{
"results": {
"exams_ar": {
"alias": "exams_ar",
"acc,none": 0.515828677839851,
"acc_stderr,none": 0.021585885942816244,
"acc_norm,none": 0.515828677839851,
"acc_norm_stderr,none": 0.021585885942816244
}
},
"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
},
"test_split": "test",
"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": 0.0
}
}
},
"versions": {
"exams_ar": 0.0
},
"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": "vllm",
"model_args": "pretrained=/tmp/7b-alpha-v1.27.2.25,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.8",
"batch_size": 1,
"batch_sizes": [],
"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": "8e1bd48d",
"date": 1735662207.0830526,
"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": "4.47.1",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 4096,
"task_hashes": {
"exams_ar": "b1561abd56354d570ac16bf64163b0ee8dc6c507234b05f678576b09c26c644a"
},
"model_source": "vllm",
"model_name": "/tmp/7b-alpha-v1.27.2.25",
"model_name_sanitized": "__tmp__7b-alpha-v1.27.2.25",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 2867.397536365,
"end_time": 2948.510496752,
"total_evaluation_time_seconds": "81.11296038699993"
}