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lm-evaluation-harness/lm_eval/tasks/french_bench/README.md

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+# FrenchBench
+
+### Paper
+
+FrenchBench is a benchmark for evaluating French language models, introduced in the paper
+[CroissantLLM: A Truly Bilingual French-English Language Model](https://arxiv.org/abs/2402.00786).
+It is a collection of tasks that evaluate the ability of a language model to understand and generate French text.
+This benchmark is constructed both from openly available datasets, as well as newly released manually annotated data.
+
+### Citation
+
+```bibtex
+@misc{faysse2024croissantllm,
+      title={CroissantLLM: A Truly Bilingual French-English Language Model},
+      author={Manuel Faysse and Patrick Fernandes and Nuno M. Guerreiro and António Loison and Duarte M. Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro H. Martins and Antoni Bigata Casademunt and François Yvon and André F. T. Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
+      year={2024},
+      eprint={2402.00786},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+- `french_bench`: All tasks (non-perplexity based)
+- `french_bench_gen`: All official generative tasks
+- `french_bench_mc`: All official multiple choice tasks
+- `french_bench_perplexity`: All perplexity-based tasks (0 shot is recommended)
+- `french_bench_extra`: All extra tasks
+
+#### Tasks
+
+
+The following tasks evaluate tasks on the French Bench dataset using various scoring methods.
+  - french_bench_boolqa
+  - french_bench_fquadv2
+  - french_bench_fquadv2_bool
+  - french_bench_fquadv2_genq
+  - french_bench_fquadv2_hasAns
+  - french_bench_topic_based_nli
+  - french_bench_multifquad
+  - french_bench_grammar
+  - french_bench_vocab
+  - french_bench_reading_comp
+  - french_bench_xnli (modified XNLI)
+  - french_bench_orangesum_abstract
+  - french_bench_orangesum_title
+  - french_bench_trivia
+  - french_bench_hellaswag
+  - french_bench_arc_challenge
+
+The french bench also includes other tasks from various benchmarks:
+- `belebele_fra_Latn`: Belebele French
+- `wmt14-en-fr`: WMT14 English-French
+- `wmt14-fr-en`: WMT14 French-English
+
+# Not to use in few-shot
+- `crows_pairs_french`: Crows Pairs French
+- `french_bench_opus_perplexity`: Opus Perplexity
+
+
+### Usage
+
+```bash
+# openai
+lm_eval --model openai-completions --model_args engine=text-davinci-003  --tasks french_bench  --limit 100 --num_fewshot 3 --batch_size auto --output_path data/french_bench/davinci-003/results_french_bench_3shot.json
+lm_eval --model openai-completions --model_args engine=text-davinci-003  --tasks french_bench_opus_perplexity,crows_pairs_french  --limit 100 --batch_size auto --output_path data/french_bench/davinci-003/results_french_bench2_0shot.json
+
+
+lm_eval --model hf --model_args pretrained=gpt2 --tasks french_bench --device cuda:0 --limit 100 --num_fewshot 3 --batch_size 8 --output_path data/french_bench/gpt2/results_french_bench_3shot.json
+lm_eval --model hf --model_args pretrained=gpt2 --tasks french_bench_opus_perplexity,crows_pairs_french --device cuda:0 --limit 100 --batch_size auto --output_path data/french_bench/gpt2/results_french_bench2_0shot.json
+
+lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks french_bench --device cuda:0 --limit 100 --num_fewshot 3 --batch_size 4 --output_path data/french_bench/llama-2-7b-hf/results_french_bench_3shot.json
+lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks french_bench_opus_perplexity,crows_pairs_french --device cuda:0 --limit 100 --batch_size auto --output_path data/french_bench/llama-2-7b-hf/results_french_bench2_0shot.json
+```
+
+HF and Accelerate options can be added when loading a model:
+```bash
+  accelerate launch -m lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf,dtype="float16" --tasks french_bench
+```
+
+### Checklist
+
+* [x] Is the task an existing benchmark in the literature?
+  * [x] Have you referenced the original paper that introduced the task?
+  * [x] If yes, does the original paper provide a reference implementation?
+    * [x] Yes, original implementation contributed by author of the benchmark
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [x] Have you noted which, if any, published evaluation setups are matched by this variant?

lm-evaluation-harness/lm_eval/tasks/french_bench/_default_template_yaml

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+test_split: test
+fewshot_split: valid
+fewshot_config:
+  sampler: first_n

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_arc_challenge.yaml

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+group:
+  - french_bench
+  - french_bench_mc
+task: french_bench_arc_challenge
+dataset_path: manu/french_bench_arc_challenge
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+doc_to_text: "Question: {{question}}\nRéponse:"
+doc_to_target: "{{['A', 'B', 'C', 'D'].index(answerKey)}}"
+doc_to_choice: "{{choices}}"
+should_decontaminate: true
+doc_to_decontamination_query: "Question: {{question}}\nRéponse:"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_boolqa.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_extra
+description: "D'après l'information dans le contexte donné, quelle est la réponse à la question ?"
+task: french_bench_boolqa
+dataset_path: manu/french_boolq
+output_type: multiple_choice
+validation_split: valid
+doc_to_text: "\nContexte: {{passage}}\n\nQuestion: {{question}}\n"
+doc_to_choice: ["Oui", "Non"]
+# doc_to_text: "\nContexte: {{passage}}\n\nQuestion: {{question}}\n\nD'après l'information dans le contexte, la réponse est:\nA. Oui \nB. Non\n\nRéponse:"
+# doc_to_choice: ["A", "B"]
+doc_to_target: "{{[1, 0].index(label)}}"
+should_decontaminate: true
+doc_to_decontamination_query: passage
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_fquadv2.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_extra
+description: "D'après l'information dans le contexte donné, donne la réponse à la question en citant quelques mots du contexte. Si il est impossible de répondre avec les informations du contexte, répond 'Impossible'."
+task: french_bench_fquadv2
+dataset_path: manu/fquad2_test
+output_type: generate_until
+validation_split: valid
+doc_to_text: "\nContexte: {{context}}\n\nQuestion: {{question}}\n\nRéponse:"
+doc_to_target: "{% if answers.text| length > 0 %}{{answers.text[0]}}{% else %}{{['Impossible']}}{% endif %}"
+target_delimiter: " "
+should_decontaminate: true
+doc_to_decontamination_query: context
+generation_kwargs:
+  until:
+    - "\n"
+# filter_list:
+#   - name: remove_whitespace
+#     filter:
+#       - function: remove_whitespace
+#       - function: take_first
+metric_list:
+  - metric: !function utils.exact
+    aggregation: mean
+    higher_is_better: true
+  - metric: !function utils.f1
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_fquadv2_bool.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_extra
+description: "D'après l'information présente dans le contexte, est il possible de répondre à la question ?"
+task: french_bench_fquadv2_bool
+dataset_path: manu/fquad2_test
+output_type: multiple_choice
+validation_split: valid
+doc_to_text: "\nContexte: {{context}}\n\nQuestion: {{question}}\n\nD'après l'information présente dans le contexte, répondre à la question est:\nA. Possible \nB. Impossible\n\nRéponse:"
+doc_to_choice: ["A", "B"]
+doc_to_target: "{{[False, True].index(is_impossible)}}"
+should_decontaminate: true
+doc_to_decontamination_query: context
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_fquadv2_genq.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_gen
+description: "D'après l'information dans le contexte donné, quelle question a été posée pour obtenir la réponse donnée ?"
+task: french_bench_fquadv2_genq
+dataset_path: manu/fquad2_test
+output_type: generate_until
+validation_split: valid_hasAns
+test_split: test_hasAns
+fewshot_split: valid_hasAns
+doc_to_text: "\nContexte: {{context}}\n\nRéponse: {% if answers.text| length > 0 %}{{answers.text[0]}}{% else %}{{['Impossible']}}{% endif %}\n\nQuestion:"
+doc_to_target: "{{question}}"
+target_delimiter: " "
+should_decontaminate: true
+doc_to_decontamination_query: question
+generation_kwargs:
+  until:
+    - "\n"
+# filter_list:
+#   - name: remove_whitespace
+#     filter:
+#       - function: remove_whitespace
+#       - function: take_first
+metric_list:
+  - metric: !function utils.rouge1
+    higher_is_better: true
+    aggregation:  !function utils.rouge1_agg
+  - metric: !function utils.f1
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_fquadv2_hasAns.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_gen
+description: "D'après l'information dans le contexte donné, donne la réponse à la question en citant quelques mots du contexte. Si il est impossible de répondre avec les informations du contexte, répond 'Impossible'."
+task: french_bench_fquadv2_hasAns
+dataset_path: manu/fquad2_test
+output_type: generate_until
+validation_split: valid_hasAns
+test_split: test_hasAns
+fewshot_split: valid_hasAns
+doc_to_text: "\nContexte: {{context}}\n\nQuestion: {{question}}\n\nRéponse:"
+doc_to_target: "{% if answers.text| length > 0 %}{{answers.text[0]}}{% else %}{{['Impossible']}}{% endif %}"
+target_delimiter: " "
+should_decontaminate: true
+doc_to_decontamination_query: context
+generation_kwargs:
+  until:
+    - "\n"
+# filter_list:
+#   - name: remove_whitespace
+#     filter:
+#       - function: remove_whitespace
+#       - function: take_first
+metric_list:
+  - metric: !function utils.exact
+    aggregation: mean
+    higher_is_better: true
+  - metric: !function utils.f1
+    aggregation: mean
+    higher_is_better: true
+  - metric: !function utils.rouge1
+    higher_is_better: true
+    aggregation: !function utils.rouge1_agg

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_grammar.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_mc
+description: "Répond au mieux en complétant la question avec une des réponses proposées."
+dataset_path: manu/french-bench-grammar-vocab-reading
+output_type: multiple_choice
+validation_split: Grammar
+fewshot_split: Grammar
+test_split: Grammar
+#doc_to_text: "Question: {{question.strip()}}\nA: {{answerA}}\nB: {{answerB}}\nC: {{answerC}}\nD: {{answerD}}\nRéponse："
+#doc_to_choice: ["A", "B", "C", "D"]
+doc_to_text: "La phrase suivante est correcte grammaticalement:\n"
+doc_to_choice: "{{[question.replace('<...>', answerA), question.replace('<...>', answerB), question.replace('<...>', answerC), question.replace('<...>', answerD)]}}"
+doc_to_target: '{{["answerA", "answerB", "answerC", "answerD"].index("answer" + answer)}}'
+task: french_bench_grammar
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_hellaswag.yaml

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+group:
+  - french_bench
+  - french_bench_mc
+task: french_bench_hellaswag
+dataset_path: manu/french_bench_hellaswag
+output_type: multiple_choice
+training_split: validation
+validation_split: validation
+test_split: null
+process_docs: !function utils.process_docs
+doc_to_text: "{{query}}"
+doc_to_target: "{{label}}"
+doc_to_choice: "{{choices}}"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_multifquad.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_gen
+description: "D'après l'information dans le contexte donné, donne la réponse à la question en citant quelques extraits du contexte."
+task: french_bench_multifquad
+dataset_path: manu/multifquad_test
+output_type: generate_until
+validation_split: valid
+test_split: test
+fewshot_split: valid
+doc_to_text: "\nContexte: {{context}}\n\nQuestion: {{question}}\n\nRéponse:"
+doc_to_target: "{{', '.join(answers.text)}}"
+target_delimiter: " "
+should_decontaminate: true
+doc_to_decontamination_query: context
+generation_kwargs:
+  until:
+    - "\n"
+# filter_list:
+#   - name: remove_whitespace
+#     filter:
+#       - function: remove_whitespace
+#       - function: take_first
+metric_list:
+  - metric: !function utils.exact
+    aggregation: mean
+    higher_is_better: true
+  - metric: !function utils.f1
+    aggregation: mean
+    higher_is_better: true
+  - metric: !function utils.rouge1
+    higher_is_better: true
+    aggregation:  !function utils.rouge1_agg

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_opus_perplexity.yaml

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+group:
+  - french_bench_perplexity
+task: french_bench_opus_perplexity
+dataset_path: manu/opus100-en-fr
+output_type: loglikelihood_rolling
+test_split: test
+fewshot_split: validation
+validation_split: validation
+num_fewshot: 0
+doc_to_text: ""
+doc_to_target: "{{text}}"
+should_decontaminate: true
+doc_to_decontamination_query: "{{text}}"
+metric_list:
+  - metric: word_perplexity
+    aggregation: weighted_perplexity
+    higher_is_better: false
+  - metric: byte_perplexity
+    aggregation: weighted_perplexity
+    higher_is_better: false
+  - metric: bits_per_byte
+    aggregation: bits_per_byte
+    higher_is_better: false

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_orangesum_abstract.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_gen
+description: "Résume l'article en une phrase."
+task: french_bench_orangesum_abstract
+dataset_path: orange_sum
+dataset_name: abstract
+output_type: generate_until
+validation_split: validation
+fewshot_split: validation
+doc_to_text: "\nArticle: {{text}}\n\nRésumé:"
+doc_to_target: "{{summary}}"
+target_delimiter: " "
+should_decontaminate: true
+doc_to_decontamination_query: summary
+generation_kwargs:
+  until:
+    - "\n"
+# filter_list:
+#   - name: remove_whitespace
+#     filter:
+#       - function: remove_whitespace
+#       - function: take_first
+metric_list:
+  - metric: !function utils.rouge1
+    higher_is_better: true
+    aggregation:  !function utils.rouge1_agg

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_orangesum_title.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_extra
+description: "Trouve le titre de l'article."
+task: french_bench_orangesum_title
+dataset_path: orange_sum
+dataset_name: title
+output_type: generate_until
+validation_split: validation
+fewshot_split: validation
+doc_to_text: "\nArticle: {{text}}\n\nTitre:"
+doc_to_target: "{{summary}}"
+target_delimiter: " "
+should_decontaminate: true
+doc_to_decontamination_query: summary
+generation_kwargs:
+  until:
+    - "\n"
+# filter_list:
+#   - name: remove_whitespace
+#     filter:
+#       - function: remove_whitespace
+#       - function: take_first
+metric_list:
+  - metric: !function utils.rouge1
+    higher_is_better: true
+    aggregation:  !function utils.rouge1_agg

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_reading_comp.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_extra
+# description: "Répond au mieux en complétant la question avec une des réponses proposées."
+dataset_path: manu/french-bench-grammar-vocab-reading
+output_type: multiple_choice
+validation_split: Reading
+fewshot_split: Reading
+test_split: Reading
+# doc_to_text: "Context: {{context}}\nQuestion: {{question.strip()}}\nA: {{answerA}}\nB: {{answerB}}\nC: {{answerC}}\nD: {{answerD}}\nRéponse："
+# doc_to_choice: "{{['A: '+answerA, 'B: '+answerB, 'C: '+answerC, 'D: '+answerD]}}"
+doc_to_text: "Context: {{context}}\n\n"
+doc_to_choice: "{{[question.replace('<...>', answerA) if '<...>' in question else question + ' ' +answerA, question.replace('<...>', answerB) if '<...>' in question else question + ' ' + answerB, question.replace('<...>', answerC) if '<...>' in question else question + ' ' + answerC, question.replace('<...>', answerD) if '<...>' in question else question + ' ' + answerD]}}"
+doc_to_target: '{{["answerA", "answerB", "answerC", "answerD"].index("answer" + answer)}}'
+# doc_to_choice: "{{['A: '+answerA, 'B: '+answerB, 'C: '+answerC, 'D: '+answerD]}}"
+# doc_to_target: answer
+task: french_bench_reading_comp
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_topic_based_nli.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_extra
+description: "A propos du thème spécifié, l'avis client est il positif, négatif, ou neutre ?"
+task: french_bench_topic_based_nli
+dataset_path: manu/topic_based_nli_test
+output_type: multiple_choice
+validation_split: valid
+# doc_to_text: "\nAvis Client: {{text}}\n\nEn considèrant uniquement le thème \"{{topic}}\", l'avis client est plutot:\nA. Positif \nB. Négatif\nC. Mitigé \nD. Neutre\nE. Absent\n\nRéponse:"
+# doc_to_choice: ["A", "B", "C", "D", "E"]
+doc_to_text: "\nAvis Client: {{text}}\n\nA propos du thème \"{{topic}}\", l'avis client est"
+doc_to_choice: ['positif', 'négatif', 'neutre']
+doc_to_target: "{{['positif', 'negatif', 'neutre'].index(polarity)}}"
+should_decontaminate: true
+doc_to_decontamination_query: texte
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_trivia.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_gen
+task: french_bench_trivia
+dataset_path: manu/french-trivia
+output_type: generate_until
+validation_split: train
+test_split: train
+fewshot_split: train
+doc_to_text: "{{Question}}\nAnswer:"
+doc_to_target: "{{Answer}}"
+target_delimiter: " "
+should_decontaminate: true
+doc_to_decontamination_query: Question
+generation_kwargs:
+  until:
+    - "\n"
+# filter_list:
+#   - name: remove_whitespace
+#     filter:
+#       - function: remove_whitespace
+#       - function: take_first
+metric_list:
+  - metric: !function utils.exact
+    aggregation: mean
+    higher_is_better: true
+  - metric: !function utils.f1
+    aggregation: mean
+    higher_is_better: true
+  - metric: !function utils.rouge1
+    higher_is_better: true
+    aggregation:  !function utils.rouge1_agg
+  - metric: !function utils.is_included
+    higher_is_better: true
+    aggregation: mean

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_vocab.yaml

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+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_mc
+# description: "Répond au mieux en complétant la question avec une des réponses proposées."
+dataset_path: manu/french-bench-grammar-vocab-reading
+output_type: multiple_choice
+validation_split: Vocabulary
+fewshot_split: Vocabulary
+test_split: Vocabulary
+# doc_to_text: "Question: {{question.strip()}}\nA: {{answerA}}\nB: {{answerB}}\nC: {{answerC}}\nD: {{answerD}}\nRéponse："
+# doc_to_choice: ["A", "B", "C", "D"]
+doc_to_text: "La phrase suivante est logique sémantiquement:\n"
+doc_to_choice: "{{[question.replace('<...>', answerA), question.replace('<...>', answerB), question.replace('<...>', answerC), question.replace('<...>', answerD)]}}"
+doc_to_target: '{{["answerA", "answerB", "answerC", "answerD"].index("answer" + answer)}}'
+task: french_bench_vocab
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_wikitext_fr.yaml

@@ -0,0 +1,25 @@
+group:
+  - french_bench_perplexity
+task: french_bench_wikitext_fr
+dataset_path: asi/wikitext_fr
+dataset_name: wikitext-35
+output_type: loglikelihood_rolling
+training_split: train
+validation_split: validation
+test_split: test
+num_fewshot: 0
+doc_to_text: ""
+doc_to_target: !function preprocess_wikitext.wikitext_detokenizer
+process_results: !function preprocess_wikitext.process_results
+should_decontaminate: true
+doc_to_decontamination_query: "{{paragraph}}"
+metric_list:
+  - metric: word_perplexity
+    aggregation: weighted_perplexity
+    higher_is_better: false
+  - metric: byte_perplexity
+    aggregation: weighted_perplexity
+    higher_is_better: false
+  - metric: bits_per_byte
+    aggregation: bits_per_byte
+    higher_is_better: false

lm-evaluation-harness/lm_eval/tasks/french_bench/french_bench_xnli.yaml

@@ -0,0 +1,21 @@
+include: "_default_template_yaml"
+group:
+  - french_bench
+  - french_bench_extra
+description: "La prémisse et l'hypothèse sont elles en accord, neutres en elles, ou en contradiction ?"
+dataset_path: xnli
+dataset_name: fr
+output_type: multiple_choice
+validation_split: validation
+fewshot_split: validation
+test_split: test
+# doc_to_text: "\nPrémisse: {{premise}}\n\nHypothèse: {{hypothesis}}\n\nLa prémisse et l'hypothèse sont:\nA. En accord\nB. Neutre\nC. En contradiction\nRéponse:"
+# doc_to_choice: "{{['A: En accord', 'B: Neutre', 'C: En contradiction']}}"
+doc_to_text: "\nPrémisse: {{premise}}\n\nHypothèse: {{hypothesis}}\n\nLa prémisse et l'hypothèse sont"
+doc_to_choice: "{{['en accord', 'neutres entre elles', 'en contradiction']}}"
+doc_to_target: label
+task: french_bench_xnli
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true

lm-evaluation-harness/lm_eval/tasks/french_bench/preprocess_wikitext.py

@@ -0,0 +1,48 @@
+import re
+
+
+def wikitext_detokenizer(doc):
+    string = doc["paragraph"]
+    # contractions
+    string = string.replace("s '", "s'")
+    string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string)
+    # number separators
+    string = string.replace(" @-@ ", "-")
+    string = string.replace(" @,@ ", ",")
+    string = string.replace(" @.@ ", ".")
+    # punctuation
+    string = string.replace(" : ", ": ")
+    string = string.replace(" ; ", "; ")
+    string = string.replace(" . ", ". ")
+    string = string.replace(" ! ", "! ")
+    string = string.replace(" ? ", "? ")
+    string = string.replace(" , ", ", ")
+    # double brackets
+    string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string)
+    string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string)
+    string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string)
+    string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string)
+    string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string)
+    # miscellaneous
+    string = string.replace("= = = =", "====")
+    string = string.replace("= = =", "===")
+    string = string.replace("= =", "==")
+    string = string.replace(" " + chr(176) + " ", chr(176))
+    string = string.replace(" \n", "\n")
+    string = string.replace("\n ", "\n")
+    string = string.replace(" N ", " 1 ")
+    string = string.replace(" 's", "'s")
+
+    return string
+
+
+def process_results(doc, results):
+    (loglikelihood,) = results
+    # IMPORTANT: wikitext counts number of words in *original doc before detokenization*
+    _words = len(re.split(r"\s+", doc["paragraph"]))
+    _bytes = len(doc["paragraph"].encode("utf-8"))
+    return {
+        "word_perplexity": (loglikelihood, _words),
+        "byte_perplexity": (loglikelihood, _bytes),
+        "bits_per_byte": (loglikelihood, _bytes),
+    }

lm-evaluation-harness/lm_eval/tasks/french_bench/utils.py

@@ -0,0 +1,102 @@
+import collections
+import re
+import string
+
+import datasets
+import evaluate
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+
+    def remove_articles(text):
+        regex = re.compile(r"\b(un|une|des|le|la|les)\b", re.UNICODE)
+        return re.sub(regex, " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return "".join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def get_tokens(s):
+    if not s:
+        return []
+    return normalize_answer(s).split()
+
+
+# Exact match (the normalized answer exactly match the gold answer)
+def exact(predictions, references):
+    return int(normalize_answer(references[0]) == normalize_answer(predictions[0]))
+
+
+# The F-score of predicted tokens versus the gold answer
+def f1(predictions, references):
+    gold_toks = get_tokens(references[0])
+    pred_toks = get_tokens(predictions[0])
+    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
+    num_same = sum(common.values())
+    if len(gold_toks) == 0 or len(pred_toks) == 0:
+        # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
+        return int(gold_toks == pred_toks)
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(pred_toks)
+    recall = 1.0 * num_same / len(gold_toks)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def rouge1(items):
+    """
+    # passthrough for efficiency
+    """
+    return items
+
+
+def rouge1_agg(items):
+    """
+    Higher is better
+    """
+    refs = list(zip(*items))[0]
+    preds = list(zip(*items))[1]
+    rouge_scorer = evaluate.load("rouge")
+    return rouge_scorer.compute(predictions=preds, references=refs)["rouge1"]
+
+
+def is_included(items):
+    """
+    # passthrough for efficiency
+    """
+    if items[0] in items[1]:
+        return True
+    return False
+
+
+def preprocess(text):
+    text = text.strip()
+    # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
+    text = text.replace(" [title]", ". ")
+    text = re.sub("\\[.*?\\]", "", text)
+    text = text.replace("  ", " ")
+    return text
+
+
+def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+    def _process_doc(doc):
+        ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
+        out_doc = {
+            "query": preprocess(doc["activity_label"] + ": " + ctx),
+            "choices": [preprocess(ending) for ending in doc["endings"]],
+            "gold": int(doc["label"]),
+        }
+        return out_doc
+
+    return dataset.map(_process_doc)

lm-evaluation-harness/lm_eval/tasks/glue/README.md

@@ -0,0 +1,72 @@
+# GLUE
+**NOTE**: GLUE benchmark tasks do not provide publicly accessible labels for their test sets, so we default to the validation sets for all sub-tasks.
+
+### Paper
+
+Title: `GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding`
+
+Abstract: https://openreview.net/pdf?id=rJ4km2R5t7
+
+The General Language Understanding Evaluation (GLUE) benchmark is a collection of
+resources for training, evaluating, and analyzing natural language understanding
+systems. GLUE consists of:
+- A benchmark of nine sentence- or sentence-pair language understanding tasks built
+on established existing datasets and selected to cover a diverse range of dataset
+sizes, text genres, and degrees of difficulty, and
+- A diagnostic dataset designed to evaluate and analyze model performance with
+respect to a wide range of linguistic phenomena found in natural language.
+
+Homepage: https://gluebenchmark.com/
+
+### Citation
+
+```
+@inproceedings{wang-etal-2018-glue,
+    title = "{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
+    author = "Wang, Alex  and
+      Singh, Amanpreet  and
+      Michael, Julian  and
+      Hill, Felix  and
+      Levy, Omer  and
+      Bowman, Samuel",
+    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
+    month = nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W18-5446",
+    doi = "10.18653/v1/W18-5446",
+    pages = "353--355",
+    abstract = "Human ability to understand language is \textit{general, flexible, and robust}. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions.",
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `glue`: Run all Glue subtasks.
+
+#### Tasks
+
+* `cola`
+* `mnli`
+* `mrpc`
+* `qnli`
+* `qqp`
+* `rte`
+* `sst`
+* `wnli`
+
+### 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-evaluation-harness/lm_eval/tasks/glue/cola/default.yaml

@@ -0,0 +1,16 @@
+group: glue
+task: cola
+dataset_path: glue
+dataset_name: cola
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence}}\nQuestion: Does this sentence make sense?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["no", "yes"]
+should_decontaminate: true
+doc_to_decontamination_query: sentence
+metric_list:
+  - metric: mcc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/glue/mnli/default.yaml

@@ -0,0 +1,14 @@
+group: glue
+task: mnli
+dataset_path: glue
+dataset_name: mnli
+output_type: multiple_choice
+training_split: train
+validation_split: validation_matched
+doc_to_text: !function utils.doc_to_text
+doc_to_target: label
+doc_to_choice: ["True", "Neither", "False"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/glue/mnli/mismatch.yaml

@@ -0,0 +1,3 @@
+include: default.yaml
+task: mnli_mismatch
+validation_split: validation_mismatched

lm-evaluation-harness/lm_eval/tasks/glue/mnli/utils.py

@@ -0,0 +1,6 @@
+def doc_to_text(doc) -> str:
+    return "{}\nQuestion: {} True, False or Neither?\nAnswer:".format(
+        doc["premise"],
+        doc["hypothesis"].strip()
+        + ("" if doc["hypothesis"].strip().endswith(".") else "."),
+    )

lm-evaluation-harness/lm_eval/tasks/glue/mrpc/default.yaml

@@ -0,0 +1,15 @@
+group: glue
+task: mrpc
+dataset_path: glue
+dataset_name: mrpc
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "Sentence 1: {{sentence1}}\nSentence 2: {{sentence2}}\nQuestion: Do both sentences mean the same thing?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["no", "yes"]
+metric_list:
+  - metric: acc
+  - metric: f1
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/glue/qnli/default.yaml

@@ -0,0 +1,14 @@
+group: glue
+task: qnli
+dataset_path: glue
+dataset_name: qnli
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{question}}\n{{sentence}}\nQuestion: Does this response answer the question?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["yes", "no"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/glue/qqp/default.yaml

@@ -0,0 +1,15 @@
+group: glue
+task: qqp
+dataset_path: glue
+dataset_name: qqp
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "Question 1: {{question1}}\nQuestion 2: {{question2}}\nQuestion: Do both questions ask the same thing?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["no", "yes"]
+metric_list:
+  - metric: acc
+  - metric: f1
+metadata:
+  version: 2.0

lm-evaluation-harness/lm_eval/tasks/glue/rte/default.yaml

@@ -0,0 +1,14 @@
+group: glue
+task: rte
+dataset_path: glue
+dataset_name: rte
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence1}}\nQuestion: {{sentence2}} True or False?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["True", "False"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/glue/sst2/default.yaml

@@ -0,0 +1,14 @@
+group: glue
+task: sst2
+dataset_path: glue
+dataset_name: sst2
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence}}\nQuestion: Is this sentence positive or negative?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["negative", "positive"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/glue/wnli/default.yaml

@@ -0,0 +1,14 @@
+group: glue
+task: wnli
+dataset_path: glue
+dataset_name: wnli
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence1}}\nQuestion: {{sentence2}} True or False?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["False", "True"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 2.0

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_civil_engineering.yaml

@@ -0,0 +1,3 @@
+dataset_name: Civil-Engineering
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_civil_engineering

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_construction.yaml

@@ -0,0 +1,3 @@
+dataset_name: Construction
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_construction

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_criminal_law.yaml

@@ -0,0 +1,3 @@
+dataset_name: Criminal-Law
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_criminal_law

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_ecology.yaml

@@ -0,0 +1,3 @@
+dataset_name: Ecology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_ecology

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_economics.yaml

@@ -0,0 +1,3 @@
+dataset_name: Economics
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_economics

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_education.yaml

@@ -0,0 +1,3 @@
+dataset_name: Education
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_education

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_electrical_engineering.yaml

@@ -0,0 +1,3 @@
+dataset_name: Electrical-Engineering
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_electrical_engineering

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_information_technology.yaml

@@ -0,0 +1,3 @@
+dataset_name: Information-Technology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_information_technology

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_social_welfare.yaml

@@ -0,0 +1,3 @@
+dataset_name: Social-Welfare
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_social_welfare

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct/kmmlu_direct_taxation.yaml

@@ -0,0 +1,3 @@
+dataset_name: Taxation
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_taxation

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/_direct_hard_kmmlu_yaml

@@ -0,0 +1,27 @@
+group:
+    - kmmlu
+    - kmmlu_hard_direct
+dataset_path: HAERAE-HUB/KMMLU-HARD
+output_type: generate_until
+test_split: test
+fewshot_split: dev
+doc_to_text: "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답："
+doc_to_target: "{{['A', 'B', 'C', 'D'][answer-1]}}"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+    regexes_to_ignore:
+          - " "
+generation_kwargs:
+  until:
+    - "Q:"
+    - "\n\n"
+    - "</s>"
+    - "."
+  do_sample: false
+  temperature: 0.0
+metadata:
+  version: 2.0

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_accounting.yaml

@@ -0,0 +1,3 @@
+dataset_name: accounting
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_accounting

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_aviation_engineering_and_maintenance.yaml

@@ -0,0 +1,3 @@
+dataset_name: aviation_engineering_and_maintenance
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_aviation_engineering_and_maintenance

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_criminal_law.yaml

@@ -0,0 +1,3 @@
+dataset_name: criminal_law
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_criminal_law

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_ecology.yaml

@@ -0,0 +1,3 @@
+dataset_name: ecology
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_ecology

lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_economics.yaml

@@ -0,0 +1,3 @@
+dataset_name: economics
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_economics