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PiperOrigin-RevId: 725233618

Author: tensorflower-gardener

Diff for: docs/nlp/customize_encoder.ipynb

@@ -497,7 +497,7 @@
       "source": [
         "#### Customize Feedforward Layer\n",
         "\n",
-        "Similarly, one could also customize the feedforward layer.\n",
+        "Similiarly, one could also customize the feedforward layer.\n",
         "\n",
         "See [the source of `nlp.layers.GatedFeedforward`](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/gated_feedforward.py) for how to implement a customized feedforward layer.\n",
         "\n",

Diff for: docs/vision/object_detection.ipynb

@@ -66,7 +66,7 @@
         "This tutorial demonstrates how to:\n",
         "\n",
         "1. Use models from the Tensorflow Model Garden(TFM) package.\n",
-        "2. Fine-tune a pre-trained RetinaNet with ResNet-50 as backbone for object detection.\n",
+        "2. Fine-tune a pre-trained RetinanNet with ResNet-50 as backbone for object detection.\n",
         "3. Export the tuned RetinaNet model"
       ]
     },
@@ -323,7 +323,7 @@
         "\n",
         "Use the `retinanet_resnetfpn_coco` experiment configuration, as defined by `tfm.vision.configs.retinanet.retinanet_resnetfpn_coco`.\n",
         "\n",
-        "The configuration defines an experiment to train a RetinaNet with Resnet-50 as backbone, FPN as decoder. Default Configuration is trained on [COCO](https://cocodataset.org/) train2017 and evaluated on [COCO](https://cocodataset.org/) val2017.\n",
+        "The configuration defines an experiment to train a RetinanNet with Resnet-50 as backbone, FPN as decoder. Default Configuration is trained on [COCO](https://cocodataset.org/) train2017 and evaluated on [COCO](https://cocodataset.org/) val2017.\n",
         "\n",
         "There are also other alternative experiments available such as\n",
         "`retinanet_resnetfpn_coco`, `retinanet_spinenet_coco`, `fasterrcnn_resnetfpn_coco` and more. One can switch to them by changing the experiment name argument to the `get_exp_config` function.\n",
@@ -538,7 +538,7 @@
         "id": "m-QW7DoKbD8z"
       },
       "source": [
-        "### Create category index dictionary to map the labels to corresponding label names."
+        "### Create category index dictionary to map the labels to coressponding label names."
       ]
     },
     {
@@ -573,7 +573,7 @@
       },
       "source": [
         "### Helper function for visualizing the results from TFRecords.\n",
-        "Use `visualize_boxes_and_labels_on_image_array` from `visualization_utils` to draw bounding boxes on the image."
+        "Use `visualize_boxes_and_labels_on_image_array` from `visualization_utils` to draw boudning boxes on the image."
       ]
     },
     {

Diff for: docs/vision/semantic_segmentation.ipynb

@@ -341,7 +341,7 @@
         "\n",
         "Use the `mnv2_deeplabv3_pascal` experiment configuration, as defined by `tfm.vision.configs.semantic_segmentation.mnv2_deeplabv3_pascal`.\n",
         "\n",
-        "Please find all the registered experiments [here](https://www.tensorflow.org/api_docs/python/tfm/core/exp_factory/get_exp_config)\n",
+        "Please find all the registered experiements [here](https://www.tensorflow.org/api_docs/python/tfm/core/exp_factory/get_exp_config)\n",
         "\n",
         "The configuration defines an experiment to train a [DeepLabV3](https://arxiv.org/pdf/1706.05587.pdf) model with MobilenetV2 as backbone and [ASPP](https://arxiv.org/pdf/1606.00915v2.pdf) as decoder.\n",
         "\n",
@@ -420,7 +420,7 @@
         "exp_config.task.train_data.dtype = 'float32'\n",
         "exp_config.task.train_data.output_size = [HEIGHT, WIDTH]\n",
         "exp_config.task.train_data.preserve_aspect_ratio = False\n",
-        "exp_config.task.train_data.seed = 21 # Reproducible Training Data\n",
+        "exp_config.task.train_data.seed = 21 # Reproducable Training Data\n",
         "\n",
         "# Validation Data Config\n",
         "exp_config.task.validation_data.input_path = val_data_tfrecords\n",
@@ -429,7 +429,7 @@
         "exp_config.task.validation_data.output_size = [HEIGHT, WIDTH]\n",
         "exp_config.task.validation_data.preserve_aspect_ratio = False\n",
         "exp_config.task.validation_data.groundtruth_padded_size = [HEIGHT, WIDTH]\n",
-        "exp_config.task.validation_data.seed = 21 # Reproducible Validation Data\n",
+        "exp_config.task.validation_data.seed = 21 # Reproducable Validation Data\n",
         "exp_config.task.validation_data.resize_eval_groundtruth = True # To enable validation loss"
       ]
     },

Diff for: official/projects/waste_identification_ml/circularnet-docs/content/_index.md

@@ -27,7 +27,7 @@
         * [Aperture size (f-number)](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/factors.md#aperture-size-f-number)
         * [Shutter speed](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/factors.md#shutter-speed)
     * [Table of specifications](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/table-of-specs.md)
-* [Choose edge device hardware](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-edge-device.md)
+* [Choose edge device hardware](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-edge-device/_index.md)
 
 **[Deploy CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/_index.md)**