Update README.md

README.md

@@ -17,4 +17,5 @@ Pretrained T5 model with nanoT5:
 - ~900m parameters, 16 layers in encoder, 32 layers in decoder
 - sentencepiece tokenizer with 48k vocab & byte-pair fallback
   - handles whitespaces etc correctly (unlike standard T5 tokenizer)
-- 1024 ctx during pretrain
+- 1024 ctx during pretrain
+- `relative_attention_num_buckets` increased to 48 from standard 32 for context length upscaling