implementing hugging face few shot inference

Hugging Face/Hugging_Face_Few_Shot_Learning_with_Inference_API.ipynb

@@ -70,7 +70,7 @@
     ]
    },
    "source": [
-    "**Description:** This notebook demonstrates how to utilize the <a href=\"https://huggingface.co/docs/inference-endpoints/index\">inference endpoints</a> of hugging face models. Additionally, it demonstrates how to use few shot learning for a specific task in a model."
+    "**Description:** This notebook demonstrates how to utilize the <a href=\"https://huggingface.co/docs/inference-endpoints/index\">inference endpoints</a> (additional information can be found here: <a href=\"https://huggingface.co/docs/api-inference/index#-hosted-inference-api\">link</a>) of hugging face models. Additionally, it demonstrates how to use few shot learning for a specific task in a model."
    ]
   },
   {
@@ -183,6 +183,14 @@
    "source": [
     "### Add the Model and API token\n",
     "\n",
+    "#### Steps to get API token\n",
+    "- Create an account on Hugging Face\n",
+    "- Log in, and click on profile icon (top right corner)\n",
+    "- Go to settings\n",
+    "- Click on Access tokens\n",
+    "- 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. "
    ]
   },
@@ -291,7 +299,7 @@
    "id": "f0724801-389c-4184-b3a1-a3491573e24e",
    "metadata": {},
    "source": [
-    " The model usually takes time to load in the hugging face server. For example, model gpt-neo-1.3B takes approximately 212 seconds"
+    "><b>The model usually takes time to load in the hugging face server. For example, model gpt-neo-1.3B takes approximately 212 seconds</b>"
    ]
   },
   {
@@ -302,7 +310,9 @@
     "tags": []
    },
    "source": [
-    "### Zero-shot"
+    "### Zero-shot\n",
+    "\n",
+    "Zero-shot learning means to generate meaningful responses from model for tasks or topics it has never been explicitly trained on, showcasing a capacity to generalize and understand novel concepts without specific examples during training."
    ]
   },
   {
@@ -357,7 +367,9 @@
     "tags": []
    },
    "source": [
-    "### One-shot"
+    "### One-shot\n",
+    "\n",
+    "One-shot learning refers to the model's ability to understand and generate meaningful responses after being exposed to a single example or prompt during the inference phase, showcasing its capacity to generalize knowledge from limited input."
    ]
   },
   {
@@ -412,7 +424,9 @@
    "id": "96831b89-e92f-4ddb-8703-0124c26c8613",
    "metadata": {},
    "source": [
-    "### Two-shot"
+    "### Two-shot\n",
+    "\n",
+    "Similar to one-shot, we will have the model exposed to two examples to generalize knowledge and make predictions."
    ]
   },
   {