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lm-eval-data

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picocreator commited on Feb 23, 2024

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d7a4441

1 Parent(s): 7b1ec6f

updated multilang

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compile-results.ipynb +6 -4
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +252 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +283 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +548 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +423 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +248 -0
lm-eval-output/meta-llama/Llama-2-7b-chat-hf/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +3 -0
lm-eval-output/mistralai/Mistral-7B-v0.1/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +30 -30
lm-eval-output/mistralai/Mistral-7B-v0.1/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +2 -2
lm-eval-output/mistralai/Mistral-7B-v0.1/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +20 -20
lm-eval-output/mistralai/Mistral-7B-v0.1/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +2 -2
lm-eval-output/mistralai/Mistral-7B-v0.1/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +34 -34
lm-eval-output/mistralai/Mistral-7B-v0.1/xcopa/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +2 -2
lm-eval-output/mistralai/Mistral-7B-v0.1/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +36 -36
lm-eval-output/mistralai/Mistral-7B-v0.1/xnli/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +2 -2
lm-eval-output/mistralai/Mistral-7B-v0.1/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +26 -26
lm-eval-output/mistralai/Mistral-7B-v0.1/xstorycloze/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +2 -2
lm-eval-output/mistralai/Mistral-7B-v0.1/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json +15 -15
lm-eval-output/mistralai/Mistral-7B-v0.1/xwinograd/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log +2 -2
summary/bf16-all-results-and-groups.csv +2 -2
summary/bf16-eng-results.csv +2 -2
summary/bf16-eng-summary.csv +2 -2
summary/bf16-multilang-results.csv +2 -2
summary/bf16-multilang-summary.csv +2 -2
summary/bf16-sorted-eng-results.csv +2 -2
summary/bf16-sorted-eng-summary.csv +2 -2
summary/bf16-sorted-multilang-summary.csv +2 -2
summary/compiled-lm-eval-results.json +2 -2

compile-results.ipynb CHANGED Viewed

@@ -837,6 +837,8 @@
     "# Multilang results\n",
     "multiLang_tTest = [\"xcopa_*\", \"xnli_*\", \"xstorycloze_*\", \"xwinograd_*\", \"lambada_openai_*\",     \"pawsx_*\"]\n",
     "multiLang_tGrps = [\"xcopa\",   \"xnli\",   \"xstorycloze\",   \"xwinograd\",   \"lambada_multilingual\", \"pawsx\"]\n",
     "\n",
     "multilang_grp = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=[] )\n",
     "multilang_test = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=multiLang_tTest )\n",
@@ -847,10 +849,10 @@
     "multilang_grp_sorted.to_csv('summary/bf16-sorted-multilang-summary.csv', index=False)\n",
     "\n",
     "# All other results\n",
-    "eng_grp = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[], exGroups=multiLang_tGrps )\n",
-    "eng_grp_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[], exGroups=multiLang_tGrps, sort=True )\n",
-    "eng_test = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[\"*\"], exGroups=multiLang_tGrps, exResults=multiLang_tTest )\n",
-    "eng_test_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[\"*\"], exGroups=multiLang_tGrps, exResults=multiLang_tTest, sort=True )\n",
     "\n",
     "eng_grp.to_csv('summary/bf16-eng-summary.csv', index=False)\n",
     "eng_test.to_csv('summary/bf16-eng-results.csv', index=False)\n",

     "# Multilang results\n",
     "multiLang_tTest = [\"xcopa_*\", \"xnli_*\", \"xstorycloze_*\", \"xwinograd_*\", \"lambada_openai_*\",     \"pawsx_*\"]\n",
     "multiLang_tGrps = [\"xcopa\",   \"xnli\",   \"xstorycloze\",   \"xwinograd\",   \"lambada_multilingual\", \"pawsx\"]\n",
+    "# Both test and groups, merged into a single list\n",
+    "multiLang_joint = multiLang_tTest + multiLang_tGrps\n",
     "\n",
     "multilang_grp = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=[] )\n",
     "multilang_test = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=multiLang_tGrps, inResults=multiLang_tTest )\n",
     "multilang_grp_sorted.to_csv('summary/bf16-sorted-multilang-summary.csv', index=False)\n",
     "\n",
     "# All other results\n",
+    "eng_grp = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[], exGroups=multiLang_joint, exResults=multiLang_joint )\n",
+    "eng_grp_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[], exGroups=multiLang_joint, exResults=multiLang_joint, sort=True )\n",
+    "eng_test = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[\"*\"], exGroups=multiLang_joint, exResults=multiLang_joint )\n",
+    "eng_test_sorted = generate_result_table( inConfig = { \"dtype\": \"bfloat16\" }, inGroups=[\"*\"], inResults=[\"*\"], exGroups=multiLang_joint, exResults=multiLang_joint, sort=True )\n",
     "\n",
     "eng_grp.to_csv('summary/bf16-eng-summary.csv', index=False)\n",
     "eng_test.to_csv('summary/bf16-eng-results.csv', index=False)\n",

lm-eval-output/meta-llama/Llama-2-7b-chat-hf/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json ADDED Viewed

	@@ -0,0 +1,252 @@

+{
+  "results": {
+    "lambada_multilingual": {
+      "perplexity,none": 57.042172854076604,
+      "perplexity_stderr,none": 26.965652387645022,
+      "acc,none": 0.4823986027556763,
+      "acc_stderr,none": 0.09072647847465312,
+      "alias": "lambada_multilingual"
+    },
+    "lambada_openai_mt_de": {
+      "perplexity,none": 107.88641822332492,
+      "perplexity_stderr,none": 8.346927407066568,
+      "acc,none": 0.3811372016301184,
+      "acc_stderr,none": 0.006766279471108957,
+      "alias": " - lambada_openai_mt_de"
+    },
+    "lambada_openai_mt_en": {
+      "perplexity,none": 3.26738661283498,
+      "perplexity_stderr,none": 0.08670177584512109,
+      "acc,none": 0.7073549388705609,
+      "acc_stderr,none": 0.006338717071166964,
+      "alias": " - lambada_openai_mt_en"
+    },
+    "lambada_openai_mt_es": {
+      "perplexity,none": 70.10198485582532,
+      "perplexity_stderr,none": 4.9188446355598225,
+      "acc,none": 0.3782262759557539,
+      "acc_stderr,none": 0.006756224989789186,
+      "alias": " - lambada_openai_mt_es"
+    },
+    "lambada_openai_mt_fr": {
+      "perplexity,none": 39.30187309975079,
+      "perplexity_stderr,none": 2.704778816496143,
+      "acc,none": 0.492140500679216,
+      "acc_stderr,none": 0.006965117003048606,
+      "alias": " - lambada_openai_mt_fr"
+    },
+    "lambada_openai_mt_it": {
+      "perplexity,none": 64.65320147864703,
+      "perplexity_stderr,none": 4.8964710849417665,
+      "acc,none": 0.4531340966427324,
+      "acc_stderr,none": 0.006935309823023549,
+      "alias": " - lambada_openai_mt_it"
+    }
+  },
+  "groups": {
+    "lambada_multilingual": {
+      "perplexity,none": 57.042172854076604,
+      "perplexity_stderr,none": 26.965652387645022,
+      "acc,none": 0.4823986027556763,
+      "acc_stderr,none": 0.09072647847465312,
+      "alias": "lambada_multilingual"
+    }
+  },
+  "configs": {
+    "lambada_openai_mt_de": {
+      "task": "lambada_openai_mt_de",
+      "group": [
+        "lambada_multilingual"
+      ],
+      "dataset_path": "EleutherAI/lambada_openai",
+      "dataset_name": "de",
+      "test_split": "test",
+      "doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
+      "doc_to_target": "{{' '+text.split(' ')[-1]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "perplexity",
+          "aggregation": "perplexity",
+          "higher_is_better": false
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "loglikelihood",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "{{text}}",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "lambada_openai_mt_en": {
+      "task": "lambada_openai_mt_en",
+      "group": [
+        "lambada_multilingual"
+      ],
+      "dataset_path": "EleutherAI/lambada_openai",
+      "dataset_name": "en",
+      "test_split": "test",
+      "doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
+      "doc_to_target": "{{' '+text.split(' ')[-1]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "perplexity",
+          "aggregation": "perplexity",
+          "higher_is_better": false
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "loglikelihood",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "{{text}}",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "lambada_openai_mt_es": {
+      "task": "lambada_openai_mt_es",
+      "group": [
+        "lambada_multilingual"
+      ],
+      "dataset_path": "EleutherAI/lambada_openai",
+      "dataset_name": "es",
+      "test_split": "test",
+      "doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
+      "doc_to_target": "{{' '+text.split(' ')[-1]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "perplexity",
+          "aggregation": "perplexity",
+          "higher_is_better": false
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "loglikelihood",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "{{text}}",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "lambada_openai_mt_fr": {
+      "task": "lambada_openai_mt_fr",
+      "group": [
+        "lambada_multilingual"
+      ],
+      "dataset_path": "EleutherAI/lambada_openai",
+      "dataset_name": "fr",
+      "test_split": "test",
+      "doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
+      "doc_to_target": "{{' '+text.split(' ')[-1]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "perplexity",
+          "aggregation": "perplexity",
+          "higher_is_better": false
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "loglikelihood",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "{{text}}",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "lambada_openai_mt_it": {
+      "task": "lambada_openai_mt_it",
+      "group": [
+        "lambada_multilingual"
+      ],
+      "dataset_path": "EleutherAI/lambada_openai",
+      "dataset_name": "it",
+      "test_split": "test",
+      "doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}",
+      "doc_to_target": "{{' '+text.split(' ')[-1]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "perplexity",
+          "aggregation": "perplexity",
+          "higher_is_better": false
+        },
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "loglikelihood",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "{{text}}",
+      "metadata": {
+        "version": 1.0
+      }
+    }
+  },
+  "versions": {
+    "lambada_multilingual": "N/A",
+    "lambada_openai_mt_de": 1.0,
+    "lambada_openai_mt_en": 1.0,
+    "lambada_openai_mt_es": 1.0,
+    "lambada_openai_mt_fr": 1.0,
+    "lambada_openai_mt_it": 1.0
+  },
+  "n-shot": {
+    "lambada_multilingual": 0,
+    "lambada_openai_mt_de": 0,
+    "lambada_openai_mt_en": 0,
+    "lambada_openai_mt_es": 0,
+    "lambada_openai_mt_fr": 0,
+    "lambada_openai_mt_it": 0
+  },
+  "config": {
+    "model": "hf",
+    "model_args": "pretrained=meta-llama/Llama-2-7b-chat-hf,dtype=bfloat16,trust_remote_code=True",
+    "batch_size": "auto",
+    "batch_sizes": [
+      64
+    ],
+    "device": null,
+    "use_cache": null,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "gen_kwargs": null
+  },
+  "git_hash": "4d19ea9"
+}

lm-eval-output/meta-llama/Llama-2-7b-chat-hf/lambada_multilingual/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/taskrun.log ADDED Viewed

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lm-eval-output/meta-llama/Llama-2-7b-chat-hf/pawsx/dtype=bfloat16,trust_remote_code=True-num_fewshot=-1-nvidia-gpu/results.json ADDED Viewed

	@@ -0,0 +1,283 @@

+{
+  "results": {
+    "pawsx": {
+      "acc,none": 0.44192857142857145,
+      "acc_stderr,none": 0.04262578926290293,
+      "alias": "pawsx"
+    },
+    "paws_de": {
+      "acc,none": 0.3875,
+      "acc_stderr,none": 0.010896386585483742,
+      "alias": " - paws_de"
+    },
+    "paws_en": {
+      "acc,none": 0.419,
+      "acc_stderr,none": 0.011035415270622925,
+      "alias": " - paws_en"
+    },
+    "paws_es": {
+      "acc,none": 0.3865,
+      "acc_stderr,none": 0.010891197550868481,
+      "alias": " - paws_es"
+    },
+    "paws_fr": {
+      "acc,none": 0.406,
+      "acc_stderr,none": 0.010983729838291724,
+      "alias": " - paws_fr"
+    },
+    "paws_ja": {
+      "acc,none": 0.5045,
+      "acc_stderr,none": 0.011182683094883898,
+      "alias": " - paws_ja"
+    },
+    "paws_ko": {
+      "acc,none": 0.536,
+      "acc_stderr,none": 0.011154111668060216,
+      "alias": " - paws_ko"
+    },
+    "paws_zh": {
+      "acc,none": 0.454,
+      "acc_stderr,none": 0.011135708419359803,
+      "alias": " - paws_zh"
+    }
+  },
+  "groups": {
+    "pawsx": {
+      "acc,none": 0.44192857142857145,
+      "acc_stderr,none": 0.04262578926290293,
+      "alias": "pawsx"
+    }
+  },
+  "configs": {
+    "paws_de": {
+      "task": "paws_de",
+      "group": "pawsx",
+      "dataset_path": "paws-x",
+      "dataset_name": "de",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "",
+      "doc_to_target": "label",
+      "doc_to_choice": "{{[sentence1+\", richtig? Ja, \"+sentence2, sentence1+\", richtig? Nein, \"+sentence2]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "paws_en": {
+      "task": "paws_en",
+      "group": "pawsx",
+      "dataset_path": "paws-x",
+      "dataset_name": "en",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "",
+      "doc_to_target": "label",
+      "doc_to_choice": "{{[sentence1+\", right? Yes, \"+sentence2, sentence1+\", right? No, \"+sentence2]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "paws_es": {
+      "task": "paws_es",
+      "group": "pawsx",
+      "dataset_path": "paws-x",
+      "dataset_name": "es",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "",
+      "doc_to_target": "label",
+      "doc_to_choice": "{{[sentence1+\", verdad? Sí, \"+sentence2, sentence1+\", verdad? No, \"+sentence2]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "paws_fr": {
+      "task": "paws_fr",
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+      "doc_to_text": "def doc_to_text(doc: Dict) -> int:\n    \"\"\"\n    Return index of the correct choice.\n\n    Note: We are using the \"multiple input\" mode of the multiple-choice\n        output-type, which means we use different contexts with the same target\n        for the different choices, rather than the same context and different targets.\n    \"\"\"\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
+      "doc_to_target": "def doc_to_target(doc: Dict) -> str:\n    \"\"\"\n    Return the target completion.\n\n    Note that this does not depend on the correct choice as we are using\n    \"multiple input\" mode.\n    \"\"\"\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
+      "doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n    \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
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+      "target_delimiter": " ",
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+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
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+    "xwinograd_fr": {
+      "task": "xwinograd_fr",
+      "group": [
+        "xwinograd"
+      ],
+      "dataset_path": "Muennighoff/xwinograd",
+      "dataset_name": "fr",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc: Dict) -> int:\n    \"\"\"\n    Return index of the correct choice.\n\n    Note: We are using the \"multiple input\" mode of the multiple-choice\n        output-type, which means we use different contexts with the same target\n        for the different choices, rather than the same context and different targets.\n    \"\"\"\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
+      "doc_to_target": "def doc_to_target(doc: Dict) -> str:\n    \"\"\"\n    Return the target completion.\n\n    Note that this does not depend on the correct choice as we are using\n    \"multiple input\" mode.\n    \"\"\"\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
+      "doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n    \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
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+          "aggregation": "mean",
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+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
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+    "xwinograd_jp": {
+      "task": "xwinograd_jp",
+      "group": [
+        "xwinograd"
+      ],
+      "dataset_path": "Muennighoff/xwinograd",
+      "dataset_name": "jp",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc: Dict) -> int:\n    \"\"\"\n    Return index of the correct choice.\n\n    Note: We are using the \"multiple input\" mode of the multiple-choice\n        output-type, which means we use different contexts with the same target\n        for the different choices, rather than the same context and different targets.\n    \"\"\"\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
+      "doc_to_target": "def doc_to_target(doc: Dict) -> str:\n    \"\"\"\n    Return the target completion.\n\n    Note that this does not depend on the correct choice as we are using\n    \"multiple input\" mode.\n    \"\"\"\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
+      "doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n    \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
+      "description": "",
+      "target_delimiter": " ",
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+          "aggregation": "mean",
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+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
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+    "xwinograd_pt": {
+      "task": "xwinograd_pt",
+      "group": [
+        "xwinograd"
+      ],
+      "dataset_path": "Muennighoff/xwinograd",
+      "dataset_name": "pt",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc: Dict) -> int:\n    \"\"\"\n    Return index of the correct choice.\n\n    Note: We are using the \"multiple input\" mode of the multiple-choice\n        output-type, which means we use different contexts with the same target\n        for the different choices, rather than the same context and different targets.\n    \"\"\"\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
+      "doc_to_target": "def doc_to_target(doc: Dict) -> str:\n    \"\"\"\n    Return the target completion.\n\n    Note that this does not depend on the correct choice as we are using\n    \"multiple input\" mode.\n    \"\"\"\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
+      "doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n    \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
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+      "target_delimiter": " ",
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+          "aggregation": "mean",
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+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
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+    "xwinograd_ru": {
+      "task": "xwinograd_ru",
+      "group": [
+        "xwinograd"
+      ],
+      "dataset_path": "Muennighoff/xwinograd",
+      "dataset_name": "ru",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc: Dict) -> int:\n    \"\"\"\n    Return index of the correct choice.\n\n    Note: We are using the \"multiple input\" mode of the multiple-choice\n        output-type, which means we use different contexts with the same target\n        for the different choices, rather than the same context and different targets.\n    \"\"\"\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
+      "doc_to_target": "def doc_to_target(doc: Dict) -> str:\n    \"\"\"\n    Return the target completion.\n\n    Note that this does not depend on the correct choice as we are using\n    \"multiple input\" mode.\n    \"\"\"\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
+      "doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n    \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
+      "description": "",
+      "target_delimiter": " ",
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+          "aggregation": "mean",
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+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
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+    "xwinograd_zh": {
+      "task": "xwinograd_zh",
+      "group": [
+        "xwinograd"
+      ],
+      "dataset_path": "Muennighoff/xwinograd",
+      "dataset_name": "zh",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc: Dict) -> int:\n    \"\"\"\n    Return index of the correct choice.\n\n    Note: We are using the \"multiple input\" mode of the multiple-choice\n        output-type, which means we use different contexts with the same target\n        for the different choices, rather than the same context and different targets.\n    \"\"\"\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
+      "doc_to_target": "def doc_to_target(doc: Dict) -> str:\n    \"\"\"\n    Return the target completion.\n\n    Note that this does not depend on the correct choice as we are using\n    \"multiple input\" mode.\n    \"\"\"\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
+      "doc_to_choice": "def doc_to_choice(doc: Dict) -> List[str]:\n    \"\"\"Return the choices that will be used as contexts in \"multiple input\" mode.\"\"\"\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
+      "description": "",
+      "target_delimiter": " ",
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+      "should_decontaminate": false,
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+    "batch_size": "auto",
+    "batch_sizes": [
+      64
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+    "use_cache": null,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "gen_kwargs": null
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+  "git_hash": "4d19ea9"
+}

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   "results": {
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       "alias": "lambada_multilingual"
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       "alias": " - lambada_openai_mt_en"
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-      "perplexity_stderr,none": 1.790606090078102,
-      "acc,none": 0.42790607413157383,
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       "alias": " - lambada_openai_mt_es"
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     "lambada_openai_mt_fr": {
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-      "perplexity_stderr,none": 1.1061897900321798,
-      "acc,none": 0.5214438191344848,
-      "acc_stderr,none": 0.006959568274744848,
       "alias": " - lambada_openai_mt_fr"
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     "lambada_openai_mt_it": {
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-      "perplexity_stderr,none": 1.6707259318257452,
-      "acc,none": 0.49039394527459734,
-      "acc_stderr,none": 0.006964691949428186,
       "alias": " - lambada_openai_mt_it"
     }
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   "groups": {
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-      "perplexity_stderr,none": 8.199911438395738,
-      "acc,none": 0.5190374539103435,
-      "acc_stderr,none": 0.07089117907004505,
       "alias": "lambada_multilingual"
     }
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@@ -240,7 +240,7 @@
     "model_args": "pretrained=mistralai/Mistral-7B-v0.1,dtype=bfloat16,trust_remote_code=True",
     "batch_size": "auto",
     "batch_sizes": [
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     "device": null,
     "use_cache": null,
@@ -248,5 +248,5 @@
     "bootstrap_iters": 100000,
     "gen_kwargs": null
   },
-  "git_hash": "da066fa"
 }

 {
   "results": {
     "lambada_multilingual": {
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       "alias": "lambada_multilingual"
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       "alias": " - lambada_openai_mt_de"
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       "alias": " - lambada_openai_mt_en"
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       "alias": " - lambada_openai_mt_es"
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       "alias": " - lambada_openai_mt_fr"
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       "alias": " - lambada_openai_mt_it"
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       "alias": "lambada_multilingual"
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     "batch_size": "auto",
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     "device": null,
     "use_cache": null,
     "bootstrap_iters": 100000,
     "gen_kwargs": null
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+  "git_hash": "4d19ea9"
 }

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   "results": {
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-      "acc_stderr,none": 0.05538778178867068,
       "alias": "pawsx"
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       "alias": " - paws_de"
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       "alias": " - paws_fr"
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@@ -279,5 +279,5 @@
     "bootstrap_iters": 100000,
     "gen_kwargs": null
   },
-  "git_hash": "da066fa"
 }

 {
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       "alias": "pawsx"
     },
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       "alias": " - paws_de"
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 }

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       "alias": "xcopa"
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@@ -26,8 +26,8 @@
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       "alias": " - xcopa_qu"
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@@ -36,35 +36,35 @@
       "alias": " - xcopa_sw"
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       "alias": " - xcopa_ta"
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       "alias": " - xcopa_vi"
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       "alias": " - xcopa_zh"
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     "bootstrap_iters": 100000,
     "gen_kwargs": null
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-  "git_hash": "da066fa"
 }

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       "alias": "xcopa"
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       "alias": " - xcopa_ht"
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       "alias": " - xcopa_id"
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       "alias": " - xcopa_it"
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       "alias": " - xcopa_qu"
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       "alias": " - xcopa_vi"
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       "alias": " - xcopa_zh"
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 }

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     "bootstrap_iters": 100000,
     "gen_kwargs": null
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-  "git_hash": "da066fa"
 }

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