Update README.md

README.md

@@ -22,7 +22,7 @@ A CLIP ViT-B/32 model trained with the Quilt-1M dataset (https://quilt1m.github.
 
 As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. 
 
-The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. 
+The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
 
 ## Direct Use
 
@@ -34,17 +34,17 @@ Image classification and other image task fine-tuning, linear probe image classi
 
 ### Intended Use
 
-The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
+The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models.
 
 #### Primary intended uses
 
 The primary intended users of these models are AI researchers.
 
-We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
+We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision histopathology models.
 
 ### Out-of-Scope Use Cases
 
-**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task-specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy.
 
 Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
 
@@ -59,7 +59,7 @@ Curated from educational videos on Youtube QUILT-1M contributes the largest data
 
 # Evaluation
 
-Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark) and results can be found in the paper on a list of varying histology tasks and datasets.
+Evaluation done with code in the [CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark) and results can be found in the paper on a list of varying histology tasks and datasets.
 
 
 # Disclaimer