Docs: Typo Fix (#5642)

Corrected "labelled" to "labeled" in [README.md].

This pull request addresses a minor typo found in repository. The typo
has been corrected to improve clarity and maintain the quality of the
documentation.

This change is purely cosmetic and does not affect functionality.

# Description
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Please also include relevant motivation and context. List any
dependencies that are required for this change. -->

Closes #<issue_number>

**Type of change**
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PR according to the type of change -->

- Bug fix (non-breaking change which fixes an issue)
- New feature (non-breaking change which adds functionality)
- Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- Refactor (change restructuring the codebase without changing
functionality)
- Improvement (change adding some improvement to an existing
functionality)
- Documentation update

**How Has This Been Tested**
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**Checklist**
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- 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
- I confirm My changes generate no new warnings
- I have added tests that prove my fix is effective or that my feature
works
- I have added relevant notes to the CHANGELOG.md file (See
https://keepachangelog.com/)

Co-authored-by: Sara Han <[email protected]>

Author: RahulK4102

README.md

@@ -84,7 +84,7 @@ The community uses Argilla to create amazing open-source [datasets](https://hugg
 AI teams from companies like [the Red Cross](https://510.global/), [Loris.ai](https://loris.ai/) and [Prolific](https://www.prolific.com/) use Argilla to improve the quality and efficiency of AI projects. They shared their experiences in our [AI community meetup](https://lu.ma/embed-checkout/evt-IQtRiSuXZCIW6FB).
 
 - AI for good: [the Red Cross presentation](https://youtu.be/ZsCqrAhzkFU?feature=shared) showcases how the Red Cross domain experts and AI team collaborated by classifying and redirecting requests from refugees of the Ukrainian crisis to streamline the support processes of the Red Cross.
-- Customer support: during [the Loris meetup](https://youtu.be/jWrtgf2w4VU?feature=shared) they showed how their AI team uses unsupervised and few-shot contrastive learning to help them quickly validate and gain labelled samples for a huge amount of multi-label classifiers.
+- Customer support: during [the Loris meetup](https://youtu.be/jWrtgf2w4VU?feature=shared) they showed how their AI team uses unsupervised and few-shot contrastive learning to help them quickly validate and gain labeled samples for a huge amount of multi-label classifiers.
 - Research studies: [the showcase from Prolific](https://youtu.be/ePDlhIxnuAs?feature=shared) announced their integration with our platform. They use it to actively distribute data collection projects among their annotating workforce. This allows Prolific to quickly and efficiently collect high-quality data for research studies.
 
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