diff --git "a/BERTopic_hub_starter.ipynb" "b/BERTopic_hub_starter.ipynb"
--- "a/BERTopic_hub_starter.ipynb"
+++ "b/BERTopic_hub_starter.ipynb"
@@ -1,42 +1,44 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
+ "metadata": {
+ "id": "3YOdAD8OzLqz"
+ },
"source": [
"# Sharing BERTopic models on the Hugging Face Hub\n",
"\n",
"This notebook shows the steps involved in sharing a BERTopic model on the Hugging Face Hub. As an example, we'll train a topic model on GitHub issue titles for the Transformers library. "
- ],
- "metadata": {
- "id": "3YOdAD8OzLqz"
- }
+ ]
},
{
+ "attachments": {},
"cell_type": "markdown",
- "source": [
- "First we need to install `BERTopic` along with the `huggingface_hub` library. We can optionally also install [`safetensors`](https://huggingface.co/docs/safetensors/index). `safetensors` Safetensors is a new simple format for storing tensors safely (as opposed to pickle) that is still fast (zero-copy). If this library is installed, BERTopic can use the `safetensor` format for model serialization. "
- ],
"metadata": {
"id": "TkOqnFiZzVRZ"
- }
+ },
+ "source": [
+ "First we need to install `BERTopic` along with the `huggingface_hub` library. We can optionally also install [`safetensors`](https://huggingface.co/docs/safetensors/index). `safetensors` Safetensors is a new simple format for storing tensors safely (as opposed to pickle) that is still fast (zero-copy). If this library is installed, BERTopic can use the `safetensor` format for model serialization. "
+ ]
},
{
"cell_type": "code",
- "source": [
- "%pip install git+https://github.com/MaartenGr/BERTopic huggingface_hub safetensors -qqq"
- ],
+ "execution_count": null,
"metadata": {
- "id": "nGhtumgZTvlE",
- "outputId": "6de65d7d-e963-4d3a-f927-0a3ece0277b4",
"colab": {
"base_uri": "https://localhost:8080/"
+ },
+ "id": "nGhtumgZTvlE",
+ "outputId": "6de65d7d-e963-4d3a-f927-0a3ece0277b4",
+ "vscode": {
+ "languageId": "python"
}
},
- "execution_count": null,
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
@@ -61,122 +63,59 @@
" Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
+ ],
+ "source": [
+ "%pip install git+https://github.com/MaartenGr/BERTopic huggingface_hub safetensors -qqq"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
+ "metadata": {
+ "id": "Lfx9GTwnz6S4"
+ },
"source": [
"We can use a [dataset](https://github.com/nlp-with-transformers/notebooks) that has been created for the [Natural Language Processing with Transformers](https://github.com/nlp-with-transformers/notebooks) book. This dataset contains issue titles, along with some metadata for the Transformers library GitHub repository. \n",
"\n",
"GitHub issues are an example of a domain where me might assume some sort of topics exist in the corpus, but we probablydon't have an exact sense of what all of these topics would be. This is the type of problem where topic modelling can give us a better sense of the corpus and potentially be useful for classifying new issues into topics. \n",
"\n",
"We'll start by loading the data into a pandas DataFrame. "
- ],
- "metadata": {
- "id": "Lfx9GTwnz6S4"
- }
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "nnMEq1vMT5Kv",
+ "vscode": {
+ "languageId": "python"
+ }
+ },
+ "outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"dataset_url = \"https://raw.githubusercontent.com/nlp-with-transformers/notebooks/main/data/github-issues-transformers.jsonl\"\n",
"df_issues = pd.read_json(dataset_url, lines=True)\n"
- ],
- "metadata": {
- "id": "nnMEq1vMT5Kv"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "code",
- "source": [
- "df_issues.head(4)"
- ],
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 494
},
"id": "s4kh0GNZtkg7",
- "outputId": "fea4924d-bf9f-4bde-80be-793b7fe32bf7"
+ "outputId": "fea4924d-bf9f-4bde-80be-793b7fe32bf7",
+ "vscode": {
+ "languageId": "python"
+ }
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- "execution_count": null,
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": [
- " url \\\n",
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- "\n",
- " html_url id \\\n",
- "0 https://github.com/huggingface/transformers/is... 849568459 \n",
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- "\n",
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- "\n",
- " created_at updated_at closed_at author_association \\\n",
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- "\n",
- " active_lock_reason body \\\n",
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- "\n",
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"text/html": [
"\n",
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@@ -399,106 +338,192 @@
"
\n",
" \n",
" "
+ ],
+ "text/plain": [
+ " url \\\n",
+ "0 https://api.github.com/repos/huggingface/trans... \n",
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+ "3 https://api.github.com/repos/huggingface/trans... \n",
+ "\n",
+ " repository_url \\\n",
+ "0 https://api.github.com/repos/huggingface/trans... \n",
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+ "\n",
+ " labels_url \\\n",
+ "0 https://api.github.com/repos/huggingface/trans... \n",
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+ "\n",
+ " comments_url \\\n",
+ "0 https://api.github.com/repos/huggingface/trans... \n",
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+ "3 https://api.github.com/repos/huggingface/trans... \n",
+ "\n",
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+ "0 https://api.github.com/repos/huggingface/trans... \n",
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+ "3 https://api.github.com/repos/huggingface/trans... \n",
+ "\n",
+ " html_url id \\\n",
+ "0 https://github.com/huggingface/transformers/is... 849568459 \n",
+ "1 https://github.com/huggingface/transformers/is... 849544374 \n",
+ "2 https://github.com/huggingface/transformers/is... 849529761 \n",
+ "3 https://github.com/huggingface/transformers/is... 849499734 \n",
+ "\n",
+ " node_id number \\\n",
+ "0 MDU6SXNzdWU4NDk1Njg0NTk= 11046 \n",
+ "1 MDU6SXNzdWU4NDk1NDQzNzQ= 11045 \n",
+ "2 MDU6SXNzdWU4NDk1Mjk3NjE= 11044 \n",
+ "3 MDU6SXNzdWU4NDk0OTk3MzQ= 11043 \n",
+ "\n",
+ " title ... milestone comments \\\n",
+ "0 Potential incorrect application of layer norm ... ... NaN 0 \n",
+ "1 Multi-GPU seq2seq example evaluation significa... ... NaN 0 \n",
+ "2 [DeepSpeed] ZeRO stage 3 integration: getting ... ... NaN 0 \n",
+ "3 Can't load model to estimater ... NaN 0 \n",
+ "\n",
+ " created_at updated_at closed_at author_association \\\n",
+ "0 2021-04-03 03:37:32 2021-04-03 03:37:32 NaT NONE \n",
+ "1 2021-04-03 00:52:24 2021-04-03 00:52:24 NaT NONE \n",
+ "2 2021-04-02 23:40:42 2021-04-03 00:00:18 NaT COLLABORATOR \n",
+ "3 2021-04-02 21:51:44 2021-04-02 21:51:44 NaT NONE \n",
+ "\n",
+ " active_lock_reason body \\\n",
+ "0 None In BlenderbotSmallDecoder, layer norm is appl... \n",
+ "1 None \\r\\n### Who can help\\r\\n@patil-suraj @sgugger ... \n",
+ "2 None **[This is not yet alive, preparing for the re... \n",
+ "3 None I was trying to follow the Sagemaker instructi... \n",
+ "\n",
+ " performed_via_github_app pull_request \n",
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+ "1 NaN None \n",
+ "2 NaN None \n",
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+ "\n",
+ "[4 rows x 26 columns]"
]
},
+ "execution_count": 4,
"metadata": {},
- "execution_count": 4
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "df_issues.head(4)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
- "source": [
- "We can train our topic model on a subset of the data and hold back some examples which we can treat as new data. This mirrors a situtation where we might use BERTopic model in a production setting. "
- ],
"metadata": {
"id": "TFgpuxbbMTsm"
- }
+ },
+ "source": [
+ "We can train our topic model on a subset of the data and hold back some examples which we can treat as new data. This mirrors a situtation where we might use BERTopic model in a production setting. "
+ ]
},
{
"cell_type": "code",
- "source": [
- "len(df_issues)"
- ],
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oknWZx1s0pQv",
- "outputId": "bf7176c8-1922-480b-d5e9-f2f58ba49054"
+ "outputId": "bf7176c8-1922-480b-d5e9-f2f58ba49054",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"9930"
]
},
+ "execution_count": 10,
"metadata": {},
- "execution_count": 10
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "len(df_issues)"
]
},
{
"cell_type": "code",
- "source": [
- "df_issues_train = df_issues[:9000]"
- ],
+ "execution_count": null,
"metadata": {
- "id": "sFJLTOxv1VLR"
+ "id": "sFJLTOxv1VLR",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "df_issues_train = df_issues[:9000]"
+ ]
},
{
"cell_type": "code",
- "source": [
- "df_issues_test = df_issues[9000:]"
- ],
+ "execution_count": null,
"metadata": {
- "id": "6LYCGoHH1ZL0"
+ "id": "6LYCGoHH1ZL0",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "df_issues_test = df_issues[9000:]"
+ ]
},
{
+ "attachments": {},
"cell_type": "markdown",
- "source": [
- "BERTopic expects a list of strings as input so let's grab the title column and turn this into a list. "
- ],
"metadata": {
"id": "iSc2dIr9Mil5"
- }
+ },
+ "source": [
+ "BERTopic expects a list of strings as input so let's grab the title column and turn this into a list. "
+ ]
},
{
"cell_type": "code",
- "source": [
- "issue_titles = df_issues_train['title'].to_list()"
- ],
+ "execution_count": null,
"metadata": {
- "id": "TgKrVC5MVlo6"
+ "id": "TgKrVC5MVlo6",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "issue_titles = df_issues_train['title'].to_list()"
+ ]
},
{
"cell_type": "code",
- "source": [
- "issue_titles[:3]"
- ],
+ "execution_count": null,
"metadata": {
- "id": "dTbWPHF-roqO",
- "outputId": "512b5629-005d-4567-9108-1f2b77c8bf7c",
"colab": {
"base_uri": "https://localhost:8080/"
+ },
+ "id": "dTbWPHF-roqO",
+ "outputId": "512b5629-005d-4567-9108-1f2b77c8bf7c",
+ "vscode": {
+ "languageId": "python"
}
},
- "execution_count": null,
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"['Potential incorrect application of layer norm in BlenderbotSmallDecoder',\n",
@@ -506,64 +531,74 @@
" '[DeepSpeed] ZeRO stage 3 integration: getting started and issues']"
]
},
+ "execution_count": 16,
"metadata": {},
- "execution_count": 16
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "issue_titles[:3]"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
+ "metadata": {
+ "id": "vdPHupl-roiW"
+ },
"source": [
"## Training our model\n",
"\n",
"We'll train a BERTopic model using fairly standard settings."
- ],
- "metadata": {
- "id": "vdPHupl-roiW"
- }
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "74t-ARnOVw5K",
+ "vscode": {
+ "languageId": "python"
+ }
+ },
+ "outputs": [],
"source": [
"from bertopic import BERTopic\n",
"from bertopic.representation import KeyBERTInspired"
- ],
- "metadata": {
- "id": "74t-ARnOVw5K"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "code",
- "source": [
- "representation_model = KeyBERTInspired()"
- ],
+ "execution_count": null,
"metadata": {
- "id": "epdxCx2Kr3GO"
+ "id": "epdxCx2Kr3GO",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "representation_model = KeyBERTInspired()"
+ ]
},
{
"cell_type": "code",
- "source": [
- "topic_model = BERTopic(\"english\", verbose=True, nr_topics=30, representation_model=representation_model)"
- ],
+ "execution_count": null,
"metadata": {
- "id": "hyf7ay5QrzI7"
+ "id": "hyf7ay5QrzI7",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "topic_model = BERTopic(\"english\", verbose=True, nr_topics=30, representation_model=representation_model)"
+ ]
},
{
"cell_type": "code",
- "source": [
- "topics, probs = topic_model.fit_transform(issue_titles)"
- ],
+ "execution_count": null,
"metadata": {
- "id": "v16KfKlUoUpc",
- "outputId": "0fa2103c-f7dc-4a5d-bb9d-66fe6811365a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 570,
@@ -734,223 +769,227 @@
"8c49969a967649a39af9f95602a8b3ba",
"de918ff77f67407689fc683c8acd2d91"
]
+ },
+ "id": "v16KfKlUoUpc",
+ "outputId": "0fa2103c-f7dc-4a5d-bb9d-66fe6811365a",
+ "vscode": {
+ "languageId": "python"
}
},
- "execution_count": null,
"outputs": [
{
- "output_type": "display_data",
"data": {
- "text/plain": [
- "Downloading (…)e9125/.gitattributes: 0%| | 0.00/1.18k [00:00\n",
@@ -1224,30 +1232,70 @@
" \n",
" \n",
" "
+ ],
+ "text/plain": [
+ " Topic Count Name \\\n",
+ "0 -1 2106 -1_bert_tensorflow_model_models \n",
+ "1 0 1774 0_bert_bertforsequenceclassification_berttoken... \n",
+ "2 1 1122 1_gpt2_trainertrain_gpt_trainer \n",
+ "3 2 516 2_typos_typo_fix_fixed \n",
+ "4 3 464 3_s2s_seq2seq_examplesseq2seq_seq2seqdataset \n",
+ "5 4 404 4_modelcard_modelcards_card_model \n",
+ "6 5 368 5_attributeerror_valueerror_typeerror_error \n",
+ "7 6 347 6_summarization_summaries_questionansweringpip... \n",
+ "8 7 329 7_longformer_tf_longformers_tftrainer \n",
+ "9 8 227 8_testing_ci_tests_tf \n",
+ "\n",
+ " Representation \\\n",
+ "0 [bert, tensorflow, model, models, tf, tokenize... \n",
+ "1 [bert, bertforsequenceclassification, berttoke... \n",
+ "2 [gpt2, trainertrain, gpt, trainer, training, c... \n",
+ "3 [typos, typo, fix, fixed, correction, error, c... \n",
+ "4 [s2s, seq2seq, examplesseq2seq, seq2seqdataset... \n",
+ "5 [modelcard, modelcards, card, model, cards, mo... \n",
+ "6 [attributeerror, valueerror, typeerror, error,... \n",
+ "7 [summarization, summaries, questionansweringpi... \n",
+ "8 [longformer, tf, longformers, tftrainer, longf... \n",
+ "9 [testing, ci, tests, tf, test, slow, t5, bench... \n",
+ "\n",
+ " Representative_Docs \n",
+ "0 [t5 model card, TFDistilBERT ValueError when l... \n",
+ "1 [The output to be used for getting sentence em... \n",
+ "2 [Training GPT2 and Reformer from scratch. , A... \n",
+ "3 [fix typo, Fix doc link in README, [doc] typo ... \n",
+ "4 [[s2s] --eval_max_generate_length, [s2s] s/alp... \n",
+ "5 [Add model card, Add model card, Model Card fo... \n",
+ "6 [TypeError: on_init_end() got an unexpected ke... \n",
+ "7 [Bug in the question answering pipeline, Add t... \n",
+ "8 [TF Longformer, Fix TF Longformer, Fix TF Long... \n",
+ "9 [Fix the CI, Ci test tf super slow, TF Slow te... "
]
},
+ "execution_count": 22,
"metadata": {},
- "execution_count": 22
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "freq.head(10)"
]
},
{
"cell_type": "code",
- "source": [
- "topic_model.visualize_topics()"
- ],
+ "execution_count": null,
"metadata": {
- "id": "lW3a5RNFqjvB",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 667
},
- "outputId": "eb25f4d2-2ed6-4d59-e5ed-b1477dfebc77"
+ "id": "lW3a5RNFqjvB",
+ "outputId": "eb25f4d2-2ed6-4d59-e5ed-b1477dfebc77",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
"outputs": [
{
- "output_type": "display_data",
"data": {
"text/html": [
"\n",
@@ -1283,70 +1331,80 @@
""
]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
}
+ ],
+ "source": [
+ "topic_model.visualize_topics()"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
- "source": [
- "We can also view topics over time"
- ],
"metadata": {
"id": "sgWV1tAiM4Cc"
- }
+ },
+ "source": [
+ "We can also view topics over time"
+ ]
},
{
"cell_type": "code",
- "source": [
- "timestamps = df_issues_train['created_at']"
- ],
+ "execution_count": null,
"metadata": {
- "id": "fCHa0NMZt3Kd"
+ "id": "fCHa0NMZt3Kd",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "timestamps = df_issues_train['created_at']"
+ ]
},
{
"cell_type": "code",
- "source": [
- "topics_over_time = topic_model.topics_over_time(issue_titles, timestamps, nr_bins=20)"
- ],
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xI7rB5h3tcPN",
- "outputId": "43ca3f25-eb8a-41cc-b04d-b909a016fa7d"
+ "outputId": "43ca3f25-eb8a-41cc-b04d-b909a016fa7d",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"20it [00:14, 1.36it/s]\n"
]
}
+ ],
+ "source": [
+ "topics_over_time = topic_model.topics_over_time(issue_titles, timestamps, nr_bins=20)"
]
},
{
"cell_type": "code",
- "source": [
- "topic_model.visualize_topics_over_time(topics_over_time, top_n_topics=10)"
- ],
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 467
},
"id": "dEzZpm-1t_aI",
- "outputId": "57f2e701-6be7-4c51-a32d-7ed53de09010"
+ "outputId": "57f2e701-6be7-4c51-a32d-7ed53de09010",
+ "vscode": {
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+ }
},
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- "output_type": "display_data",
"data": {
"text/html": [
"\n",
@@ -1382,37 +1440,43 @@
""
]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
}
+ ],
+ "source": [
+ "topic_model.visualize_topics_over_time(topics_over_time, top_n_topics=10)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
+ "metadata": {
+ "id": "zdt2wnLWNcbS"
+ },
"source": [
"## Pushing our BERTopic model to the Hugging Face Hub 🤗\n",
"\n",
"We can use the new BERTopic Hub intergration to push our models to the Hugging Face hub. Sharing models to the Hub makes it easier for others (or our future self) to use or adapt our topic models for further use. "
- ],
- "metadata": {
- "id": "zdt2wnLWNcbS"
- }
+ ]
},
{
"cell_type": "code",
- "source": [
- "from huggingface_hub import notebook_login"
- ],
+ "execution_count": null,
"metadata": {
- "id": "fAGXCaRByZiA"
+ "id": "fAGXCaRByZiA",
+ "vscode": {
+ "languageId": "python"
+ }
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import notebook_login"
+ ]
},
{
"cell_type": "code",
- "source": [
- "notebook_login()"
- ],
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -1438,31 +1502,47 @@
]
},
"id": "UQHF0yd_y4P0",
- "outputId": "303cc7c1-c499-4168-a8ad-bd39ddff7c94"
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},
- "execution_count": null,
"outputs": [
{
- "output_type": "display_data",
"data": {
- "text/plain": [
- "VBox(children=(HTML(value=' Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. "
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@@ -20324,4 +20451,4 @@
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