update model card

README.md

@@ -38,9 +38,9 @@ The model was trained on (image, text) pairs obtained from the Farfecth dataset[
 
 ## Limitations, Bias and Fiarness
 
-We acknowledge certain limitations of FashionCLIP and expect that it inherits certain limitations and biases present in the original CLIP model. We do not expect our fine tuning to significantly augment these limitation: we acknowledge that the fashion data we use makes explicit assumptions about the notion of gender as in "blue shoes for woman" that inevitably to associate aspects of clothing to specific people.
+We acknowledge certain limitations of FashionCLIP and expect that it inherits certain limitations and biases present in the original CLIP model. We do not expect our fine-tuning to significantly augment these limitations: we acknowledge that the fashion data we use makes explicit assumptions about the notion of gender as in "blue shoes for a woman" that inevitably associate aspects of clothing with specific people.
 
-Our investingations also suggests that the data used introduces certain limitaions in FashionCLIP. From the textual modality, given that most captions dervied from the Farfetch dataset are long, we observe that FashionCLIP maybe more performant in longer queries than shorter ones. From the image modality, FashionCLIP is also biased towards standard product images (centered, white background).
+Our investigations also suggest that the data used introduces certain limitations in FashionCLIP. From the textual modality, given that most captions derived from the Farfetch dataset are long, we observe that FashionCLIP may be more performant in longer queries than shorter ones. From the image modality, FashionCLIP is also biased towards standard product images (centered, white background).
 
 Model selection, i.e. selecting an appropariate stopping critera during fine-tuning, remains an open challenge. We observed that using loss on an in-domain (i.e. same distribution as test) validation dataset is a poor selection critera when out-of-domain generalization (i.e. across different datasets) is desired, even when the dataset used is relatively diverse and large.