diff --git a/README.md b/README.md
index 3873972..791d1c4 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
- $${\Huge{\textbf{\textsf{\color{#2E8B57}Bio\color{#4682B4}fit}}}}$$
+ $${\Huge{\textbf{\textsf{\color{#2E8B57}Bio\color{red}fit}}}}$$
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@@ -29,7 +29,7 @@ and configurable processing pipelines. Here are some of the main features of Bio
- [CSR (SciPy)](https://github.com/scipy/scipy)
- [Arrow](https://github.com/apache/arrow)
- 🤗 [Datasets](https://github.com/huggingface/datasets)
- - [Biosets](https://github.com/psmyth94/biosets)
+ - [biofit](https://github.com/psmyth94/biofit)
- **Machine Learning Models:** Supports a wide range of machine learning models, including:
- [Scikit-learn](https://github.com/scikit-learn/scikit-learn)
- [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
@@ -98,11 +98,11 @@ print(preprocessor)
preprocessed_data = preprocessor.fit_transform(dataset)
```
-Biofit is made with Biosets in mind. You can pass the loaded dataset instead of a string
+Biofit is made with biofit in mind. You can pass the loaded dataset instead of a string
to load the preprocessors:
```python
-from biosets import load_dataset
+from biofit import load_dataset
dataset = load_dataset("csv", data_files="my_file.csv", experiment_type="snp")
@@ -120,7 +120,7 @@ Biofit allows you to create custom preprocessing pipelines using the
`sklearn` and Biofit in a single operation:
```python
-from biosets import load_dataset
+from biofit import load_dataset
from biofit.preprocessing import LogTransformer, PreprocessorPipeline
from sklearn.preprocessing import StandardScaler