From 26d681754f6802e5e4986637ecdb91c1843f50ec Mon Sep 17 00:00:00 2001 From: Piotr Czapla Date: Thu, 7 Nov 2019 09:29:52 +0100 Subject: [PATCH] Fix spelling errors in README.md --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index be34c23..1018c53 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ We released 7 language models trained on corresponding Wikipedia dumps: - ru_multifit_paper_version - zh_multifit_paper_version -To fetch the model just use `ulmfit.from_pretreined` function. +To fetch the model just use `multifit.from_pretrained` function. Here are some example notebook showing how to train a classifier using a pretrained models. - [./notebooks/CLS-JA.ipynb](./notebooks/CLS-JA.ipynb) - example of classifier trained on amazon CLS JA music. - [./notebooks/MLDoc-JA-multifit_fp16.ipynb](./notebooks/MLDoc-JA-multifit_fp16.ipynb) - example of a faster multifit training using fp16 on MDLDoc. @@ -45,7 +45,10 @@ Sentiment classification results on CLS dataset [Prettenhofer and Stein, 2010](h ## How to use it with fastai v1.0 You can use the pretrained models with fastai library as follows: ``` -exp = ulmfit.from_pretrained("name of the model") +from fastai.text import * +import multifit + +exp = multifit.from_pretrained("name of the model") fa_config = exp.pretrain_lm.tokenizer.get_fastai_config(add_open_file_processor=True) data_lm = (TextList.from_folder(imdb_path, **fa_config) .filter_by_folder(include=['train', 'test', 'unsup'])