Update README.md

README.md

@@ -36,11 +36,22 @@ This allows the model to be used for tasks like:
 - visual question answering (VQA)
 - chat-like conversations by feeding the image and the previous conversation as prompt to the model
 
-## Intended uses & limitations
+## Direct Use and Downstream Use
 
 You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
 fine-tuned versions on a task that interests you.
 
+## Bias, Risks, and Limitations
+
+BLIP2-FlanT5 uses off-the-shelf Flan-T5 as the language model. It inherits the same risks and limitations from [Flan-T5](https://arxiv.org/pdf/2210.11416.pdf):
+
+> Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.
+
+BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet.  As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
+
+BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
+
+
 ### How to use
 
 For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example), or refer to the snippets below depending on your usecase: