Update phrasing and prose based on https://github.com/huggingface/hub…

…-docs/pull/1340/files

README.md

@@ -38,7 +38,7 @@ If you just want to get started, we recommend our [UI demo](https://demo.argilla
 
 ## Why use Argilla?
 
-Whether you are working on monitoring and improving complex **generative tasks** involving LLM pipelines with RAG, or you are working on a **predictive task** for things like AB-testing of span- and text-classification models. Our versatile tools helps you ensure **your data work pays off**.
+Argilla can be used for collecting human feedback for a wide variety of AI projects like traditional NLP (text classification, NER, etc.), LLMs (RAG, preference tuning, etc.), or multimodal models (text to image, etc.). Argilla's programmatic approach lets you build workflows for continuous evaluation and model improvement. The goal of Argilla is to ensure your data work pays off by quickly iterating on the right data and models.
 
 ### Improve your AI output quality through data quality
 
@@ -66,18 +66,18 @@ We are an open-source community-driven project and we love to hear from you. Her
 
 ### Open-source datasets and models
 
-Argilla is a tool that can be used to achieve and keep **high-quality data standards** with a **focus on NLP and LLMs**. Our community uses Argilla to create amazing open-source [datasets](https://huggingface.co/datasets?other=argilla) and [models](https://huggingface.co/models?other=distilabel), and **we love contributions to open-source** ourselves too.
+The community uses Argilla to create amazing open-source [datasets](https://huggingface.co/datasets?library=library:argilla&sort=trending) and [models](https://huggingface.co/models?other=distilabel).
 
-- Our [cleaned UltraFeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) and the [Notus](https://huggingface.co/argilla/notus-7b-v1) and [Notux](https://huggingface.co/argilla/notux-8x7b-v1) models, where we improved benchmark and empirical human judgment for the Mistral and Mixtral models with cleaner data using **human feedback**.
-- Our [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) and the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B), show how we improve model performance by filtering out 50% of the original dataset through **human and AI feedback**.
+- [Cleaned UltraFeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) used to fine tune the [Notus](https://huggingface.co/argilla/notus-7b-v1) and [Notux](https://huggingface.co/argilla/notux-8x7b-v1) models. The original UltraFeedback dataset was curated using Argilla UI filters to find and report a bug in the original data generation code. Based on this data curation process, Argilla built this new version of the UltraFeedback dataset and fine-tuned Notus, outperforming Zephyr on several benchmarks.
+- [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) used to fine tune the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B). This dataset was built combining Argilla human curation with distilabel, leading to an improved version of the Intel Orca dataset and outperforming models fine tuned on the original dataset.
 
-### Internal Use cases
+### Examples Use cases
 
-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 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 their experts and AI team collaborate** 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**.
-- Research studies: [the showcase from Prolific](https://youtu.be/ePDlhIxnuAs?feature=shared) announced their integration with our tool. They use it to actively **distribute data collection projects** among their annotating workforce. This allows them to quickly and **efficiently collect high-quality data** for their research studies.
+- 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.
+- 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.
 
 ## 👨‍💻 Getting started
 

argilla-v1/README.md

@@ -68,18 +68,18 @@ We are an open-source community-driven project and we love to hear from you. Her
 
 ### Open-source datasets and models
 
-Argilla is a tool that can be used to achieve and keep **high-quality data standards** with a **focus on NLP and LLMs**. Our community uses Argilla to create amazing open-source [datasets](https://huggingface.co/datasets?other=argilla) and [models](https://huggingface.co/models?other=distilabel), and **we love contributions to open-source** ourselves too.
+The community uses Argilla to create amazing open-source [datasets](https://huggingface.co/datasets?library=library:argilla&sort=trending) and [models](https://huggingface.co/models?other=distilabel).
 
-- Our [cleaned UltraFeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) and the [Notus](https://huggingface.co/argilla/notus-7b-v1) and [Notux](https://huggingface.co/argilla/notux-8x7b-v1) models, where we improved benchmark and empirical human judgment for the Mistral and Mixtral models with cleaner data using **human feedback**.
-- Our [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) and the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B), show how we improve model performance by filtering out 50% of the original dataset through **human and AI feedback**.
+- [Cleaned UltraFeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) used to fine tune the [Notus](https://huggingface.co/argilla/notus-7b-v1) and [Notux](https://huggingface.co/argilla/notux-8x7b-v1) models. The original UltraFeedback dataset was curated using Argilla UI filters to find and report a bug in the original data generation code. Based on this data curation process, Argilla built this new version of the UltraFeedback dataset and fine-tuned Notus, outperforming Zephyr on several benchmarks.
+- [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) used to fine tune the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B). This dataset was built combining Argilla human curation with distilabel, leading to an improved version of the Intel Orca dataset and outperforming models fine tuned on the original dataset.
 
-### Internal Use cases
+### Examples Use cases
 
-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 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 their experts and AI team collaborate** 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**.
-- 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 them to quickly and **efficiently collect high-quality data** for their research studies.
+- 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.
+- 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.
 
 ## 👨‍💻 Getting started
 

argilla/README.md

@@ -65,18 +65,18 @@ We are an open-source community-driven project and we love to hear from you. Her
 
 ### Open-source datasets and models
 
-Argilla is a tool that can be used to achieve and keep **high-quality data standards** with a **focus on NLP and LLMs**. Our community uses Argilla to create amazing open-source [datasets](https://huggingface.co/datasets?other=argilla) and [models](https://huggingface.co/models?other=distilabel), and **we love contributions to open-source** ourselves too.
+The community uses Argilla to create amazing open-source [datasets](https://huggingface.co/datasets?library=library:argilla&sort=trending) and [models](https://huggingface.co/models?other=distilabel).
 
-- Our [cleaned UltraFeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) and the [Notus](https://huggingface.co/argilla/notus-7b-v1) and [Notux](https://huggingface.co/argilla/notux-8x7b-v1) models, where we improved benchmark and empirical human judgment for the Mistral and Mixtral models with cleaner data using **human feedback**.
-- Our [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) and the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B), show how we improve model performance by filtering out 50% of the original dataset through **human and AI feedback**.
+- [Cleaned UltraFeedback dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) used to fine tune the [Notus](https://huggingface.co/argilla/notus-7b-v1) and [Notux](https://huggingface.co/argilla/notux-8x7b-v1) models. The original UltraFeedback dataset was curated using Argilla UI filters to find and report a bug in the original data generation code. Based on this data curation process, Argilla built this new version of the UltraFeedback dataset and fine-tuned Notus, outperforming Zephyr on several benchmarks.
+- [distilabeled Intel Orca DPO dataset](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) used to fine tune the [improved OpenHermes model](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B). This dataset was built combining Argilla human curation with distilabel, leading to an improved version of the Intel Orca dataset and outperforming models fine tuned on the original dataset.
 
-### Internal Use cases
+### Examples Use cases
 
-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 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 their experts and AI team collaborate** 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**.
-- 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 them to quickly and **efficiently collect high-quality data** for their research studies.
+- 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.
+- 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.
 
 ## 👨‍💻 Getting started
 
@@ -153,4 +153,3 @@ To help our community with the creation of contributions, we have created our [c
 <img  src="https://contrib.rocks/image?repo=argilla-io/argilla" />
 
 </a>
-