cookbook feedback

docs/sections/pipeline_samples/tutorials/GenerateSentencePair.ipynb

@@ -91,9 +91,9 @@
    "source": [
     "### (optional) Deploy Argilla\n",
     "\n",
-    "You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already have deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/). \n",
+    "You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/). \n",
     "\n",
-    "Along with that, you will need to install argilla as distilabel extra."
+    "Along with that, you will need to install Argilla as a distilabel extra."
    ]
   },
   {

docs/sections/pipeline_samples/tutorials/clean_existing_dataset.ipynb

@@ -102,9 +102,9 @@
    "source": [
     "### (optional) Deploy Argilla\n",
     "\n",
-    "You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already have deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/). \n",
+    "You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/). \n",
     "\n",
-    "Along with that, you will need to install argilla as distilabel extra."
+    "Along with that, you will need to install Argilla as a distilabel extra."
    ]
   },
   {
@@ -127,7 +127,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this case, we will clean a preference dataset, so we will use the `Intel/orca_dpo_pairs` dataset from the [Hugging Face Hub](https://huggingface.co/datasets/Intel/orca_dpo_pairs)."
+    "In this case, we will clean a preference dataset, so we will use the [`Intel/orca_dpo_pairs`](https://huggingface.co/datasets/Intel/orca_dpo_pairs) dataset from the Hugging Face Hub."
    ]
   },
   {

docs/sections/pipeline_samples/tutorials/generate_preference_dataset.ipynb

@@ -97,12 +97,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "### (optional) Deploy Argilla\n",
     "\n",
-    "You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already have deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/). \n",
+    "You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/). \n",
     "\n",
-    "Along with that, you will need to install argilla as distilabel extra."
+    "Along with that, you will need to install Argilla as a distilabel extra."
    ]
   },
   {
@@ -134,7 +133,7 @@
    "source": [
     "### Load the dataset\n",
     "\n",
-    "We will use as source data the `argilla/10Kprompts-mini` dataset from the [Hugging Face Hub](https://huggingface.co/datasets/argilla/10Kprompts-mini).\n",
+    "We will use as source data the [`argilla/10Kprompts-mini`](https://huggingface.co/datasets/argilla/10Kprompts-mini) dataset from the Hugging Face Hub.\n",
     "\n",
     "<iframe\n",
     "  src=\"https://huggingface.co/datasets/argilla/10Kprompts-mini/embed/viewer/default/train\"\n",
@@ -182,7 +181,7 @@
    "source": [
     "### Generate responses\n",
     "\n",
-    "We need to generate the responses for the given instructions. We will use two different models available in the Hugging Face Hub through the Serverless Inference API: [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). We will also indicate the generation parameters for each model.\n",
+    "We need to generate the responses for the given instructions. We will use two different models available on the Hugging Face Hub through the Serverless Inference API: [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). We will also indicate the generation parameters for each model.\n",
     "\n",
     "- Component: `TextGeneration` task with LLMs using `InferenceEndpointsLLM`\n",
     "- Input columns: `instruction`\n",