fix merge conflicts

Author: fchollet

.github/workflows/continuous_integration.yml

@@ -13,7 +13,7 @@ jobs:
     - name: Ensure files are formatted with black
       run: |
         pip install --upgrade pip
-        pip install black==22.1.0
+        pip install black~=22.3.0
         black --check --diff ./examples
   docker-image:
     runs-on: ubuntu-latest

examples/nlp/md/multi_label_classification.md

@@ -6,7 +6,7 @@
 **Description:** Implementing a large-scale multi-label text classification model.
 
 
-<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team\keras-io\blob\master\examples\nlp/ipynb/multi_label_classification.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team\keras-io\blob\master\examples\nlp/multi_label_classification.py)
+<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team\keras-io\blob\master\examples\nlp/ipynb/multi_label_classification.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team\keras-io\blob\master\examples\nlp/multi_label_classification.py)
 
 
 

guides/keras_nlp/transformer_pretraining.py

@@ -251,7 +251,7 @@ def preprocess(inputs):
     # Split the masking layer outputs into a (features, labels, and weights)
     # tuple that we can use with keras.Model.fit().
     features = {
-        "tokens": outputs["tokens"],
+        "token_ids": outputs["token_ids"],
         "mask_positions": outputs["mask_positions"],
     }
     labels = outputs["mask_ids"]
@@ -277,7 +277,7 @@ def preprocess(inputs):
 
 We have two features:
 
-1. `"tokens"`, where some tokens have been replaced with our mask token id.
+1. `"token_ids"`, where some tokens have been replaced with our mask token id.
 2. `"mask_positions"`, which keeps track of which tokens we masked out.
 
 Our labels are simply the ids we masked out.
@@ -350,12 +350,12 @@ def preprocess(inputs):
 
 # Create the pretraining model by attaching a masked language model head.
 inputs = {
-    "tokens": keras.Input(shape=(SEQ_LENGTH,), dtype=tf.int32),
+    "token_ids": keras.Input(shape=(SEQ_LENGTH,), dtype=tf.int32),
     "mask_positions": keras.Input(shape=(PREDICTIONS_PER_SEQ,), dtype=tf.int32),
 }
 
 # Encode the tokens.
-encoded_tokens = encoder_model(inputs["tokens"])
+encoded_tokens = encoder_model(inputs["token_ids"])
 
 # Predict an output word for each masked input token.
 # We use the input token embedding to project from our encoded vectors to

requirements.txt

@@ -1,16 +1,15 @@
-black==22.3.1
-pygments>=2.7.4
-markdown>=3.3
-matplotlib
-jinja2
-requests
-mdx_truly_sane_lists
-pathlib
-PyYAML
-pandas
-jupyter
-tensorflow==2.11.0
-tensorflow_datasets
-keras-tuner
-keras-cv
-keras-nlp
+black~=22.3.0
+pygments>=2.7.4
+markdown>=3.3
+matplotlib
+jinja2
+requests
+mdx_truly_sane_lists
+PyYAML
+pandas
+jupyter
+tensorflow~=2.11.0
+tensorflow_datasets
+keras-tuner
+keras-cv
+keras-nlp