Datasets:

YanAdjeNole
/

eppc-2shot

+{
+  "results": {
+    "EppcExtraction": {
+      "alias": "EppcExtraction",
+      "evaluate_eppc,none": {
+        "code": {
+          "P": 0.1858,
+          "R": 0.3801,
+          "f1": 0.2496
+        },
+        "sub-code": {
+          "P": 0.0548,
+          "R": 0.1679,
+          "f1": 0.0826
+        },
+        "span": {
+          "P": 0.037,
+          "R": 0.444,
+          "f1": 0.0684
+        }
+      },
+      "evaluate_eppc_stderr,none": "N/A"
+    }
+  },
+  "group_subtasks": {
+    "EppcExtraction": []
+  },
+  "configs": {
+    "EppcExtraction": {
+      "task": "EppcExtraction",
+      "tag": "Eppc",
+      "dataset_path": "YanAdjeNole/EPPC_Miner_Recognition_new_test",
+      "dataset_name": "default",
+      "test_split": "test",
+      "fewshot_split": "test",
+      "doc_to_text": "{{query}}",
+      "doc_to_target": "{{answer}}",
+      "unsafe_code": false,
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 1,
+      "metric_list": [
+        {
+          "metric": "def evaluate_eppc(items):\n    \"\"\" pass the parameters\"\"\"\n   \n    return items\n",
+          "aggregation": "def evaluate_eppc_agg(items):\n    true_answer = [item[0] for item in items]\n    pred_answer = [item[1] for item in items]\n    \n\n    ## code\n    true_codes = []\n    pred_codes = []\n\n    ## sub-code\n    true_sub_codes = []\n    pred_sub_codes = []\n\n    ## span\n    true_spans = []\n    pred_spans = []\n    # for t_answer, p_answer in zip(true_answer, pred_answer):\n    #     # t_answer = ast.literal_eval(t_answer)\n    #     t_answer = safe_json_loads(t_answer)\n        \n    #     if not is_valid_format(p_answer):\n    #         p_answer = fix_structure(p_answer)\n    #     p_answer = safe_json_loads(p_answer)\n    \n    for t_answer, p_answer in zip(true_answer, pred_answer):\n        t_answer = safe_json_loads(t_answer)\n        p_answer = safe_json_loads(p_answer)\n\n        \n        code = [anno.get(\"Code\") for anno in t_answer.get(\"results\")]\n        pred_code = [pred.get(\"Code\") for pred in p_answer.get(\"results\")]\n        true_codes.append(code)\n        pred_codes.append(pred_code)\n\n        sub_code = [anno.get(\"Sub-code\") for anno in t_answer.get(\"results\")]\n        pred_sub_code = [pred.get(\"Sub-code\") for pred in p_answer.get(\"results\")]\n        true_sub_codes.append(sub_code)\n        pred_sub_codes.append(pred_sub_code)\n\n        span = [anno.get(\"Span\") for anno in t_answer.get(\"results\")]\n        extracted_span = [pred.get(\"Span\") for pred in p_answer.get(\"results\")]\n        true_spans.append(span)\n        pred_spans.append(extracted_span)\n\n    \n    precision_code, recall_code, f1_code = calculate_code(true_codes, pred_codes)\n    precision_subcode, recall_subcode, f1_subcode = calculate_subcode(true_sub_codes, pred_sub_codes)\n    precision_span, recall_span, f1_span = relaxed_match_evaluation_with_full_containment(true_spans, pred_spans, jaccard_threshold=0.6)\n    \n    return {\"code\": {\"P\": round(precision_code,4), \"R\": round(recall_code,4), \"f1\": round(f1_code,4)},\n            \"sub-code\": {\"P\": round(precision_subcode,4), \"R\": round(recall_subcode,4), \"f1\": round(f1_subcode,4)},\n            \"span\": {\"P\": round(precision_span,4), \"R\": round(recall_span,4), \"f1\": round(f1_span,4)}}\n",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "generate_until",
+      "generation_kwargs": {
+        "until": [
+          "JSON_END"
+        ],
+        "do_sample": false,
+        "temperature": 0.0,
+        "max_gen_toks": 1024
+      },
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "{{query}}",
+      "metadata": {
+        "version": "1.0",
+        "pretrained": "meta-llama/Llama-3.2-1B-Instruct",
+        "tensor_parallel_size": 2,
+        "gpu_memory_utilization": 0.9,
+        "max_model_len": 8192
+      }
+    }
+  },
+  "versions": {
+    "EppcExtraction": "1.0"
+  },
+  "n-shot": {
+    "EppcExtraction": 1
+  },
+  "higher_is_better": {
+    "EppcExtraction": {
+      "evaluate_eppc": true
+    }
+  },
+  "n-samples": {
+    "EppcExtraction": {
+      "original": 1933,
+      "effective": 1933
+    }
+  },
+  "config": {
+    "model": "vllm",
+    "model_args": "pretrained=meta-llama/Llama-3.2-1B-Instruct,tensor_parallel_size=2,gpu_memory_utilization=0.90,max_model_len=8192",
+    "batch_size": "auto",
+    "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": "50e970f",
+  "date": 1763502949.190076,
+  "pretty_env_info": "PyTorch version: 2.8.0+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Red Hat Enterprise Linux release 8.10 (Ootpa) (x86_64)\nGCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-26)\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.28\n\nPython version: 3.11.14 | packaged by conda-forge | (main, Oct 22 2025, 22:46:25) [GCC 14.3.0] (64-bit runtime)\nPython platform: Linux-4.18.0-553.52.1.el8_10.x86_64-x86_64-with-glibc2.28\nIs CUDA available: True\nCUDA runtime version: Could not collect\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 570.133.20\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\nByte Order:          Little Endian\nCPU(s):              64\nOn-line CPU(s) list: 0-63\nThread(s) per core:  1\nCore(s) per socket:  32\nSocket(s):           2\nNUMA node(s):        2\nVendor ID:           GenuineIntel\nCPU family:          6\nModel:               106\nModel name:          Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz\nStepping:            6\nCPU MHz:             3300.000\nCPU max MHz:         3400.0000\nCPU min MHz:         800.0000\nBogoMIPS:            5200.00\nL1d cache:           48K\nL1i cache:           32K\nL2 cache:            1280K\nL3 cache:            49152K\nNUMA node0 CPU(s):   0-31\nNUMA node1 CPU(s):   32-63\nFlags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.3\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.8.0\n[pip3] torchaudio==2.8.0\n[pip3] torchvision==0.23.0\n[pip3] triton==3.4.0\n[conda] numpy                     2.2.6                    pypi_0    pypi\n[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi\n[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi\n[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi\n[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi\n[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi\n[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi\n[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi\n[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi\n[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi\n[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi\n[conda] nvidia-nccl-cu12          2.27.3                   pypi_0    pypi\n[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi\n[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi\n[conda] torch                     2.8.0                    pypi_0    pypi\n[conda] torchaudio                2.8.0                    pypi_0    pypi\n[conda] torchvision               0.23.0                   pypi_0    pypi\n[conda] triton                    3.4.0                    pypi_0    pypi",
+  "transformers_version": "4.57.1",
+  "lm_eval_version": "0.4.9.1",
+  "upper_git_hash": null,
+  "tokenizer_pad_token": [
+    "<|eot_id|>",
+    "128009"
+  ],
+  "tokenizer_eos_token": [
+    "<|eot_id|>",
+    "128009"
+  ],
+  "tokenizer_bos_token": [
+    "<|begin_of_text|>",
+    "128000"
+  ],
+  "eot_token_id": 128009,
+  "max_length": 8192,
+  "task_hashes": {
+    "EppcExtraction": "02420f16e84f65ce4c17feef66936f7bb237835353ea5b12d0eba4acfa7849d0"
+  },
+  "model_source": "vllm",
+  "model_name": "meta-llama/Llama-3.2-1B-Instruct",
+  "model_name_sanitized": "meta-llama__Llama-3.2-1B-Instruct",
+  "system_instruction": null,
+  "system_instruction_sha": null,
+  "fewshot_as_multiturn": false,
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "chat_template_sha": "5816fce10444e03c2e9ee1ef8a4a1ea61ae7e69e438613f3b17b69d0426223a4",
+  "start_time": 2523150.540459601,
+  "end_time": 2523727.205256163,
+  "total_evaluation_time_seconds": "576.664796561934"
+}