Update README.md

README.md

@@ -100,26 +100,33 @@ Eprint = {arXiv:2405.20204},
 }
 ```
 
+## FAQ
 
-**notice: our emperical study shows that text-text cosine similarity is normally larger than text-image cosine similarity!**
+### I encounter this problem, what should I do?
+
+```
+ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_clip.JinaCLIPConfig'> and you passed <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_cli.JinaCLIPConfig'>. Fix one of those so they match!
+```
+
+There was a bug in Transformers library between 4.40.x to 4.41.1. You can update transformers to >4.41.2 or <=4.40.0
+
+### Givne one query, how can I merge its text-text and text-image cosine similarity?
+
+Our emperical study shows that text-text cosine similarity is normally larger than text-image cosine similarity!
 If you want to merge two scores, we recommended 2 ways:
 
 1. weighted average of text-text sim and text-image sim:
 
 ```python
-# pseudo code
-alpha = 0.6
-beta = 0.4
-
-combined_scores = alpha * sim(query, document) + beta * sim(text, image)
+combined_scores = sim(text, text) + lambda * sim(text, image)  # optimal lambda depends on your dataset, but in general lambda=2 can be a good choice.
 ```
 
 2. apply z-score normalization before merging scores:
 
 ```python
 # pseudo code
-query_document_mean = np.mean(cos_sim_query_documents)
-query_document_std = np.std(cos_sim_query_documents)
+query_document_mean = np.mean(cos_sim_text_texts)
+query_document_std = np.std(cos_sim_text_texts)
 text_image_mean = np.mean(cos_sim_text_images)
 text_image_std = np.std(cos_sim_text_images)