Update README.md

README.md

@@ -13,7 +13,7 @@ base_model:
 <!-- Provide a quick summary of what the model is/does. -->
 
 ModularStarEncoder-finetuned is an encoder built on top of [ModularStarEncoder-1B Pre-trained](https://huggingface.co/andreagurioli1995/ModularStarEncoder) on [SynthCode2Code2NL](https://huggingface.co/datasets/andreagurioli1995/SynthCode2Code2NL-neardedup). 
-ModularStarEncoder, fine-tuned, is an encoder for code-to-code and nl-to-code retrieval tasks, enabling the end user to select the model size that meets their memory and computational constraints.
+ModularStarEncoder, fine-tuned, is an encoder for code-to-code and text-to-code retrieval tasks, enabling the end user to select the model size that meets their memory and computational constraints.
 We built ModularStarEncoder on top of [StarCoder-2](https://huggingface.co/bigcode/starcoder2-15b), reducing its size from 15B to 1B parameters in bfloat16.
 
 The model is finetuned with [CLIP objective](https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/loss.py).
@@ -83,7 +83,7 @@ The pre-training and fine-tuning were conducted on 512 NVIDIA Ampere (64GB) GPUs
 
 ### Evaluation
 
-Here we briefly show our codeSearchNet (codeXGLUE) results between different layers:
+Here we briefly show our codeSearchNet (codeXGLUE) results between different layers; for full results over text-to-code and code-to-code refer to the article:
 
 | Layer           | Avg. MRR     |
 |--------------------------|-----------|