Add support for configurable chrF metric parameters in task YAML, fix…

lm_eval/api/metrics.py

@@ -5,6 +5,7 @@
 import re
 import string
 from collections.abc import Iterable
+from functools import partial
 from typing import Callable, List, Optional, Sequence, TypeVar
 
 import numpy as np
@@ -99,7 +100,7 @@ def bleu(items):
 
 
 @register_aggregation("chrf")
-def chrf(items):
+def chrf(items, char_order=6, word_order=0, **kwargs):
     """chrF++ is a tool for automatic evaluation of machine translation output
     based on character n-gram precision and recall enhanced with word n-grams.
     Source: https://github.com/m-popovic/chrF
@@ -110,7 +111,9 @@ def chrf(items):
     refs = list(zip(*items))[0]
     preds = list(zip(*items))[1]
     refs, preds = _sacreformat(refs, preds)
-    return sacrebleu.corpus_chrf(preds, refs).score
+    return sacrebleu.corpus_chrf(
+        preds, refs, char_order=char_order, word_order=word_order, **kwargs
+    ).score
 
 
 @register_aggregation("ter")
@@ -482,7 +485,7 @@ def _bootstrap_internal_no_mp(
     chunk_size = min(1000, iters)
     from tqdm import tqdm
 
-    print(f"bootstrapping for stddev: {f.__name__}")
+    print(f"bootstrapping for stddev: {getattr(f, '__name__', repr(f))}")
 
     # A single loop replaces the multiprocessing pool.
     for i in tqdm(range(iters // chunk_size)):
@@ -515,7 +518,7 @@ def bootstrap_stderr(
         chunk_size = min(1000, iters)
         from tqdm import tqdm
 
-        print("bootstrapping for stddev:", f.__name__)
+        print("bootstrapping for stddev:", getattr(f, "__name__", repr(f)))
         with mp.Pool(mp.cpu_count()) as pool:
             for bootstrap in tqdm(
                 pool.imap(
@@ -533,7 +536,9 @@ def bootstrap_stderr(
 
 
 def stderr_for_metric(
-    metric: Callable[[Sequence[T]], float], bootstrap_iters: int
+    metric: Callable[[Sequence[T]], float],
+    bootstrap_iters: int,
+    metric_kwargs: dict = None,
 ) -> Optional[Callable[[Sequence[T]], float]]:
     """
     Return a function that estimates the standard error of `metric(xs)`.
@@ -548,6 +553,9 @@ def stderr_for_metric(
         # return no function (don't compute stderr) if bootstrap iters = 0
         return None
 
+    if metric_kwargs is None:
+        metric_kwargs = {}
+
     bootstrappable = [
         median,
         matthews_corrcoef,
@@ -560,7 +568,10 @@ def stderr_for_metric(
     ]
 
     if metric in bootstrappable:
-        return lambda x: bootstrap_stderr(metric, x, iters=bootstrap_iters)
+        metric_with_kwargs = (
+            partial(metric, **metric_kwargs) if metric_kwargs else metric
+        )
+        return lambda x: bootstrap_stderr(metric_with_kwargs, x, iters=bootstrap_iters)
 
     stderr = {mean: mean_stderr, acc_all: acc_all_stderr}
 

lm_eval/evaluator_utils.py

@@ -111,14 +111,21 @@ def calculate_aggregate_metric(self, bootstrap_iters=100000) -> None:
                 # TODO: Handle this better and allow other aggregate functions other than mean.
                 agg_fn = mean
             metric_key = f"{metric},{filter_key}"
-            self.agg_metrics[metric_key] = agg_fn(items)
+            metric_kwargs = {}
+            if (
+                hasattr(self.task, "_metric_fn_kwargs")
+                and metric in self.task._metric_fn_kwargs
+            ):
+                metric_kwargs = self.task._metric_fn_kwargs[metric]
+            self.agg_metrics[metric_key] = agg_fn(items, **metric_kwargs)
             self.sample_len = len(items)  # TODO: same sample size for each metric?
             if isinstance(bootstrap_iters, int):
                 stderr_fn = stderr_for_metric(
                     metric=agg_fn,
                     bootstrap_iters=min(bootstrap_iters, 100)
                     if metric in ["bleu", "chrf", "ter"]
                     else bootstrap_iters,
+                    metric_kwargs=metric_kwargs,
                 )
                 self.agg_metrics[f"{metric}_stderr,{filter_key}"] = (
                     stderr_fn(items) if (stderr_fn and len(items) > 1) else "N/A"