mirror of
https://github.com/qdrant/fastembed.git
synced 2026-07-23 11:20:51 -05:00
Replace Data Source (#206)
* Re-run of identical hardware and generate graphs * Re-run of identical hardware and generate graphs Fixes https://github.com/qdrant/fastembed/issues/174 * Change dataset source * Refactor code for better readability and maintainability * Inline outputs * Replace hard coded constants with dataset specific n_dim * fix: fix binary quant from scratch notebook * fix: fix result table, explain corner case --------- Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
This commit is contained in:
Nirant
@@ -14,23 +14,33 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:00:06.460001Z",
|
||||
"start_time": "2024-06-06T17:00:04.214098Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install matplotlib tqdm pandas numpy --quiet"
|
||||
"!pip install matplotlib tqdm pandas numpy datasets --quiet --upgrade"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:00:07.041784Z",
|
||||
"start_time": "2024-06-06T17:00:06.461658Z"
|
||||
},
|
||||
"id": "WBVTItUX4yyr"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from datasets import load_dataset\n",
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
@@ -52,8 +62,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:01:09.343230Z",
|
||||
"start_time": "2024-06-06T17:00:07.042526Z"
|
||||
},
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 250
|
||||
@@ -61,58 +75,24 @@
|
||||
"id": "REJpFqkG7EG2",
|
||||
"outputId": "7a43c0ae-fbcc-45fe-fd58-bfe691297b22"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 26/26 [00:10<00:00, 2.45it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(1000000, 1536)"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_openai_vectors(force_download: bool = False):\n",
|
||||
" res = []\n",
|
||||
" for i in tqdm(range(26)):\n",
|
||||
" if force_download:\n",
|
||||
" !wget https://huggingface.co/api/datasets/KShivendu/dbpedia-entities-openai-1M/parquet/KShivendu--dbpedia-entities-openai-1M/train/{i}.parquet\n",
|
||||
" df = pd.read_parquet(f\"{i}.parquet\", engine=\"pyarrow\")\n",
|
||||
" res.append(np.stack(df.openai))\n",
|
||||
" del df\n",
|
||||
"\n",
|
||||
" openai_vectors = np.concatenate(res)\n",
|
||||
" del res\n",
|
||||
" return openai_vectors\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"openai_vectors = get_openai_vectors(force_download=False)\n",
|
||||
"openai_vectors.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## ㆓ Binary Conversion\n",
|
||||
"\n",
|
||||
"Here, we will use 0 as the threshold for the binary conversion. All values greater than 0 will be set to 1, and others will remain 0. This is a simple and effective way to convert continuous values into binary values for OpenAI embeddings."
|
||||
"# Download from Huggingface Hub\n",
|
||||
"ds = load_dataset(\n",
|
||||
" \"Qdrant/dbpedia-entities-openai3-text-embedding-3-large-3072-100K\", split=\"train\"\n",
|
||||
")\n",
|
||||
"openai_vectors = np.array(ds[\"text-embedding-3-large-3072-embedding\"])\n",
|
||||
"del ds"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"id": "0JM2-Bj2Jkab"
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:01:10.900963Z",
|
||||
"start_time": "2024-06-06T17:01:09.344842Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -120,6 +100,30 @@
|
||||
"openai_bin[openai_vectors > 0] = 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:01:10.906827Z",
|
||||
"start_time": "2024-06-06T17:01:10.901820Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "3072"
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"n_dim = openai_vectors.shape[1]\n",
|
||||
"n_dim"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -131,8 +135,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:01:10.909730Z",
|
||||
"start_time": "2024-06-06T17:01:10.908166Z"
|
||||
},
|
||||
"id": "FqshI-GlIERd"
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -141,7 +149,7 @@
|
||||
" scores = np.dot(openai_vectors, openai_vectors[idx])\n",
|
||||
" dot_results = np.argsort(scores)[-limit:][::-1]\n",
|
||||
"\n",
|
||||
" bin_scores = 1536 - np.logical_xor(openai_bin, openai_bin[idx]).sum(axis=1)\n",
|
||||
" bin_scores = n_dim - np.logical_xor(openai_bin, openai_bin[idx]).sum(axis=1)\n",
|
||||
" bin_results = np.argsort(bin_scores)[-(limit * oversampling) :][::-1]\n",
|
||||
"\n",
|
||||
" return len(set(dot_results).intersection(set(bin_results))) / limit"
|
||||
@@ -156,8 +164,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:01:25.206592Z",
|
||||
"start_time": "2024-06-06T17:01:10.911971Z"
|
||||
},
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
@@ -169,110 +181,128 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0%| | 0/4 [00:00<?, ?it/s]"
|
||||
" 0%| | 0/4 [00:00<?, ?it/s]\n",
|
||||
" 0%| | 0/2 [00:00<?, ?it/s]\u001b[A\n",
|
||||
" 50%|█████ | 1/2 [00:02<00:02, 2.05s/it]\u001b[A"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 1, 'limit': 10, 'recall': 0.8}\n"
|
||||
"{'sampling_rate': 1, 'limit': 3, 'mean_acc': 0.9}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 2/2 [00:33<00:00, 16.98s/it]\n",
|
||||
" 25%|██▌ | 1/4 [00:33<01:41, 33.96s/it]"
|
||||
"\n",
|
||||
"100%|██████████| 2/2 [00:04<00:00, 2.02s/it]\u001b[A\n",
|
||||
" 25%|██▌ | 1/4 [00:04<00:12, 4.05s/it]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 1, 'limit': 100, 'recall': 0.708}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": []
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 2, 'limit': 10, 'recall': 0.95}\n"
|
||||
"{'sampling_rate': 1, 'limit': 10, 'mean_acc': 0.8300000000000001}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 2/2 [00:32<00:00, 16.38s/it]\n",
|
||||
" 50%|█████ | 2/4 [01:06<01:06, 33.26s/it]"
|
||||
"\n",
|
||||
" 0%| | 0/2 [00:00<?, ?it/s]\u001b[A\n",
|
||||
" 50%|█████ | 1/2 [00:01<00:01, 1.72s/it]\u001b[A"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 2, 'limit': 100, 'recall': 0.877}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": []
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 3, 'limit': 10, 'recall': 0.96}\n"
|
||||
"{'sampling_rate': 2, 'limit': 3, 'mean_acc': 1.0}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 2/2 [00:32<00:00, 16.49s/it]\n",
|
||||
" 75%|███████▌ | 3/4 [01:39<00:33, 33.13s/it]"
|
||||
"\n",
|
||||
"100%|██████████| 2/2 [00:03<00:00, 1.76s/it]\u001b[A\n",
|
||||
" 50%|█████ | 2/4 [00:07<00:07, 3.75s/it]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 3, 'limit': 100, 'recall': 0.937}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": []
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 5, 'limit': 10, 'recall': 0.9800000000000001}\n"
|
||||
"{'sampling_rate': 2, 'limit': 10, 'mean_acc': 0.9700000000000001}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 2/2 [00:32<00:00, 16.47s/it]\n",
|
||||
"100%|██████████| 4/4 [02:12<00:00, 33.17s/it]"
|
||||
"\n",
|
||||
" 0%| | 0/2 [00:00<?, ?it/s]\u001b[A\n",
|
||||
" 50%|█████ | 1/2 [00:01<00:01, 1.72s/it]\u001b[A"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 5, 'limit': 100, 'recall': 0.977}\n"
|
||||
"{'sampling_rate': 3, 'limit': 3, 'mean_acc': 1.0}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"100%|██████████| 2/2 [00:03<00:00, 1.69s/it]\u001b[A\n",
|
||||
" 75%|███████▌ | 3/4 [00:10<00:03, 3.58s/it]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 3, 'limit': 10, 'mean_acc': 0.9800000000000001}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
" 0%| | 0/2 [00:00<?, ?it/s]\u001b[A\n",
|
||||
" 50%|█████ | 1/2 [00:01<00:01, 1.68s/it]\u001b[A"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 5, 'limit': 3, 'mean_acc': 1.0}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"100%|██████████| 2/2 [00:03<00:00, 1.65s/it]\u001b[A\n",
|
||||
"100%|██████████| 4/4 [00:14<00:00, 3.57s/it]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'sampling_rate': 5, 'limit': 10, 'mean_acc': 0.99}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -285,119 +315,53 @@
|
||||
],
|
||||
"source": [
|
||||
"number_of_samples = 10\n",
|
||||
"limits = [10, 100]\n",
|
||||
"limits = [3, 10]\n",
|
||||
"sampling_rate = [1, 2, 3, 5]\n",
|
||||
"results = []\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def mean_accuracy(number_of_samples, limit, sampling_rate):\n",
|
||||
" return np.mean([accuracy(i, limit=limit, oversampling=sampling_rate) for i in range(number_of_samples)])\n",
|
||||
" return np.mean(\n",
|
||||
" [accuracy(i, limit=limit, oversampling=sampling_rate) for i in range(number_of_samples)]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"for i in tqdm(sampling_rate):\n",
|
||||
" for j in tqdm(limits):\n",
|
||||
" result = {\"sampling_rate\": i, \"limit\": j, \"recall\": mean_accuracy(number_of_samples, j, i)}\n",
|
||||
" result = {\n",
|
||||
" \"sampling_rate\": i,\n",
|
||||
" \"limit\": j,\n",
|
||||
" \"mean_acc\": mean_accuracy(number_of_samples, j, i),\n",
|
||||
" }\n",
|
||||
" print(result)\n",
|
||||
" results.append(result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## ㆓ Binary Conversion\n",
|
||||
"\n",
|
||||
"Here, we will use 0 as the threshold for the binary conversion. All values greater than 0 will be set to 1, and others will remain 0. This is a simple and effective way to convert continuous values into binary values for OpenAI embeddings."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-06-06T17:01:25.247495Z",
|
||||
"start_time": "2024-06-06T17:01:25.213508Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>sampling_rate</th>\n",
|
||||
" <th>limit</th>\n",
|
||||
" <th>recall</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.800</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.708</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.950</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.877</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.960</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.937</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>0.980</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.977</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" sampling_rate limit recall\n",
|
||||
"0 1 10 0.800\n",
|
||||
"1 1 100 0.708\n",
|
||||
"2 2 10 0.950\n",
|
||||
"3 2 100 0.877\n",
|
||||
"4 3 10 0.960\n",
|
||||
"5 3 100 0.937\n",
|
||||
"6 5 10 0.980\n",
|
||||
"7 5 100 0.977"
|
||||
]
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>sampling_rate</th>\n <th>limit</th>\n <th>mean_acc</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>3</td>\n <td>0.90</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>10</td>\n <td>0.83</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>3</td>\n <td>1.00</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2</td>\n <td>10</td>\n <td>0.97</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3</td>\n <td>3</td>\n <td>1.00</td>\n </tr>\n <tr>\n <th>5</th>\n <td>3</td>\n <td>10</td>\n <td>0.98</td>\n </tr>\n <tr>\n <th>6</th>\n <td>5</td>\n <td>3</td>\n <td>1.00</td>\n </tr>\n <tr>\n <th>7</th>\n <td>5</td>\n <td>10</td>\n <td>0.99</td>\n </tr>\n </tbody>\n</table>\n</div>",
|
||||
"text/plain": " sampling_rate limit mean_acc\n0 1 3 0.90\n1 1 10 0.83\n2 2 3 1.00\n3 2 10 0.97\n4 3 3 1.00\n5 3 10 0.98\n6 5 3 1.00\n7 5 10 0.99"
|
||||
},
|
||||
"execution_count": 19,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -408,22 +372,13 @@
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"| sampling_rate | limit | accuracy |\n",
|
||||
"|---------------|-------|----------|\n",
|
||||
"| 1 | 10 | 0.800 |\n",
|
||||
"| 1 | 100 | 0.708 |\n",
|
||||
"| 2 | 10 | 0.950 |\n",
|
||||
"| 2 | 100 | 0.877 |\n",
|
||||
"| 4 | 10 | 0.970 |\n",
|
||||
"| 4 | 100 | 0.956 |\n",
|
||||
"| 8 | 10 | 0.990 |\n",
|
||||
"| 8 | 100 | 0.990 |\n",
|
||||
"| 16 | 10 | 1.000 |\n",
|
||||
"| 16 | 100 | 0.998 |"
|
||||
]
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
@@ -432,7 +387,8 @@
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
@@ -445,7 +401,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.17"
|
||||
"version": "3.10.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user