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chardet==5.2.0 charset-normalizer==3.4.0 click==8.1.7 cryptography==43.0.1 dataclasses-json==0.6.7 datasets==3.0.1 dill==0.3.8 distro==1.9.0 emoji==2.14.0 eval-type-backport==0.2.0 faiss-cpu==1.9.0 filelock==3.16.1 filetype==1.2.0 frozenlist==1.4.1 fsspec[http]==2024.6.1 greenlet==3.1.1 h11==0.14.0 httpcore==1.0.6 httpx==0.27.2 huggingface-hub==0.26.0 idna==3.10 jinja2==3.1.4 jiter==0.6.1 joblib==1.4.2 jsonpatch==1.33 jsonpath-python==1.0.6 jsonpointer==3.0.0 langchain==0.2.16 langchain-community==0.2.16 langchain-core==0.2.38 langchain-huggingface==0.0.3 langchain-openai==0.1.23 langchain-text-splitters==0.2.4 langdetect==1.0.9 langsmith==0.1.136 lxml==5.3.0 markupsafe==3.0.2 marshmallow==3.23.0 mpmath==1.3.0 multidict==6.1.0 multiprocess==0.70.16 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 tabulate==0.9.0 tenacity==8.5.0 threadpoolctl==3.5.0 tiktoken==0.8.0 tokenizers==0.20.1 torch==2.5.0 tqdm==4.66.5 transformers==4.45.2 triton==3.1.0 typing-extensions==4.12.2 typing-inspect==0.9.0 tzdata==2024.2 unstructured==0.15.7 unstructured-client==0.26.1 urllib3==2.2.3 wrapt==1.16.0 xlsxwriter==3.2.0 xxhash==3.5.0 yarl==1.15.4\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JRkQn1rGTr1h", + "outputId": "941c05f0-d7a6-4572-b0e2-7aebd591ced1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━��━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.1/58.1 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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", + "21632fc73a2b49d6a784c2ad6413f21f", + "3491a9317f154c7e93d222623c71ed8b", + "9c192c350a07430ea2a1db49f54eb4db", + "bba302ae936c42649a90caff653475a7", + "1ff7a3ed0fb0474f851beaea5c896e42", + "451b663517254b479513102030bbcf28", + "7afae5422e004f9c9de421d975d8aa02", + "62722c053ac0483e9ee3639b10cd2c0b", + "b96e60e8b02d4fbf813e4d888af4abe7", + "445fdd2cb00b4f88afcf211d4f1a13c0", + "32d38a1def784709bf82bc8e082f4d52", + "abb82336817a43b08dd46282857d1c78", + "1545417e82bc4f5eaddc9463b8e9976b", + "a9ac52ef3475488c80f751f94687ed14", + "5c9e9769c3f24fc18185b42783becc55", + "b6b3e6aaa275433094459b7106cdd95e", + "bdbca64fb0094d239c7d72ee9934181e", + "f5f41b10754e428c84260963466948fd", + "f9bab79cb56641cc8fb7af9f598d1331", + "4f59a3ca8df648a6ab92f4c8543f7bd4", + 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"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='