[AIR][DOC] Fix minor usage nits in the HuggingFace + AIR example

doc/source/ray-air/examples/huggingface_text_classification.ipynb

@@ -250,7 +250,7 @@
         "id": "4RRkXuteIrIh"
       },
       "source": [
-        "This notebook is built to run on any of the tasks in the list above, with any model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a version with a classification head. Depending on you model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those three parameters, then the rest of the notebook should run smoothly:"
+        "This notebook is built to run on any of the tasks in the list above, with any model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a version with a classification head. Depending on your model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those three parameters, then the rest of the notebook should run smoothly:"
       ]
     },
     {
@@ -313,7 +313,7 @@
         "id": "RzfPtOMoIrIu"
       },
       "source": [
-        "The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set (with more keys for the mismatched validation and test set in the special case of `mnli`)."
+        "The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation, and test set (with more keys for the mismatched validation and test set in the special case of `mnli`)."
       ]
     },
     {
@@ -363,9 +363,9 @@
         "id": "YVx71GdAIrJH"
       },
       "source": [
-        "Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers `Tokenizer` which will (as the name indicates) tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary) and put it in a format the model expects, as well as generate the other inputs that model requires.\n",
+        "Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers' `Tokenizer`, which will (as the name indicates) tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary) and put it in a format the model expects, as well as generate the other inputs that model requires.\n",
         "\n",
-        "To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure:\n",
+        "To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure that:\n",
         "\n",
         "- we get a tokenizer that corresponds to the model architecture we want to use,\n",
         "- we download the vocabulary used when pretraining this specific checkpoint."
@@ -469,9 +469,9 @@
         "id": "2C0hcmp9IrJQ"
       },
       "source": [
-        "We can them write the function that will preprocess our samples. We just feed them to the `tokenizer` with the argument `truncation=True`. This will ensure that an input longer that what the model selected can handle will be truncated to the maximum length accepted by the model.\n",
+        "We can then write the function that will preprocess our samples. We just feed them to the `tokenizer` with the argument `truncation=True`. This will ensure that an input longer than what the model selected can handle will be truncated to the maximum length accepted by the model.\n",
         "\n",
-        "We use a `BatchMapper` to create a Ray AIR preprocessor that will map the function to the dataset in a distributed fashion. It will be ran during training and prediction."
+        "We use a `BatchMapper` to create a Ray AIR preprocessor that will map the function to the dataset in a distributed fashion. It will run during training and prediction."
       ]
     },
     {
@@ -524,13 +524,13 @@
         "\n",
         "We will not go into details about each specific component of the training (see the [original notebook](https://github.com/huggingface/notebooks/blob/6ca682955173cc9d36ffa431ddda505a048cbe80/examples/text_classification.ipynb) for that). The tokenizer is the same as we have used to encoded the dataset before.\n",
         "\n",
-        "The main difference when using the Ray AIR is that we need to create our 🤗 Transformers `Trainer` inside a function (`trainer_init_per_worker`) and return it. That function will be passed to the `HuggingFaceTrainer` and ran on every Ray worker. The training will then proceed by the means of PyTorch DDP.\n",
+        "The main difference when using the Ray AIR is that we need to create our 🤗 Transformers `Trainer` inside a function (`trainer_init_per_worker`) and return it. That function will be passed to the `HuggingFaceTrainer` and will run on every Ray worker. The training will then proceed by the means of PyTorch DDP.\n",
         "\n",
-        "Make sure that you initialize the model, metric and tokenizer inside that function. Otherwise, you may run into serialization errors.\n",
+        "Make sure that you initialize the model, metric, and tokenizer inside that function. Otherwise, you may run into serialization errors.\n",
         "\n",
-        "Furthermore, `push_to_hub=True` is not yet supported. Ray will however checkpoint the model at every epoch, allowing you to push it to hub manually. We will do that after the training.\n",
+        "Furthermore, `push_to_hub=True` is not yet supported. Ray will, however, checkpoint the model at every epoch, allowing you to push it to hub manually. We will do that after the training.\n",
         "\n",
-        "If you wish to use thrid party logging libraries, such as MLFlow or Weights&Biases, do not set them in `TrainingArguments` (they will be automatically disabled) - instead, you should be passing Ray AIR callbacks to `HuggingFaceTrainer`'s `run_config`. In this example, we will use MLFlow."
+        "If you wish to use thrid party logging libraries, such as MLflow or Weights&Biases, do not set them in `TrainingArguments` (they will be automatically disabled) - instead, you should pass Ray AIR callbacks to `HuggingFaceTrainer`'s `run_config`. In this example, we will use MLflow."
       ]
     },
     {
@@ -600,7 +600,7 @@
       "source": [
         "With our `trainer_init_per_worker` complete, we can now instantiate the `HuggingFaceTrainer`. Aside from the function, we set the `scaling_config`, controlling the amount of workers and resources used, and the `datasets` we will use for training and evaluation.\n",
         "\n",
-        "We specify the `MlflowLoggerCallback` inside the `run_config`, and pass the preprocessor we have defined earlier as an argument. It will be included with the returned `Checkpoint`, meaning it will also be applied during inference."
+        "We specify the `MlflowLoggerCallback` inside the `run_config`, and pass the preprocessor we have defined earlier as an argument. The preprocessor will be included with the returned `Checkpoint`, meaning it will also be applied during inference."
       ]
     },
     {
@@ -633,7 +633,7 @@
         "id": "XvS136zKhYba"
       },
       "source": [
-        "Finally, we call the `fit` method to being training with Ray AIR. We will save the `Result` object to a variable so we can access metrics and checkpoints."
+        "Finally, we call the `fit` method to start training with Ray AIR. We will save the `Result` object to a variable so we can access metrics and checkpoints."
       ]
     },
     {
@@ -2269,7 +2269,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.9"
+      "version": "3.9.6"
     },
     "vscode": {
       "interpreter": {