Merge branch 'upstream' into 'mmlu-pro'

add tokenizer logs info (#1731)

See merge request shijie.yu/lm-evaluation-harness!4

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_amh.yaml

+dataset_name: amh
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_amh

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_eng.yaml

+dataset_name: eng
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_eng

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_ewe.yaml

+dataset_name: ewe
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_ewe

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_fra.yaml

+dataset_name: fra
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_fra

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_hau.yaml

+dataset_name: hau
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_hau

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_ibo.yaml

+dataset_name: ibo
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_ibo

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_kin.yaml

+dataset_name: kin
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_kin

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_lin.yaml

+dataset_name: lin
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_lin

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_lug.yaml

+dataset_name: lug
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_lug

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_orm.yaml

+dataset_name: orm
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_orm

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_sna.yaml

+dataset_name: sna
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_sna

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_sot.yaml

+dataset_name: sot
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_sot

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_swa.yaml

+dataset_name: swa
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_swa

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_twi.yaml

+dataset_name: twi
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_twi

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_wol.yaml

+dataset_name: wol
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_wol

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_xho.yaml

+dataset_name: xho
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_xho

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_yor.yaml

+dataset_name: yor
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_yor

lm_eval/tasks/afrimmlu/direct/afrimmlu_direct_zul.yaml

+dataset_name: zul
+include: afrimmlu_common_yaml
+task: afrimmlu_direct_zul

lm_eval/tasks/afrimmlu/direct/utils.py

+from sklearn.metrics import 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
+
+
+def weighted_f1_score(items):
+    unzipped_list = list(zip(*items))
+    golds = unzipped_list[0]
+    preds = unzipped_list[1]
+    fscore = f1_score(golds, preds, average="weighted")
+    return fscore

lm_eval/tasks/afrimmlu/fewshot.sh

+lm_eval --model hf \
+        --model_args pretrained=masakhane/African-ultrachat-alpaca  \
+        --tasks afrimmlu_direct_amh,afrimmlu_direct_eng,afrimmlu_direct_ewe,afrimmlu_direct_fra,afrimmlu_direct_hau,afrimmlu_direct_ibo,afrimmlu_direct_kin,afrimmlu_direct_lin,afrimmlu_direct_lug,afrimmlu_direct_orm,afrimmlu_direct_sna,afrimmlu_direct_sot,afrimmlu_direct_twi,afrimmlu_direct_wol,afrimmlu_direct_xho,afrimmlu_direct_yor,afrimmlu_direct_zul   \
+        --device cuda:0     \
+        --batch_size 1 \
+        --num_fewshot 0 \
+        --verbosity DEBUG \
+        --wandb_args project=afrimmlu