Refactor:Fixed typos and grammar (#156)

neuralmagic · Jul 29, 2021 · de70f82 · de70f82
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diff --git a/examples/huggingface-transformers/squad_eval.py b/examples/huggingface-transformers/squad_eval.py
@@ -39,7 +39,7 @@
                         'deepsparse', 'onnxruntime'. Default is 'deepsparse'
   -s NUM_SOCKETS, --num-sockets NUM_SOCKETS
                         For DeepSparse only. The number of physical cores to
-                        run the eval on. Defaults to None where is uses all
+                        run the eval on. Defaults to None where it uses all
                         sockets available on the system
   --max-sequence-length MAX_SEQUENCE_LENGTH
                         the max sequence length for model inputs. Default is

diff --git a/examples/ultralytics-yolo/README.md b/examples/ultralytics-yolo/README.md
@@ -30,7 +30,7 @@ The dependencies for this example can be installed using `pip`:
 ```bash
 pip3 install -r requirements.txt
 ```
-
+Note: upgrade pip using `python -m pip install -U pip` before installing requirements
 ## SparseZoo Stubs
 The following examples may be run with local ONNX YOLO models, or by using pre-trained, pre-sparsified YOLO models
 from the [SparseZoo](https://sparsezoo.neuralmagic.com/).
@@ -44,13 +44,13 @@ run the following examples.
 
 | Model Name     |      Stub      | Description |
 |----------|-------------|-------------|
-| yolov3-pruned-aggressive | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97 | Sparse YOLOv3 model train with full FP32 precision that recovers 97% of its baseline mAP |
+| yolov3-pruned-aggressive | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97 | Sparse YOLOv3 model trained with full FP32 precision that recovers 97% of its baseline mAP |
 | yolov3-pruned_quant-aggressive | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94 | Sparse INT8 quantized YOLOv3 model that recovers 94% of its baseline mAP |
 | yolov3-base | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none | Dense full precision YOLOv3-SPP model |
 
 
 ## Annotation Example
-`annotate.py` is a script for using YOLO sparsified (and not sparsified) models
+`annotate.py` is a script for using YOLO sparsified (or non-sparsified) models
 to run inferences on images, videos, or webcam streams. For a full list of options
 `python annotate.py -h`.
 
@@ -64,7 +64,7 @@ python annotate.py \
     --no-save  # webcam only
 ```
 
-In addition to webcam `--source` can take a path to a `.jpg` file, directory or glob path
+In addition to webcam, `--source` can take a path to a `.jpg` file, directory or glob path
 of `.jpg` files, or path to a `.mp4` video file.  If source is an integer and no
 corresponding webcam is available, an exception will be raised.
 

diff --git a/examples/ultralytics-yolo/annotate.py b/examples/ultralytics-yolo/annotate.py
@@ -26,7 +26,7 @@
                    [--model-config MODEL_CONFIG]
                    model_filepath
 
-Annotate images, videos, and streams with sparsified YOLO models
+Annotate images, videos, and streams with sparsified or non-sparsified YOLO models
 
 positional arguments:
   model_filepath        The full file path of the ONNX model file or SparseZoo