Update README.md

README.md

@@ -17,7 +17,7 @@ We fine-tuned GPT-J on an instruction dataset created by the [Stanford Alpaca te
 
 The dataset was slightly reworked in order to match the GPT-J fine-tuning format with [Mesh Transformer Jax](https://github.com/kingoflolz/mesh-transformer-jax) on TPUs. [Here is the final dataset we used](https://huggingface.co/datasets/nlpcloud/instructions-dataset-adapted-from-stanford-alpaca-for-gpt-j).
 
-The base GPT-J models needs few-shot learning in order to properly understand what you want. [See more details here about how to properly use few-shot learning](https://nlpcloud.com/effectively-using-gpt-j-gpt-neo-gpt-3-alternatives-few-shot-learning.html). For example let's say that you want to correct spelling with GPT-J. Here is an example of a prompt you had to use:
+The base GPT-J model needs few-shot learning in order to properly understand what you want. [See more details here about how to properly use few-shot learning](https://nlpcloud.com/effectively-using-gpt-j-gpt-neo-gpt-3-alternatives-few-shot-learning.html). For example let's say that you want to correct spelling with GPT-J. Here is an example of a prompt you had to use:
 
 ```text
 I love goin to the beach.