From 5d13c7ff466402a63ce090b20ca1b6719b9165eb Mon Sep 17 00:00:00 2001 From: Florian Cafiero Date: Wed, 18 Dec 2024 08:56:38 +0100 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 12bd1e04..c12728e5 100755 --- a/README.md +++ b/README.md @@ -213,7 +213,7 @@ python train_svm.py data/feats_tests_train.csv --test_path unseen.csv --norms -- If you have generated samples in a rolling fashion (using `--sampling` in `load_corpus.py`), you can visualize how author predictions evolve across consecutive text segments. For example: ```bash -# Creating rolling samples (e.g., 1000-word segments) +# e.g., with 1000 word segments python load_corpus.py -s data/long_text/*.txt -t chars -n 3 -o rolling_train --sampling --units words --sample_size 1000 python load_corpus.py -s data/long_text_to_predict/*.txt -t chars -n 3 -o rolling_unknown -f rolling_train_feats.json --sampling --units words --sample_size 1000