From 8fed473f88d3647cd93aa4486d5c768111e1d106 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rog=C3=A9rio=20Chaves?= Date: Mon, 6 Jan 2025 12:27:03 +0100 Subject: [PATCH] Remove use_hhem, pytorch is hard to install in cpu-only linux aarch64 --- .../ragas/langevals_ragas/faithfulness.py | 13 +- evaluators/ragas/poetry.lock | 357 +----------------- evaluators/ragas/pyproject.toml | 13 - poetry.lock | 4 - ts-integration/evaluators.generated.ts | 10 - 5 files changed, 3 insertions(+), 394 deletions(-) diff --git a/evaluators/ragas/langevals_ragas/faithfulness.py b/evaluators/ragas/langevals_ragas/faithfulness.py index 48e9374..176767f 100644 --- a/evaluators/ragas/langevals_ragas/faithfulness.py +++ b/evaluators/ragas/langevals_ragas/faithfulness.py @@ -19,7 +19,7 @@ prepare_llm, ) from pydantic import Field -from ragas.metrics import Faithfulness, FaithfulnesswithHHEM +from ragas.metrics import Faithfulness from langchain_core.prompt_values import StringPromptValue @@ -37,10 +37,6 @@ class RagasFaithfulnessResult(EvaluationResult): class RagasFaithfulnessSettings(RagasSettings): - use_hhem: bool = Field( - default=False, - description="Whether to use Vectara's HHEM-2.1-Open for faithfulness scoring.", - ) autodetect_dont_know: bool = Field( default=True, description="Whether to autodetect 'I don't know' in the output to avoid failing the evaluation.", @@ -63,11 +59,6 @@ class RagasFaithfulnessEvaluator( docs_url = "https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/faithfulness/" is_guardrail = False - @classmethod - def preload(cls): - cls.faithfulnessHHEM = FaithfulnesswithHHEM() - super().preload() - def evaluate(self, entry: RagasFaithfulnessEntry) -> SingleEvaluationResult: llm, _ = prepare_llm(self, self.settings) @@ -81,7 +72,7 @@ def evaluate(self, entry: RagasFaithfulnessEntry) -> SingleEvaluationResult: if skip: return skip - scorer = self.faithfulnessHHEM if self.settings.use_hhem else Faithfulness() + scorer = Faithfulness() scorer.llm = llm _original_create_statements = scorer._create_statements diff --git a/evaluators/ragas/poetry.lock b/evaluators/ragas/poetry.lock index fa75e94..cf333a0 100644 --- a/evaluators/ragas/poetry.lock +++ b/evaluators/ragas/poetry.lock @@ -1451,20 +1451,6 @@ files = [ {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, ] -[[package]] -name = "intel-openmp" -version = "2021.4.0" -description = "Intel OpenMP* Runtime Library" -optional = false -python-versions = "*" -files = [ - {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"}, - {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"}, - {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"}, - {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"}, - {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"}, -] - [[package]] name = "ipykernel" version = "6.29.5" @@ -2217,41 +2203,6 @@ files = [ [package.dependencies] traitlets = "*" -[[package]] -name = "mkl" -version = "2021.4.0" -description = "IntelĀ® oneAPI Math Kernel Library" -optional = false -python-versions = "*" -files = [ - {file = "mkl-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:67460f5cd7e30e405b54d70d1ed3ca78118370b65f7327d495e9c8847705e2fb"}, - {file = "mkl-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:636d07d90e68ccc9630c654d47ce9fdeb036bb46e2b193b3a9ac8cfea683cce5"}, - {file = "mkl-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:398dbf2b0d12acaf54117a5210e8f191827f373d362d796091d161f610c1ebfb"}, - {file = "mkl-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:439c640b269a5668134e3dcbcea4350459c4a8bc46469669b2d67e07e3d330e8"}, - {file = "mkl-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:ceef3cafce4c009dd25f65d7ad0d833a0fbadc3d8903991ec92351fe5de1e718"}, -] - -[package.dependencies] -intel-openmp = "==2021.*" -tbb = "==2021.*" - -[[package]] -name = "mpmath" -version = "1.3.0" -description = "Python library for arbitrary-precision floating-point arithmetic" -optional = false -python-versions = "*" -files = [ - {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, - {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, -] - -[package.extras] -develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] -docs = ["sphinx"] -gmpy = ["gmpy2 (>=2.1.0a4)"] -tests = ["pytest (>=4.6)"] - [[package]] name = "multidict" version = "6.1.0" @@ -2403,25 +2354,6 @@ files = [ {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"}, ] -[[package]] -name = "networkx" -version = "3.4.2" -description = "Python package for creating and manipulating graphs and networks" -optional = false -python-versions = ">=3.10" -files = [ - {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"}, - {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"}, -] - -[package.extras] -default = ["matplotlib (>=3.7)", "numpy (>=1.24)", "pandas (>=2.0)", "scipy (>=1.10,!=1.11.0,!=1.11.1)"] -developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"] -doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.15)", "sphinx (>=7.3)", "sphinx-gallery (>=0.16)", "texext (>=0.6.7)"] -example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=1.9)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"] -extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"] -test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"] - [[package]] name = "nltk" version = "3.9.1" @@ -2492,149 +2424,6 @@ files = [ {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"}, ] -[[package]] -name = "nvidia-cublas-cu12" -version = "12.1.3.1" -description = "CUBLAS native runtime libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"}, - {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"}, -] - -[[package]] -name = "nvidia-cuda-cupti-cu12" -version = "12.1.105" -description = "CUDA profiling tools runtime libs." -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"}, - {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"}, -] - -[[package]] -name = "nvidia-cuda-nvrtc-cu12" -version = "12.1.105" -description = "NVRTC native runtime libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"}, - {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"}, -] - -[[package]] -name = "nvidia-cuda-runtime-cu12" -version = "12.1.105" -description = "CUDA Runtime native Libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"}, - {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"}, -] - -[[package]] -name = "nvidia-cudnn-cu12" -version = "8.9.2.26" -description = "cuDNN runtime libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"}, -] - -[package.dependencies] -nvidia-cublas-cu12 = "*" - -[[package]] -name = "nvidia-cufft-cu12" -version = "11.0.2.54" -description = "CUFFT native runtime libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"}, - {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"}, -] - -[[package]] -name = "nvidia-curand-cu12" -version = "10.3.2.106" -description = "CURAND native runtime libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"}, - {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"}, -] - -[[package]] -name = "nvidia-cusolver-cu12" -version = "11.4.5.107" -description = "CUDA solver native runtime libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"}, - {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"}, -] - -[package.dependencies] -nvidia-cublas-cu12 = "*" -nvidia-cusparse-cu12 = "*" -nvidia-nvjitlink-cu12 = "*" - -[[package]] -name = "nvidia-cusparse-cu12" -version = "12.1.0.106" -description = "CUSPARSE native runtime libraries" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"}, - {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"}, -] - -[package.dependencies] -nvidia-nvjitlink-cu12 = "*" - -[[package]] -name = "nvidia-nccl-cu12" -version = "2.20.5" -description = "NVIDIA Collective Communication Library (NCCL) Runtime" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"}, - {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"}, -] - -[[package]] -name = "nvidia-nvjitlink-cu12" -version = "12.4.127" -description = "Nvidia JIT LTO Library" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4abe7fef64914ccfa909bc2ba39739670ecc9e820c83ccc7a6ed414122599b83"}, - {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"}, - {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:fd9020c501d27d135f983c6d3e244b197a7ccad769e34df53a42e276b0e25fa1"}, -] - -[[package]] -name = "nvidia-nvtx-cu12" -version = "12.1.105" -description = "NVIDIA Tools Extension" -optional = false -python-versions = ">=3" -files = [ - {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"}, - {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"}, -] - [[package]] name = "openai" version = "1.55.2" @@ -4352,23 +4141,6 @@ pure-eval = "*" [package.extras] tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"] -[[package]] -name = "sympy" -version = "1.13.1" -description = "Computer algebra system (CAS) in Python" -optional = false -python-versions = ">=3.8" -files = [ - {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"}, - {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"}, -] - -[package.dependencies] -mpmath = ">=1.1.0,<1.4" - -[package.extras] -dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"] - [[package]] name = "tabulate" version = "0.9.0" @@ -4383,19 +4155,6 @@ files = [ [package.extras] widechars = ["wcwidth"] -[[package]] -name = "tbb" -version = "2021.13.1" -description = "IntelĀ® oneAPI Threading Building Blocks (oneTBB)" -optional = false -python-versions = "*" -files = [ - {file = "tbb-2021.13.1-py2.py3-none-manylinux1_i686.whl", hash = "sha256:bb5bdea0c0e9e6ad0739e7a8796c2635ce9eccca86dd48c426cd8027ac70fb1d"}, - {file = "tbb-2021.13.1-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:d916359dc685579d09e4b344241550afc1cc034f7f5ec7234c258b6680912d70"}, - {file = "tbb-2021.13.1-py3-none-win32.whl", hash = "sha256:00f5e5a70051650ddd0ab6247c0549521968339ec21002e475cd23b1cbf46d66"}, - {file = "tbb-2021.13.1-py3-none-win_amd64.whl", hash = "sha256:cbf024b2463fdab3ebe3fa6ff453026358e6b903839c80d647e08ad6d0796ee9"}, -] - [[package]] name = "tenacity" version = "8.5.0" @@ -4490,97 +4249,6 @@ dev = ["tokenizers[testing]"] docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"] testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"] -[[package]] -name = "torch" -version = "2.3.0" -description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" -optional = false -python-versions = ">=3.8.0" -files = [ - {file = "torch-2.3.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:d8ea5a465dbfd8501f33c937d1f693176c9aef9d1c1b0ca1d44ed7b0a18c52ac"}, - {file = "torch-2.3.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:09c81c5859a5b819956c6925a405ef1cdda393c9d8a01ce3851453f699d3358c"}, - {file = "torch-2.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:1bf023aa20902586f614f7682fedfa463e773e26c58820b74158a72470259459"}, - {file = "torch-2.3.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:758ef938de87a2653bba74b91f703458c15569f1562bf4b6c63c62d9c5a0c1f5"}, - {file = "torch-2.3.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:493d54ee2f9df100b5ce1d18c96dbb8d14908721f76351e908c9d2622773a788"}, - {file = "torch-2.3.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:bce43af735c3da16cc14c7de2be7ad038e2fbf75654c2e274e575c6c05772ace"}, - {file = "torch-2.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:729804e97b7cf19ae9ab4181f91f5e612af07956f35c8b2c8e9d9f3596a8e877"}, - {file = "torch-2.3.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:d24e328226d8e2af7cf80fcb1d2f1d108e0de32777fab4aaa2b37b9765d8be73"}, - {file = "torch-2.3.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b0de2bdc0486ea7b14fc47ff805172df44e421a7318b7c4d92ef589a75d27410"}, - {file = "torch-2.3.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a306c87a3eead1ed47457822c01dfbd459fe2920f2d38cbdf90de18f23f72542"}, - {file = "torch-2.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:f9b98bf1a3c8af2d4c41f0bf1433920900896c446d1ddc128290ff146d1eb4bd"}, - {file = "torch-2.3.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:dca986214267b34065a79000cee54232e62b41dff1ec2cab9abc3fc8b3dee0ad"}, - {file = "torch-2.3.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:20572f426965dd8a04e92a473d7e445fa579e09943cc0354f3e6fef6130ce061"}, - {file = "torch-2.3.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e65ba85ae292909cde0dde6369826d51165a3fc8823dc1854cd9432d7f79b932"}, - {file = "torch-2.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:5515503a193781fd1b3f5c474e89c9dfa2faaa782b2795cc4a7ab7e67de923f6"}, - {file = "torch-2.3.0-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:6ae9f64b09516baa4ef890af0672dc981c20b1f0d829ce115d4420a247e88fba"}, - {file = "torch-2.3.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:cd0dc498b961ab19cb3f8dbf0c6c50e244f2f37dbfa05754ab44ea057c944ef9"}, - {file = "torch-2.3.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e05f836559251e4096f3786ee99f4a8cbe67bc7fbedba8ad5e799681e47c5e80"}, - {file = "torch-2.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:4fb27b35dbb32303c2927da86e27b54a92209ddfb7234afb1949ea2b3effffea"}, - {file = "torch-2.3.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:760f8bedff506ce9e6e103498f9b1e9e15809e008368594c3a66bf74a8a51380"}, -] - -[package.dependencies] -filelock = "*" -fsspec = "*" -jinja2 = "*" -mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""} -networkx = "*" -nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} -sympy = "*" -triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""} -typing-extensions = ">=4.8.0" - -[package.extras] -opt-einsum = ["opt-einsum (>=3.3)"] -optree = ["optree (>=0.9.1)"] - -[[package]] -name = "torch" -version = "2.3.0+cpu" -description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" -optional = false -python-versions = ">=3.8.0" -files = [ - {file = "torch-2.3.0+cpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:e3c220702d82c7596924150e0499fbbffcf62a88a59adc860fa357cd8dc1c302"}, - {file = "torch-2.3.0+cpu-cp310-cp310-win_amd64.whl", hash = "sha256:ab0c05525195b8fecdf2ea75968ed32ccd87dff16381b6e13249babb4a9596ff"}, - {file = "torch-2.3.0+cpu-cp311-cp311-linux_x86_64.whl", hash = "sha256:97a38b25ee0e3d020691e7846efbca62a3d8a57645c027dcb5ba0adfec36fe55"}, - {file = "torch-2.3.0+cpu-cp311-cp311-win_amd64.whl", hash = "sha256:a8ac195974be6f067245bae8156b8c06fb0a723b0eed8f2e244b5dd58c7e2a49"}, - {file = "torch-2.3.0+cpu-cp312-cp312-linux_x86_64.whl", hash = "sha256:a8982e52185771591dad577a124a7770f72f288f8ae5833317b1e329c0d2f07e"}, - {file = "torch-2.3.0+cpu-cp312-cp312-win_amd64.whl", hash = "sha256:483131a7997995d867313ee902743084e844e830ab2a0c5e079c61ec2da3cd17"}, - {file = "torch-2.3.0+cpu-cp38-cp38-linux_x86_64.whl", hash = "sha256:8c52484880d5fbe511cffc255dd34847ddeced3f94334c6bf7eb2b0445f10cb4"}, - {file = "torch-2.3.0+cpu-cp38-cp38-win_amd64.whl", hash = "sha256:28a11bcc0d709b397d675cff689707019b8cc122e6bf328b57b900f47c36f156"}, - {file = "torch-2.3.0+cpu-cp39-cp39-linux_x86_64.whl", hash = "sha256:1e86e225e472392440ace378ba3165b5e87648e8b5fbf16adc41c0df881c38b8"}, - {file = "torch-2.3.0+cpu-cp39-cp39-win_amd64.whl", hash = "sha256:5c2afdff80203eaabf4c223a294c2f465020b3360e8e87f76b52ace9c5801ebe"}, -] - -[package.dependencies] -filelock = "*" -fsspec = "*" -jinja2 = "*" -mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""} -networkx = "*" -sympy = "*" -typing-extensions = ">=4.8.0" - -[package.extras] -opt-einsum = ["opt-einsum (>=3.3)"] -optree = ["optree (>=0.9.1)"] - -[package.source] -type = "legacy" -url = "https://download.pytorch.org/whl/cpu" -reference = "pytorch_cpu" - [[package]] name = "tornado" version = "6.4.2" @@ -4706,29 +4374,6 @@ torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", video = ["av (==9.2.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] -[[package]] -name = "triton" -version = "2.3.0" -description = "A language and compiler for custom Deep Learning operations" -optional = false -python-versions = "*" -files = [ - {file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"}, - {file = "triton-2.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c3d9607f85103afdb279938fc1dd2a66e4f5999a58eb48a346bd42738f986dd"}, - {file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"}, - {file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"}, - {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"}, - {file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"}, -] - -[package.dependencies] -filelock = "*" - -[package.extras] -build = ["cmake (>=3.20)", "lit"] -tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"] -tutorials = ["matplotlib", "pandas", "tabulate", "torch"] - [[package]] name = "typing-extensions" version = "4.12.2" @@ -5081,4 +4726,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "2101688b0b0fe78bc689b05204022551da5455b9b0356b6a015a5cb659760a02" +content-hash = "1cc8d3c246a2ff7793c64fcf8a0c5110b6d03429ed87427e20f281aee86f7f3d" diff --git a/evaluators/ragas/pyproject.toml b/evaluators/ragas/pyproject.toml index 615d98f..99a2524 100644 --- a/evaluators/ragas/pyproject.toml +++ b/evaluators/ragas/pyproject.toml @@ -7,10 +7,6 @@ authors = [ "Yevhenii Budnyk ", ] license = "MIT" -[[tool.poetry.source]] -name = "pytorch_cpu" -url = "https://download.pytorch.org/whl/cpu" -priority = "explicit" [tool.poetry.dependencies] python = "^3.11" @@ -23,15 +19,6 @@ transformers = "^4.47.1" rapidfuzz = "^3.11.0" sacrebleu = "^2.5.1" rouge-score = "^0.1.2" -[[tool.poetry.dependencies.torch]] -version = "^2.2.2+cpu" -source = "pytorch_cpu" -markers = "sys_platform == 'linux'" - -[[tool.poetry.dependencies.torch]] -version = "^2.2.2" -source = "pypi" -markers = "sys_platform != 'linux'" [tool.poetry.group.test.dependencies] pytest = "^7.4.2" diff --git a/poetry.lock b/poetry.lock index ec037c1..de57c85 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2329,10 +2329,6 @@ ragas = "0.2.9" rapidfuzz = "^3.11.0" rouge-score = "^0.1.2" sacrebleu = "^2.5.1" -torch = [ - {version = ">=2.2.2+cpu,<3.0.0", markers = "sys_platform == \"linux\""}, - {version = ">=2.2.2,<3.0.0", markers = "sys_platform != \"linux\""}, -] transformers = "^4.47.1" [package.source] diff --git a/ts-integration/evaluators.generated.ts b/ts-integration/evaluators.generated.ts index 6855cbf..b286cde 100644 --- a/ts-integration/evaluators.generated.ts +++ b/ts-integration/evaluators.generated.ts @@ -255,11 +255,6 @@ export type Evaluators = { * @default 2048 */ max_tokens: number; - /** - * @description Whether to use Vectara's HHEM-2.1-Open for faithfulness scoring. - * @default false - */ - use_hhem: boolean; /** * @description Whether to autodetect 'I don't know' in the output to avoid failing the evaluation. * @default true @@ -1264,11 +1259,6 @@ This evaluator assesses the extent to which the generated answer is consistent w "The maximum number of tokens allowed for evaluation, a too high number can be costly. Entries above this amount will be skipped.", default: 2048, }, - use_hhem: { - description: - "Whether to use Vectara's HHEM-2.1-Open for faithfulness scoring.", - default: false, - }, autodetect_dont_know: { description: "Whether to autodetect 'I don't know' in the output to avoid failing the evaluation.",