Merge pull request #76 from bigcode-project/loubnabnl-patch-2

Update README.md

README.md

@@ -27,11 +27,11 @@ Below are the features and tasks of this framework:
 
 - Tasks:
     - 4 code generation **Python** tasks (with unit tests): [HumanEval](https://huggingface.co/datasets/openai_humaneval), [APPS](https://huggingface.co/datasets/codeparrot/apps), [MBPP](https://huggingface.co/datasets/mbpp) and [DS-1000](https://github.com/HKUNLP/DS-1000/) for both completion (left-to-right) and insertion (FIM) mode.
-    - [MultiPL-E](https://github.com/nuprl/MultiPL-E) evaluation suite (HumanEval translated into **18** programming languages)
+    - [MultiPL-E](https://github.com/nuprl/MultiPL-E) evaluation suite (HumanEval translated into **18** programming languages).
     - [Pal](https://github.com/reasoning-machines/pal) Program-aided Language Models evaluation for grade school math problems : [GSM8K](https://huggingface.co/datasets/gsm8k) and [GSM-HARD](https://huggingface.co/datasets/reasoning-machines/gsm-hard). These problems are solved by generating reasoning chains of text and code.
     - Code to text task from [CodeXGLUE](https://huggingface.co/datasets/code_x_glue_ct_code_to_text) (zero-shot & fine-tuning) for 6 languages: **Python, Go, Ruby, Java, JavaScript and PHP.**  Documentation translation task from [CodeXGLUE](https://huggingface.co/datasets/code_x_glue_tt_text_to_text).
-    - [CoNaLa](https://huggingface.co/datasets/neulab/conala) for **Python** code generation (2-shot setting and evaluation with BLEU score)
-    - [Concode](https://huggingface.co/datasets/code_x_glue_tc_text_to_code) for **Java** code generation (2-shot setting and evaluation with BLEU score)
+    - [CoNaLa](https://huggingface.co/datasets/neulab/conala) for **Python** code generation (2-shot setting and evaluation with BLEU score).
+    - [Concode](https://huggingface.co/datasets/code_x_glue_tc_text_to_code) for **Java** code generation (2-shot setting and evaluation with BLEU score).
     - 3 multilingual downstream classification tasks: [Java Complexity prediction](https://huggingface.co/datasets/codeparrot/codecomplex), [Java code equivalence prediction](https://huggingface.co/datasets/code_x_glue_cc_clone_detection_big_clone_bench), [C code defect prediction](https://huggingface.co/datasets/code_x_glue_cc_defect_detection).
 
 More details about each task can be found in  the documentation in [`docs/README.md`](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/docs/README.md).
@@ -169,7 +169,7 @@ $ sudo docker run -v $(pwd)/generations_py.json:/app/generations_py.json:ro -it
 To implement a new task in this evaluation harness, see the guide in [`docs/guide`](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/docs/guide.md). The are also contribution guidelines in this [`CONTRIBUTING.md`](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/CONTRIBUTING.md)
 
 ## Documentation
-We provide documentation for the existing benchmarks and how we make the evaluation in [`docs/README.md`](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/docs/README.md).
+We provide documentation for the existing benchmarks and how to run the evaluation in [`docs/README.md`](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/docs/README.md).
 
 ## Remarks
 * Currenltly, we use data parallel evaluation across multiple GPUs using `accelerate`, this assumes that you can fit the model in one GPU.