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mypy-extensions==1.0.0 nest-asyncio==1.6.0 networkx==3.4.1 nltk==3.9.1 numpy==1.26.4 nvidia-cublas-cu12==12.4.5.8 nvidia-cuda-cupti-cu12==12.4.127 nvidia-cuda-nvrtc-cu12==12.4.127 nvidia-cuda-runtime-cu12==12.4.127 nvidia-cudnn-cu12==9.1.0.70 nvidia-cufft-cu12==11.2.1.3 nvidia-curand-cu12==10.3.5.147 nvidia-cusolver-cu12==11.6.1.9 nvidia-cusparse-cu12==12.3.1.170 nvidia-nccl-cu12==2.21.5 nvidia-nvjitlink-cu12==12.4.127 nvidia-nvtx-cu12==12.4.127 openai==1.52.0 orjson==3.10.7 packaging==24.1 pandas==2.2.3 pillow==11.0.0 propcache==0.2.0 psutil==6.1.0 pyarrow==17.0.0 pycparser==2.22 pydantic==2.9.2 pydantic-core==2.23.4 pypdf==5.0.1 python-dateutil==2.8.2 python-iso639==2024.4.27 python-magic==0.4.27 python-pptx==1.0.2 pytz==2024.2 pyyaml==6.0.2 rapidfuzz==3.10.0 regex==2024.9.11 requests==2.32.3 requests-toolbelt==1.0.0 safetensors==0.4.5 scikit-learn==1.5.2 scipy==1.14.1 sentence-transformers==3.2.0 six==1.16.0 sniffio==1.3.1 soupsieve==2.6 sqlalchemy==2.0.36 sympy==1.13.1 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m95.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m906.4/906.4 MB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.9/9.9 MB\u001b[0m \u001b[31m116.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.5/209.5 MB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.0/314.0 kB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "cudf-cu12 24.6.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 2.2.3 which is incompatible.\n", "cudf-cu12 24.6.1 requires pyarrow<16.2.0a0,>=16.1.0, but you have pyarrow 17.0.0 which is incompatible.\n", "fastai 2.7.17 requires torch<2.5,>=1.10, but you have torch 2.5.0 which is incompatible.\n", "gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.14.1 which is incompatible.\n", "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.\n", "jupyter-server 1.24.0 requires anyio<4,>=3.1.0, but you have anyio 4.6.2.post1 which is incompatible.\n", "torchaudio 2.4.1+cu121 requires torch==2.4.1, but you have torch 2.5.0 which is incompatible.\n", "torchvision 0.19.1+cu121 requires torch==2.4.1, but you have torch 2.5.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ] }, { "cell_type": "code", "source": [ "links = ['https://www.gallup.com/workplace/390776/percent-feel-employer-cares-wellbeing-plummets.aspx', 'https://newsroom.cigna.com/business-case-addressing-loneliness-workforce', 'https://www.gallup.com/workplace/608675/new-workplace-employee-engagement-stagnates.aspx', 'https://www.wsj.com/lifestyle/workplace/american-workers-loneliness-research-35793dc4', 'https://hbr.org/resources/pdfs/comm/achievers/hbr_achievers_report_sep13.pdf', 'https://unmind.com/blog/wellbeing-washing?locale=en', 'https://www.mckinsey.com/industries/healthcare/our-insights/how-prioritizing-health-is-a-prescription-for-us-prosperity', 'https://www.workhuman.com/resources/reports-guides/amplifying-wellbeing-at-work-and-beyond/', 'https://d341ezm4iqaae0.cloudfront.net/ews/20220817145319/Work-Wellbeing-2022-Insights-Report-.pdf', 'https://www.gallup.com/394373/indicator-employee-engagement.aspx', 'https://thrivemyway.com/burnout-stats/', 'https://www.mckinsey.com/featured-insights/diversity-and-inclusion/the-state-of-burnout-for-women-in-the-workplace', 'https://www.thelancet.com/journals/lanam/article/PIIS2667-193X(21)00087-9/fulltext', 'https://www.apa.org/pubs/reports/work-well-being/2022-mental-health-support', 'https://www.gallup.com/workplace/393497/world-trillion-workplace-problem.aspx', 'https://www.apa.org/pubs/reports/work-in-america/2023-workplace-health-well-being', 'https://www.forbes.com/sites/tracybrower/2024/06/04/how-the-modern-workplace-affects-loneliness/', 'https://www.aihr.com/blog/employee-experience-statistics/', 'https://www.wellable.co/resources/employee-wellness-industry-trends-reports/2024/', 'https://www.mindsharepartners.org/mentalhealthatworkreport-2021', 'https://www.ringover.com/blog/worst-industries-for-burnout', 'https://www.usnews.com/news/health-news/articles/2024-02-01/loneliness-is-plaguing-americans-in-2024-poll', 'https://www.gallup.com/workplace/397058/increasing-importance-best-friend-work.aspx', 'https://www.psychiatry.org/News-room/News-Releases/New-APA-Poll-One-in-Three-Americans-Feels-Lonely-E', 'https://www.workhuman.com/blog/how-recognition-can-help-reduce-stress-and-burnout/', 'https://news.harvard.edu/gazette/story/2023/10/how-social-isolation-loneliness-can-shorten-your-life/', 'https://www.youtube.com/watch?v=sgthz0xu_Gw', 'https://www.quantumworkplace.com/future-of-work/remote-work-statistics', 'https://www.zippia.com/advice/employee-wellness-statistics/', 'https://www.zippia.com/advice/employee-engagement-statistics/', 'https://www.nytimes.com/2024/05/06/well/loneliness-mental-health-age.html', 'https://www.nami.org/about-mental-illness/mental-health-by-the-numbers/', 'https://www.aarp.org/pri/topics/health/coverage-access/medicare-spends-more-on-socially-isolated-older-adults/', 'https://www.forbes.com/sites/williamhaseltine/2024/05/29/battling-loneliness-the-new-public-health-crisis/', 'https://apnews.com/article/loneliness-workplace-job-wellness-c2dee7b29121a470afb0a6792387e28b',\n", "'https://www.mckinsey.com/industries/healthcare/our-insights/national-surveys-reveal-disconnect-between-employees-and-employers-around-mental-health-need', 'https://www.fastcompany.com/91140052/workplace-loneliness-2024-increase-virtual-employees-gallup', 'https://www.gallup.com/workplace/645566/employees-worldwide-feel-lonely.aspx', 'https://resumelab.com/career-advice/mental-health-at-work', 'https://www.hhs.gov/sites/default/files/sg-social-connection-fact-cards.pdf', 'https://www.gallup.com/workplace/349484/state-of-the-global-workplace.aspx', 'https://www.weforum.org/agenda/2023/07/business-benefits-of-boosting-employee-health-and-well-being/', 'https://www.cbsnews.com/video/how-workplace-loneliness-can-impact-job-personal-life/', 'https://theharrispoll.com/briefs/workplace-mental-health-awareness-week/', 'https://www.workhuman.com/why-workhuman/case-studies/eaton-driving-retention-with-recognition/']" ], "metadata": { "id": "vNbUItlBHf22" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain_community.document_loaders import PyPDFLoader, UnstructuredURLLoader\n", "import requests\n", "\n", "def load_documents_from_url(url):\n", " try:\n", " # Check if it's a PDF\n", " if url.endswith(\".pdf\"):\n", " try:\n", " loader = PyPDFLoader(url)\n", " return loader.load()\n", " except Exception as e:\n", " print(f\"Error loading PDF from {url}: {e}\")\n", " return None\n", "\n", " # Fetch the content and check for video pages\n", " try:\n", " response = requests.head(url, timeout=10) # Timeout for fetching headers\n", " content_type = response.headers.get('Content-Type', '')\n", " except Exception as e:\n", " print(f\"Error fetching headers from {url}: {e}\")\n", " return None\n", "\n", " # Ignore video content (flagged for now)\n", " if 'video' in content_type:\n", " return None\n", " if 'youtube' in url:\n", " return None\n", "\n", " # Otherwise, treat it as an HTML page\n", " try:\n", " loader = UnstructuredURLLoader([url])\n", " return loader.load()\n", " except Exception as e:\n", " print(f\"Error loading HTML from {url}: {e}\")\n", " return None\n", " except Exception as e:\n", " print(f\"General error loading from {url}: {e}\")\n", " return None" ], "metadata": { "id": "6qGbwTMFHiPb" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "documents = []\n", "for link in links:\n", " doc = load_documents_from_url(link)\n", " #print(f\"loaded doc from {link}\")\n", " if doc:\n", " documents.extend(doc)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sekHJutbHwTb", "outputId": "19dd5e52-1fdc-431b-ae09-b7591253aa54" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:pypdf.generic._base:could not convert string to float: b'0.00-5031024' : FloatObject (b'0.00-5031024') invalid; use 0.0 instead\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Error fetching headers from https://www.usnews.com/news/health-news/articles/2024-02-01/loneliness-is-plaguing-americans-in-2024-poll: HTTPSConnectionPool(host='www.usnews.com', port=443): Read timed out. (read timeout=10)\n", "Error loading PDF from https://www.hhs.gov/sites/default/files/sg-social-connection-fact-cards.pdf: Check the url of your file; returned status code 403\n" ] } ] }, { "cell_type": "code", "source": [ "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(\n", " chunk_size = 750,\n", " chunk_overlap = 20,\n", " length_function = len\n", ")" ], "metadata": { "id": "NSzhEHcPIhwz" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "training_documents = text_splitter.split_documents(documents)" ], "metadata": { "id": "PJQZy1n2Imbh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "len(training_documents)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "er-5Ut_uIp2s", "outputId": "a816bbec-2cba-4bab-a39d-ad7a0f64d273" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "674" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "code", "source": [ "import uuid\n", "\n", "id_set = set()\n", "\n", "for document in training_documents:\n", " id = str(uuid.uuid4())\n", " while id in id_set:\n", " id = uuid.uuid4()\n", " id_set.add(id)\n", " document.metadata[\"id\"] = id" ], "metadata": { "id": "wjUTJOMhIq40" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "training_split_documents = training_documents[:300]\n", "val_split_documents = training_documents[300:350]\n", "test_split_documents = training_documents[350:400]" ], "metadata": { "id": "LA823RrcIvFP" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import os\n", "import getpass\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter Your OpenAI API Key: \")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hJ-vyjLoJ7PJ", "outputId": "50558fd8-e657-47f1-a497-5a40d2b2ff19" }, "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your OpenAI API Key: ··········\n" ] } ] }, { "cell_type": "code", "source": [ "from langchain_openai import ChatOpenAI\n", "\n", "qa_chat_model = ChatOpenAI(\n", " model=\"gpt-4o-mini\",\n", " temperature=0\n", ")" ], "metadata": { "id": "BDecxbNyKFkt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain_core.prompts import ChatPromptTemplate\n", "\n", "qa_prompt = \"\"\"\\\n", "Given the following context, you must generate questions based on only the provided context.\n", "\n", "You are to generate {n_questions} questions which should be provided in the following format:\n", "\n", "1. QUESTION #1\n", "2. QUESTION #2\n", "...\n", "\n", "Context:\n", "{context}\n", "\"\"\"\n", "\n", "qa_prompt_template = ChatPromptTemplate.from_template(qa_prompt)" ], "metadata": { "id": "U0hNkr5VLZ9R" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "question_generation_chain = qa_prompt_template | qa_chat_model" ], "metadata": { "id": "M3M3RG-wLfRb" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import tqdm\n", "\n", "def create_questions(documents, n_questions):\n", " questions = {}\n", " relevant_docs = {}\n", " for doc in tqdm.tqdm(documents, desc=\"Processing documents\"):\n", " context = doc.page_content\n", " doc_id = doc.metadata[\"id\"]\n", "\n", " # Generate questions for this document\n", " result = question_generation_chain.invoke({\"context\": context, \"n_questions\": n_questions})\n", " generated_questions = result.content.strip().split('\\n')\n", "\n", " # Process each generated question\n", " for q in generated_questions:\n", " # Remove the numbering from the question\n", " q = q.split('. ', 1)[-1].strip()\n", "\n", " # Generate a unique ID for this question\n", " question_id = str(uuid.uuid4())\n", "\n", " # Add the question to our questions dict\n", " questions[question_id] = q\n", "\n", " # Add the document ID to our relevant_docs dict\n", " relevant_docs[question_id] = [doc_id]\n", "\n", " return questions, relevant_docs" ], "metadata": { "id": "v-JFty4-Lkb5" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "training_questions, training_relevant_contexts = create_questions(training_split_documents,2)\n", "val_questions, val_relevant_contexts = create_questions(val_split_documents,2)\n", "test_questions, test_relevant_contexts = create_questions(test_split_documents,2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NFFKli-fNPn8", "outputId": "1b894311-6ddd-45dd-94f3-8e2ea3f94083" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Processing documents: 100%|██████████| 300/300 [05:59<00:00, 1.20s/it]\n", "Processing documents: 100%|██████████| 50/50 [00:55<00:00, 1.12s/it]\n", "Processing documents: 100%|██████████| 50/50 [01:11<00:00, 1.44s/it]\n" ] } ] }, { "cell_type": "code", "source": [ "import json\n", "\n", "training_corpus = {train_item.metadata[\"id\"] : train_item.page_content for train_item in training_split_documents}\n", "\n", "train_dataset = {\n", " \"questions\" : training_questions,\n", " \"relevant_contexts\" : training_relevant_contexts,\n", " \"corpus\" : training_corpus\n", "}\n", "\n", "with open(\"training_dataset.jsonl\", \"w\") as f:\n", " json.dump(train_dataset, f)" ], "metadata": { "id": "BH6TFaP2NVjO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "val_corpus = {val_item.metadata[\"id\"] : val_item.page_content for val_item in val_split_documents}\n", "\n", "val_dataset = {\n", " \"questions\" : val_questions,\n", " \"relevant_contexts\" : val_relevant_contexts,\n", " \"corpus\" : val_corpus\n", "}\n", "\n", "with open(\"val_dataset.jsonl\", \"w\") as f:\n", " json.dump(val_dataset, f)" ], "metadata": { "id": "SWCkYR8lNXIM" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_corpus = {test_item.metadata[\"id\"] : test_item.page_content for test_item in test_split_documents}\n", "\n", "test_dataset = {\n", " \"questions\" : test_questions,\n", " \"relevant_contexts\" : test_relevant_contexts,\n", " \"corpus\" : train_corpus\n", "}\n", "\n", "with open(\"test_dataset.jsonl\", \"w\") as f:\n", " json.dump(test_dataset, f)" ], "metadata": { "id": "UEXTp4OcNYzD" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sentence_transformers import SentenceTransformer\n", "\n", "model = SentenceTransformer(\"Snowflake/snowflake-arctic-embed-l\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 532, "referenced_widgets": [ "ed6ab8ab108846049d47d8d39a954c31", 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"0f0100984b4344d68d727e3508c53a16", "a73d238f34384914bd1f0f42c71c754c", "9fbdacdc59be44409b9fe75fb037c7c0", "fff261285abe4e60a428ccf0eac23a5d", "9c37f8e4cdfc4d3ba7f12371134700ee", "008e3c737cef47a3acbb7c74373e4e06", "7a939420558040caa2668f79dfc8c182", "f1fa907f5b994794816608a699363e44", "f02e46fd9b54438e8a83931bca32740d", "df535bc4bc844df7ac0800e393ff71aa", "7dce82f05ea14f92a72dfef573e4e8e5", "0151275c683140a396065418160e355d", "666fdf790e864379900265070099a36c", "9c6514a9e0744ab2a4398b6350ca3be1", "5bfc6d885a2242f9be7e1480f0450693", "5daa6405560248ff9f5aef554a6238f6", "4beb10451dd447ab81f3ba03bd992529", "f47277bb047a4bf0a7924e30e0f1f8e3", "63cc4fd0d3ff4e56a7f3507b050563bf", "b82df6d9cdcb449c9c63336f95adbb89", "6a08635e59fb47b8857eefd64aefe81b", "2808cd4b5ca541598ca30d19dbf91ac9", "ecf6793471ba44f3ade76d178ba48bae", "5e794c3060ed46de93c0d301b9c48008", "b2c9cf8cd66d4c88904d5b391838d87b", "b0e7fe0b498d4d80a9546fae542cb865", "8544b95bcda44d319551301aaad7032e", "f6b9aa5ef4014993a9714b312b613c73", "9e8dbe71a5c4455a9d68ed0cf0981cf3", "3a5fe0e8001747eaa66a006e2173c613", "baa0567c3a0647079a1755c49c770e67", "3272795756004da39a368c2fabed14d6", "156eae127222422cb6795b657d5bf7b0", "2a1b0a3c52ec49ba8e7b132289e89e19", "340c39c1f90a4e5586a79466b5add247", "3a02fe133c4a4c39a7072d1804b4dace", "4b984065d09146d9894f67bc1041b52d", "e24c2804e85044a794f81165fe469e6b", "3cc569d1022c45debade2ca81abfe2ac", "2deed4b01c894278be90cce890c24106", "e141de42450a4e1b9b2244b0cac70efa", "c0ac97daba8a4a9488daa65fd6e37d93", "b01841c4b7e74aa9b0aa7693fb262f83", "ec25d346ae6a437a8166dec98f75c113", "88c1ac5855a445b99b63c2027da5e088", "261a9c58eb6749fe9eebd8ab09f61a10", "870b18218e004df1a405350fad9527cd", "a5bb5baa139b46a188fd5005a5fe7b90", "1d19b6f936b04a9e9272d645ca385dd2", "4a3f47481730493083f0bfad759e543d", "5efb25cd0cd64baa9d9533113f41ec69", "dfc207c19b314794b8187f1e23697ba0", "ddbef823ee114c8bb2c17460a8d2f2af", "6dfda67e6aed47b48fb59df1adc1856f", "1adabe9dbb454f61bef31338d3bed2c2", "d4e64e7998d048e2b50990f2f6c383ee", "d5a5e901d60f4fac95a63149a6e08422", "167fcc2d60b64b93a59a42a24425b661", "e60059db53d0453bb6bd66778451b630", "4d31490981d04167a7b8794cc627f95c", "21fd1f3af7b04518b8f49daeb363fbcd", "fad6044d5d37432cb3d8de772bdc3f36", "787968006938465ab8d3d9292935f8d5", "de4cf93021ad4c11b13b0628861b04ff", "21fc3f315a8b4dba9ac35ec496f16b5d", "c3ed0268f1194050a4e37165facc572a", "3c3ccbeab1fb4213a943e0addcf46917", "b3497678f8da429d8067a079d813878e", "d1bc9b38a78d409db3a720d3828253cc", "ecd067f1488640f79d8c5e343e5a83bd", "4d5bc51656d34254843945dc1a4bbc89", "9fcd1e8468ac42c6a8ce932a93521d86", "009a8bdd870f4e4396b2491a7cd66763", "86c7ad55145744c08f9e140b6b343917", "212a945d726046fb9a000f7f49e159e4", "0fd6738484dc4fcc96c300dd4a614237", "99aaca60704640aeaef40a50c4730149", "0956b919e44d4d1ba6c3978d455fd884", "926dd0b38c804463801ecf4543b519ec", "74a5dce48b8c45d5bd1ae9e1151ee5a1", "e3dd0c91d25242f8a18a9efc1588131c", "bffcd97f0bba4d63bec73c223f299294", "b6f84ef812bd48498db848f10fe25034", "6adc789aeb5e43b4b82f1a5cc63fb131", "b2d3fc85a54a40399f3958254c9ac025", "3577429457834b66871cafd3baf800a4", "1e8a0dcc8bf84da3b802f91cc76838af", "0ec2968c42e14b1b962d2d60872678d2", "8635c0f849dd4689aa1cb7a8aabe8e30", "f7d9a232f9d6485a890f05a2c5e903d7", "a693e1967c3e456e8965bc59909cf2ea", "5357b6f6a1a64768bcb75ae118ba6cca", "d02813b86e3846599fde13abf7512937" ] }, "id": "cbAGn_KGPY7f", "outputId": "e76d9b85-677a-4c91-a25c-b08d8c7876cd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", " from tqdm.autonotebook import tqdm, trange\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co./settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00" ], "text/html": [ "\n", "
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StepTraining LossValidation LossCosine Accuracy@1Cosine Accuracy@3Cosine Accuracy@5Cosine Accuracy@10Cosine Precision@1Cosine Precision@3Cosine Precision@5Cosine Precision@10Cosine Recall@1Cosine Recall@3Cosine Recall@5Cosine Recall@10Cosine Ndcg@10Cosine Mrr@10Cosine Map@100Dot Accuracy@1Dot Accuracy@3Dot Accuracy@5Dot Accuracy@10Dot Precision@1Dot Precision@3Dot Precision@5Dot Precision@10Dot Recall@1Dot Recall@3Dot Recall@5Dot Recall@10Dot Ndcg@10Dot Mrr@10Dot Map@100
30No logNo log0.8100000.9400000.9600000.9800000.8100000.3133330.1920000.0980000.8100000.9400000.9600000.9800000.9022930.8764290.8781710.8100000.9400000.9600000.9800000.8100000.3133330.1920000.0980000.8100000.9400000.9600000.9800000.9022930.8764290.878171
50No logNo log0.8300000.9500000.9600000.9900000.8300000.3166670.1920000.0990000.8300000.9500000.9600000.9900000.9122130.8868450.8877540.8300000.9500000.9600000.9900000.8300000.3166670.1920000.0990000.8300000.9500000.9600000.9900000.9122130.8868450.887754
60No logNo log0.8200000.9500000.9600000.9900000.8200000.3166670.1920000.0990000.8200000.9500000.9600000.9900000.9105580.8845400.8854490.8200000.9500000.9600000.9900000.8200000.3166670.1920000.0990000.8200000.9500000.9600000.9900000.9105580.8845400.885449
90No logNo log0.8200000.9200000.9700000.9900000.8200000.3066670.1940000.0990000.8200000.9200000.9700000.9900000.9104130.8844290.8853380.8200000.9200000.9700000.9900000.8200000.3066670.1940000.0990000.8200000.9200000.9700000.9900000.9104130.8844290.885338
100No logNo log0.8200000.9300000.9700000.9900000.8200000.3100000.1940000.0990000.8200000.9300000.9700000.9900000.9097960.8835950.8845040.8200000.9300000.9700000.9900000.8200000.3100000.1940000.0990000.8200000.9300000.9700000.9900000.9097960.8835950.884504
120No logNo log0.8100000.9300000.9700000.9800000.8100000.3100000.1940000.0980000.8100000.9300000.9700000.9800000.9032150.8775950.8793380.8100000.9300000.9700000.9800000.8100000.3100000.1940000.0980000.8100000.9300000.9700000.9800000.9032150.8775950.879338
150No logNo log0.8100000.9300000.9700000.9800000.8100000.3100000.1940000.0980000.8100000.9300000.9700000.9800000.9036530.8780950.8798380.8100000.9300000.9700000.9800000.8100000.3100000.1940000.0980000.8100000.9300000.9700000.9800000.9036530.8780950.879838

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Computing widget examples: 0%| | 0/1 [00:00=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2024.6.1)\n", "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (24.1)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.2)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.32.3)\n", "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.5)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4.0)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.2.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.8.30)\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "0f92bbe647084ac7a97c9292e3c0af70", "6ed689f73979402c85210759d39789c5", "29c07d0c464948ea8899257c9dd1009c", "287d4166802148ab9e415b92edcf0efe", "4496a53b5c1e47ef8f0025991ce68242", "3430c0b599f94762aa80f7472a467fd7", "d986567e867a42d9939700039608d696", "d65fa132972b49fdb9d2695f205cc224", "16e3844463254d86bbf4ae1eb3414019", "68d80f7b2d2c4e0c87904a8c5c7867ea", "82f545a023584726a20efa74316138df", "8b72b915d8e84d8c95b8a89d48c400e2", "7725d7ff977b4c6a95df2f88f96018ef", "724ae7a08dd044e4be0fe899d6669afa", "72b8b2332d24489dbfbc9edb9ecadbad", "377abb647c304782bb3d0e2d4aa551bd", "4b5829de40334c8d80fe096319231b3b", "de1d9c77afa941be846a7f82f2c121c6", "cce7ca27217f4ab0ac8e4c043cfc5de5", "19dd68bdb7db4e0e982883e62aac6b99" ] }, "id": "Qv5naM8UgQba", "outputId": "ce6d210a-a3ec-4f70-ed37-6f430ce5b4c3" }, "execution_count": 67, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='