From 7a8c9a39d673488357640812070e355e3b4cc069 Mon Sep 17 00:00:00 2001 From: cfli <545999961@qq.com> Date: Wed, 30 Oct 2024 23:43:40 +0800 Subject: [PATCH] tmp rm docs --- .gitignore | 4 +- docs/Makefile | 20 - docs/README.md | 10 - docs/make.bat | 35 - docs/requirements.txt | 3 - docs/source/API/abc.rst | 5 - docs/source/API/evaluation.rst | 2 - docs/source/API/inference.rst | 2 - docs/source/C-MTEB.rst | 47 -- docs/source/Introduction/installation.rst | 28 - docs/source/Introduction/quick_start.ipynb | 472 ----------- docs/source/_static/img/BAAI_logo.png | Bin 62137 -> 0 bytes docs/source/_static/img/C_MTEB.png | Bin 529368 -> 0 bytes docs/source/bge/bge_icl.rst | 2 - docs/source/bge/bge_m3.rst | 2 - docs/source/bge/bge_reranker.rst | 2 - docs/source/bge/bge_v1.rst | 49 -- docs/source/bge/introduction.rst | 5 - docs/source/conf.py | 53 -- docs/source/index.rst | 77 -- docs/source/tutorial/1_Embedding.rst | 12 - docs/source/tutorial/1_Embedding/1.1.1.ipynb | 395 --------- docs/source/tutorial/1_Embedding/1.2.1.ipynb | 486 ----------- docs/source/tutorial/1_Embedding/1.2.2.ipynb | 419 --------- docs/source/tutorial/1_Embedding/1.2.3.ipynb | 414 --------- docs/source/tutorial/2_Metrics.rst | 10 - 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-# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/README.md b/docs/README.md deleted file mode 100644 index 6908c194..00000000 --- a/docs/README.md +++ /dev/null @@ -1,10 +0,0 @@ -install the required pkgs: -``` -pip install -r requirements.txt -``` - - -to host the webpages locally: -``` -python -m http.server -``` \ No newline at end of file diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 747ffb7b..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 3d310f10..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -sphinx -myst-nb -furo \ No newline at end of file diff --git a/docs/source/API/abc.rst b/docs/source/API/abc.rst deleted file mode 100644 index 310a7a87..00000000 --- a/docs/source/API/abc.rst +++ /dev/null @@ -1,5 +0,0 @@ -Abstract Class -============== - -.. autoclass:: FlagEmbedding.abc.inference.AbsEmbedder - :members: \ No newline at end of file diff --git a/docs/source/API/evaluation.rst b/docs/source/API/evaluation.rst deleted file mode 100644 index 0e6b32f8..00000000 --- a/docs/source/API/evaluation.rst +++ /dev/null @@ -1,2 +0,0 @@ -Evaluation -========== \ No newline at end of file diff --git a/docs/source/API/inference.rst b/docs/source/API/inference.rst deleted file mode 100644 index 3fe80034..00000000 --- a/docs/source/API/inference.rst +++ /dev/null @@ -1,2 +0,0 @@ -Inference -========= \ No newline at end of file diff --git a/docs/source/C-MTEB.rst b/docs/source/C-MTEB.rst deleted file mode 100644 index fac2ec33..00000000 --- a/docs/source/C-MTEB.rst +++ /dev/null @@ -1,47 +0,0 @@ -.. C-MTEB -.. ====== - -.. Introduction -.. ------------ - -.. `C-MTEB `_ is a benchmark for chinese text embedding. It contains 35 -.. datasets in 6 different tasks, providing a comprehensive evaluation to the quality of an embedding model on Chinese. - - -.. .. image:: ../_static/img/C_MTEB.png -.. :width: 700 -.. :align: center - - -.. Installation -.. ------------ - -.. C-MTEB is developed based on MTEB, you can install C-MTEB by: - -.. .. code:: bash - -.. pip install -U C_MTEB - -.. or install by FlagEmbedding's repo: - -.. .. code:: bash - -.. git clone https://github.com/FlagOpen/FlagEmbedding.git -.. cd FlagEmbedding/C_MTEB -.. pip install -e . - -.. Citing the Work -.. --------------- - -.. There are more details in our publication. If you find C-MTEB useful, you can cite it by: - -.. .. code:: - -.. @misc{c-pack, -.. title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, -.. author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, -.. year={2023}, -.. eprint={2309.07597}, -.. archivePrefix={arXiv}, -.. primaryClass={cs.CL} -.. } \ No newline at end of file diff --git a/docs/source/Introduction/installation.rst b/docs/source/Introduction/installation.rst deleted file mode 100644 index a4809b46..00000000 --- a/docs/source/Introduction/installation.rst +++ /dev/null @@ -1,28 +0,0 @@ -:github_url: https://github.com/AI4Finance-Foundation/FinRL - -Installation -============ - -Using pip: ----------- - -.. code:: bash - - pip install -U FlagEmbedding - -Install from sources: ---------------------- - -Clone the repository and install - -.. code:: bash - - git clone https://github.com/FlagOpen/FlagEmbedding.git - cd FlagEmbedding - pip install . - -For development in editable mode: - -.. code:: bash - - pip install -e . \ No newline at end of file diff --git a/docs/source/Introduction/quick_start.ipynb b/docs/source/Introduction/quick_start.ipynb deleted file mode 100644 index 4eed81cb..00000000 --- a/docs/source/Introduction/quick_start.ipynb +++ /dev/null @@ -1,472 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quick Start" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, we will show how to use BGE models on a text retrieval task in 5 minutes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 0: Preparation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, install FlagEmbedding in the environment." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -U FlagEmbedding" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is a super tiny courpus with only 10 sentences, which will be the dataset we use.\n", - "\n", - "Each sentence is a concise discription of a famous people in specific domain." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "corpus = [\n", - " \"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\",\n", - " \"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\",\n", - " \"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\",\n", - " \"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\",\n", - " \"Eminem is a renowned rapper and one of the best-selling music artists of all time.\",\n", - " \"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\",\n", - " \"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\",\n", - " \"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\",\n", - " \"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\",\n", - " \"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\",\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to know which one of these people could be an expert of neural network and who he/she is. \n", - "\n", - "Thus we generate the following query:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "query = \"Who could be an expert of neural network?\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Text -> Embedding" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's use a [BGE embedding model](https://huggingface.co/BAAI/bge-base-en-v1.5) to create sentence embedding for the corpus." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from FlagEmbedding import FlagModel\n", - "\n", - "# get the BGE embedding model\n", - "model = FlagModel('BAAI/bge-base-en-v1.5',\n", - " query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages:\",\n", - " use_fp16=True)\n", - "\n", - "# get the embedding of the query and corpus\n", - "corpus_embeddings = model.encode(corpus)\n", - "query_embedding = model.encode(query)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The embedding of each sentence is a vector with length 768. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape of the query embedding: (768,)\n", - "shape of the corpus embeddings: (10, 768)\n" - ] - } - ], - "source": [ - "print(\"shape of the query embedding: \", query_embedding.shape)\n", - "print(\"shape of the corpus embeddings:\", corpus_embeddings.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the following print line to take a look at the first 10 elements of the query embedding vector." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.00790005 -0.00683443 -0.00806659 0.00756918 0.04374858 0.02838556\n", - " 0.02357143 -0.02270943 -0.03611493 -0.03038301]\n" - ] - } - ], - "source": [ - "print(query_embedding[:10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Calculate Similarity" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we have the embeddings of the query and the corpus. The next step is to calculate the similarity between the query and each sentence in the corpus. Here we use the dot product/inner product as our similarity metric." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.39290053 0.6031525 0.32672375 0.6082418 0.39446455 0.35350388\n", - " 0.4626108 0.40196604 0.5284606 0.36792332]\n" - ] - } - ], - "source": [ - "sim_scores = query_embedding @ corpus_embeddings.T\n", - "print(sim_scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The result is a list of score representing the query's similarity to: [sentence 0, sentence 1, sentence 2, ...]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Ranking" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After we have the similarity score of the query to each sentence in the corpus, we can rank them from large to small." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3, 1, 8, 6, 7, 4, 0, 9, 5, 2]\n" - ] - } - ], - "source": [ - "# get the indices in sorted order\n", - "sorted_indices = sorted(range(len(sim_scores)), key=lambda k: sim_scores[k], reverse=True)\n", - "print(sorted_indices)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now from the ranking, the sentence with index 3 is the best answer to our query \"Who could be an expert of neural network?\"\n", - "\n", - "And that person is Geoffrey Hinton!" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\n" - ] - } - ], - "source": [ - "print(corpus[3])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "According to the order of indecies, we can print out the ranking of people that our little retriever got." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Score of 0.608: \"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\"\n", - "Score of 0.603: \"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\"\n", - "Score of 0.528: \"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\"\n", - "Score of 0.463: \"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\"\n", - "Score of 0.402: \"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\"\n", - "Score of 0.394: \"Eminem is a renowned rapper and one of the best-selling music artists of all time.\"\n", - "Score of 0.393: \"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\"\n", - "Score of 0.368: \"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\"\n", - "Score of 0.354: \"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\"\n", - "Score of 0.327: \"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\"\n" - ] - } - ], - "source": [ - "# iteratively print the score and corresponding sentences in descending order\n", - "\n", - "for i in sorted_indices:\n", - " print(f\"Score of {sim_scores[i]:.3f}: \\\"{corpus[i]}\\\"\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the ranking, not surprisingly, the similarity scores of the query and the discriptions of Geoffrey Hinton and Fei-Fei Li is way higher than others, following by those of Andrew Ng and Sam Altman. \n", - "\n", - "While the key phrase \"neural network\" in the query does not appear in any of those discriptions, the BGE embedding model is still powerful enough to get the semantic meaning of query and corpus well." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Evaluate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We've seen the embedding model performed pretty well on the \"neural network\" query. What about the more general quality?\n", - "\n", - "Let's generate a very small dataset of queries and corresponding ground truth answers. Note that the ground truth answers are the indices of sentences in the corpus." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "queries = [\n", - " \"Who could be an expert of neural network?\",\n", - " \"Who might had won Grammy?\",\n", - " \"Won Academy Awards\",\n", - " \"One of the most famous female singers.\",\n", - " \"Inventor of AlexNet\",\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "ground_truth = [\n", - " [1, 3],\n", - " [0, 4, 5],\n", - " [2, 7, 9],\n", - " [5],\n", - " [3],\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we repeat the steps we covered above to get the predicted ranking of each query." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[3, 1, 8, 6, 7, 4, 0, 9, 5, 2],\n", - " [5, 0, 3, 4, 1, 9, 7, 2, 6, 8],\n", - " [3, 2, 7, 5, 9, 0, 1, 4, 6, 8],\n", - " [5, 0, 4, 7, 1, 9, 2, 3, 6, 8],\n", - " [3, 1, 8, 6, 0, 7, 5, 9, 4, 2]]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# use bge model to generate embeddings for all the queries\n", - "queries_embedding = model.encode(queries)\n", - "# compute similarity scores\n", - "scores = queries_embedding @ corpus_embeddings.T\n", - "# get he final rankings\n", - "rankings = [sorted(range(len(sim_scores)), key=lambda k: sim_scores[k], reverse=True) for sim_scores in scores]\n", - "rankings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Mean Reciprocal Rank ([MRR](https://en.wikipedia.org/wiki/Mean_reciprocal_rank)) is a widely used metric in information retrieval to evaluate the effectiveness of a system. Here we use that to have a very rough idea how our system performs." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def MRR(preds, labels, cutoffs):\n", - " mrr = [0 for _ in range(len(cutoffs))]\n", - " for pred, label in zip(preds, labels):\n", - " for i, c in enumerate(cutoffs):\n", - " for j, index in enumerate(pred):\n", - " if j < c and index in label:\n", - " mrr[i] += 1/(j+1)\n", - " break\n", - " mrr = [k/len(preds) for k in mrr]\n", - " return mrr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We choose to use 1 and 5 as our cutoffs, with the result of 0.8 and 0.9 respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MRR@1: 0.8\n", - "MRR@5: 0.9\n" - ] - } - ], - "source": [ - "cutoffs = [1, 5]\n", - "mrrs = MRR(rankings, ground_truth, cutoffs)\n", - "for i, c in enumerate(cutoffs):\n", - " print(f\"MRR@{c}: {mrrs[i]}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/_static/img/BAAI_logo.png b/docs/source/_static/img/BAAI_logo.png deleted file mode 100644 index c39cc6fdc0f1c7bdc72826aed4a42b3959914a44..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 62137 zcmXtAcOaGR`+v@H(ov{m9;V3788Cems$7va* zDC4L^p<%@Ddc5D?U+-JzzMtp0#(j;?^|?#1vM^-Z!M_7R5H@2YgChvClZ+tS^zb z;XRXIZ>zeb$P%s2GEEYh3;c#T)ZXH{@uoEEkHZp87YrYU|$B@Ll-**0<>KQ~z&21-jn$4YVR{ zKh9)%QT7-udhIb@JhP{-sZKSjZ*R!L(M=~QE;`0(#?HYoj$=>H(jtu&xjpfvZ+CJ0 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