Update README.md

README.md

@@ -10,7 +10,7 @@ language:
 - en
 ---
 
-# BigBird-ITC-BASE fine-tuned on MP-DocVQA
+# BigBird-BASE-ITC fine-tuned on MP-DocVQA
 
 This is BigBird-base trained on TriviaQA from [Google hub](https://huggingface.co/google/bigbird-base-trivia-itc) and fine-tuned on Multipage DocVQA (MP-DocVQA) dataset.
 * Due to Huggingface implementation, the global tokens are defined according to the Internal Transformer Construction (ITC) strategy.
@@ -27,13 +27,13 @@ Here is how to use this model to get the features of a given text in PyTorch:
 from transformers import BigBirdForQuestionAnswering
 
 # by default its in `block_sparse` mode with num_random_blocks=3, block_size=64
-model = BigBirdForQuestionAnswering.from_pretrained("rubentito/BigBird-ITC-MPDocVQA")
+model = BigBirdForQuestionAnswering.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa")
 
 # you can change `attention_type` to full attention like this:
-model = BigBirdForQuestionAnswering.from_pretrained("rubentito/BigBird-ITC-MPDocVQA", attention_type="original_full")
+model = BigBirdForQuestionAnswering.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa", attention_type="original_full")
 
 # you can change `block_size` & `num_random_blocks` like this:
-model = BigBirdForQuestionAnswering.from_pretrained("rubentito/BigBird-ITC-MPDocVQA", block_size=16, num_random_blocks=2)
+model = BigBirdForQuestionAnswering.from_pretrained("rubentito/bigbird-base-itc-mpdocvqa", block_size=16, num_random_blocks=2)
 
 question = "Replace me by any text you'd like."
 context = "Put some context for answering"