From 826e4aa278a37ff69563d0b461d9eca414759323 Mon Sep 17 00:00:00 2001 From: Kalpesh Krishna Date: Tue, 6 Jun 2023 13:22:34 -0400 Subject: [PATCH] Version update, 0.1.4 --> 0.1.5 --- README.md | 9 ++++++--- pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index ec98a21..857137b 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,16 @@ # FActScore [![made-with-python](https://img.shields.io/badge/Made%20with-Python-red.svg)](#python) +[![arxiv](https://img.shields.io/badge/arXiv-2305.14251-b31b1b.svg)](https://arxiv.org/abs/2305.14251) [![PyPI version factscore](https://badge.fury.io/py/factscore.svg)](https://pypi.python.org/pypi/factscore/) [![Downloads](https://pepy.tech/badge/factscore)](https://pepy.tech/project/factscore) -This is the official release accompanying our preprint, [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://tinyurl.com/FActScore). FActScore is available as a PIP package as well. +This is the official release accompanying our preprint, [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://arxiv.org/abs/2305.14251). FActScore is available as a PIP package as well. If you find FActScore useful, please cite: ``` @article{ factscore, - title={ {FActScore}: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation }, + title={ {FActScore}: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation }, author={ Min, Sewon and Krishna, Kalpesh and Lyu, Xinxi and Lewis, Mike and Yih, Wen-tau and Koh, Pang Wei and Iyyer, Mohit and Zettlemoyer, Luke and Hajishirzi, Hannaneh }, year={ 2023 }, journal={ arXiv preprint arXiv:2305.14251 }, @@ -27,7 +28,7 @@ pip install -r requirements.txt Make a new Python 3.7+ environment using `virtualenv` or `conda`. ```bash -pip install factscore +pip install --upgrade factscore python -m spacy download en_core_web_sm ``` @@ -47,6 +48,7 @@ This command does the following. **Troubleshooting**: - If you get a `ERROR 429: Too Many Requests` error while downloading the DB file, please download the DB from [this Google Drive link](https://drive.google.com/file/d/1mekls6OGOKLmt7gYtHs0WGf5oTamTNat/view?usp=sharing) and place it under `--data_dir` (`.cache/factscore` by default). +- If everything else fails, consider downloading the files manually from [this link](https://drive.google.com/drive/folders/1bLHGu_imkZVtX6O0mpZ-G0-4ofTLM1ZA?usp=share_link) and placing them in `--data_dir` and `--model_dir`, see [`factscore/download_data.py`](factscore/download_data.py) for more details. ## Running FActScore using a command line @@ -69,6 +71,7 @@ python -m factscore.factscorer --input_path {input_path} --model_name {estimator - `--n_samples`: If specified, it runs the model on a subset of the data. - `--verbose`: If specified, it shows the progress bar. - `--print_rate_limit_error`: It specified, it prints out rate limit errors from OpenAI API. +- `--cost_estimate`: This flag decides the type of OpenAI API cost estimation that we provide before calling it. It can be `"consider_cache"` (default) or `"ignore_cache"`. This command uses the English Wikipedia from 2023/04/01 as a knowledge source. See [this section](#To-use-a-custom-knowledge-source) to use your own database as a knowledge source! diff --git a/pyproject.toml b/pyproject.toml index db58dbe..5715e06 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "factscore" -version = "0.1.4" +version = "0.1.5" description = "FactScore is an automatic evaluation metric for factual precision in long-form text generation. It uses large language models and retrieval to break down generations into atomic facts and then measure the correctness with respect to a knowledge source (like Wikipedia)." authors = ["Sewon Min ", "Kalpesh Krishna ", "Xinxi Lyu "] license = "MIT"