Merge branch 'big-refactor' into haileyschoelkopf-patch-2

lm_eval/tasks/hendrycks_ethics/utils.py

@@ -15,7 +15,7 @@ def _preproc_doc(doc):
     return doc
 
 
-def doc_to_text(doc):
+def doc_to_text(doc) -> str:
     doc = _preproc_doc(doc)
     return f"Scenario 1: {doc['scenarios'][0]}\nScenario 2: {doc['scenarios'][1]}\nQuestion: Is Scenario 1 preferable?\nAnswer:"
 

lm_eval/tasks/mgsm/README.md

+# MGSM
+
+### Paper
+
+Title: `Language Models are Multilingual Chain-of-Thought Reasoners`
+
+Abstract: https://arxiv.org/abs/2210.03057
+
+Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057).
+
+The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
+- Spanish
+- French
+- German
+- Russian
+- Chinese
+- Japanese
+- Thai
+- Swahili
+- Bengali
+- Telugu
+
+GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
+
+You can find the input and targets for each of the ten languages (and English) as `.tsv` files.
+We also include few-shot exemplars that are also manually translated from each language in `exemplars.py`.
+
+Homepage: https://github.com/google-research/url-nlp/tree/main/mgsm
+
+
+### Citation
+
+```
+@misc{cobbe2021training,
+    title={Training Verifiers to Solve Math Word Problems},
+    author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
+    year={2021},
+    eprint={2110.14168},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+@misc{shi2022language,
+    title={Language Models are Multilingual Chain-of-Thought Reasoners},
+    author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei},
+    year={2022},
+    eprint={2210.03057},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `mgsm_direct`: Direct question
+  * `mgsm_direct_bn`: Bengali
+  * `mgsm_direct_de`: German
+  * `mgsm_direct_en`: English
+  * `mgsm_direct_es`: Spanish
+  * `mgsm_direct_fr`: French
+  * `mgsm_direct_ja`: Japanese
+  * `mgsm_direct_ru`: Russian
+  * `mgsm_direct_sw`: Swahili
+  * `mgsm_direct_te`: Telugu
+  * `mgsm_direct_th`: Thai
+  * `mgsm_direct_zh`: Chinese
+* `mgsm_cot_native`: Question with Answer followed by CoT prompt in the same language as the dataset.
+  * `mgsm_cot_native_bn`: Bengali
+  * `mgsm_cot_native_de`: German
+  * `mgsm_cot_native_en`: English
+  * `mgsm_cot_native_es`: Spanish
+  * `mgsm_cot_native_fr`: French
+  * `mgsm_cot_native_ja`: Japanese
+  * `mgsm_cot_native_ru`: Russian
+  * `mgsm_cot_native_sw`: Swahili
+  * `mgsm_cot_native_te`: Telugu
+  * `mgsm_cot_native_th`: Thai
+  * `mgsm_cot_native_zh`: Chinese
+
+Examplar Samples: https://github.com/google-research/url-nlp/blob/main/mgsm/exemplars.py
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/mgsm/direct/direct_yaml

+# This file will be included in the generated language-specific task configs.
+# It doesn't have a yaml file extension as it is not meant to be imported directly
+# by the harness.
+group: mgsm_direct
+dataset_path: juletxara/mgsm
+dataset_name: null  # Overridden by language-specific config.
+output_type: greedy_until
+training_split: train
+test_split: test
+target_delimiter: ""
+generation_kwargs:
+  until:
+    - "\n\n"
+    - "\n"
+  do_sample: false
+  temperature: 0.0
+filter_list:
+  - name: remove_whitespace
+    filter:
+      - function: remove_whitespace
+      - function: take_first
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true

lm_eval/tasks/mgsm/direct/mgsm_direct_bn.yaml

+# Generated by utils.py
+dataset_name: bn
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"প্রশ্ন:
+  "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_bn

lm_eval/tasks/mgsm/direct/mgsm_direct_de.yaml

+# Generated by utils.py
+dataset_name: de
+doc_to_target: '{% if answer is not none %}{{answer[7+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAntwort"}}{% else %}{{"Frage:
+  "+question+"\nAntwort"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_de

lm_eval/tasks/mgsm/direct/mgsm_direct_en.yaml

+# Generated by utils.py
+dataset_name: en
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"Question:
+  "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_en

lm_eval/tasks/mgsm/direct/mgsm_direct_es.yaml

+# Generated by utils.py
+dataset_name: es
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"Pregunta:
+  "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_es

lm_eval/tasks/mgsm/direct/mgsm_direct_fr.yaml

+# Generated by utils.py
+dataset_name: fr
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"Question
+  : "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_fr

lm_eval/tasks/mgsm/direct/mgsm_direct_ja.yaml

+# Generated by utils.py
+dataset_name: ja
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"問題: "+question+"\nAnswer"}}{%
+  endif %}'
+include: direct_yaml
+task: mgsm_direct_ja

lm_eval/tasks/mgsm/direct/mgsm_direct_ru.yaml

+# Generated by utils.py
+dataset_name: ru
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"Задача:
+  "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_ru

lm_eval/tasks/mgsm/direct/mgsm_direct_sw.yaml

+# Generated by utils.py
+dataset_name: sw
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"Swali:
+  "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_sw

lm_eval/tasks/mgsm/direct/mgsm_direct_te.yaml

+# Generated by utils.py
+dataset_name: te
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"ప్రశ్న:
+  "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_te

lm_eval/tasks/mgsm/direct/mgsm_direct_th.yaml

+# Generated by utils.py
+dataset_name: th
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"โจทย์:
+  "+question+"\nAnswer"}}{% endif %}'
+include: direct_yaml
+task: mgsm_direct_th

lm_eval/tasks/mgsm/direct/mgsm_direct_zh.yaml

+# Generated by utils.py
+dataset_name: zh
+doc_to_target: '{% if answer is not none %}{{answer[6+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nAnswer"}}{% else %}{{"问题: "+question+"\nAnswer"}}{%
+  endif %}'
+include: direct_yaml
+task: mgsm_direct_zh

lm_eval/tasks/mgsm/native_cot/cot_yaml

+# This file will be included in the generated language-specific task configs.
+# It doesn't have a yaml file extension as it is not meant to be imported directly
+# by the harness.
+group: mgsm_cot_native
+dataset_path: juletxara/mgsm
+dataset_name: null  # Overridden by language-specific config.
+output_type: greedy_until
+training_split: train
+test_split: test
+target_delimiter: ""
+generation_kwargs:
+  until:
+    - "\n\n"
+    - "\n"
+  do_sample: false
+  temperature: 0.0
+target_delimiter: " "
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true

lm_eval/tasks/mgsm/native_cot/mgsm_cot_native_bn.yaml

+# Generated by utils.py
+dataset_name: bn
+doc_to_target: '{% if answer is not none %}{{answer[16+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nধাপে ধাপে উত্তর:"}}{% else
+  %}{{"প্রশ্ন: "+question+"\nধাপে ধাপে উত্তর:"}}{% endif %}'
+filter:
+- function: regex
+  regex_pattern: The answer is (\-?[0-9\.\,]+)
+- function: take_first
+filter_list:
+- name: get-answer
+include: cot_yaml
+task: mgsm_bn_direct

lm_eval/tasks/mgsm/native_cot/mgsm_cot_native_de.yaml

+# Generated by utils.py
+dataset_name: de
+doc_to_target: '{% if answer is not none %}{{answer[28+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nSchritt-für-Schritt-Antwort:"}}{%
+  else %}{{"Frage: "+question+"\nSchritt-für-Schritt-Antwort:"}}{% endif %}'
+filter:
+- function: regex
+  regex_pattern: The answer is (\-?[0-9\.\,]+)
+- function: take_first
+filter_list:
+- name: get-answer
+include: cot_yaml
+task: mgsm_de_direct

lm_eval/tasks/mgsm/native_cot/mgsm_cot_native_en.yaml

+# Generated by utils.py
+dataset_name: en
+doc_to_target: '{% if answer is not none %}{{answer[20+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nStep-by-Step Answer:"}}{% else
+  %}{{"Question: "+question+"\nStep-by-Step Answer:"}}{% endif %}'
+filter:
+- function: regex
+  regex_pattern: The answer is (\-?[0-9\.\,]+)
+- function: take_first
+filter_list:
+- name: get-answer
+include: cot_yaml
+task: mgsm_en_direct

lm_eval/tasks/mgsm/native_cot/mgsm_cot_native_es.yaml

+# Generated by utils.py
+dataset_name: es
+doc_to_target: '{% if answer is not none %}{{answer[22+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nRespuesta paso a paso:"}}{%
+  else %}{{"Pregunta: "+question+"\nRespuesta paso a paso:"}}{% endif %}'
+filter:
+- function: regex
+  regex_pattern: The answer is (\-?[0-9\.\,]+)
+- function: take_first
+filter_list:
+- name: get-answer
+include: cot_yaml
+task: mgsm_es_direct

lm_eval/tasks/mgsm/native_cot/mgsm_cot_native_fr.yaml

+# Generated by utils.py
+dataset_name: fr
+doc_to_target: '{% if answer is not none %}{{answer[25+1]}}{% else %}{{answer_number|string}}{%
+  endif %}'
+doc_to_text: '{% if answer is not none %}{{question+"\nRéponse étape par étape :"}}{%
+  else %}{{"Question : "+question+"\nRéponse étape par étape :"}}{% endif %}'
+filter:
+- function: regex
+  regex_pattern: The answer is (\-?[0-9\.\,]+)
+- function: take_first
+filter_list:
+- name: get-answer
+include: cot_yaml
+task: mgsm_fr_direct