Update README.md

README.md

@@ -45,9 +45,8 @@ Both image recognition (classification, retrieval, etc) and text generation (ima
 
 # Uses
 
-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 and retrieval, and generalization capabilities of language-vision learning in general. We also hope it can be used for interdisciplinary studies of the impact of such model, eg when used as component in VLMs or other multi-modal models. 
+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 and retrieval, and generalization capabilities of language-vision learning in general. We also hope it can be used for interdisciplinary studies of the impact of such model, eg when used as component in VLMs or other multi-modal models. 
 
-The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
 Details on the DataComp-1.4B training dataset can be found in [DataComp repository](https://github.com/mlfoundations/datacomp) and the [DataComp NeurIPS Oral paper](https://openreview.net/forum?id=dVaWCDMBof). 
 
 ## Direct Use
@@ -67,7 +66,7 @@ Image classification, retrieval and image captioning. Linear probing and full fi
 
 As per the OpenAI models,
 
-**Any** deployed use case of the model (that is, in form of an end product) - 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 error prone and thus unsafe. 
+**Any** deployed use case of the model (that is, in form of an end product) - 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 error prone and thus unsafe.
 
 Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
 
@@ -81,7 +80,7 @@ Further the above notice, the DataComp-1.4B dataset used in training of these mo
 
 This model was trained on [DataComp-1.4B](https://github.com/mlfoundations/datacomp), [DataComp paper](https://openreview.net/forum?id=dVaWCDMBof), [DataComp-1.4B metadata at HF](https://huggingface.co/datasets/mlfoundations/datacomp_1b)  (also known as DataComp-XL), which contains 1.4 Billion image-text samples.
 
-**IMPORTANT NOTE:** Open datasets democratize research and experimentation around large-scale multi-modal model training and handling of curated or uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to train on the dataset only for research purposes. Be aware when obtaining the dataset for training for research purposes, that this large-scale dataset is automatically curated. Keep in mind that the automatic curation of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the links contained in metadata with caution and follow them only at your own risk. Same is valid for the downloaded samples for training, view them only if you are a well-trained large-scale data scientist prepared to be confronted with extremely diverse content. While filtering out samples based on various safety classifiers strongly reduced the chance for encountering potentially harmful content when viewing, the possibility for subjectively strongly discomforting content being still present in the dataset cannot be entirely excluded. Open datasets provided to broad research and other interested communities allow for transparent investigation of benefits that come along with training large-scale models as well as of pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. The training dataset is not recommended to be used for creating any ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
+**IMPORTANT NOTE:** Our recommendation is to use the model pre-trained on open DataComp-1.4B dataset only for research purposes. Open datasets provided to broad research and other interested communities allow reproducible and transparent studies of model training and resulting capabilities, the resulting models are basic research artefacts and as such are not recommended to be used for creating any ready-to-go industrial products.
 
 ## Training Procedure