79 lines
1.9 KiB
Plaintext
79 lines
1.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "61f56afc-bc15-46a4-8eb1-d940c332cf52",
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"metadata": {},
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"source": [
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"# Meeting minutes creator\n",
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"\n",
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"In this colab, we make a meeting minutes program.\n",
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"\n",
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"It includes useful code to connect your Google Drive to your colab.\n",
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"\n",
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"Upload your own audio to make this work!!\n",
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"\n",
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"https://colab.research.google.com/drive/1KSMxOCprsl1QRpt_Rq0UqCAyMtPqDQYx?usp=sharing\n",
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"\n",
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"This should run nicely on a low-cost or free T4 box."
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]
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},
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{
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"cell_type": "markdown",
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"id": "501aa674",
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"metadata": {},
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"source": [
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"### BUT FIRST - Something cool - really showing you how \"model inference\" works via OpenAI"
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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": null,
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"id": "e9289ba7-200c-43a9-b67a-c5ce826c9537",
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"metadata": {},
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"outputs": [],
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"source": [
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"from visualizer import TokenPredictor, create_token_graph, visualize_predictions\n",
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"\n",
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"message = \"In one sentence, describe the color orange to someone who has never been able to see\"\n",
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"model_name = \"gpt-4.1-mini\"\n",
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"\n",
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"predictor = TokenPredictor(model_name)\n",
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"predictions = predictor.predict_tokens(message)\n",
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"G = create_token_graph(model_name, predictions)\n",
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"plt = visualize_predictions(G)\n",
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"plt.show()"
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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": null,
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"id": "540a8255",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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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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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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