Upload example.ipynb
Browse files- example.ipynb +1682 -0
example.ipynb
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| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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|
| 11 |
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},
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| 12 |
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|
| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 1414 |
+
"ab0c301db6104a37abb4051f9f90a896",
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| 1415 |
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"eb262072aa73425ba9bc5324f5920665",
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| 1416 |
+
"c475ac1a2f904d038944cb7e4a65812e",
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| 1417 |
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"e1af2b8270ca43aa9c9b0f7f4acd5300",
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| 1418 |
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"cd7b4ce4f200489cbfbf0174b0561511"
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+
]
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| 1420 |
+
},
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| 1421 |
+
"id": "4PwffMXrLsVy",
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| 1422 |
+
"outputId": "16bf648d-2275-40b0-c1db-fee21ee94699"
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| 1423 |
+
},
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| 1424 |
+
"execution_count": 4,
|
| 1425 |
+
"outputs": [
|
| 1426 |
+
{
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| 1427 |
+
"output_type": "stream",
|
| 1428 |
+
"name": "stdout",
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| 1429 |
+
"text": [
|
| 1430 |
+
"\n",
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| 1431 |
+
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
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| 1432 |
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" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
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" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
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" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
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" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
|
| 1436 |
+
" \n",
|
| 1437 |
+
" To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
|
| 1438 |
+
"Token: \n",
|
| 1439 |
+
"Add token as git credential? (Y/n) y\n",
|
| 1440 |
+
"Token is valid (permission: write).\n",
|
| 1441 |
+
"\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
|
| 1442 |
+
"You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
|
| 1443 |
+
"Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
|
| 1444 |
+
"\n",
|
| 1445 |
+
"git config --global credential.helper store\n",
|
| 1446 |
+
"\n",
|
| 1447 |
+
"Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
|
| 1448 |
+
"Token has not been saved to git credential helper.\n",
|
| 1449 |
+
"Your token has been saved to /root/.cache/huggingface/token\n",
|
| 1450 |
+
"Login successful\n"
|
| 1451 |
+
]
|
| 1452 |
+
},
|
| 1453 |
+
{
|
| 1454 |
+
"output_type": "display_data",
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"data": {
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"text/plain": [
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+
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
|
| 1458 |
+
],
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+
"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
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"version_minor": 0,
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"model_id": "afb4cf60647746cea3e1ef02bd4637fb"
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+
}
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},
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"metadata": {}
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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| 1473 |
+
"colab": {
|
| 1474 |
+
"base_uri": "https://localhost:8080/",
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+
"height": 101,
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| 1476 |
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"referenced_widgets": [
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| 1477 |
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"7ed17347ad114ee6a3f5bbe44547458e",
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| 1478 |
+
"c25700be00a04f41b6792c9a3e75cffc",
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| 1479 |
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"fa3fb7b518bf4d4f8b1201d16fe0767f",
|
| 1480 |
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"9ff4dbe4ff744d2aae82de08feead57f",
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| 1481 |
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"15c7a3f8f13d4812a436fe6ea3fb7573",
|
| 1482 |
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"895122e40b324be1aae88771173ac719",
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| 1483 |
+
"bb72a3986e8d4dd2a551cdcada837881",
|
| 1484 |
+
"8dd8f7deb5d64e80b30e348470ec21e6",
|
| 1485 |
+
"c63216e05f8547ce9682b8570b954252",
|
| 1486 |
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"c5b57f3fa4b847baa7548ce653d9aaa2",
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| 1487 |
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"b465c6b89cc74a358d7b0aff13d5c058"
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+
]
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| 1489 |
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},
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"id": "vnRfcWYuJFSd",
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+
"outputId": "e662c39e-f5a9-412e-b9cc-632baed6b263"
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| 1492 |
+
},
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+
"outputs": [
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| 1494 |
+
{
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| 1495 |
+
"output_type": "stream",
|
| 1496 |
+
"name": "stderr",
|
| 1497 |
+
"text": [
|
| 1498 |
+
"config.json not found in HuggingFace Hub.\n",
|
| 1499 |
+
"WARNING:huggingface_hub.hub_mixin:config.json not found in HuggingFace Hub.\n"
|
| 1500 |
+
]
|
| 1501 |
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},
|
| 1502 |
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{
|
| 1503 |
+
"output_type": "display_data",
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"data": {
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"text/plain": [
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"Fetching 8 files: 0%| | 0/8 [00:00<?, ?it/s]"
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+
],
|
| 1508 |
+
"application/vnd.jupyter.widget-view+json": {
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| 1509 |
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"version_major": 2,
|
| 1510 |
+
"version_minor": 0,
|
| 1511 |
+
"model_id": "7ed17347ad114ee6a3f5bbe44547458e"
|
| 1512 |
+
}
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| 1513 |
+
},
|
| 1514 |
+
"metadata": {}
|
| 1515 |
+
},
|
| 1516 |
+
{
|
| 1517 |
+
"output_type": "stream",
|
| 1518 |
+
"name": "stderr",
|
| 1519 |
+
"text": [
|
| 1520 |
+
"WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
|
| 1521 |
+
]
|
| 1522 |
+
}
|
| 1523 |
+
],
|
| 1524 |
+
"source": [
|
| 1525 |
+
"from huggingface_hub import from_pretrained_keras\n",
|
| 1526 |
+
"\n",
|
| 1527 |
+
"model = from_pretrained_keras(\"mabzak/lstm-id-yt-comment\")"
|
| 1528 |
+
]
|
| 1529 |
+
},
|
| 1530 |
+
{
|
| 1531 |
+
"cell_type": "code",
|
| 1532 |
+
"source": [
|
| 1533 |
+
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
|
| 1534 |
+
"import pandas as pd\n",
|
| 1535 |
+
"from keras.preprocessing.text import Tokenizer\n",
|
| 1536 |
+
"from tensorflow.keras.preprocessing.sequence import pad_sequences"
|
| 1537 |
+
],
|
| 1538 |
+
"metadata": {
|
| 1539 |
+
"id": "ASQghJpLYx_I"
|
| 1540 |
+
},
|
| 1541 |
+
"execution_count": 13,
|
| 1542 |
+
"outputs": []
|
| 1543 |
+
},
|
| 1544 |
+
{
|
| 1545 |
+
"cell_type": "code",
|
| 1546 |
+
"source": [
|
| 1547 |
+
"# Gantilah 'comments.csv' dengan nama file CSV yang sesuai\n",
|
| 1548 |
+
"df = pd.read_csv('https://huggingface.co/datasets/mabzak/indonesia-youtube-comment/raw/main/comment1.csv')\n",
|
| 1549 |
+
"texts = df['text']\n",
|
| 1550 |
+
"\n",
|
| 1551 |
+
"# Membuang baris dengan nilai teks yang hilang\n",
|
| 1552 |
+
"texts = texts.dropna()\n",
|
| 1553 |
+
"\n",
|
| 1554 |
+
"# Tokenisasi dan pembuatan representasi numerik dari kata-kata\n",
|
| 1555 |
+
"tokenizer = Tokenizer()\n",
|
| 1556 |
+
"tokenizer.fit_on_texts(texts)\n",
|
| 1557 |
+
"total_words = len(tokenizer.word_index) + 1"
|
| 1558 |
+
],
|
| 1559 |
+
"metadata": {
|
| 1560 |
+
"id": "JRJ2mLn4XQcM"
|
| 1561 |
+
},
|
| 1562 |
+
"execution_count": 15,
|
| 1563 |
+
"outputs": []
|
| 1564 |
+
},
|
| 1565 |
+
{
|
| 1566 |
+
"cell_type": "code",
|
| 1567 |
+
"source": [
|
| 1568 |
+
"tokenizer"
|
| 1569 |
+
],
|
| 1570 |
+
"metadata": {
|
| 1571 |
+
"colab": {
|
| 1572 |
+
"base_uri": "https://localhost:8080/"
|
| 1573 |
+
},
|
| 1574 |
+
"id": "7S_MvtZAb3nr",
|
| 1575 |
+
"outputId": "e3262896-35df-4f40-dbba-571c46349dd9"
|
| 1576 |
+
},
|
| 1577 |
+
"execution_count": 64,
|
| 1578 |
+
"outputs": [
|
| 1579 |
+
{
|
| 1580 |
+
"output_type": "execute_result",
|
| 1581 |
+
"data": {
|
| 1582 |
+
"text/plain": [
|
| 1583 |
+
"<keras.src.preprocessing.text.Tokenizer at 0x7d12a85ebe80>"
|
| 1584 |
+
]
|
| 1585 |
+
},
|
| 1586 |
+
"metadata": {},
|
| 1587 |
+
"execution_count": 64
|
| 1588 |
+
}
|
| 1589 |
+
]
|
| 1590 |
+
},
|
| 1591 |
+
{
|
| 1592 |
+
"cell_type": "code",
|
| 1593 |
+
"source": [
|
| 1594 |
+
"input_sequences = []\n",
|
| 1595 |
+
"for line in texts:\n",
|
| 1596 |
+
" token_list = tokenizer.texts_to_sequences([line])[0]\n",
|
| 1597 |
+
" for i in range(1, len(token_list)):\n",
|
| 1598 |
+
" n_gram_sequence = token_list[:i+1]\n",
|
| 1599 |
+
" input_sequences.append(n_gram_sequence)\n",
|
| 1600 |
+
"\n",
|
| 1601 |
+
"max_sequence_length = max([len(x) for x in input_sequences])\n",
|
| 1602 |
+
"input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')\n",
|
| 1603 |
+
"predictors, label = input_sequences[:, :-1], input_sequences[:, -1]"
|
| 1604 |
+
],
|
| 1605 |
+
"metadata": {
|
| 1606 |
+
"id": "PZC9THPtWHx7"
|
| 1607 |
+
},
|
| 1608 |
+
"execution_count": 16,
|
| 1609 |
+
"outputs": []
|
| 1610 |
+
},
|
| 1611 |
+
{
|
| 1612 |
+
"cell_type": "code",
|
| 1613 |
+
"source": [
|
| 1614 |
+
"import numpy as np\n",
|
| 1615 |
+
"\n",
|
| 1616 |
+
"def predict_next_sentence(seed_text, model, tokenizer, max_sequence_length, num_words):\n",
|
| 1617 |
+
" generated_sentence = seed_text\n",
|
| 1618 |
+
"\n",
|
| 1619 |
+
" for _ in range(num_words):\n",
|
| 1620 |
+
" token_list = tokenizer.texts_to_sequences([generated_sentence])[0]\n",
|
| 1621 |
+
" token_list = pad_sequences([token_list], maxlen=max_sequence_length-1, padding='pre')\n",
|
| 1622 |
+
" predicted_probs = model.predict(token_list, verbose=0)[0]\n",
|
| 1623 |
+
" predicted_index = np.argmax(predicted_probs)\n",
|
| 1624 |
+
"\n",
|
| 1625 |
+
" # Temukan kata yang sesuai dengan indeks prediksi\n",
|
| 1626 |
+
" output_word = \"\"\n",
|
| 1627 |
+
" for word, index in tokenizer.word_index.items():\n",
|
| 1628 |
+
" if index == predicted_index:\n",
|
| 1629 |
+
" output_word = word\n",
|
| 1630 |
+
" break\n",
|
| 1631 |
+
"\n",
|
| 1632 |
+
" # Tambahkan kata ke kalimat yang dihasilkan\n",
|
| 1633 |
+
" generated_sentence += \" \" + output_word\n",
|
| 1634 |
+
"\n",
|
| 1635 |
+
" return generated_sentence\n"
|
| 1636 |
+
],
|
| 1637 |
+
"metadata": {
|
| 1638 |
+
"id": "JfUx3ItpV1k_"
|
| 1639 |
+
},
|
| 1640 |
+
"execution_count": 17,
|
| 1641 |
+
"outputs": []
|
| 1642 |
+
},
|
| 1643 |
+
{
|
| 1644 |
+
"cell_type": "code",
|
| 1645 |
+
"source": [
|
| 1646 |
+
"# Menggunakan seed text untuk memprediksi kalimat berikutnya\n",
|
| 1647 |
+
"seed_text = \"masa depan negara\"\n",
|
| 1648 |
+
"predicted_sentence = predict_next_sentence(seed_text, model, tokenizer, max_sequence_length, num_words=10)\n",
|
| 1649 |
+
"print(\"Kalimat yang Diprediksi:\", predicted_sentence)"
|
| 1650 |
+
],
|
| 1651 |
+
"metadata": {
|
| 1652 |
+
"colab": {
|
| 1653 |
+
"base_uri": "https://localhost:8080/"
|
| 1654 |
+
},
|
| 1655 |
+
"id": "qcO83u2fVsKE",
|
| 1656 |
+
"outputId": "cbfebba6-ceb1-4bb0-d76d-305ce3491287"
|
| 1657 |
+
},
|
| 1658 |
+
"execution_count": 21,
|
| 1659 |
+
"outputs": [
|
| 1660 |
+
{
|
| 1661 |
+
"output_type": "stream",
|
| 1662 |
+
"name": "stdout",
|
| 1663 |
+
"text": [
|
| 1664 |
+
"Kalimat yang Diprediksi: masa depan negara ini saya yakin bisa di pimpin oleh orang yg baik\n"
|
| 1665 |
+
]
|
| 1666 |
+
}
|
| 1667 |
+
]
|
| 1668 |
+
},
|
| 1669 |
+
{
|
| 1670 |
+
"cell_type": "markdown",
|
| 1671 |
+
"source": [
|
| 1672 |
+
"\n",
|
| 1673 |
+
"\n",
|
| 1674 |
+
"---\n",
|
| 1675 |
+
"\n"
|
| 1676 |
+
],
|
| 1677 |
+
"metadata": {
|
| 1678 |
+
"id": "sGs-qtsIltSr"
|
| 1679 |
+
}
|
| 1680 |
+
}
|
| 1681 |
+
]
|
| 1682 |
+
}
|