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update notebooks
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‎notebooks/llmu/Classify_Endpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/Classify_Endpoint.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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"</a>"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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"# The Classify Endpoint\n",
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"\n",
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"In this lab, we'll learn how to use Cohere's Classify cohere endpoint. This codelab accompanies the [Classify endpoint lesson](https://docs.cohere.com/docs/classify-endpoint/) of LLM University.\n",
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"\n",
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"This is part of a bigger [colab](https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/Hello_World_Meet_Language_AI.ipynb#) containing more endpoints, please feel free to check it out!"
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"In this lab, we'll learn how to use Cohere's Classify cohere endpoint. This codelab accompanies the [Classify endpoint lesson](https://docs.cohere.com/docs/classify-endpoint/) of LLM University."
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]
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},
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{
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"outputs": [],
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"source": [
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"# Install the libraries\n",
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"# TODO: upgrade to \"cohere>5\"",
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"! pip install \"cohere<5\" altair umap-learn > /dev/null"
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"# TODO: upgrade to \"cohere>5\"\n",
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"! pip install cohere altair umap-learn > /dev/null"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"id": "YYi9JxxFRWX7"
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},
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import the libraries\n",
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"import textwrap as tr\n",
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"\n",
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"# Setup the Cohere client\n",
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"api_key = 'api_key' # Paste your API key here. Remember to not share it publicly\n",
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"co = cohere.Client(api_key)"
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"co = cohere.Client(\"COHERE_API_KEY\") # Get your API key here: https://dashboard.cohere.com/api-keys"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"metadata": {
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"id": "-lSi_UmQEfy_"
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},
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"outputs": [],
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"source": [
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"# Create the training examples for the classifier\n",
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"from cohere.responses.classify import Example\n",
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"\n",
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"examples = [Example(\"I’m so proud of you\", \"positive\"), \n",
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" Example(\"What a great time to be alive\", \"positive\"), \n",
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" Example(\"That’s awesome work\", \"positive\"), \n",
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" Example(\"The service was amazing\", \"positive\"), \n",
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" Example(\"I love my family\", \"positive\"), \n",
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" Example(\"They don't care about me\", \"negative\"), \n",
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" Example(\"I hate this place\", \"negative\"), \n",
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" Example(\"The most ridiculous thing I've ever heard\", \"negative\"), \n",
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" Example(\"I am really frustrated\", \"negative\"), \n",
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" Example(\"This is so unfair\", \"negative\"),\n",
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" Example(\"This made me think\", \"neutral\"), \n",
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" Example(\"The good old days\", \"neutral\"), \n",
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" Example(\"What's the difference\", \"neutral\"), \n",
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" Example(\"You can't ignore this\", \"neutral\"), \n",
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" Example(\"That's how I see it\", \"neutral\") \n",
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" ]"
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"from cohere import ClassifyExample\n",
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"\n",
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"examples = [ClassifyExample(text=\"I’m so proud of you\", label=\"positive\"), \n",
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" ClassifyExample(text=\"What a great time to be alive\", label=\"positive\"), \n",
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" ClassifyExample(text=\"That’s awesome work\", label=\"positive\"), \n",
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" ClassifyExample(text=\"The service was amazing\", label=\"positive\"), \n",
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" ClassifyExample(text=\"I love my family\", label=\"positive\"), \n",
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" ClassifyExample(text=\"They don't care about me\", label=\"negative\"), \n",
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" ClassifyExample(text=\"I hate this place\", label=\"negative\"), \n",
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" ClassifyExample(text=\"The most ridiculous thing I've ever heard\", label=\"negative\"), \n",
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" ClassifyExample(text=\"I am really frustrated\", label=\"negative\"), \n",
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" ClassifyExample(text=\"This is so unfair\", label=\"negative\"),\n",
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" ClassifyExample(text=\"This made me think\", label=\"neutral\"), \n",
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" ClassifyExample(text=\"The good old days\", label=\"neutral\"), \n",
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" ClassifyExample(text=\"What's the difference\", label=\"neutral\"), \n",
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" ClassifyExample(text=\"You can't ignore this\", label=\"neutral\"), \n",
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" ClassifyExample(text=\"That's how I see it\", label=\"neutral\")]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 9,
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"metadata": {
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"id": "QpC1Z4xEEWs0"
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},
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" \"This is the worst thing\",\n",
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" \"I cannot stand this any longer\",\n",
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" \"This is really annoying\",\n",
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" \"I am just plain fed up\"\n",
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" ]"
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" \"I am just plain fed up\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 10,
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"metadata": {
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"id": "etTr200IRszm"
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},
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 11,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"text": [
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"Input: Hello, world! What a beautiful day\n",
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"Prediction: positive\n",
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"Confidence: 0.83\n",
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"Confidence: 0.40\n",
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"----------\n",
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"Input: It was a great time with great people\n",
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"Prediction: positive\n",
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"Confidence: 0.99\n",
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"Confidence: 0.49\n",
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"----------\n",
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"Input: Great place to work\n",
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"Prediction: positive\n",
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"Confidence: 0.91\n",
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"Confidence: 0.50\n",
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"----------\n",
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"Input: That was a wonderful evening\n",
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"Prediction: positive\n",
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"Confidence: 0.96\n",
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"Confidence: 0.48\n",
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"----------\n",
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"Input: Maybe this is why\n",
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"Prediction: neutral\n",
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"Confidence: 0.70\n",
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"Confidence: 0.45\n",
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"----------\n",
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"Input: Let's start again\n",
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"Prediction: neutral\n",
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"Confidence: 0.83\n",
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"Confidence: 0.42\n",
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"----------\n",
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"Input: That's how I see it\n",
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"Prediction: neutral\n",
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"Confidence: 1.00\n",
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"Confidence: 0.53\n",
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"----------\n",
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"Input: These are all facts\n",
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"Prediction: neutral\n",
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"Confidence: 0.78\n",
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"Confidence: 0.41\n",
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"----------\n",
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"Input: This is the worst thing\n",
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"Prediction: negative\n",
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"Confidence: 0.93\n",
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"Confidence: 0.52\n",
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"----------\n",
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"Input: I cannot stand this any longer\n",
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"Prediction: negative\n",
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"Confidence: 0.93\n",
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"Confidence: 0.52\n",
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"----------\n",
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"Input: This is really annoying\n",
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"Prediction: negative\n",
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"Confidence: 0.99\n",
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"Confidence: 0.56\n",
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"----------\n",
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"Input: I am just plain fed up\n",
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"Prediction: negative\n",
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"Confidence: 1.00\n",
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"Confidence: 0.57\n",
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"----------\n"
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]
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}
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"version": "3.10.0"
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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},
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"vscode": {
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"interpreter": {

‎notebooks/llmu/Embed_Endpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/Embed_Endpoint.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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"</a>"
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]
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},
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{
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"source": [
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"# The Embed Endpoint\n",
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"\n",
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"In this lab, we'll learn how to analyze a text dataset using Cohere's Embed cohere endpoint. This colab accompanies the [Classify endpoint lesson](https://docs.cohere.com/docs/embed-endpoint/) of LLM University.\n",
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"\n",
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"This is part of a bigger [colab](https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/Hello_World_Meet_Language_AI.ipynb#) containing more endpoints, please feel free to check it out!"
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"In this lab, we'll learn how to analyze a text dataset using Cohere's Embed cohere endpoint. This colab accompanies the [Classify endpoint lesson](https://docs.cohere.com/docs/embed-endpoint/) of LLM University."
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]
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{
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"outputs": [],
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"source": [
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"# Install the libraries\n",
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"# TODO: upgrade to \"cohere>5\"",
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"! pip install \"cohere<5\" altair umap-learn -q"
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"# TODO: upgrade to \"cohere>5\"\n",
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"! pip install cohere altair umap-learn -q"
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]
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},
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{
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"import textwrap as tr\n",
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"\n",
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"# Setup the Cohere client\n",
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"api_key = 'COHERE_API_KEY' # Paste your API key here. Remember to not share it publicly\n",
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"co = cohere.Client(api_key)"
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"co = cohere.Client(\"COHERE_API_KEY\") # Get your API key here: https://dashboard.cohere.com/api-keys"
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]
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"language_info": {
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"version": "3.11.4"
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},
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"vscode": {
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"interpreter": {

‎notebooks/llmu/End_To_End_Wikipedia_Search.ipynb

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{
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/End_To_End_Wikipedia_Search.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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"</a>"
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]
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],
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"# TODO: upgrade to \"cohere>5\"\n",
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"!pip install cohere weaviate-client"
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"! pip install cohere weaviate-client"
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{
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"import weaviate\n",
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"import cohere\n",
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"\n",
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"# Add your Cohere API key here\n",
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"# You can obtain a key by signing up in https://dashboard.cohere.com/ or https://docs.cohere.com/reference/key\n",
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"cohere_api_key = ''\n",
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"\n",
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"co = cohere.Client(cohere_api_key)\n",
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"# Setup the Cohere client\n",
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"co = cohere.Client(\"COHERE_API_KEY\") # Get your API key here: https://dashboard.cohere.com/api-keys\n",
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"\n",
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"# Connect to the Weaviate demo databse containing 10M wikipedia vectors\n",
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"# This uses a public READ-ONLY Weaviate API key\n",
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"name": "python3"
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"language_info": {
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"version": "3.11.4"
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"nbformat": 4,

‎notebooks/llmu/Introduction_to_RAG.ipynb

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‎notebooks/llmu/RAG_over_Large_Scale_Data.ipynb

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"id": "CSKXot1mf3-B"
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},
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"source": [
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/RAG_over_Large_Scale_Data.ipynb\">\n",
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/RAG_over_Large_Scale_Data.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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"</a>"
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}
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],
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"source": [
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"! pip install \"cohere<5\" -q"
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"! pip install cohere -q"
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]
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},
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{

‎notebooks/llmu/RAG_with_Chat_Embed_and_Rerank.ipynb

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"id": "ctaLvRUsfpj8"
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/RAG_with_Chat_Embed_and_Rerank.ipynb\">\n",
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/RAG_with_Chat_Embed_and_Rerank.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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},
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"outputs": [],
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"source": [
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"! pip install \"cohere<5\" hnswlib unstructured -q"
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"! pip install cohere hnswlib unstructured -q"
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{

‎notebooks/llmu/RAG_with_Connectors.ipynb

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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/RAG_with_Connectors.ipynb\">\n",
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"<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/RAG_with_Connectors.ipynb\">\n",
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" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
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}
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],
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"! pip install \"cohere<5\" -q"
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"! pip install cohere -q"
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{
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"from cohere import ChatConnector\n",
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"from typing import List\n",
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"\n",
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"co = cohere.Client(\"COHERE_API_KEY\") # Get your API key here: https://dashboard.cohere.com/api-keys\n",
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"from google.colab import userdata\n",
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"co = cohere.Client(userdata.get('COHERE_API_KEY'))"
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"co = cohere.Client(\"COHERE_API_KEY\") # Get your API key here: https://dashboard.cohere.com/api-keys"
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]
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},
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{

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