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ckpts/universal/global_step20/zero/16.post_attention_layernorm.weight/exp_avg.pt

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+size 9372

ckpts/universal/global_step20/zero/21.mlp.dense_h_to_4h_swiglu.weight/exp_avg.pt

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ckpts/universal/global_step20/zero/6.mlp.dense_h_to_4h.weight/exp_avg_sq.pt

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lm-evaluation-harness/lm_eval/tasks/belebele/belebele_gaz_Latn.yaml

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+"fewshot_split": "gaz_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_gaz_Latn"
+"test_split": "gaz_Latn"

lm-evaluation-harness/lm_eval/tasks/belebele/belebele_mri_Latn.yaml

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+"fewshot_split": "mri_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_mri_Latn"
+"test_split": "mri_Latn"

lm-evaluation-harness/lm_eval/tasks/belebele/belebele_tur_Latn.yaml

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+"fewshot_split": "tur_Latn"
+"include": "_default_template_yaml"
+"task": "belebele_tur_Latn"
+"test_split": "tur_Latn"

lm-evaluation-harness/lm_eval/tasks/belebele/belebele_ukr_Cyrl.yaml

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+"fewshot_split": "ukr_Cyrl"
+"include": "_default_template_yaml"
+"task": "belebele_ukr_Cyrl"
+"test_split": "ukr_Cyrl"

lm-evaluation-harness/lm_eval/tasks/belebele/belebele_urd_Arab.yaml

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+"fewshot_split": "urd_Arab"
+"include": "_default_template_yaml"
+"task": "belebele_urd_Arab"
+"test_split": "urd_Arab"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_anatomy.yaml

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+"dataset_name": "anatomy"
+"description": "以下是关于解剖学的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_anatomy"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_chinese_driving_rule.yaml

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+"dataset_name": "chinese_driving_rule"
+"description": "以下是关于中国驾驶规则的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_chinese_driving_rule"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_clinical_knowledge.yaml

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+"dataset_name": "clinical_knowledge"
+"description": "以下是关于临床知识的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_clinical_knowledge"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_college_law.yaml

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+"dataset_name": "college_law"
+"description": "以下是关于大学法律的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_college_law"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_electrical_engineering.yaml

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+"dataset_name": "electrical_engineering"
+"description": "以下是关于电气工程的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_electrical_engineering"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_elementary_chinese.yaml

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+"dataset_name": "elementary_chinese"
+"description": "以下是关于小学语文的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_elementary_chinese"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_ethnology.yaml

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+"dataset_name": "ethnology"
+"description": "以下是关于民族学的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_ethnology"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_global_facts.yaml

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+"dataset_name": "global_facts"
+"description": "以下是关于全球事实的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_global_facts"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_high_school_geography.yaml

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+"dataset_name": "high_school_geography"
+"description": "以下是关于高中地理的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_high_school_geography"

lm-evaluation-harness/lm_eval/tasks/cmmlu/cmmlu_default_professional_psychology.yaml

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+"dataset_name": "professional_psychology"
+"description": "以下是关于专业心理学的单项选择题，请直接给出正确答案的选项。\n\n"
+"include": "_default_template_yaml"
+"task": "cmmlu_professional_psychology"

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_english.yaml

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+group:
+  - crows_pairs
+  - social_bias
+  - loglikelihood
+task: crows_pairs_english
+dataset_path: BigScienceBiasEval/crows_pairs_multilingual
+dataset_name: english
+test_split: test
+output_type: multiple_choice
+doc_to_text: ""
+doc_to_target: 0
+doc_to_choice: !function utils.doc_to_choice
+target_delimiter: ""
+process_results: !function utils.process_results
+metric_list:
+  - metric: likelihood_diff
+    aggregation: mean
+    higher_is_better: false
+  - metric: pct_stereotype
+    aggregation: mean
+    higher_is_better: false
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_english_autre.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_english_autre
+dataset_name: english
+process_docs: !function utils.filter_autre

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_english_disability.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_english_disability
+dataset_name: english
+process_docs: !function utils.filter_disability

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_english_gender.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_english_gender
+dataset_name: english
+process_docs: !function utils.filter_gender

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_english_race_color.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_english_race_color
+dataset_name: english
+process_docs: !function utils.filter_race_color

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_english_religion.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_english_religion
+dataset_name: english
+process_docs: !function utils.filter_religion

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_english_sexual_orientation.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_english_sexual_orientation
+dataset_name: english
+process_docs: !function utils.filter_orientation

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_french.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_french
+dataset_name: french

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_french_autre.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_french_autre
+dataset_name: french
+process_docs: !function utils.filter_autre

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_french_disability.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_french_disability
+dataset_name: french
+process_docs: !function utils.filter_disability

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_french_gender.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_french_gender
+dataset_name: french
+process_docs: !function utils.filter_gender

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_french_nationality.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_french_nationality
+dataset_name: french
+process_docs: !function utils.filter_nationality

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_french_religion.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_french_religion
+dataset_name: french
+process_docs: !function utils.filter_religion

lm-evaluation-harness/lm_eval/tasks/crows_pairs/crows_pairs_french_sexual_orientation.yaml

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+include: crows_pairs_english.yaml
+task: crows_pairs_french_sexual_orientation
+dataset_name: french
+process_docs: !function utils.filter_orientation

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

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+import datasets
+
+
+def process_results(doc, results):
+    lls, _ = zip(*results)
+
+    likelihood1, likelihood2 = lls
+
+    # Calculate the absolute difference in loglikelihoods
+    diff = abs(likelihood1 - likelihood2)
+
+    # if stereotypical sentence more likely (loglikelihood higher)
+    # then treat this as predicting stereotyped sentence
+    acc = 1.0 if likelihood1 > likelihood2 else 0.0
+
+    return {"likelihood_diff": diff, "pct_stereotype": acc}
+
+
+def doc_to_choice(doc):
+    return [doc["sent_more"], doc["sent_less"]]
+
+
+def filter_dataset(dataset: datasets.Dataset, bias_type: str) -> datasets.Dataset:
+    return dataset.filter(lambda example: example["bias_type"].startswith(bias_type))
+
+
+def filter_race_color(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "race-color")
+
+
+def filter_socio(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "socioeconomic")
+
+
+def filter_gender(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "gender")
+
+
+def filter_age(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "age")
+
+
+def filter_religion(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "religion")
+
+
+def filter_disability(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "disability")
+
+
+def filter_orientation(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "sexual-orientation")
+
+
+def filter_nationality(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "nationality")
+
+
+def filter_appearance(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "physical-appearance")
+
+
+def filter_autre(dataset: datasets.Dataset) -> datasets.Dataset:
+    return filter_dataset(dataset, "autre")

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

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+# SuperGLUE
+
+### Paper
+
+Title: `SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems`
+Abstract: `https://w4ngatang.github.io/static/papers/superglue.pdf`
+
+SuperGLUE is a benchmark styled after GLUE with a new set of more difficult language
+understanding tasks.
+
+Homepage: https://super.gluebenchmark.com/
+
+### Citation
+
+```
+@inproceedings{NEURIPS2019_4496bf24,
+    author = {Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel},
+    booktitle = {Advances in Neural Information Processing Systems},
+    editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
+    pages = {},
+    publisher = {Curran Associates, Inc.},
+    title = {SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
+    url = {https://proceedings.neurips.cc/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf},
+    volume = {32},
+    year = {2019}
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `super-glue-lm-eval-v1`: SuperGLUE eval adapted from LM Eval V1
+* `super-glue-t5-prompt`: SuperGLUE prompt and evaluation that matches the T5 paper (if using accelerate, will error if record is included.)
+
+#### Tasks
+
+Comparison between validation split score on T5x and LM-Eval (T5x models converted to HF)
+| T5V1.1 Base | SGLUE | BoolQ | CB        | Copa | MultiRC | ReCoRD | RTE | WiC | WSC |
+| ----------- | ------| ----- | --------- | ---- | ------- | ------ | --- | --- | --- |
+| T5x | 69.47 | 78.47(acc) | 83.93(f1) 87.5(acc) | 50(acc) | 73.81(f1) 33.26(em) | 70.09(em) 71.34(f1) | 78.7(acc) | 63.64(acc) | 75(acc) |
+| LM-Eval | 71.35 | 79.36(acc) | 83.63(f1) 87.5(acc) | 63(acc) | 73.45(f1) 33.26(em) | 69.85(em) 68.86(f1) | 78.34(acc) | 65.83(acc) | 75.96(acc) |
+
+
+
+* `super-glue-lm-eval-v1`
+    -  `boolq`
+    - `cb`
+    - `copa`
+    - `multirc`
+    - `record`
+    - `rte`
+    - `wic`
+    - `wsc`
+
+* `super-glue-t5-prompt`
+    - `super_glue-boolq-t5-prompt`
+    - `super_glue-cb-t5-prompt`
+    - `super_glue-copa-t5-prompt`
+    - `super_glue-multirc-t5-prompt`
+    - `super_glue-record-t5-prompt`
+    - `super_glue-rte-t5-prompt`
+    - `super_glue-wic-t5-prompt`
+    - `super_glue-wsc-t5-prompt`
+
+### 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/super_glue/boolq/default.yaml

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+group:
+  - super-glue-lm-eval-v1
+task: boolq
+dataset_path: super_glue
+dataset_name: boolq
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{passage}}\nQuestion: {{question}}?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["no", "yes"]
+should_decontaminate: true
+doc_to_decontamination_query: passage
+metric_list:
+  - metric: acc
+metadata:
+  version: 2.0

lm-evaluation-harness/lm_eval/tasks/super_glue/boolq/seq2seq.yaml

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+group:
+  - super-glue-lm-eval-v1-seq2seq
+task: "boolq-seq2seq"
+dataset_path: super_glue
+dataset_name: boolq
+output_type: generate_until
+training_split: train
+validation_split: validation
+doc_to_text: "{{passage}}\nQuestion: {{question}}?\nAnswer:"
+doc_to_target: label
+doc_to_choice: [' no', ' yes']
+target_delimiter: ""
+generation_kwargs:
+  until:
+    - "\n\n"
+    - "\n"
+  do_sample: false
+  temperature: 0.0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 0.0

lm-evaluation-harness/lm_eval/tasks/super_glue/boolq/t5-prompt.yaml

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+group:
+  - super-glue-t5-prompt
+task: super_glue-boolq-t5-prompt
+dataset_path: super_glue
+dataset_name: boolq
+training_split: train
+validation_split: validation
+output_type: generate_until
+doc_to_text: "boolq passage: {{passage}} question: {{question}}"
+doc_to_target: label
+doc_to_choice: ['False', 'True']
+generation_kwargs:
+  until:
+    - "</s>"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 0.0

lm-evaluation-harness/lm_eval/tasks/super_glue/cb/default.yaml

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+group:
+  - super-glue-lm-eval-v1
+task: cb
+dataset_path: super_glue
+dataset_name: cb
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{premise}}\nQuestion: {{hypothesis}}. True, False, or Neither?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ['True', 'False', 'Neither']
+metric_list:
+  - metric: acc
+  - metric: f1
+    aggregation: !function "aggregate.cb_multi_fi"
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/super_glue/cb/t5-prompt.yaml

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+group:
+  - super-glue-t5-prompt
+task: super_glue-cb-t5-prompt
+dataset_path: super_glue
+dataset_name: cb
+training_split: train
+validation_split: validation
+output_type: generate_until
+doc_to_text: "cb hypothesis: {{hypothesis}} premise: {{premise}}"
+doc_to_target: label
+doc_to_choice: ['entailment', 'contradiction', 'neutral']
+generation_kwargs:
+  until:
+    - "</s>"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+  - metric: !function "t5_utils.mean_3class_f1"
+    aggregation: !function "t5_utils.agg_mean_3class_f1"
+    higher_is_better: true
+metadata:
+  version: 0.0

lm-evaluation-harness/lm_eval/tasks/super_glue/cb/t5_utils.py

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+import sklearn.metrics
+
+
+def mean_3class_f1(predictions, references):  # This is a passthrough function
+    string_label = ["entailment", "contradiction", "neutral"]
+    predictions = (
+        string_label.index(predictions[0]) if predictions[0] in string_label else 0
+    )
+    references = string_label.index(references[0])
+
+    return (predictions, references)
+
+
+def agg_mean_3class_f1(items):
+    predictions, references = zip(*items)
+
+    """Computes the unweighted average of the F1 per class."""
+    metric_str = "fbeta_score"
+    metric_fn_kwargs = {
+        "beta": 1,
+        "labels": range(3),
+        "average": "macro",
+    }
+
+    def _fn(predictions, references):
+        metric_fn = getattr(sklearn.metrics, metric_str)
+        metric_val = metric_fn(references, predictions, **metric_fn_kwargs)
+        return metric_val
+
+    return _fn(predictions, references)

lm-evaluation-harness/lm_eval/tasks/super_glue/copa/default.yaml

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+group:
+  - super-glue-lm-eval-v1
+task: copa
+dataset_path: super_glue
+dataset_name: copa
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: !function utils.doc_to_text
+doc_to_target: !function utils.doc_to_target
+doc_to_choice: !function utils.doc_to_choice
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/super_glue/copa/t5-prompt.yaml

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+group:
+  - super-glue-t5-prompt
+task: super_glue-copa-t5-prompt
+dataset_path: super_glue
+dataset_name: copa
+training_split: train
+validation_split: validation
+output_type: generate_until
+doc_to_text: "copa choice1: {{choice1}} choice2: {{choice2}} premise: {{premise}} question: {{question}}"
+doc_to_target: label
+doc_to_choice: ['choice1', 'choice2']
+generation_kwargs:
+  until:
+    - "</s>"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 0.0

lm-evaluation-harness/lm_eval/tasks/super_glue/copa/utils.py

@@ -0,0 +1,21 @@
+def convert_choice(choice):
+    return choice[0].lower() + choice[1:]
+
+
+def doc_to_text(doc):
+    # Drop the period
+    connector = {
+        "cause": "because",
+        "effect": "therefore",
+    }[doc["question"]]
+    return doc["premise"].strip()[:-1] + f" {connector}"
+
+
+def doc_to_target(doc):
+    correct_choice = doc["choice1"] if doc["label"] == 0 else doc["choice2"]
+    # Connect the sentences
+    return " " + convert_choice(correct_choice)
+
+
+def doc_to_choice(doc):
+    return [" " + convert_choice(doc["choice1"]), " " + convert_choice(doc["choice2"])]

lm-evaluation-harness/lm_eval/tasks/super_glue/wic/default.yaml

@@ -0,0 +1,15 @@
+group:
+  - super-glue-lm-eval-v1
+task: "wic"
+dataset_path: super_glue
+dataset_name: wic
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "Sentence 1: {{sentence1}}\nSentence 2: {{sentence2}}\nQuestion: Is the word '{{sentence1[start1:end1]}}' used in the same way in the two sentences above?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ['no', 'yes']
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/super_glue/wic/t5-prompt.yaml

@@ -0,0 +1,22 @@
+group:
+  - super-glue-t5-prompt
+task: super_glue-wic-t5-prompt
+dataset_path: super_glue
+dataset_name: wic
+training_split: train
+validation_split: validation
+output_type: generate_until
+doc_to_text: "wic sentence1: {{sentence1}} sentence2: {{sentence2}} word: {{word}}"
+doc_to_target: label
+doc_to_choice: ['False', 'True']
+generation_kwargs:
+  until:
+    - "</s>"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 0.0

lm-evaluation-harness/lm_eval/tasks/super_glue/wsc/default.yaml

@@ -0,0 +1,15 @@
+group:
+  - super-glue-lm-eval-v1
+task: wsc
+dataset_path: super_glue
+dataset_name: wsc.fixed
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: !function preprocess_wsc.default_doc_to_text
+doc_to_target: label
+doc_to_choice: ['no', 'yes']
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/super_glue/wsc/preprocess_wsc.py

@@ -0,0 +1,17 @@
+from lm_eval.utils import general_detokenize
+
+
+def default_doc_to_text(x):
+    raw_passage = x["text"]
+    # NOTE: HuggingFace span indices are word-based not character-based.
+    pre = " ".join(raw_passage.split()[: x["span2_index"]])
+    post = raw_passage[len(pre) + len(x["span2_text"]) + 1 :]
+    passage = general_detokenize(pre + " *{}*".format(x["span2_text"]) + post)
+    noun = x["span1_text"]
+    pronoun = x["span2_text"]
+    text = (
+        f"Passage: {passage}\n"
+        + f'Question: In the passage above, does the pronoun "*{pronoun}*" refer to "*{noun}*"?\n'
+        + "Answer:"
+    )
+    return text

lm-evaluation-harness/lm_eval/tasks/super_glue/wsc/t5-prompt.yaml

@@ -0,0 +1,20 @@
+group:
+  - super-glue-t5-prompt
+task: super_glue-wsc-t5-prompt
+dataset_path: super_glue
+dataset_name: wsc.fixed
+training_split: train
+validation_split: validation
+output_type: generate_until
+doc_to_text: !function "t5_utils.doc_to_text"
+process_results: !function "t5_utils.process_results"
+doc_to_target: label
+generation_kwargs:
+  until:
+    - "</s>"
+metric_list:
+  - metric: accuracy
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm-evaluation-harness/lm_eval/tasks/super_glue/wsc/t5_utils.py

@@ -0,0 +1,104 @@
+import re
+from typing import List
+
+
+def doc_to_text(x):
+    text = re.sub(r" X ", " *" + x["span2_text"] + "* ", _wsc_inputs(x))
+    return "wsc: " + text
+
+
+def _wsc_inputs(x):
+    words = x["text"].split(" ")
+
+    # We would need some special logic to handle the case where the pronoun is the
+    # first or last word in the text. None of the examples in WSC seem to have
+    # this, so we are ignoring these cases.
+    assert x["span2_index"] > 0
+    assert x["span2_index"] < len(words)
+    pronoun_index = x["span2_index"]
+
+    def create_input():
+        assert words[pronoun_index] == x["span2_text"]
+
+        return " ".join(
+            [
+                " ".join(words[:pronoun_index]),
+                "X",
+                " ".join(words[pronoun_index + 1 :]),
+            ]
+        )
+
+    # Handle some special cases.
+    if (
+        x["text"]
+        == 'The boy continued to whip the pony , and eventually the pony threw him over. John laughed out quite loud. "Good for him," he said. '
+    ):
+        return (
+            "The boy continued to whip the pony , and eventually the pony threw "
+            'him over. John laughed out quite loud. "Good for X ," he said.'
+        )
+
+    # Using the span2_index, we get 'use' instead of 'it'.
+    if (
+        x["text"]
+        == "When they had eventually calmed down a bit , and had gotten home, Mr. Farley put the magic pebble in an iron safe . Some day they might want to use it , but really for now, what more could they wish for?"
+    ):
+        return (
+            "When they had eventually calmed down a bit , and had gotten home, "
+            "Mr. Farley put the magic pebble in an iron safe . Some day they might "
+            "want to use X , but really for now, what more could they wish for?"
+        )
+
+    return create_input()
+
+
+DETERMINERS = {
+    "a",
+    "an",
+    "few",
+    "her",
+    "his",
+    "each",
+    "every",
+    "many",
+    "much",
+    "my",
+    "our",
+    "some",
+    "that",
+    "the",
+    "their",
+    "these",
+    "this",
+    "those",
+    "which",
+    "whose",
+    "your",
+}
+
+
+def clean(s: str) -> str:
+    """Ignore capitalization and determiners."""
+    s = s.strip().lower()
+    return " ".join([w for w in s.split(" ") if w not in DETERMINERS])
+
+
+def process_results(docs: dict, resps: List):
+    prediction = clean(resps[0])
+    reference = clean(docs["span1_text"])
+
+    if ("'" in prediction) != ("'" in reference):
+        # referent is "Bob's hat" as predicting the referent.
+        predicted_referent = False
+    else:
+        prediction_words = set(prediction.split(" "))
+        referent_words = set(reference.split(" "))
+
+        # Handle cases where the prediction is "fuzzy bunny" and the referent is
+        # "bunny".
+        predicted_referent = prediction_words.issubset(
+            referent_words
+        ) or referent_words.issubset(prediction_words)
+
+    acc = 1.0 if predicted_referent == docs["label"] else 0.0
+    return {"accuracy": acc}