| { | |
| "results": { | |
| "gat": { | |
| "acc,none": 0.33400225761946567, | |
| "acc_stderr,none": 0.003661710170227351, | |
| "alias": "gat" | |
| }, | |
| "gat_algebra": { | |
| "alias": " - gat_algebra", | |
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| "acc_stderr,none": 0.008543671687979955 | |
| }, | |
| "gat_analogy": { | |
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| "acc_stderr,none": 0.008690892996182613 | |
| }, | |
| "gat_arithmetic": { | |
| "alias": " - gat_arithmetic", | |
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| "acc_stderr,none": 0.0086606873206029 | |
| }, | |
| "gat_association": { | |
| "alias": " - gat_association", | |
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| "acc_stderr,none": 0.01514355305056311 | |
| }, | |
| "gat_comparisons": { | |
| "alias": " - gat_comparisons", | |
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| "acc_stderr,none": 0.010750488821112222 | |
| }, | |
| "gat_completion": { | |
| "alias": " - gat_completion", | |
| "acc,none": 0.4388429752066116, | |
| "acc_stderr,none": 0.014271960233219975 | |
| }, | |
| "gat_contextual": { | |
| "alias": " - gat_contextual", | |
| "acc,none": 0.36809815950920244, | |
| "acc_stderr,none": 0.013360860368019332 | |
| }, | |
| "gat_geometry": { | |
| "alias": " - gat_geometry", | |
| "acc,none": 0.26301369863013696, | |
| "acc_stderr,none": 0.023076407542407414 | |
| }, | |
| "gat_reading": { | |
| "alias": " - gat_reading", | |
| "acc,none": 0.4888468809073724, | |
| "acc_stderr,none": 0.009721453573508959 | |
| } | |
| }, | |
| "groups": { | |
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| "acc_stderr,none": 0.003661710170227351, | |
| "alias": "gat" | |
| } | |
| }, | |
| "group_subtasks": { | |
| "gat": [ | |
| "gat_analogy", | |
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| "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", | |
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| "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}}", | |
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| } | |
| }, | |
| "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}}", | |
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| "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}}", | |
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| }, | |
| "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}}", | |
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| "dataset_name": "comparisons", | |
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| "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}}", | |
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| "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", | |
| "dataset_name": "completion", | |
| "dataset_kwargs": { | |
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| }, | |
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| "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}}", | |
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| }, | |
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| "task": "gat_contextual", | |
| "dataset_path": "lm_eval/tasks/gat/gat_data/gat.py", | |
| "dataset_name": "contextual", | |
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| "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}}", | |
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| "model": "vllm", | |
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| "git_hash": "8e1bd48d", | |
| "date": 1735750231.7451465, | |
| "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": [ | |
| "<unk>", | |
| "0" | |
| ], | |
| "tokenizer_eos_token": [ | |
| "</s>", | |
| "2" | |
| ], | |
| "tokenizer_bos_token": [ | |
| "<s>", | |
| "1" | |
| ], | |
| "eot_token_id": 2, | |
| "max_length": 4096, | |
| "task_hashes": {}, | |
| "model_source": "vllm", | |
| "model_name": "inceptionai/jais-adapted-13b-chat", | |
| "model_name_sanitized": "inceptionai__jais-adapted-13b-chat", | |
| "system_instruction": null, | |
| "system_instruction_sha": null, | |
| "fewshot_as_multiturn": false, | |
| "chat_template": null, | |
| "chat_template_sha": null, | |
| "start_time": 4550.419244017, | |
| "end_time": 5184.983570193, | |
| "total_evaluation_time_seconds": "634.5643261759997" | |
| } |