Merge branch 'main' into humaneval

# Conflicts:
#	lm_eval/api/task.py

lm_eval/tasks/afrimmlu/translate/afrimmlu_translate_yor.yaml

+dataset_name: yor
+include: afrimmlu_common_translate_yaml
+task: afrimmlu_translate_yor

lm_eval/tasks/afrimmlu/translate/afrimmlu_translate_zul.yaml

+dataset_name: zul
+include: afrimmlu_common_translate_yaml
+task: afrimmlu_translate_zul

lm_eval/tasks/afrimmlu/translate/utils.py

+from lm_eval.utils import weighted_f1_score
+
+
+def doc_to_choice(doc):
+    choices = eval(doc["choices"])
+    return choices
+
+
+def doc_to_text(doc):
+    output = """You are a highly knowledgeable and intelligent artificial intelligence
+                model answers multiple-choice questions about '{subject}'
+
+                Question: '''{question}'''
+
+                Choices:
+                        A: ''{choice1}'''
+                        B: ''{choice2}'''
+                        C: ''{choice3}'''
+                        D: ''{choice4}'''
+
+                Answer:  """
+
+    choices = eval(doc["choices"])
+    text = output.format(
+        subject=doc["subject"],
+        question=doc["question"],
+        choice1=choices[0],
+        choice2=choices[1],
+        choice3=choices[2],
+        choice4=choices[3],
+    )
+    return text

lm_eval/tasks/afrimmlu/utils.py

+from lm_eval.utils import weighted_f1_score
+
+
+def doc_to_choice(doc):
+    choices = eval(doc["choices"])
+    return choices
+
+
+def doc_to_text(doc):
+    output = """You are a highly knowledgeable and intelligent artificial intelligence
+                model answers multiple-choice questions about '{subject}'
+
+                Question: '''{question}'''
+
+                Choices:
+                        A: ''{choice1}'''
+                        B: ''{choice2}'''
+                        C: ''{choice3}'''
+                        D: ''{choice4}'''
+
+                Answer:  """
+
+    choices = eval(doc["choices"])
+    text = output.format(
+        subject=doc["subject"],
+        question=doc["question"],
+        choice1=choices[0],
+        choice2=choices[1],
+        choice3=choices[2],
+        choice4=choices[3],
+    )
+    return text

lm_eval/tasks/afrixnli/README.md

+# IrokoBench
+
+### Paper
+
+IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models
+https://arxiv.org/pdf/2406.03368
+
+IrokoBench is a human-translated benchmark dataset for 16 typologically diverse
+low-resource African languages covering three tasks: natural language inference (AfriXNLI),
+mathematical reasoning (AfriMGSM), and multi-choice knowledge-based QA (AfriMMLU).
+
+
+### Citation
+
+```
+@misc{adelani2024irokobenchnewbenchmarkafrican,
+      title={IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models},
+      author={David Ifeoluwa Adelani and Jessica Ojo and Israel Abebe Azime and Jian Yun Zhuang and Jesujoba O. Alabi and Xuanli He and Millicent Ochieng and Sara Hooker and Andiswa Bukula and En-Shiun Annie Lee and Chiamaka Chukwuneke and Happy Buzaaba and Blessing Sibanda and Godson Kalipe and Jonathan Mukiibi and Salomon Kabongo and Foutse Yuehgoh and Mmasibidi Setaka and Lolwethu Ndolela and Nkiruka Odu and Rooweither Mabuya and Shamsuddeen Hassan Muhammad and Salomey Osei and Sokhar Samb and Tadesse Kebede Guge and Pontus Stenetorp},
+      year={2024},
+      eprint={2406.03368},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2406.03368},
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `afrixnli`: All afrixnli tasks
+* `afrixnli_en_direct`: afrixnli_en_direct evaluates models performance using the anli prompt on the curated dataset
+* `afrixnli_native_direct`: afrixnli_native_direct evaluates models performance using the anli prompt translated to the
+respective languages on the curated dataset
+* `afrixnli_translate`: afrixnli_translate evaluates models using the anli prompt in translate-test setting
+* `afrixnli_manual_direct`: afrixnli_manual_direct evaluates models performance using Lai's prompt on the curated dataset
+* `afrixnli_manual_translate`: afrixnli_manual_translate evaluates models using Lai's prompt in translate-test setting
+
+#### Tasks
+* `afrixnli_en_direct_{language_code}`: each task evaluates for one language
+* `afrixnli_native_direct_{language_code}`: each task evaluates for one language
+* `afrixnli_translate_{language_code}`: each task evaluates for one language
+* `afrixnli_manual_direct_{language_code}`: each task evaluates for one language
+* `afrixnli_manual_translate_{language_code}`: each task evaluates for one language
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [x] Is the task an existing benchmark in the literature?
+  * [x] 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:
+* [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?
+  * [x] Checked for equivalence with v0.3.0 LM Evaluation Harness

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_amh.yaml

+# Generated by utils.py
+dataset_name: amh
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_amh

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_eng.yaml

+# Generated by utils.py
+dataset_name: eng
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_eng

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_ewe.yaml

+# Generated by utils.py
+dataset_name: ewe
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_ewe

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_fra.yaml

+# Generated by utils.py
+dataset_name: fra
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_fra

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_hau.yaml

+# Generated by utils.py
+dataset_name: hau
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_hau

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_ibo.yaml

+# Generated by utils.py
+dataset_name: ibo
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_ibo

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_kin.yaml

+# Generated by utils.py
+dataset_name: kin
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_kin

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_lin.yaml

+# Generated by utils.py
+dataset_name: lin
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_lin

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_lug.yaml

+# Generated by utils.py
+dataset_name: lug
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_lug

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_orm.yaml

+# Generated by utils.py
+dataset_name: orm
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_orm

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_sna.yaml

+# Generated by utils.py
+dataset_name: sna
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_sna

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_sot.yaml

+# Generated by utils.py
+dataset_name: sot
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_sot

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_swa.yaml

+# Generated by utils.py
+dataset_name: swa
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_swa

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_twi.yaml

+# Generated by utils.py
+dataset_name: twi
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_twi

lm_eval/tasks/afrixnli/anli prompt/en-direct/afrixnli_en_direct_wol.yaml

+# Generated by utils.py
+dataset_name: wol
+include: afrixnli_en_direct_yaml
+task: afrixnli_en_direct_wol