diff --git a/README.md b/README.md
index fdf4130124..a8717e3ff3 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ document embeddings, including our proposed [Flair embeddings](https://www.aclwe
 * **A PyTorch NLP framework.** Our framework builds directly on [PyTorch](https://pytorch.org/), making it easy to
 train your own models and experiment with new approaches using Flair embeddings and classes.
 
-Now at [version 0.14.0](https://github.com/flairNLP/flair/releases)!
+Now at [version 0.15.0](https://github.com/flairNLP/flair/releases)!
 
 
 ## State-of-the-Art Models
diff --git a/docs/conf.py b/docs/conf.py
index 3f5d95cb04..7ac895ac0e 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -7,8 +7,8 @@
 from sphinx_github_style import get_linkcode_resolve
 from torch.nn import Module
 
-version = "0.14.0"
-release = "0.14.0"
+version = "0.15.0"
+release = "0.15.0"
 project = "flair"
 author = importlib_metadata.metadata(project)["Author"]
 copyright = f"2023 {author}"
diff --git a/docs/tutorial/intro.md b/docs/tutorial/intro.md
index b8af9b5667..beb2c3422d 100644
--- a/docs/tutorial/intro.md
+++ b/docs/tutorial/intro.md
@@ -89,4 +89,8 @@ The output shows that the sentence "_I love Berlin and New York._" was tagged as
 
 ## Summary
 
-Congrats, you now know how to use Flair to find entities and detect sentiment!
\ No newline at end of file
+Congrats, you now know how to use Flair to find entities and detect sentiment!
+
+## Next steps
+
+If you want to know more about Flair, next check out [Tutorial 1](tutorial-basics/) that gives an intro into the basics of Flair!
\ No newline at end of file
diff --git a/docs/tutorial/tutorial-basics/basic-types.md b/docs/tutorial/tutorial-basics/basic-types.md
index 5ddf247166..2ff20c2af9 100644
--- a/docs/tutorial/tutorial-basics/basic-types.md
+++ b/docs/tutorial/tutorial-basics/basic-types.md
@@ -87,7 +87,8 @@ This print-out includes the token index (3) and the lexical value of the token (
 When you create a [`Sentence`](#flair.data.Sentence) as above, the text is automatically tokenized (segmented into words) using the [segtok](https://pypi.org/project/segtok/) library.
 
 ```{note}
-You can also use a different tokenizer if you like. To learn more about this, check out our tokenization tutorial.
+You can also use a different tokenizer by passing a different [`Tokenizer`](#flair.tokenization.Tokenizer ) to the Sentence 
+when you initialize it.
 ```
 
 
diff --git a/docs/tutorial/tutorial-training/how-to-load-prepared-dataset.md b/docs/tutorial/tutorial-training/how-to-load-prepared-dataset.md
index b53aeef917..ac9b38c524 100644
--- a/docs/tutorial/tutorial-training/how-to-load-prepared-dataset.md
+++ b/docs/tutorial/tutorial-training/how-to-load-prepared-dataset.md
@@ -115,7 +115,8 @@ This will print out the created dictionary:
 Dictionary with 17 tags: PROPN, PUNCT, ADJ, NOUN, VERB, DET, ADP, AUX, PRON, PART, SCONJ, NUM, ADV, CCONJ, X, INTJ, SYM
 ```
 
-#### Dictionaries for other label types
+
+### Printing label statistics
 
 If you don't know the label types in a corpus, just call [`Corpus.make_label_dictionary`](#flair.data.Corpus.make_label_dictionary) with
 any random label name (e.g. `corpus.make_label_dictionary(label_type='abcd')`). This will print
@@ -139,17 +140,6 @@ tense_dictionary = corpus.make_label_dictionary(label_type='number')
 If you print these dictionaries, you will find that the POS dictionary contains 50 tags and the number dictionary only 2 for this corpus (singular and plural).
 
 
-#### Dictionaries for other corpora types
-
-The method [`Corpus.make_label_dictionary`](#flair.data.Corpus.make_label_dictionary) can be used for any corpus, including text classification corpora:
-
-```python
-# create label dictionary for a text classification task
-from flair.datasets import TREC_6
-corpus = TREC_6()
-corpus.make_label_dictionary('question_class')
-```
-
 ### The MultiCorpus Object
 
 If you want to train multiple tasks at once, you can use the [`MultiCorpus`](#flair.data.MultiCorpus) object.
@@ -175,6 +165,7 @@ The [`MultiCorpus`](#flair.data.MultiCorpus) inherits from [`Corpus`](#flair.dat
 Flair supports many datasets out of the box. It usually automatically downloads and sets up the data the first time you
 call the corresponding constructor ID.
 The datasets are split into multiple modules, however they all can be imported from `flair.datasets` too.
+
 You can look up the respective modules to find the possible datasets.
 
 The following datasets are supported:
diff --git a/flair/__init__.py b/flair/__init__.py
index 341f630e43..4f27b4c2ad 100644
--- a/flair/__init__.py
+++ b/flair/__init__.py
@@ -34,7 +34,7 @@
     device = torch.device("cpu")
 
 # global variable: version
-__version__ = "0.14.0"
+__version__ = "0.15.0"
 """The current version of the flair library installed."""
 
 # global variable: arrow symbol
diff --git a/setup.py b/setup.py
index 0573896c19..faf1540a29 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 
 setup(
     name="flair",
-    version="0.14.0",
+    version="0.15.0",
     description="A very simple framework for state-of-the-art NLP",
     long_description=Path("README.md").read_text(encoding="utf-8"),
     long_description_content_type="text/markdown",