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[Docs] Fix spelling and grammar mistakes (#28825)

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* Fix typos and grammar mistakes in docs and examples

* Fix typos in docstrings and comments

* Fix spelling of `tokenizer` in model tests

* Remove erroneous spaces in decorators

* Remove extra spaces in Markdown link texts
This commit is contained in:
Klaus Hipp
2024-02-02 08:45:00 +01:00
committed by GitHub
parent 2418c64a1c
commit 721ee783ca
134 changed files with 185 additions and 186 deletions
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4
examples/flax/language-modeling/README.md
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@@ -449,7 +449,7 @@ are 8 TPU cores on 4 chips (each chips has 2 cores), while "8 GPU" are 8 GPU chi
For comparison one can run the same pre-training with PyTorch/XLA on TPU. To set up PyTorch/XLA on Cloud TPU VMs, please
refer to [this](https://cloud.google.com/tpu/docs/pytorch-xla-ug-tpu-vm) guide.
Having created the tokenzier and configuration in `norwegian-roberta-base`, we create the following symbolic links:
Having created the tokenizer and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./
@@ -499,7 +499,7 @@ python3 xla_spawn.py --num_cores ${NUM_TPUS} run_mlm.py --output_dir="./runs" \
For comparison you can run the same pre-training with PyTorch on GPU. Note that we have to make use of `gradient_accumulation`
because the maximum batch size that fits on a single V100 GPU is 32 instead of 128.
Having created the tokenzier and configuration in `norwegian-roberta-base`, we create the following symbolic links:
Having created the tokenizer and configuration in `norwegian-roberta-base`, we create the following symbolic links:
```bash
ln -s ~/transformers/examples/pytorch/language-modeling/run_mlm.py ./

2
examples/flax/question-answering/run_qa.py
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@@ -674,7 +674,7 @@ def main():
raise ValueError("--do_train requires a train dataset")
train_dataset = raw_datasets["train"]
if data_args.max_train_samples is not None:
# We will select sample from whole data if agument is specified
# We will select sample from whole data if argument is specified
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
# Create train feature from dataset

2
examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py
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@@ -62,7 +62,7 @@ from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risk.
check_min_version("4.38.0.dev0")
require_version("datasets>=2.14.0", "To fix: pip install -r examples/flax/speech-recogintion/requirements.txt")
require_version("datasets>=2.14.0", "To fix: pip install -r examples/flax/speech-recognition/requirements.txt")
logger = logging.getLogger(__name__)

2
examples/flax/vision/run_image_classification.py
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@@ -330,7 +330,7 @@ def main():
# Initialize datasets and pre-processing transforms
# We use torchvision here for faster pre-processing
# Note that here we are using some default pre-processing, for maximum accuray
# Note that here we are using some default pre-processing, for maximum accuracy
# one should tune this part and carefully select what transformations to use.
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
train_dataset = torchvision.datasets.ImageFolder(