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{
  "results": {
    "exams_ar": {
      "alias": "exams_ar",
      "acc,none": 0.4506517690875233,
      "acc_stderr,none": 0.021491266540407467,
      "acc_norm,none": 0.4506517690875233,
      "acc_norm_stderr,none": 0.021491266540407467
    }
  },
  "group_subtasks": {
    "exams_ar": []
  },
  "configs": {
    "exams_ar": {
      "task": "exams_ar",
      "tag": [
        "multiple_choice"
      ],
      "dataset_path": "lm_eval/tasks/exams_ar",
      "dataset_name": "exams_ar",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "validation_split": "validation",
      "test_split": "test",
      "fewshot_split": "validation",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n\n    def _process_docs(doc):\n        def format_example(doc, keys):\n            \"\"\"\n            <prompt>\n            \u0633\u0624\u0627\u0644:\n            A. <choice1>\n            B. <choice2>\n            C. <choice3>\n            D. <choice4>\n            \u0627\u062c\u0627\u0628\u0629:\n            \"\"\"\n            \n            question =  doc[\"question\"].strip()\n            \n            choices = \"\".join(\n                [f\"{key}. {choice}\\n\" for key, choice in zip(keys,  doc[\"choices\"])]\n            )\n            prompt = f\"\u0627\u0644\u0633\u0624\u0627\u0644: {question}\\n{choices} \\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:\"\n            return prompt\n\n        def _format_subject(subject):\n            arabic_words = subtasks_ar[subtasks.index(subject)]\n            return arabic_words\n\n        keys = [\"A\", \"B\", \"C\", \"D\"]\n    \n        subject = doc['id'].split(\"-\")[0]\n        description = f\"\ufed2\ufef4\ufee3\ufe8d \ufef2\ufee0\ufef3 \ufe84\ufeb4\ufe8c\ufedf\ufe93 \ufe8d\ufefc\ufea8\ufe98\ufef3\ufe8d\ufead \ufee2\ufee7 \ufee2\ufe98\ufecb\ufea9\ufea9 (\ufee2\ufecb \ufe8d\ufefa\ufe9f\ufe8e\ufe91\ufe8e\ufe97) \ufea1\ufeee\ufedf {_format_subject(subject)} \\n\" #\ufee2\ufee7 \ufed2\ufec0\ufee0\ufedb \ufe8e\ufea8\ufe97\ufead \ufe88\ufe9f\ufe8e\ufe91\ufe93 \ufeed\ufe8e\ufea3\ufea9\ufe93 \ufee2\ufee7 \ufe90\ufef4\ufee7 'A\u060c B\u060c C\u060c D' \ufea9\ufeee\ufee7 \ufeb5\ufeae\ufea3\\n\"\n\n        out_doc = {\n            \"idx\": doc[\"idx\"],\n            \"id\": doc[\"id\"],\n            'dsecription': description,\n            \"query\":  format_example(doc, keys), # \"Question: \" + doc[\"question\"]['stem'] + \"\\nAnswer:\",\n            \"choices\": keys,\n            \"gold\": [\"A\", \"B\", \"C\", \"D\"].index(doc[\"label\"]),\n        }\n        return out_doc\n\n    return dataset.map(_process_docs)\n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "description",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 5,
      "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": "query",
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "exams_ar": 1.0
  },
  "n-shot": {
    "exams_ar": 5
  },
  "higher_is_better": {
    "exams_ar": {
      "acc": true,
      "acc_norm": true
    }
  },
  "n-samples": {
    "exams_ar": {
      "original": 537,
      "effective": 537
    }
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=inceptionai/jais-family-13b-chat,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
    "model_num_parameters": 13027571240,
    "model_dtype": "torch.float32",
    "model_revision": "main",
    "model_sha": "0ef8b4f80429609890816d912b331d3b95864707",
    "batch_size": "auto",
    "batch_sizes": [
      8
    ],
    "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": "150ae04f",
  "date": 1737023418.5168922,
  "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: 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 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\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:                      48 bits physical, 48 bits virtual\nByte Order:                         Little Endian\nCPU(s):                             48\nOn-line CPU(s) list:                0-47\nVendor ID:                          AuthenticAMD\nModel name:                         AMD EPYC 7V13 64-Core Processor\nCPU family:                         25\nModel:                              1\nThread(s) per core:                 1\nCore(s) per socket:                 48\nSocket(s):                          1\nStepping:                           1\nBogoMIPS:                           4890.87\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 rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor:                  Microsoft\nVirtualization type:                full\nL1d cache:                          1.5 MiB (48 instances)\nL1i cache:                          1.5 MiB (48 instances)\nL2 cache:                           24 MiB (48 instances)\nL3 cache:                           192 MiB (6 instances)\nNUMA node(s):                       2\nNUMA node0 CPU(s):                  0-23\nNUMA node1 CPU(s):                  24-47\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:             Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass:    Vulnerable\nVulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds:                Not affected\nVulnerability Tsx async abort:      Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
  "transformers_version": "4.48.0",
  "upper_git_hash": "2e5cd5395faf76fea1afc96dd0f7161a9d3aa145",
  "tokenizer_pad_token": [
    "<|endoftext|>",
    "0"
  ],
  "tokenizer_eos_token": [
    "<|endoftext|>",
    "0"
  ],
  "tokenizer_bos_token": [
    "<|endoftext|>",
    "0"
  ],
  "eot_token_id": 0,
  "max_length": 2048,
  "task_hashes": {},
  "model_source": "hf",
  "model_name": "inceptionai/jais-family-13b-chat",
  "model_name_sanitized": "inceptionai__jais-family-13b-chat",
  "system_instruction": null,
  "system_instruction_sha": null,
  "fewshot_as_multiturn": false,
  "chat_template": null,
  "chat_template_sha": null,
  "start_time": 3042.082462715,
  "end_time": 4392.50396786,
  "total_evaluation_time_seconds": "1350.4215051449996"
}