Update README.md

README.md

@@ -7,10 +7,9 @@ datasets:
 inference: true
 ---
 
-# CodeT5 for code summarization (base-sized model)
+# CodeT5-base for Code Summarization
 
-[CodeT5-base](https://huggingface.co/Salesforce/codet5-base) model fine-tuned on CodeSearchNet data
-from [Husain et al., 2019](https://arxiv.org/abs/1909.09436) in a multi-lingual training setting (
+[CodeT5-base](https://huggingface.co/Salesforce/codet5-base) model fine-tuned on CodeSearchNet data in a multi-lingual training setting (
 Ruby/JavaScript/Go/Python/Java/PHP) for code summarization. It was introduced in this EMNLP 2021
 paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/abs/2109.00859)
 by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi. Please check out more
@@ -24,7 +23,7 @@ Here is how to use this model:
 from transformers import RobertaTokenizer, T5ForConditionalGeneration
 
 if __name__ == '__main__':
-    tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base')
+    tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base-multi-sum')
     model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base-multi-sum')
 
     text = """def svg_to_image(string, size=None):
@@ -49,13 +48,12 @@ if __name__ == '__main__':
 
 ## Fine-tuning data
 
-We employ the filtered version of CodeSearchNet data
+We employ the filtered version of CodeSearchNet data [[Husain et al., 2019](https://arxiv.org/abs/1909.09436)]
 from [CodeXGLUE](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text) benchmark for fine-tuning on
 code summarization. The data is tokenized with our pre-trained code-specific BPE (Byte-Pair Encoding) tokenizer. One can
-prepare text (or code) for the model using RobertaTokenizer, with the vocab files
-from [codet5-base](https://huggingface.co/Salesforce/codet5-base).
+prepare text (or code) for the model using RobertaTokenizer with the vocab files from [codet5-base](https://huggingface.co/Salesforce/codet5-base).
 
-### Data Statistic
+### Data statistic
 
 | Programming Language | Training |  Dev   |  Test  |
 | :------------------- | :------: | :----: | :----: |
@@ -68,14 +66,13 @@ from [codet5-base](https://huggingface.co/Salesforce/codet5-base).
 
 ## Training procedure
 
-We fine-tune codet5-base on six PLs (Ruby/JavaScript/Go/Python/Java/PHP) in the multi-task learning setting. We employ
-balanced sampling to avoid biasing towards high-resource tasks. Please refer to
-the [paper](https://arxiv.org/abs/2109.00859) for more details.
+We fine-tune codet5-base on these six programming languages (Ruby/JavaScript/Go/Python/Java/PHP) in the multi-task learning setting. We employ the
+balanced sampling to avoid biasing towards high-resource tasks. Please refer to the [paper](https://arxiv.org/abs/2109.00859) for more details.
 
 ## Evaluation results
 
-Unlike the paper allowing to select different best checkpoints for different tasks, here we employ one checkpoint for
-all PLs. Besides, we remove the prefix to specify the PL in training and inference. The results on the test set are shown as below:
+Unlike the paper allowing to select different best checkpoints for different programming languages (PLs), here we employ one checkpoint for
+all PLs. Besides, we remove the task control prefix to specify the PL in training and inference. The results on the test set are shown as below:
 
 | Model       |   Ruby    | Javascript |    Go     |  Python   |   Java    |    PHP    |  Overall  |
 | ----------- | :-------: | :--------: | :-------: | :-------: | :-------: | :-------: | :-------: |
@@ -85,10 +82,10 @@ all PLs. Besides, we remove the prefix to specify the PL in training and inferen
 | [CodeBERT](https://arxiv.org/pdf/2002.08155.pdf)    | 12.16 | 14.90  | 18.07 | 19.06 | 17.65 | 25.16 | 17.83 |
 | [PLBART](https://arxiv.org/pdf/2002.08155.pdf)    | 14.11 |15.56  |  18.91 |   19.30 |  18.45 |  23.58 |  18.32 | 
 | [CodeT5-small](https://arxiv.org/abs/2109.00859)    |14.87    | 15.32   |  19.25    | 20.04   |  19.92   |  25.46   |  19.14 | 
-| [CodeT5-base](https://arxiv.org/abs/2109.00859)    |  15.24   |  16.16   |  19.56   |  20.01   |  20.31   |  26.03   |  19.55 | 
-| [CodeT5-base-multi-sum](https://arxiv.org/abs/2109.00859)    | 15.24       | 16.18       | 19.95   |    20.42       | 20.26   |    26.10   |    19.69 | 
+| [CodeT5-base](https://arxiv.org/abs/2109.00859)    |  **15.24**   |  16.16   |  19.56   |  20.01   |  **20.31**   |  26.03   |  19.55 | 
+| [CodeT5-base-multi-sum](https://arxiv.org/abs/2109.00859)    | **15.24**  | **16.18**  | **19.95**   |   **20.42**    | 20.26  | **26.10**  |  **19.69** | 
 
-### BibTeX entry and citation info
+## Citation
 
 ```bibtex
 @inproceedings{