update README

README.md

 # Bert-large infer
 
-### Fine-tuning BERT on SQuAD1.0
+### inference BERT on SQuAD1.0
 
 The [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py) script
 allows to fine-tune any model from our [hub](https://huggingface.co/models) (as long as its architecture has a `ForQuestionAnswering` version in the library) on a question-answering dataset (such as SQuAD, or any other QA dataset available in the `datasets` library, or your own csv/jsonlines files) as long as they are structured the same way as SQuAD. You might need to tweak the data processing inside the script if your data is structured differently.
@@ -16,7 +16,7 @@ Note that if your dataset contains samples with no possible answers (like SQuAD
 - [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
 - This fine-tuned model is available as a checkpoint under the reference [`bert-large-uncased-whole-word-masking-finetuned-squad`](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad).
 
-This example code fine-tunes BERT on the SQuAD1.0 dataset.
+This example code inference BERT on the SQuAD1.0 dataset.
 
 ```bash
 python /nx/transformers/examples/pytorch/question-answering/run_qa.py \