From 2105a8ac311168b05ca433f8a38774698c666211 Mon Sep 17 00:00:00 2001
From: regisss <15324346+regisss@users.noreply.github.com>
Date: Tue, 11 Apr 2023 19:03:20 +0200
Subject: [PATCH] Fix typos (#963)

---
 optimum/commands/export/tflite.py                  | 2 +-
 optimum/exporters/tflite/base.py                   | 6 +++---
 optimum/exporters/tflite/convert.py                | 2 +-
 optimum/utils/preprocessing/text_classification.py | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/optimum/commands/export/tflite.py b/optimum/commands/export/tflite.py
index de453c0fb0..bae5042ef7 100644
--- a/optimum/commands/export/tflite.py
+++ b/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."
         ),
     )
diff --git a/optimum/exporters/tflite/base.py b/optimum/exporters/tflite/base.py
index 934b444d78..46970bc970 100644
--- a/optimum/exporters/tflite/base.py
+++ b/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.
diff --git a/optimum/exporters/tflite/convert.py b/optimum/exporters/tflite/convert.py
index 7494c32b46..c1a2010355 100644
--- a/optimum/exporters/tflite/convert.py
+++ b/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.
diff --git a/optimum/utils/preprocessing/text_classification.py b/optimum/utils/preprocessing/text_classification.py
index 17933dc049..3e95d20469 100644
--- a/optimum/utils/preprocessing/text_classification.py
+++ b/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: