",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": "def doc_to_choice(doc):\n    choices = eval(doc[\"choices\"])\n    return choices\n",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "f1",
+          "aggregation": "def weighted_f1_score(items):\n    from sklearn.metrics import f1_score\n\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average=\"weighted\")\n    return fscore\n",
+          "average": "weighted",
+          "hf_evaluate": true,
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true,
+          "regexes_to_ignore": [
+            ",",
+            "\\$"
+          ]
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true,
+          "regexes_to_ignore": [
+            ",",
+            "\\$"
+          ]
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "afrimmlu_direct_zul": {
+      "task": "afrimmlu_direct_zul",
+      "tag": [
+        "afrimmlu",
+        "afrimmlu_direct"
+      ],
+      "group": [
+        "afrimmlu",
+        "afrimmlu_direct"
+      ],
+      "dataset_path": "masakhane/afrimmlu",
+      "dataset_name": "zul",
+      "validation_split": "validation",
+      "test_split": "test",
+      "fewshot_split": "validation",
+      "doc_to_text": "def doc_to_text(doc):\n    output = \"\"\"You are a highly knowledgeable and intelligent artificial intelligence\n                model answers multiple-choice questions about {subject}\n\n                Question: {question}\n\n                Choices:\n                        A: {choice1}\n                        B: {choice2}\n                        C: {choice3}\n                        D: {choice4}\n\n                Answer:  \"\"\"\n\n    choices = eval(doc[\"choices\"])\n    text = output.format(\n        subject=doc[\"subject\"],\n        question=doc[\"question\"],\n        choice1=choices[0],\n        choice2=choices[1],\n        choice3=choices[2],\n        choice4=choices[3],\n    )\n    return text\n",
+      "doc_to_target": "{{['A', 'B', 'C', 'D'].index(answer)}}",
+      "doc_to_choice": "def doc_to_choice(doc):\n    choices = eval(doc[\"choices\"])\n    return choices\n",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "f1",
+          "aggregation": "def weighted_f1_score(items):\n    from sklearn.metrics import f1_score\n\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average=\"weighted\")\n    return fscore\n",
+          "average": "weighted",
+          "hf_evaluate": true,
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true,
+          "regexes_to_ignore": [
+            ",",
+            "\\$"
+          ]
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true,
+          "regexes_to_ignore": [
+            ",",
+            "\\$"
+          ]
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "afrixnli_en_direct_xho": {
+      "task": "afrixnli_en_direct_xho",
+      "tag": [
+        "afrixnli",
+        "afrixnli_en_direct"
+      ],
+      "group": [
+        "afrixnli",
+        "afrixnli_en_direct"
+      ],
+      "dataset_path": "masakhane/afrixnli",
+      "dataset_name": "xho",
+      "validation_split": "validation",
+      "test_split": "test",
+      "fewshot_split": "validation",
+      "doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
+      "doc_to_target": "def doc_to_target(doc):\n    replacements = {0: \"True\", 1: \"Neither\", 2: \"False\"}\n    return replacements[doc[\"label\"]]\n",
+      "doc_to_choice": [
+        "True",
+        "Neither",
+        "False"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "f1",
+          "aggregation": "def weighted_f1_score(items):\n    from sklearn.metrics import f1_score\n\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average=\"weighted\")\n    return fscore\n",
+          "average": "weighted",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "premise",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "afrixnli_en_direct_zul": {
+      "task": "afrixnli_en_direct_zul",
+      "tag": [
+        "afrixnli",
+        "afrixnli_en_direct"
+      ],
+      "group": [
+        "afrixnli",
+        "afrixnli_en_direct"
+      ],
+      "dataset_path": "masakhane/afrixnli",
+      "dataset_name": "zul",
+      "validation_split": "validation",
+      "test_split": "test",
+      "fewshot_split": "validation",
+      "doc_to_text": "{{premise}}\nQuestion: {{hypothesis}} True, False, or Neither?\nAnswer:",
+      "doc_to_target": "def doc_to_target(doc):\n    replacements = {0: \"True\", 1: \"Neither\", 2: \"False\"}\n    return replacements[doc[\"label\"]]\n",
+      "doc_to_choice": [
+        "True",
+        "Neither",
+        "False"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "f1",
+          "aggregation": "def weighted_f1_score(items):\n    from sklearn.metrics import f1_score\n\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average=\"weighted\")\n    return fscore\n",
+          "average": "weighted",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true,
+          "ignore_case": true,
+          "ignore_punctuation": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "premise",
+      "metadata": {
+        "version": 1.0
+      }
+    }
+  },
+  "versions": {
+    "afrimgsm_direct_xho": 2.0,
+    "afrimgsm_direct_zul": 2.0,
+    "afrimmlu_direct_xho": 1.0,
+    "afrimmlu_direct_zul": 1.0,
+    "afrixnli_en_direct_xho": 1.0,
+    "afrixnli_en_direct_zul": 1.0
+  },
+  "n-shot": {
+    "afrimgsm_direct_xho": 0,
+    "afrimgsm_direct_zul": 0,
+    "afrimmlu_direct_xho": 0,
+    "afrimmlu_direct_zul": 0,
+    "afrixnli_en_direct_xho": 0,
+    "afrixnli_en_direct_zul": 0
+  },
+  "higher_is_better": {
+    "afrimgsm_direct_xho": {
+      "exact_match": true
+    },
+    "afrimgsm_direct_zul": {
+      "exact_match": true
+    },
+    "afrimmlu_direct_xho": {
+      "f1": true,
+      "acc": true
+    },
+    "afrimmlu_direct_zul": {
+      "f1": true,
+      "acc": true
+    },
+    "afrixnli_en_direct_xho": {
+      "f1": true,
+      "acc": true
+    },
+    "afrixnli_en_direct_zul": {
+      "f1": true,
+      "acc": true
+    }
+  },
+  "n-samples": {
+    "afrixnli_en_direct_zul": {
+      "original": 600,
+      "effective": 600
+    },
+    "afrixnli_en_direct_xho": {
+      "original": 600,
+      "effective": 600
+    },
+    "afrimmlu_direct_zul": {
+      "original": 500,
+      "effective": 500
+    },
+    "afrimmlu_direct_xho": {
+      "original": 500,
+      "effective": 500
+    },
+    "afrimgsm_direct_zul": {
+      "original": 250,
+      "effective": 250
+    },
+    "afrimgsm_direct_xho": {
+      "original": 250,
+      "effective": 250
+    }
+  },
+  "config": {
+    "model": "hf",
+    "model_args": "pretrained=CohereForAI/aya-23-8B",
+    "model_num_parameters": 8028033024,
+    "model_dtype": "torch.float16",
+    "model_revision": "main",
+    "model_sha": "5f6dc63f4b4071cff399af26e576f4540554236d",
+    "batch_size": "auto:4",
+    "batch_sizes": [
+      8,
+      32,
+      64,
+      64
+    ],
+    "device": null,
+    "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": "15ffb0d",
+  "date": 1727750420.4119575,
+  "pretty_env_info": "PyTorch version: 2.4.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.140\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA RTX A6000\nGPU 1: NVIDIA RTX A6000\n\nNvidia driver version: 535.129.03\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\nAddress sizes:                      40 bits physical, 48 bits virtual\nByte Order:                         Little Endian\nCPU(s):                             28\nOn-line CPU(s) list:                0-27\nVendor ID:                          AuthenticAMD\nModel name:                         AMD EPYC-Rome Processor\nCPU family:                         23\nModel:                              49\nThread(s) per core:                 1\nCore(s) per socket:                 1\nSocket(s):                          28\nStepping:                           0\nBogoMIPS:                           4999.23\nFlags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr wbnoinvd arat npt nrip_save umip rdpid arch_capabilities\nVirtualization:                     AMD-V\nL1d cache:                          896 KiB (28 instances)\nL1i cache:                          896 KiB (28 instances)\nL2 cache:                           14 MiB (28 instances)\nL3 cache:                           448 MiB (28 instances)\nNUMA node(s):                       1\nNUMA node0 CPU(s):                  0-27\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit:        Not affected\nVulnerability L1tf:                 Not affected\nVulnerability Mds:                  Not affected\nVulnerability Meltdown:             Not affected\nVulnerability Mmio stale data:      Not affected\nVulnerability Retbleed:             Mitigation; untrained return thunk; SMT disabled\nVulnerability Spec rstack overflow: Mitigation; SMT disabled\nVulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds:                Not affected\nVulnerability Tsx async abort:      Not affected\n\nVersions of relevant libraries:\n[pip3] flake8==4.0.1\n[pip3] numpy==1.25.2\n[pip3] torch==2.4.1+cu121\n[pip3] torchaudio==2.4.1+cu121\n[pip3] torchvision==0.19.1+cu121\n[pip3] triton==3.0.0\n[conda] Could not collect",
+  "transformers_version": "4.45.1",
+  "upper_git_hash": null,
+  "tokenizer_pad_token": [
+    "<PAD>",
+    "0"
+  ],
+  "tokenizer_eos_token": [
+    "<|END_OF_TURN_TOKEN|>",
+    "255001"
+  ],
+  "tokenizer_bos_token": [
+    "<BOS_TOKEN>",
+    "5"
+  ],
+  "eot_token_id": 255001,
+  "max_length": 8192,
+  "task_hashes": {
+    "afrixnli_en_direct_zul": "011b872bfe35d1ead7694b59c7023bc079845f39fb417791e0c2e19e49c8ce6e",
+    "afrixnli_en_direct_xho": "812b77def909fef6b7ec5373d4bfa09d6a6f5b2971b0bcad3e81a1f94d743411",
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+  "model_name": "CohereForAI/aya-23-8B",
+  "model_name_sanitized": "CohereForAI__aya-23-8B",
+  "system_instruction": null,
+  "system_instruction_sha": null,
+  "fewshot_as_multiturn": false,
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+  "start_time": 6430.178763367,
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+}