Spaces:
AIR-Bench
/
Running on CPU Upgrade

nan's picture
feat: add revision and timestamp information
df659d0
raw
history blame
3.42 kB
from dataclasses import dataclass, make_dataclass
from src.benchmarks import BenchmarksQA, BenchmarksLongDoc
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modification is needed
@dataclass
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
never_hidden: bool = False
COL_NAME_AVG = "Average ⬆️"
COL_NAME_RETRIEVAL_MODEL = "Retrieval Model"
COL_NAME_RERANKING_MODEL = "Reranking Model"
COL_NAME_RETRIEVAL_MODEL_LINK = "Retrieval Model LINK"
COL_NAME_RERANKING_MODEL_LINK = "Reranking Model LINK"
COL_NAME_RANK = "Rank 🏆"
COL_NAME_REVISION = "Revision"
COL_NAME_TIMESTAMP = "Submission Date"
def get_default_auto_eval_column_dict():
auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(
["rank", ColumnContent, ColumnContent(COL_NAME_RANK, "number", True)]
)
auto_eval_column_dict.append(
["retrieval_model", ColumnContent, ColumnContent(COL_NAME_RETRIEVAL_MODEL, "markdown", True, hidden=False, never_hidden=True)]
)
auto_eval_column_dict.append(
["reranking_model", ColumnContent, ColumnContent(COL_NAME_RERANKING_MODEL, "markdown", True, hidden=False, never_hidden=True)]
)
auto_eval_column_dict.append(
["revision", ColumnContent, ColumnContent(COL_NAME_REVISION, "markdown", True, never_hidden=True)]
)
auto_eval_column_dict.append(
["timestamp", ColumnContent, ColumnContent(COL_NAME_TIMESTAMP, "date", True, never_hidden=True)]
)
auto_eval_column_dict.append(
["average", ColumnContent, ColumnContent(COL_NAME_AVG, "number", True)]
)
auto_eval_column_dict.append(
["retrieval_model_link", ColumnContent, ColumnContent(COL_NAME_RETRIEVAL_MODEL, "markdown", False, hidden=True, never_hidden=False)]
)
auto_eval_column_dict.append(
["reranking_model_link", ColumnContent, ColumnContent(COL_NAME_RERANKING_MODEL, "markdown", False, hidden=True, never_hidden=False)]
)
return auto_eval_column_dict
def make_autoevalcolumn(cls_name="BenchmarksQA", benchmarks=BenchmarksQA):
auto_eval_column_dict = get_default_auto_eval_column_dict()
## Leaderboard columns
for benchmark in benchmarks:
auto_eval_column_dict.append(
[benchmark.name, ColumnContent, ColumnContent(benchmark.value.col_name, "number", True)]
)
# We use make dataclass to dynamically fill the scores from Tasks
return make_dataclass(cls_name, auto_eval_column_dict, frozen=True)
AutoEvalColumnQA = make_autoevalcolumn(
"AutoEvalColumnQA", BenchmarksQA)
AutoEvalColumnLongDoc = make_autoevalcolumn(
"AutoEvalColumnLongDoc", BenchmarksLongDoc)
# Column selection
COLS_QA = [c.name for c in fields(AutoEvalColumnQA) if not c.hidden]
COLS_LONG_DOC = [c.name for c in fields(AutoEvalColumnLongDoc) if not c.hidden]
TYPES_QA = [c.type for c in fields(AutoEvalColumnQA) if not c.hidden]
TYPES_LONG_DOC = [c.type for c in fields(AutoEvalColumnLongDoc) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumnQA) if c.displayed_by_default and not c.hidden]
QA_BENCHMARK_COLS = [t.value.col_name for t in BenchmarksQA]
LONG_DOC_BENCHMARK_COLS = [t.value.col_name for t in BenchmarksLongDoc]