From 4466f8ac9dbf617df0ffff296f6da13b6a229f6c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Leonard=20P=C3=BCttmann?= Date: Mon, 5 Sep 2022 11:18:07 +0200 Subject: [PATCH 1/2] Changed type in the sklearn section of the readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 588c363..f22d2c6 100644 --- a/README.md +++ b/README.md @@ -179,7 +179,7 @@ trainer.save_model("path/to/model") You can use *refinery* to directly pull data into a format you can apply for building [sklearn](https://github.com/scikit-learn/scikit-learn) models. This can look as follows: ```python -from refinery.adapter.embedders import build_classification_dataset +from refinery.adapter.sklearn import build_classification_dataset from sklearn.tree import DecisionTreeClassifier data = build_classification_dataset(client, "headline", "__clickbait", "distilbert-base-uncased") From 7d2a26af7171b4fb120b7c9db135eb6898525b6e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20H=C3=B6tter?= Date: Mon, 5 Sep 2022 18:02:38 +0200 Subject: [PATCH 2/2] Update README.md remove emoji from hugging face link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f22d2c6..b982228 100644 --- a/README.md +++ b/README.md @@ -122,7 +122,7 @@ Alternatively, you can `rsdk push ` via CLI, given that you h ### Adapters -#### 🤗 Hugging Face +#### Hugging Face Transformers are great, but often times, you want to finetune them for your downstream task. With *refinery*, you can do so easily by letting the SDK build the dataset for you that you can use as a plug-and-play base for your training: ```python