diff --git a/Hugging Face/Hugging_Face_Few_Shot_Learning_with_Inference_API.ipynb b/Hugging Face/Hugging_Face_Few_Shot_Learning_with_Inference_API.ipynb index 8b2f32ac46..a109756793 100644 --- a/Hugging Face/Hugging_Face_Few_Shot_Learning_with_Inference_API.ipynb +++ b/Hugging Face/Hugging_Face_Few_Shot_Learning_with_Inference_API.ipynb @@ -168,7 +168,8 @@ "- Now, create a new access token with name: `GPT_INFERENCE` and role: `read`\n", "- Copy the generated token and paste it below\n", "\n", - "We will use gpt-neo-1.3B model for our demonstration. " + "We use GPT based models since they excel in few-shot learning due to their ability to generate coherent and contextually relevant responses based on limited examples, capturing relationships in data more effectively than many other large language models.\n", + "In this demonstration, we will utilize the gpt-neo-1.3B model; additional GPT-based models can be explored here. Developed by EleutherAI, GPT⁠-⁠Neo is a series of transformer-based language models built on the GPT architecture. EleutherAI aims to create a model of GPT⁠-⁠3's scale and provide open access." ] }, { @@ -386,7 +387,7 @@ "source": [ "### Few-shot learning with custom dataset\n", "\n", - "You can also use any custom dataset and generate prompts like above. For example, below we will use twitter-sentiment-analysis. More datasets in huggingface can be found here" + "You can also use any custom dataset and generate prompts like above. For example, below we will use twitter-sentiment-analysis. More datasets in huggingface can be found here." ] }, {