Fix 'loglikelihood' typos in the api models file (#2459)

lm_eval/models/api_models.py

@@ -58,7 +58,7 @@ class TemplateAPI(TemplateLM):
         pretrained: str = None,  # `model` takes precedence over `pretrained` when passed.
         base_url: str = None,
         tokenizer: Optional[str] = None,
-        # Logliklehood tasks require a tokenizer to calculate context lengths,
+        # Loglikelihood tasks require a tokenizer to calculate context lengths,
         # however the requests can be sent as a string if the API doesn't support token inputs.
         # use tokenized_requests=False
         tokenizer_backend: Optional[
@@ -196,7 +196,7 @@ class TemplateAPI(TemplateLM):
         if not self.tokenized_requests:
             # if messages are tokenized:
             if isinstance(messages[0][0], int):
-                # assuming decoding is lossless. However, this is only for logliklehood requests
+                # assuming decoding is lossless. However, this is only for loglikelihood requests
                 # as we need to compute the context length. For generations, we don't need to tokenize.
                 messages = self.decode_batch(messages)
             if self._batch_size <= 1:
@@ -415,7 +415,7 @@ class TemplateAPI(TemplateLM):
             )
             return None
 
-    def batch_logliklehood_requests(
+    def batch_loglikelihood_requests(
         self, chunks: Iterable[List[LogLikelihoodInputs]]
     ) -> Tuple[List[List[int]], List[int], List[Tuple[str, str]]]:
         inputs = []
@@ -500,7 +500,7 @@ class TemplateAPI(TemplateLM):
         if self._concurrent <= 1:
             pbar = tqdm(desc="Requesting API", total=len(requests))
             for chunk in chunked:
-                inputs, ctxlens, cache_keys = self.batch_logliklehood_requests([chunk])
+                inputs, ctxlens, cache_keys = self.batch_loglikelihood_requests([chunk])
 
                 outputs = retry(
                     stop=stop_after_attempt(self.max_retries),
@@ -524,7 +524,7 @@ class TemplateAPI(TemplateLM):
                             )
                         pbar.update(1)
         else:
-            inputs, ctxlens, cache_keys = self.batch_logliklehood_requests(chunked)
+            inputs, ctxlens, cache_keys = self.batch_loglikelihood_requests(chunked)
             res = itertools.chain.from_iterable(
                 asyncio.run(
                     self.get_batched_requests(