docs: fastfit tutorial contains link to copied blog post (#4995)

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# Description

Change the link to the original blog post.

Closes #4994 

**Type of change**

(Remember to title the PR according to the type of change)

- [ ] Documentation update

**How Has This Been Tested**

(Please describe the tests that you ran to verify your changes.)

- [ ] `sphinx-autobuild` (read [Developer
Documentation](https://docs.argilla.io/en/latest/community/developer_docs.html#building-the-documentation)
for more details)

**Checklist**

- [ ] I added relevant documentation
- [ ] I followed the style guidelines of this project
- [ ] I did a self-review of my code
- [ ] I made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I filled out [the contributor form](https://tally.so/r/n9XrxK)
(see text above)
- [ ] I have added relevant notes to the `CHANGELOG.md` file (See
https://keepachangelog.com/)

Author: sdiazlor

docs/_source/tutorials_and_integrations/tutorials/feedback/training-fastfit-agreement.ipynb

@@ -36,7 +36,7 @@
     "\n",
     "[**FastFit**](https://github.com/IBM/fastfit) is a library that allows you to train a multi-class classifier with few-shot learning. It is based on the [**transformers**](https://huggingface.co/transformers/) library and uses a pre-trained model to fine-tune it on a small dataset. This is particularly useful when you have a small dataset and you want to train a model quickly. However, [**SetFit**](https://github.com/huggingface/setfit) is another well-know library that also allows few-shot learning with Sentence Transformers.\n",
     "\n",
-    "So, why using one and not the other? Based on this [article](https://medium.com/@meetgandhi586/comparing-setfit-fastfit-and-semantic-router-finding-the-best-nlp-chatbot-intent-detection-d8161a7ad117), where the author compares FastFit, SetFit, and Semantic Router, we can determine some distinctions.\n",
+    "So, why using one and not the other? Based on this [article](https://pub.towardsai.net/few-shot-nlp-intent-classification-d29bf85548aa), where the author compares FastFit, SetFit, and Semantic Router, we can determine some distinctions.\n",
     "\n",
     "| **Aspect**                | **FastFit**                                   | **SetFit**                                 |\n",
     "|---------------------------|-----------------------------------------------|--------------------------------------------|\n",
@@ -335,7 +335,7 @@
    ],
    "source": [
     "# Sample the dataset\n",
-    "dataset[\"train\"] = sample_dataset(dataset[\"train\"], label_column=\"intent\", num_samples_per_label=5, seed=42)\n",
+    "dataset[\"train\"] = sample_dataset(dataset[\"train\"], label_column=\"intent\", num_samples_per_label=10, seed=42)\n",
     "\n",
     "# Rename the validation split\n",
     "dataset['validation'] = dataset.pop('val')\n",

docs/_source/tutorials_and_integrations/tutorials/tutorials.md

@@ -139,7 +139,7 @@ Learn how to monitor data and model drift in a real-world scenario using differe
 
 Learn how to train an ABSA model and evaluate with Argilla.
 ```
-```{grid-item-card} # 🙌 Analyzing Annotation Metrics with FastFit Model Predictions
+```{grid-item-card} 🙌 Analyzing Annotation Metrics with FastFit Model Predictions
 :link: feedback/training-fastfit-agreement.html
 Learn how to train a FastFit model and calculate well-know annotation metrics.
 ```