Update README.md

README.md

@@ -13,7 +13,7 @@ Jellyfish-13B is a large language model with 13 billion parameters, designed spe
 
 We fine-tuned [Open-Orca/OpenOrca-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B) using the datasets related to data preprocessing tasks.
 Its performance is competitive, standing up well against prior state-of-the-art algorithms and LLMs such as OpenAI GPT 3.5 and GPT 4 ([evaluated by our previous work](https://arxiv.org/abs/2308.16361)).
-Note that Jellyfish is only a 13B model and can be run locally for low cost and data security.
+Keep in mind that Jellyfish is only a 13B model, allowing for cost-effective local execution while maintaining data security.
 
 |  Task  | Dataset | Non-LLM SoTA<sup>1</sup> | GPT-3.5<sup>2</sup> | GPT-4<sup>2</sup> | Jellyfish-13B| Jellyfish-13B-Resoning |
 | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
@@ -41,14 +41,13 @@ _For GPT-3.5, GPT-4 we used the few-shot approach, while for Jellyfish and Jelly
   [Large Language Models as Data Preprocessors](https://arxiv.org/abs/2308.16361)
   
 
-We release two versions of Jellyfish: the Jellyfish-13B (the main branch) and Jellyfish-13B-Reasoning.
-As the names suggest, Jellyfish-13B focuses on providing accurate, direct answers. 
-In contrast, Jellyfish-13B-Reasoning distills knowledge from GPT-4. It fine-tuned with data containing reasons and chain-of-thought responses for solving data preprocessing tasks 
-generated by GPT-4.
+We release two distinct versions of Jellyfish: Jellyfish-13B (the main branch) and Jellyfish-13B-Reasoning.
+As the names suggest, Jellyfish-13B is tailored to deliver precise, straightforward answers.
+In contrast, Jellyfish-13B-Reasoning, is fine-tuned with data that includes reasoning and sequential thought processes for handling data preprocessing tasks, distilling knowledge from GPT-4.
 
 The two versions are designed for different application scenarios.
 Jellyfish-13B is suitable for integration into larger data management systems due to its simple and clear responses that can be easily transformed into code.
-On the other hand, Jellyfish-13B-Reasoning is more user-oriented, with responses that provide them with in-depth data insights without the necessity for advanced coding skills or an intricate grasp of statistics..
+On the other hand, Jellyfish-13B-Reasoning is more user-oriented, with responses that provide them with in-depth data insights without the necessity for advanced coding skills or an intricate grasp of statistics.
 
 **Jellyfish paper will be coming soon!**
 
@@ -71,7 +70,7 @@ On the other hand, Jellyfish-13B-Reasoning is more user-oriented, with responses
 ## Training Details
 
 ### Training Data
-We utilized the training and validation sets from the paper [Can Foundation Models Wrangle Your Data?](https://arxiv.org/abs/2205.09911) to fine-tune Jellyfish
+We utilized the training and validation sets from the paper [Can Foundation Models Wrangle Your Data?](https://arxiv.org/abs/2205.09911) to fine-tune Jellyfish.
 The original datasets is [HazyResearch/fm_data_tasks](https://github.com/HazyResearch/fm_data_tasks).
 We revised this data and constructed an instruction tuning dataset suitable for fine-tuning LLM, mirroring the style of [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca).