Implement Hugging Face few shot inference

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 <a href=\"https://huggingface.co/EleutherAI/gpt-neo-1.3B\">gpt-neo-1.3B</a> 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 <a href=\"https://huggingface.co/EleutherAI/gpt-neo-1.3B\">gpt-neo-1.3B</a> model; additional GPT-based models can be explored <a href=\"https://huggingface.co/EleutherAI\">here</a>. Developed by <a href=\"https://www.eleuther.ai/\">EleutherAI</a>, 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 <a href=\"https://huggingface.co/datasets/carblacac/twitter-sentiment-analysis\">twitter-sentiment-analysis</a>. More datasets in huggingface can be found <a href=\"https://huggingface.co/datasets\">here</a>"
+    "You can also use any custom dataset and generate prompts like above. For example, below we will use <a href=\"https://huggingface.co/datasets/carblacac/twitter-sentiment-analysis\">twitter-sentiment-analysis</a>. More datasets in huggingface can be found <a href=\"https://huggingface.co/datasets\">here</a>."
    ]
   },
   {