You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
440 lines
20 KiB
Plaintext
440 lines
20 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "0wjP9mrldJsd"
|
|
},
|
|
"source": [
|
|
"## Visualizing the embeddings in Kangas\n",
|
|
"\n",
|
|
"In this Jupyter Notebook, we construct a Kangas DataGrid containing the data and projections of the embeddings into 2 dimensions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "4tPKQqqldJsj"
|
|
},
|
|
"source": [
|
|
"## What is Kangas?\n",
|
|
"\n",
|
|
"[Kangas](https://github.com/comet-ml/kangas/) as an open source, mixed-media, dataframe-like tool for data scientists. It was developed by [Comet](https://comet.com/), a company designed to help reduce the friction of moving models into production. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "6sNsB2iFdJsk"
|
|
},
|
|
"source": [
|
|
"### 1. Setup\n",
|
|
"\n",
|
|
"To get started, we pip install kangas, and import it."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "N8gi529adL-f",
|
|
"outputId": "c12e9973-a179-41e3-c5a8-f241804d99ad"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%pip install kangas --quiet"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {
|
|
"id": "htxjXThodRxD"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import kangas as kg"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 2. Constructing a Kangas DataGrid\n",
|
|
"\n",
|
|
"We create a Kangas Datagrid with the original data and the embeddings. The data is composed of a rows of reviews, and the embeddings are composed of 1536 floating-point values. In this example, we get the data directly from github, in case you aren't running this notebook inside OpenAI's repo.\n",
|
|
"\n",
|
|
"We use Kangas to read the CSV file into a DataGrid for further processing."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "0SxWlRTrdVJq",
|
|
"outputId": "d36c3a14-2e80-4315-e285-f39f6b008976"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Loading CSV file 'fine_food_reviews_with_embeddings_1k.csv'...\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1001it [00:00, 2412.90it/s]\n",
|
|
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 2899.16it/s]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data = kg.read_csv(\"https://raw.githubusercontent.com/openai/openai-cookbook/main/examples/data/fine_food_reviews_with_embeddings_1k.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can review the fields of the CSV file:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "bzhQgoRGeMCp",
|
|
"outputId": "791c4e40-fb28-409e-d1e9-20b753fb1215"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"DataGrid (in memory)\n",
|
|
" Name : fine_food_reviews_with_embeddings_1k\n",
|
|
" Rows : 1,000\n",
|
|
" Columns: 9\n",
|
|
"# Column Non-Null Count DataGrid Type \n",
|
|
"--- -------------------- --------------- --------------------\n",
|
|
"1 Column 1 1,000 INTEGER \n",
|
|
"2 ProductId 1,000 TEXT \n",
|
|
"3 UserId 1,000 TEXT \n",
|
|
"4 Score 1,000 INTEGER \n",
|
|
"5 Summary 1,000 TEXT \n",
|
|
"6 Text 1,000 TEXT \n",
|
|
"7 combined 1,000 TEXT \n",
|
|
"8 n_tokens 1,000 INTEGER \n",
|
|
"9 embedding 1,000 TEXT \n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"data.info()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"And get a glimpse of the first and last rows:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 349
|
|
},
|
|
"id": "Q95N832aeaBr",
|
|
"outputId": "aaea2816-e5a1-4e52-f228-c3e6aca6fa3e"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<table><th colspan='1' > row-id </th> <th colspan='1' > Column 1 </th> <th colspan='1' > ProductId </th> <th colspan='1' > UserId </th> <th colspan='1' > Score </th> <th colspan='1' > Summary </th> <th colspan='1' > Text </th> <th colspan='1' > combined </th> <th colspan='1' > n_tokens </th> <th colspan='1' > embedding </th> <tr>\n",
|
|
"<td colspan='1' > 1 </td> <td colspan='1' > 0 </td> <td colspan='1' > B003XPF9BO </td> <td colspan='1' > A3R7JR3FMEBXQB </td> <td colspan='1' > 5 </td> <td colspan='1' > where does one </td> <td colspan='1' > Wanted to save </td> <td colspan='1' > Title: where do </td> <td colspan='1' > 52 </td> <td colspan='1' > [0.007018072064 </td> <tr>\n",
|
|
"<td colspan='1' > 2 </td> <td colspan='1' > 297 </td> <td colspan='1' > B003VXHGPK </td> <td colspan='1' > A21VWSCGW7UUAR </td> <td colspan='1' > 4 </td> <td colspan='1' > Good, but not W </td> <td colspan='1' > Honestly, I hav </td> <td colspan='1' > Title: Good, bu </td> <td colspan='1' > 178 </td> <td colspan='1' > [-0.00314055196 </td> <tr>\n",
|
|
"<td colspan='1' > 3 </td> <td colspan='1' > 296 </td> <td colspan='1' > B008JKTTUA </td> <td colspan='1' > A34XBAIFT02B60 </td> <td colspan='1' > 1 </td> <td colspan='1' > Should advertis </td> <td colspan='1' > First, these sh </td> <td colspan='1' > Title: Should a </td> <td colspan='1' > 78 </td> <td colspan='1' > [-0.01757248118 </td> <tr>\n",
|
|
"<td colspan='1' > 4 </td> <td colspan='1' > 295 </td> <td colspan='1' > B000LKTTTW </td> <td colspan='1' > A14MQ40CCU8B13 </td> <td colspan='1' > 5 </td> <td colspan='1' > Best tomato sou </td> <td colspan='1' > I have a hard t </td> <td colspan='1' > Title: Best tom </td> <td colspan='1' > 111 </td> <td colspan='1' > [-0.00139322795 </td> <tr>\n",
|
|
"<td colspan='1' > 5 </td> <td colspan='1' > 294 </td> <td colspan='1' > B001D09KAM </td> <td colspan='1' > A34XBAIFT02B60 </td> <td colspan='1' > 1 </td> <td colspan='1' > Should advertis </td> <td colspan='1' > First, these sh </td> <td colspan='1' > Title: Should a </td> <td colspan='1' > 78 </td> <td colspan='1' > [-0.01757248118 </td> <tr>\n",
|
|
"<tr><td colspan='10' style='text-align: left;'>...</td></tr><td colspan='1' > 996 </td> <td colspan='1' > 623 </td> <td colspan='1' > B0000CFXYA </td> <td colspan='1' > A3GS4GWPIBV0NT </td> <td colspan='1' > 1 </td> <td colspan='1' > Strange inflamm </td> <td colspan='1' > Truthfully wasn </td> <td colspan='1' > Title: Strange </td> <td colspan='1' > 110 </td> <td colspan='1' > [0.000110913533 </td> <tr>\n",
|
|
"<td colspan='1' > 997 </td> <td colspan='1' > 624 </td> <td colspan='1' > B0001BH5YM </td> <td colspan='1' > A1BZ3HMAKK0NC </td> <td colspan='1' > 5 </td> <td colspan='1' > My favorite and </td> <td colspan='1' > You've just got </td> <td colspan='1' > Title: My favor </td> <td colspan='1' > 80 </td> <td colspan='1' > [-0.02086931467 </td> <tr>\n",
|
|
"<td colspan='1' > 998 </td> <td colspan='1' > 625 </td> <td colspan='1' > B0009ET7TC </td> <td colspan='1' > A2FSDQY5AI6TNX </td> <td colspan='1' > 5 </td> <td colspan='1' > My furbabies LO </td> <td colspan='1' > Shake the conta </td> <td colspan='1' > Title: My furba </td> <td colspan='1' > 47 </td> <td colspan='1' > [-0.00974910240 </td> <tr>\n",
|
|
"<td colspan='1' > 999 </td> <td colspan='1' > 619 </td> <td colspan='1' > B007PA32L2 </td> <td colspan='1' > A15FF2P7RPKH6G </td> <td colspan='1' > 5 </td> <td colspan='1' > got this for th </td> <td colspan='1' > all i have hear </td> <td colspan='1' > Title: got this </td> <td colspan='1' > 50 </td> <td colspan='1' > [-0.00521062919 </td> <tr>\n",
|
|
"<td colspan='1' > 1000 </td> <td colspan='1' > 999 </td> <td colspan='1' > B001EQ5GEO </td> <td colspan='1' > A3VYU0VO6DYV6I </td> <td colspan='1' > 5 </td> <td colspan='1' > I love Maui Cof </td> <td colspan='1' > My first experi </td> <td colspan='1' > Title: I love M </td> <td colspan='1' > 118 </td> <td colspan='1' > [-0.00605782261 </td> <tr>\n",
|
|
"<tr>\n",
|
|
"<td colspan='10' style=\"text-align: left;\"> [1000 rows x 9 columns] </td> <tr>\n",
|
|
"<tr><td colspan='10' style='text-align: left;'></td></tr><tr><td colspan='10' style='text-align: left;'>* Use DataGrid.save() to save to disk</td></tr><tr><td colspan='10' style='text-align: left;'>** Use DataGrid.show() to start user interface</td></tr></table>"
|
|
],
|
|
"text/plain": [
|
|
"<th colspan='1' > row-id </th> <th colspan='1' > Column 1 </th> <th colspan='1' > ProductId </th> <th colspan='1' > UserId </th> <th colspan='1' > Score </th> <th colspan='1' > Summary </th> <th colspan='1' > Text </th> <th colspan='1' > combined </th> <th colspan='1' > n_tokens </th> <th colspan='1' > embedding </th> <tr>\n",
|
|
"<td colspan='1' > 1 </td> <td colspan='1' > 0 </td> <td colspan='1' > B003XPF9BO </td> <td colspan='1' > A3R7JR3FMEBXQB </td> <td colspan='1' > 5 </td> <td colspan='1' > where does one </td> <td colspan='1' > Wanted to save </td> <td colspan='1' > Title: where do </td> <td colspan='1' > 52 </td> <td colspan='1' > [0.007018072064 </td> <tr>\n",
|
|
"<td colspan='1' > 2 </td> <td colspan='1' > 297 </td> <td colspan='1' > B003VXHGPK </td> <td colspan='1' > A21VWSCGW7UUAR </td> <td colspan='1' > 4 </td> <td colspan='1' > Good, but not W </td> <td colspan='1' > Honestly, I hav </td> <td colspan='1' > Title: Good, bu </td> <td colspan='1' > 178 </td> <td colspan='1' > [-0.00314055196 </td> <tr>\n",
|
|
"<td colspan='1' > 3 </td> <td colspan='1' > 296 </td> <td colspan='1' > B008JKTTUA </td> <td colspan='1' > A34XBAIFT02B60 </td> <td colspan='1' > 1 </td> <td colspan='1' > Should advertis </td> <td colspan='1' > First, these sh </td> <td colspan='1' > Title: Should a </td> <td colspan='1' > 78 </td> <td colspan='1' > [-0.01757248118 </td> <tr>\n",
|
|
"<td colspan='1' > 4 </td> <td colspan='1' > 295 </td> <td colspan='1' > B000LKTTTW </td> <td colspan='1' > A14MQ40CCU8B13 </td> <td colspan='1' > 5 </td> <td colspan='1' > Best tomato sou </td> <td colspan='1' > I have a hard t </td> <td colspan='1' > Title: Best tom </td> <td colspan='1' > 111 </td> <td colspan='1' > [-0.00139322795 </td> <tr>\n",
|
|
"<td colspan='1' > 5 </td> <td colspan='1' > 294 </td> <td colspan='1' > B001D09KAM </td> <td colspan='1' > A34XBAIFT02B60 </td> <td colspan='1' > 1 </td> <td colspan='1' > Should advertis </td> <td colspan='1' > First, these sh </td> <td colspan='1' > Title: Should a </td> <td colspan='1' > 78 </td> <td colspan='1' > [-0.01757248118 </td> <tr>\n",
|
|
"...\n",
|
|
"<td colspan='1' > 996 </td> <td colspan='1' > 623 </td> <td colspan='1' > B0000CFXYA </td> <td colspan='1' > A3GS4GWPIBV0NT </td> <td colspan='1' > 1 </td> <td colspan='1' > Strange inflamm </td> <td colspan='1' > Truthfully wasn </td> <td colspan='1' > Title: Strange </td> <td colspan='1' > 110 </td> <td colspan='1' > [0.000110913533 </td> <tr>\n",
|
|
"<td colspan='1' > 997 </td> <td colspan='1' > 624 </td> <td colspan='1' > B0001BH5YM </td> <td colspan='1' > A1BZ3HMAKK0NC </td> <td colspan='1' > 5 </td> <td colspan='1' > My favorite and </td> <td colspan='1' > You've just got </td> <td colspan='1' > Title: My favor </td> <td colspan='1' > 80 </td> <td colspan='1' > [-0.02086931467 </td> <tr>\n",
|
|
"<td colspan='1' > 998 </td> <td colspan='1' > 625 </td> <td colspan='1' > B0009ET7TC </td> <td colspan='1' > A2FSDQY5AI6TNX </td> <td colspan='1' > 5 </td> <td colspan='1' > My furbabies LO </td> <td colspan='1' > Shake the conta </td> <td colspan='1' > Title: My furba </td> <td colspan='1' > 47 </td> <td colspan='1' > [-0.00974910240 </td> <tr>\n",
|
|
"<td colspan='1' > 999 </td> <td colspan='1' > 619 </td> <td colspan='1' > B007PA32L2 </td> <td colspan='1' > A15FF2P7RPKH6G </td> <td colspan='1' > 5 </td> <td colspan='1' > got this for th </td> <td colspan='1' > all i have hear </td> <td colspan='1' > Title: got this </td> <td colspan='1' > 50 </td> <td colspan='1' > [-0.00521062919 </td> <tr>\n",
|
|
"<td colspan='1' > 1000 </td> <td colspan='1' > 999 </td> <td colspan='1' > B001EQ5GEO </td> <td colspan='1' > A3VYU0VO6DYV6I </td> <td colspan='1' > 5 </td> <td colspan='1' > I love Maui Cof </td> <td colspan='1' > My first experi </td> <td colspan='1' > Title: I love M </td> <td colspan='1' > 118 </td> <td colspan='1' > [-0.00605782261 </td> <tr>\n",
|
|
"<tr>\n",
|
|
"<td colspan='10' style=\"text-align: left;\"> [1000 rows x 9 columns] </td> <tr>\n",
|
|
"\n",
|
|
"* Use DataGrid.save() to save to disk\n",
|
|
"** Use DataGrid.show() to start user interface"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now, we create a new DataGrid, converting the numbers into an Embedding:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"id": "Bu0erP68dvLU"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import ast # to convert string of a list of numbers into a list of numbers\n",
|
|
"\n",
|
|
"dg = kg.DataGrid(\n",
|
|
" name=\"openai_embeddings\",\n",
|
|
" columns=data.get_columns(),\n",
|
|
" converters={\"Score\": str},\n",
|
|
")\n",
|
|
"for row in data:\n",
|
|
" embedding = ast.literal_eval(row[8])\n",
|
|
" row[8] = kg.Embedding(\n",
|
|
" embedding, \n",
|
|
" name=str(row[3]), \n",
|
|
" text=\"%s - %.10s\" % (row[3], row[4]),\n",
|
|
" projection=\"umap\",\n",
|
|
" )\n",
|
|
" dg.append(row)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The new DataGrid now has an Embedding column with proper datatype."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "gd6Od4Bmhijy",
|
|
"outputId": "9aa38221-0272-4a63-e393-706e0a0c5879"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"DataGrid (in memory)\n",
|
|
" Name : openai_embeddings\n",
|
|
" Rows : 1,000\n",
|
|
" Columns: 9\n",
|
|
"# Column Non-Null Count DataGrid Type \n",
|
|
"--- -------------------- --------------- --------------------\n",
|
|
"1 Column 1 1,000 INTEGER \n",
|
|
"2 ProductId 1,000 TEXT \n",
|
|
"3 UserId 1,000 TEXT \n",
|
|
"4 Score 1,000 TEXT \n",
|
|
"5 Summary 1,000 TEXT \n",
|
|
"6 Text 1,000 TEXT \n",
|
|
"7 combined 1,000 TEXT \n",
|
|
"8 n_tokens 1,000 INTEGER \n",
|
|
"9 embedding 1,000 EMBEDDING-ASSET \n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"dg.info()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We simply save the datagrid, and we're done."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"dg.save()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 3. Render 2D Projections\n",
|
|
"\n",
|
|
"To render the data directly in the notebook, simply show it. Note that each row contains an embedding projection. \n",
|
|
"\n",
|
|
"Scroll to far right to see embeddings projection per row.\n",
|
|
"\n",
|
|
"The color of the point in projection space represents the Score."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 771
|
|
},
|
|
"id": "Z8j-GdpiijU0",
|
|
"outputId": "20a0b1ca-3059-4384-cd8c-b32b1aa1c270"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"\n",
|
|
" <iframe\n",
|
|
" width=\"100%\"\n",
|
|
" height=\"750px\"\n",
|
|
" src=\"http://127.0.1.1:4000/?datagrid=openai_embeddings.datagrid×tamp=1685559502.7515423\"\n",
|
|
" frameborder=\"0\"\n",
|
|
" allowfullscreen\n",
|
|
" \n",
|
|
" ></iframe>\n",
|
|
" "
|
|
],
|
|
"text/plain": [
|
|
"<IPython.lib.display.IFrame at 0x7fcdd16bfbe0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"dg.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Group by \"Score\" to see rows of each group."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"\n",
|
|
" <iframe\n",
|
|
" width=\"100%\"\n",
|
|
" height=\"750px\"\n",
|
|
" src=\"http://127.0.1.1:4000/?datagrid=openai_embeddings.datagrid×tamp=1685559502.7515423&group=Score&sort=Score&rows=5&select=Score%2Cembedding\"\n",
|
|
" frameborder=\"0\"\n",
|
|
" allowfullscreen\n",
|
|
" \n",
|
|
" ></iframe>\n",
|
|
" "
|
|
],
|
|
"text/plain": [
|
|
"<IPython.lib.display.IFrame at 0x7fcd06209180>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"dg.show(group=\"Score\", sort=\"Score\", rows=5, select=\"Score,embedding\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "vLIxfmK5dJsq"
|
|
},
|
|
"source": [
|
|
"An example of this datagrid is hosted here: https://kangas.comet.com/?datagrid=/data/openai_embeddings.datagrid"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"accelerator": "TPU",
|
|
"colab": {
|
|
"gpuType": "V100",
|
|
"machine_shape": "hm",
|
|
"provenance": []
|
|
},
|
|
"gpuClass": "standard",
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.11"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|