Merge branch 'main' into chat_template

lm_eval/tasks/tinyBenchmarks/tinyWinogrande.yaml

+task: tinyWinogrande
+dataset_path: tinyBenchmarks/tinyWinogrande
+dataset_name: winogrande_xl
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+num_fewshot: 5
+doc_to_text: !function utils_winogrande.doc_to_text
+doc_to_target: !function utils_winogrande.doc_to_target
+doc_to_choice: !function utils_winogrande.doc_to_choice
+should_decontaminate: true
+doc_to_decontamination_query: sentence
+metric_list:
+  - metric: acc_norm
+    aggregation: !function agg_functions.agg_gpirt_winogrande
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/tinyBenchmarks/utils_hellaswag.py

+import re
+
+import datasets
+
+
+""" This code mirrors the utils of the original hellaswag task """
+
+
+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_eval/tasks/tinyBenchmarks/utils_truthfulqa.py

+import datasets
+import numpy as np
+import sacrebleu
+from rouge_score import rouge_scorer, scoring
+
+
+""" This code mirrors the utils of the original truthful_qa task """
+
+
+def process_results_mc2(doc, results):
+    lls, is_greedy = zip(*results)
+
+    # Split on the first `0` as everything before it is true (`1`).
+    split_idx = list(doc["mc2_targets"]["labels"]).index(0)
+    # Compute the normalized probability mass for the correct answer.
+    ll_true, ll_false = lls[:split_idx], lls[split_idx:]
+    p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))
+    p_true = p_true / (sum(p_true) + sum(p_false))
+
+    return {"acc": sum(p_true)}
+
+
+def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:
+    return dataset.map(preprocess_function)
+
+
+def preprocess_function(examples):
+    def _format_answers(answers):
+        formatted_answers = []
+        for answer in answers:
+            answer = answer.strip()
+            if len(answer):
+                # Add a period after all answers.
+                if answer[-1] != ".":
+                    formatted_answers.append(answer + ".")
+                else:
+                    formatted_answers.append(answer)
+        return formatted_answers
+
+    incorrect_answers = _format_answers(examples["incorrect_answers"])
+    correct_answers = _format_answers(examples["correct_answers"])
+    if "I have no comment." not in correct_answers:
+        correct_answers.append("I have no comment.")
+    return {
+        "question": examples["question"].strip(),
+        "correct_answers": correct_answers,
+        "incorrect_answers": incorrect_answers,
+    }
+
+
+def process_results_gen(doc, results):
+    completion = results[0]
+    true_refs, false_refs = doc["correct_answers"], doc["incorrect_answers"]
+    all_refs = true_refs + false_refs
+
+    # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.
+
+    # # BLEURT
+    # bleurt_scores_true = self.bleurt.compute(
+    #     predictions=[completion] * len(true_refs), references=true_refs
+    # )["scores"]
+    # bleurt_scores_false = self.bleurt.compute(
+    #     predictions=[completion] * len(false_refs), references=false_refs
+    # )["scores"]
+    # bleurt_correct = max(bleurt_scores_true)
+    # bleurt_incorrect = max(bleurt_scores_false)
+    # bleurt_max = bleurt_correct
+    # bleurt_diff = bleurt_correct - bleurt_incorrect
+    # bleurt_acc = int(bleurt_correct > bleurt_incorrect)
+
+    # BLEU
+    bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]
+    bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])
+    bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])
+    bleu_max = bleu_correct
+    bleu_diff = bleu_correct - bleu_incorrect
+    bleu_acc = int(bleu_correct > bleu_incorrect)
+
+    # ROUGE-N
+    rouge_scores = [rouge([ref], [completion]) for ref in all_refs]
+    # ROUGE-1
+    rouge1_scores = [score["rouge1"] for score in rouge_scores]
+    rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])
+    rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])
+    rouge1_max = rouge1_correct
+    rouge1_diff = rouge1_correct - rouge1_incorrect
+    rouge1_acc = int(rouge1_correct > rouge1_incorrect)
+    # ROUGE-2
+    rouge2_scores = [score["rouge2"] for score in rouge_scores]
+    rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])
+    rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])
+    rouge2_max = rouge2_correct
+    rouge2_diff = rouge2_correct - rouge2_incorrect
+    rouge2_acc = int(rouge2_correct > rouge2_incorrect)
+    # ROUGE-L
+    rougeL_scores = [score["rougeLsum"] for score in rouge_scores]
+    rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])
+    rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])
+    rougeL_max = rougeL_correct
+    rougeL_diff = rougeL_correct - rougeL_incorrect
+    rougeL_acc = int(rougeL_correct > rougeL_incorrect)
+
+    return {
+        # "bleurt_max": bleurt_max,
+        # "bleurt_acc": bleurt_acc,
+        # "bleurt_diff": bleurt_diff,
+        "bleu_max": bleu_max,
+        "bleu_acc": bleu_acc,
+        "bleu_diff": bleu_diff,
+        "rouge1_max": rouge1_max,
+        "rouge1_acc": rouge1_acc,
+        "rouge1_diff": rouge1_diff,
+        "rouge2_max": rouge2_max,
+        "rouge2_acc": rouge2_acc,
+        "rouge2_diff": rouge2_diff,
+        "rougeL_max": rougeL_max,
+        "rougeL_acc": rougeL_acc,
+        "rougeL_diff": rougeL_diff,
+    }
+
+
+def bleu(refs, preds):
+    """
+    Returns `t5` style BLEU scores. See the related implementation:
+    https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L41
+
+    :param refs:
+        A `list` of `list` of reference `str`s.
+    :param preds:
+        A `list` of predicted `str`s.
+    """
+    score = sacrebleu.corpus_bleu(
+        preds,
+        refs,
+        smooth_method="exp",
+        smooth_value=0.0,
+        force=False,
+        lowercase=False,
+        tokenize="intl",
+        use_effective_order=False,
+    ).score
+    return score
+
+
+def rouge(refs, preds):
+    """
+    Returns `t5` style ROUGE scores. See the related implementation:
+    https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L68
+
+    :param refs:
+        A `list` of reference `strs`.
+    :param preds:
+        A `list` of predicted `strs`.
+    """
+    rouge_types = ["rouge1", "rouge2", "rougeLsum"]
+    scorer = rouge_scorer.RougeScorer(rouge_types)
+    # Add newlines between sentences to correctly compute `rougeLsum`.
+
+    def _prepare_summary(summary):
+        summary = summary.replace(" . ", ".\n")
+        return summary
+
+    # Accumulate confidence intervals.
+    aggregator = scoring.BootstrapAggregator()
+    for ref, pred in zip(refs, preds):
+        ref = _prepare_summary(ref)
+        pred = _prepare_summary(pred)
+        aggregator.add_scores(scorer.score(ref, pred))
+    result = aggregator.aggregate()
+    return {type: result[type].mid.fmeasure * 100 for type in rouge_types}

lm_eval/tasks/tinyBenchmarks/utils_winogrande.py

+""" This code mirrors the utils of the original winogrande task """
+
+
+def doc_to_text(doc):
+    answer_to_num = {"1": 0, "2": 1}
+    return answer_to_num[doc["answer"]]
+
+
+def doc_to_target(doc):
+    idx = doc["sentence"].index("_") + 1
+    return doc["sentence"][idx:].strip()
+
+
+def doc_to_choice(doc):
+    idx = doc["sentence"].index("_")
+    options = [doc["option1"], doc["option2"]]
+    return [doc["sentence"][:idx] + opt for opt in options]