fixed encoding for seq2seq models

lm_eval/api/model.py

@@ -4,6 +4,7 @@ import json
 import logging
 import os
 from typing import List, Optional, Tuple, Type, TypeVar
+import transformers
 
 from sqlitedict import SqliteDict
 from tqdm import tqdm
@@ -296,12 +297,17 @@ class TemplateLM(LM):
             continuation = context[-n_spaces:] + continuation
             context = context[:-n_spaces]
 
-        whole_enc = self.tok_encode(context + continuation)
-        context_enc = self.tok_encode(context)
+        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
+            whole_enc = self.tok_encode(context + continuation, add_special_tokens=False)
+            context_enc = self.tok_encode(context, add_special_tokens=False)
 
             context_enc_len = len(context_enc)
             continuation_enc = whole_enc[context_enc_len:]
 
+        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
+            context_enc = self.tok_encode(context, add_special_tokens=True)
+            continuation_enc = self.tok_encode(continuation, add_special_tokens=True)
+
         return context_enc, continuation_enc
 
     def loglikelihood(

lm_eval/models/huggingface.py

@@ -707,8 +707,6 @@ class HFLM(TemplateLM):
             encoding["attention_mask"] = encoding["attention_mask"][
                 :, -left_truncate_len:
             ]
-        # print(encoding["input_ids"][0])
-        # import sys; sys.exit()
         self.tokenizer.padding_side = old_padding_side
 
         return encoding["input_ids"], encoding["attention_mask"]