525 lines
25 KiB
JSON
525 lines
25 KiB
JSON
{
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"results": {
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"minerva_math": {
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"exact_match,none": 0.1204,
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"exact_match_stderr,none": 0.004557251536754508,
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"alias": "minerva_math"
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},
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"minerva_math_algebra": {
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"alias": " - minerva_math_algebra",
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"exact_match,none": 0.17101937657961247,
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"exact_match_stderr,none": 0.010933331211377626
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},
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"minerva_math_counting_and_prob": {
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"alias": " - minerva_math_counting_and_prob",
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"exact_match,none": 0.14135021097046413,
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"exact_match_stderr,none": 0.016018641943125127
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},
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"minerva_math_geometry": {
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"alias": " - minerva_math_geometry",
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"exact_match,none": 0.07306889352818371,
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"exact_match_stderr,none": 0.011903537529007871
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},
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"minerva_math_intermediate_algebra": {
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"alias": " - minerva_math_intermediate_algebra",
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"exact_match,none": 0.07198228128460686,
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"exact_match_stderr,none": 0.008605729055597196
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},
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"minerva_math_num_theory": {
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"alias": " - minerva_math_num_theory",
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"exact_match,none": 0.08333333333333333,
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"exact_match_stderr,none": 0.011904761904761852
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},
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"minerva_math_prealgebra": {
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"alias": " - minerva_math_prealgebra",
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"exact_match,none": 0.17221584385763491,
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"exact_match_stderr,none": 0.012800751907784132
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},
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"minerva_math_precalc": {
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"alias": " - minerva_math_precalc",
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"exact_match,none": 0.06776556776556776,
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"exact_match_stderr,none": 0.010766359056008468
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}
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},
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"groups": {
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"minerva_math": {
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"exact_match,none": 0.1204,
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"exact_match_stderr,none": 0.004557251536754508,
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"alias": "minerva_math"
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}
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},
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"group_subtasks": {
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"minerva_math": [
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"minerva_math_algebra",
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"minerva_math_counting_and_prob",
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"minerva_math_geometry",
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"minerva_math_intermediate_algebra",
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"minerva_math_num_theory",
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"minerva_math_prealgebra",
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"minerva_math_precalc"
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]
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},
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"configs": {
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"minerva_math_algebra": {
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"task": "minerva_math_algebra",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "algebra",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x15165204f370>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_counting_and_prob": {
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"task": "minerva_math_counting_and_prob",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "counting_and_probability",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x15165204d3f0>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_geometry": {
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"task": "minerva_math_geometry",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "geometry",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x1516516a6170>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_intermediate_algebra": {
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"task": "minerva_math_intermediate_algebra",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "intermediate_algebra",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x1516516a5b40>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_num_theory": {
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"task": "minerva_math_num_theory",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "number_theory",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x15165202de10>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_prealgebra": {
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"task": "minerva_math_prealgebra",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "prealgebra",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x1516523d3400>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
|
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"metadata": {
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"version": 1.0
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}
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},
|
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"minerva_math_precalc": {
|
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"task": "minerva_math_precalc",
|
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"tag": [
|
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"math_word_problems"
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|
],
|
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"group": [
|
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "precalculus",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x151652326290>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
|
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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