Update README.md

README.md

@@ -176,11 +176,11 @@ Notus is a collection of fine-tuned models using Direct Preference Optimization
 
 Following a **data-first** approach, the only difference between Notus-7B-v1 and Zephyr-7B-beta is the preference dataset used for dDPO. 
 
-In particular, when we started building [distilabel](https://github.com/argilla-io/distilabel), we took some time to deep-dive into the UltraFeedback dataset. Using [Argilla](https://argilla.io/), we've found data issues in the original UltraFeedback dataset, leading to high-scores for bad responses (more details in the training data section). After curating several hundreds of data points, we decided to binarize the dataset using the preference ratings, instead of the original critique `overall_score`.
+In particular, when we started building [distilabel](https://github.com/argilla-io/distilabel), we invested time understanding and deep-diving into the UltraFeedback dataset. Using [Argilla](https://argilla.io/), we've found data issues in the original UltraFeedback dataset, leading to high-scores for bad responses (more details in the training data section). After curating several hundreds of data points, we decided to binarize the dataset using the preference ratings, instead of the original critique `overall_score`, and verified the new dataset with Argilla.
 
 Using preference ratings, instead of critiques scores, led to a new dataset where the chosen response is different in ~50% of the cases. Using this new dataset with DPO we fine-tuned Notus, a 7B model, that **surpasses Zephyr-7B-beta, Claude 2, and Cohere Command on AlpacaEval**.
 
-This model **wouldn't have been possible without the amazing [Alignment Handbook](https://github.com/huggingface/alignment-handbook)** and it's based on fruitful discussions with the HuggingFace H4 team. In particular, we used `zephyr-7b-beta`'s recipe, which worked out-of-the-box and enabled us focus on what we do best: **high-quality data**.
+This model **wouldn't have been possible without the amazing [Alignment Handbook](https://github.com/huggingface/alignment-handbook), [OpenBMB](https://www.openbmb.cn/home) for releasing the Ultrafeedback dataset**, and it's based on fruitful discussions with the HuggingFace H4 team. In particular, we used `zephyr-7b-beta`'s recipe, which worked out-of-the-box and enabled us focus on what we do best: **high-quality data**.
 
 Notus models are intended to be used as assistants via chat-like applications, and are evaluated with Chat (MT-Bench, AlpacaEval) and Academic (Open LLM Leaderboard) benchmarks for a direct comparison with the original Zephyr dDPO model and other 7B models.
 
@@ -326,7 +326,8 @@ We used a VM with 8 x A100 40GB hosted in Lambda Labs, but while experimenting w
 
 ### Training Data
 
-We used a a new curated version of [`openbmb/UltraFeedback`](https://huggingface.co/datasets/openbmb/UltraFeedback), named [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences).
+We used a a new curated version [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences).
+of [`openbmb/UltraFeedback`](https://huggingface.co/datasets/openbmb/UltraFeedback), named [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences).
 
 TL;DR
 
@@ -342,6 +343,8 @@ While we're working on fixing the original dataset (already narrowed down ~2K pr
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/M9qCKyAB_G1MbVBAPeitd.png)
 
+You can find more details about the dataset analysis and curation on the [ultrafeedback-binarized-preferences dataset card](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences).
+
 ## Prompt template
 
 We use the same prompt template as [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta):