{ "results": { "gat": { "acc,none": 0.31719553493039004, "acc_stderr,none": 0.0036673800264634595, "alias": "gat" }, "gat_algebra": { "alias": " - gat_algebra", "acc,none": 0.3484230055658627, "acc_stderr,none": 0.009179890200725068 }, "gat_analogy": { "alias": " - gat_analogy", "acc,none": 0.2837887067395264, "acc_stderr,none": 0.008606490293380746 }, "gat_arithmetic": { "alias": " - gat_arithmetic", "acc,none": 0.25653294074346705, "acc_stderr,none": 0.008379875233626235 }, "gat_association": { "alias": " - gat_association", "acc,none": 0.39617224880382773, "acc_stderr,none": 0.015137296245565172 }, "gat_comparisons": { "alias": " - gat_comparisons", "acc,none": 0.28770491803278686, "acc_stderr,none": 0.012965872987333184 }, "gat_completion": { "alias": " - gat_completion", "acc,none": 0.3371900826446281, "acc_stderr,none": 0.013596237583820002 }, "gat_contextual": { "alias": " - gat_contextual", "acc,none": 0.27223926380368096, "acc_stderr,none": 0.012330976880474218 }, "gat_geometry": { "alias": " - gat_geometry", "acc,none": 0.3287671232876712, "acc_stderr,none": 0.02462238450062787 }, "gat_reading": { "alias": " - gat_reading", "acc,none": 0.3761814744801512, "acc_stderr,none": 0.009421002319111672 } }, "groups": { "gat": { "acc,none": 0.31719553493039004, "acc_stderr,none": 0.0036673800264634595, "alias": "gat" } }, "group_subtasks": { "gat": [ "gat_analogy", "gat_association", "gat_completion", "gat_reading", "gat_algebra", "gat_arithmetic", "gat_comparisons", "gat_contextual", "gat_geometry" ] }, "configs": { "gat_algebra": { "task": "gat_algebra", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "algebra", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } }, "gat_analogy": { "task": "gat_analogy", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "analogy", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } }, "gat_arithmetic": { "task": "gat_arithmetic", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "arithmetic", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } }, "gat_association": { "task": "gat_association", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "association", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } }, "gat_comparisons": { "task": "gat_comparisons", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "comparisons", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } }, "gat_completion": { "task": "gat_completion", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "completion", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } }, "gat_contextual": { "task": "gat_contextual", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "contextual", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "description": "\u0627\u0648\u062c\u062f \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0633\u064a\u0627\u0642\u064a \u0641\u064a \u0627\u0644\u0639\u0628\u0627\u0631\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0645\u0646 \u0628\u064a\u0646 \u0627\u0644\u062e\u064a\u0627\u0631\u0627\u062a:", "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": false, "metadata": { "version": 0.0 } }, "gat_geometry": { "task": "gat_geometry", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "geometry", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } }, "gat_reading": { "task": "gat_reading", "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", "dataset_name": "reading", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "fewshot_split": "validation", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n", "doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:", "doc_to_target": "{{label}}", "doc_to_choice": [ "\u0623", "\u0628", "\u062c", "\u062f" ], "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": false, "metadata": { "version": 0.0 } } }, "versions": { "gat": 0, "gat_algebra": 0.0, "gat_analogy": 0.0, "gat_arithmetic": 0.0, "gat_association": 0.0, "gat_comparisons": 0.0, "gat_completion": 0.0, "gat_contextual": 0.0, "gat_geometry": 0.0, "gat_reading": 0.0 }, "n-shot": { "gat_algebra": 0, "gat_analogy": 0, "gat_arithmetic": 0, "gat_association": 0, "gat_comparisons": 0, "gat_completion": 0, "gat_contextual": 0, "gat_geometry": 0, "gat_reading": 0 }, "higher_is_better": { "gat": { "acc": true }, "gat_algebra": { "acc": true }, "gat_analogy": { "acc": true }, "gat_arithmetic": { "acc": true }, "gat_association": { "acc": true }, "gat_comparisons": { "acc": true }, "gat_completion": { "acc": true }, "gat_contextual": { "acc": true }, "gat_geometry": { "acc": true }, "gat_reading": { "acc": true } }, "n-samples": { "gat_analogy": { "original": 2745, "effective": 2745 }, "gat_association": { "original": 1045, "effective": 1045 }, "gat_completion": { "original": 1210, "effective": 1210 }, "gat_reading": { "original": 2645, "effective": 2645 }, "gat_algebra": { "original": 2695, "effective": 2695 }, "gat_arithmetic": { "original": 2717, "effective": 2717 }, "gat_comparisons": { "original": 1220, "effective": 1220 }, "gat_contextual": { "original": 1304, "effective": 1304 }, "gat_geometry": { "original": 365, "effective": 365 } }, "config": { "model": "vllm", "model_args": "pretrained=inceptionai/jais-family-13b-chat,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.4,download_dir=/tmp", "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": 1735755270.1942198, "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": "f64fe2f2a86055aaecced603b56097fd79201711", "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": "vllm", "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": 9075.762825732, "end_time": 9718.924999701, "total_evaluation_time_seconds": "643.1621739689999" }