mirror of
https://github.com/NicolasBohn/NexQuant.git
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Linlang
32a29a7479
* feat: parameterize cache paths with USER to avoid conflicts * guide for missing training_hyperparameters * guidance for KeyError: 'concise_reason' * fixed three bugs in the test * fix general_model task bug * fixed some bugs in the med_model scenario * delete comments * format with black * fix mypy error * fix ruff error * fix isort error * sync code * revert cache_path code * revert cache_path code * delete data mining scenario * fix factor report loop * fix LiteLLMAPIBackend log_llm_chat_content setting * refine fin factor report scenario * remove unused LogColors * fix UI * remove medical scenario docs * change **kaggle** to **data_science** * remove default dataset_path in create_debug_data * remove KAGGLE_SETTINGS in kaggle_crawler * limit litellm versions * reformat with black * change README * fix_data_science_docs * make hypothesis observations string * Hiding old versions of kaggle docs * hidding kaggle agent docs --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: yuanteli <1957922024@qq.com>
71 lines
910 B
Plaintext
71 lines
910 B
Plaintext
# Requirements for runtime.
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pydantic-settings
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python-Levenshtein
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scikit-learn
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filelock
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loguru
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fire
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fuzzywuzzy
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openai
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litellm==1.72.4
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azure.identity
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pyarrow
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rich
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tqdm
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typer
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numpy # we use numpy as default data format. So we have to install numpy
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pandas # we use pandas as default data format. So we have to install pandas
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pandarallel # parallelize pandas
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matplotlib
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langchain
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langchain-community
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tiktoken
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pymupdf # Extract shotsreens from pdf
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# PDF related
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pypdf
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azure-ai-formrecognizer
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# factor implementations
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tables
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# CI Fix Tool
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tree-sitter-python
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tree-sitter
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python-dotenv
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# infrastructure related.
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docker
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# demo related
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streamlit
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plotly
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st-theme
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randomname
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flask
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flask-cors
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# kaggle crawler
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selenium
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kaggle
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nbformat
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# tool
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setuptools-scm
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seaborn
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azure.ai.inference
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# data folder desc
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humanize
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genson
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# mlflow
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mlflow
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azureml-mlflow
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types-pytz
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# data science scenario for custom dataset
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sparse |