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Analyzing the impact of sentence length on embedding model performance

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konstantin-spiess/embenchmark

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Sentence Embedding Benchmark

Installation

Prerequisites:

  • Python 3.9 or above
  • PiP 23.3 or above

Steps:

  1. Because we are downloading and using a lot of data from huggingface, you need to create a .env file and add you huggingface token, otherwise downloading the required models and datasets might fail.
    HF_TOKEN="<your_huggingface_token>"
  2. To run the benchmark, select the task you want to run, e.g. clustering_benchmarks.ipynb and run all cells.
  3. Display the results and create all associated plots by running the plot notebook, e.g. clustering_plots.ipynb. The available task and plots are:
    Task Plot
    clustering_benchmarks.ipynb clustering_plots.ipynb
    clustering_benchmarks_cutoff.ipynb clustering_plots_cutoff.ipynb
    retrieval_benchmark_cqa.ipynb retrieval_plots.ipynb
    retrieval_benchmark_nqa_chunking.ipynb retrieval_plots.ipynb
    retrieval_benchmark_nqa_seq.ipynb retrieval_plots.ipynb
    sts_benchmarks.ipynb sts_plots.ipynb

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Analyzing the impact of sentence length on embedding model performance

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