Add new benchmark: Catalan bench (#2154)

* Add catalan_bench

* added flores_ca.yaml

* Updated some task groupings and readme

* Fix create_yamls_flores_ca.py

---------
Co-authored-by: Hailey Schoelkopf <65563625+haileyschoelkopf@users.noreply.github.com>

lm_eval/tasks/catalan_bench/piqa_ca.yaml

+task: piqa_ca
+dataset_path: projecte-aina/piqa_ca
+dataset_name: null
+output_type: multiple_choice
+training_split: null
+validation_split: validation
+test_split: null
+doc_to_text: "Pregunta: {{goal}}\nResposta:"
+doc_to_target: label
+doc_to_choice: "{{[sol1, sol2]}}"
+should_decontaminate: true
+doc_to_decontamination_query: goal
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/catalan_bench/siqa_ca.yaml

+task: siqa_ca
+dataset_path: projecte-aina/siqa_ca
+output_type: multiple_choice
+training_split: null
+validation_split: validation
+test_split: null
+doc_to_text: "Pregunta: {{context}} {{question}}\nResposta:"
+target_delimiter: " "
+doc_to_choice: "{{[answerA, answerB, answerC]}}"
+doc_to_target: "{{ (label|int) - 1 }}"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/catalan_bench/teca.yaml

+task: teca
+dataset_path: projecte-aina/teca
+dataset_name: null
+training_split: train
+validation_split: validation
+test_split: test
+output_type: multiple_choice
+process_docs: !function utils.process_doc_nli
+doc_to_text: ""
+doc_to_target: label
+target_delimiter: ""
+doc_to_choice: '{{[premise + ", correcte? Sí, " + hypothesis, premise + ", correcte? A més, " + hypothesis, premise + ", correcte? No, " + hypothesis]}}'
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/catalan_bench/utils.py

+import re
+from itertools import product
+
+import evaluate
+import transformers.data.metrics.squad_metrics as squad_metrics
+
+from lm_eval.utils import general_detokenize
+
+
+def lowercase_first_letter(text):
+    return text[0].lower() + text[1:]
+
+
+def process_doc_nli(dataset):
+    def process_fn(doc):
+        # Detokenize(remove extra whitespaces)
+        doc["premise"] = general_detokenize(doc["premise"]).strip()
+        doc["hypothesis"] = general_detokenize(doc["hypothesis"]).strip()
+        # Remove last punctuation mark in the premise
+        doc["premise"] = (
+            doc["premise"][:-1]
+            if doc["premise"].endswith((".", ",", "!", "?"))
+            else doc["premise"]
+        )
+        # Lowercase the first letter in the hypothesis
+        doc["hypothesis"] = lowercase_first_letter(doc["hypothesis"])
+        # Ensure that the hypothesis ends with a dot
+        doc["hypothesis"] = (
+            (doc["hypothesis"] + ".")
+            if not doc["hypothesis"].endswith(".")
+            else doc["hypothesis"]
+        )
+        return doc
+
+    return dataset.map(process_fn)
+
+
+def process_results_coqcat(doc, results):
+    # Get all possible answers and compute the scores
+    turn_id = len(doc["questions"])
+    answers = [doc["answers"]["input_text"][turn_id - 1]]
+    additional_answers_list = doc.get("additional_answers")
+    if additional_answers_list:
+        for key, additional_answers in additional_answers_list.items():
+            if additional_answers["input_text"][turn_id - 1].lower() not in map(
+                str.lower, answers
+            ):
+                answers.append(additional_answers["input_text"][turn_id - 1])
+
+    gold_list = answers
+    pred = results[0].strip().split("\n")[0]
+    # import code; code.interact(local=dict(globals(), **locals()))
+
+    f1_sum = 0.0
+    em_sum = 0.0
+    if len(gold_list) > 1:
+        for i in range(len(gold_list)):
+            gold_answers = gold_list[0:i] + gold_list[i + 1 :]
+            # predictions compared against (n) golds and take maximum
+            em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_answers)
+            f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_answers)
+    else:
+        em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
+        f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_list)
+    # import code; code.interact(local=dict(globals(), **locals()))
+    return {
+        "em": em_sum / max(1, len(gold_list)),
+        "f1": f1_sum / max(1, len(gold_list)),
+    }
+
+
+def process_results_qa(doc, results):
+    preds = results[0]
+    reference = doc["answers"][0]["text"]
+    # import code; code.interact(local=dict(globals(), **locals()))
+    f1_sum = squad_metrics.compute_f1(reference, preds)
+    exact_match = squad_metrics.compute_exact(reference, preds)
+    return {"f1": f1_sum, "exact_match": exact_match}
+
+
+def process_doc_cabreu(dataset):
+    def process_fn(doc):
+        # Remove duplicate spaces
+        doc["content"] = re.sub(r" +", " ", doc["content"])
+        for summary_type, index in product(
+            ["abstractive", "extractive", "extreme"], ["a1", "a2", "a3"]
+        ):
+            doc["summaries"][summary_type][index] = re.sub(
+                r" +", " ", doc["summaries"][summary_type][index]
+            )
+        return doc
+
+    return dataset.map(process_fn)
+
+
+def process_docs_paraphrases(dataset):
+    empty_docs = []
+
+    def _process_doc(doc):
+        if doc["sentence1"] not in [None, ""] and doc["sentence2"] not in [None, ""]:
+            doc["sentence1"] = general_detokenize(doc["sentence1"]).strip()
+            doc["sentence2"] = general_detokenize(doc["sentence2"]).strip()
+            # Remove final punctuation mark in the first sentence
+            if doc["sentence1"].endswith((".", ",", ";")):
+                doc["sentence1"] = doc["sentence1"][:-1]
+            # Start the second sentence in lowercase (to be used after "Yes, ...")
+            doc["sentence2"] = lowercase_first_letter(doc["sentence2"])
+            return doc
+        else:
+            empty_docs.append(doc)
+            return doc
+
+    return dataset.filter(
+        lambda doc: doc["sentence1"] not in [None, ""]
+        and doc["sentence2"] not in [None, ""]
+    ).map(_process_doc)
+
+
+def process_docs_copa_ca(dataset):
+    def _process_doc(doc):
+        doc["choice1"] = lowercase_first_letter(doc["choice1"])
+        doc["choice2"] = lowercase_first_letter(doc["choice2"])
+        return doc
+
+    return dataset.map(_process_doc)
+
+
+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"]

lm_eval/tasks/catalan_bench/wnli_ca.yaml

+task: wnli_ca
+dataset_path: projecte-aina/wnli-ca
+dataset_name: null
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: null
+doc_to_text: "{{sentence1}}\nPregunta: {{sentence2}} Cert o Fals?\nResposta:"
+doc_to_target: label
+doc_to_choice: ["Fals", "Cert"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm_eval/tasks/catalan_bench/xnli_ca.yaml

+task: xnli_ca
+dataset_path: projecte-aina/xnli-ca
+dataset_name: null
+include: ../xnli/xnli_common_yaml
+output_type: multiple_choice
+doc_to_choice: '{{[premise+", correcte? Sí, "+hypothesis,premise+", correcte? A més,
+  "+hypothesis,premise+", correcte? No, "+hypothesis]}}'
+doc_to_text: ''
+target_delimiter: ''
+process_docs: !function utils.process_doc_nli
+training_split: null
+validation_split: validation
+doc_to_target: label
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/catalan_bench/xquad_ca.yaml

+task: xquad_ca
+dataset_path: projecte-aina/xquad-ca
+dataset_name: null
+output_type: generate_until
+doc_to_text: "Context: {{context}}\n\nPregunta: {{question}}\n\nResposta:"
+doc_to_target: '{{answers[0]["text"]}}'
+validation_split: null
+test_split: test
+target_delimiter: ' '
+process_results: !function utils.process_results_qa
+generation_kwargs:
+  until:
+    - "\n"
+  do_sample: false
+  temperature: 0.0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+  - metric: f1
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/catalan_bench/xstorycloze_ca.yaml

+task: xstorycloze_ca
+dataset_path: projecte-aina/xstorycloze_ca
+dataset_name: ca
+output_type: multiple_choice
+training_split: train
+validation_split: eval
+doc_to_text: "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}"
+doc_to_target: "{{answer_right_ending-1}}"
+doc_to_choice: "{{[sentence_quiz1, sentence_quiz2]}}"
+should_decontaminate: true
+doc_to_decontamination_query: "{{[input_sentence_1, input_sentence_2, input_sentence_3, input_sentence_4]|join(' ')}}"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0