Fix typos (#963)

optimum/commands/export/tflite.py

@@ -164,7 +164,7 @@ def parse_args_tflite(parser: "ArgumentParser"):
  type=str,
  default=None,
  help=(
- "The dataset to use to calibration integer ranges when quantizing the model. This is needed to perform "
+ "The dataset to use to calibrate integer ranges when quantizing the model. This is needed to perform "
  "static quantization."
  ),
  )

optimum/exporters/tflite/base.py

@@ -67,17 +67,17 @@ class TFLiteQuantizationConfig:
  Allows to fallback to float kernels in quantization.
  inputs_dtype (`Optional[str]`, defaults to `None`):
  The data type of the inputs. If specified it must be either "int8" or "uint8". It allows to always take
- integers as inputs, it is useful for interger-only hardware.
+ integers as inputs, it is useful for integer-only hardware.
  outputs_dtype (`Optional[str]`, defaults to `None`):
  The data type of the outputs. If specified it must be either "int8" or "uint8". It allows to always output
- integers, it is useful for interger-only hardware.
+ integers, it is useful for integer-only hardware.
  calibration_dataset_name_or_path (`Optional[Union[str, Path]]`, defaults to `None`):
  The dataset to use for calibrating the quantization parameters for static quantization. If left unspecified,
  a default dataset for the considered task will be used.
  calibration_dataset_config_name (`Optional[str]`, defaults to `None`):
  The configuration name of the dataset if needed.
  num_calibration_samples (`int`, defaults to `200`):
- The number of example from the calibration dataset to use to compute the quantization parameters.
+ The number of examples from the calibration dataset to use to compute the quantization parameters.
  calibration_split (`Optional[str]`, defaults to `None`):
  The split of the dataset to use. If none is specified and the dataset contains multiple splits, the
  smallest split will be used.

optimum/exporters/tflite/convert.py

@@ -185,7 +185,7 @@ def prepare_converter_for_quantization(
 
  # Handling the calibration dataset:
  # - Either loading the default dataset if no calibration dataset was provided or the required dataset,
- # - Splitting the dataset with the provided a dataset split or with the first split if none is provided.
+ # - Splitting the dataset with the provided dataset split or with the first split if none is provided.
  # - Shuffling the split.
  # - Selecting num_calibration_samples in the dataset split.
  # - Batching the dataset.

optimum/utils/preprocessing/text_classification.py

@@ -79,7 +79,7 @@ def try_to_guess_data_keys(self, column_names: List[str]) -> Optional[Dict[str,
  return None
  elif secondary_key_name is None:
  logger.info(
- "Could not infer the secondary key in the dataset, if it does contain one, please provided it manually."
+ "Could not infer the secondary key in the dataset, if it does contain one, please provide it manually."
  )
  return {"primary": primary_key_name}
  else: