fix(docs): Fixed a link in docs (#32274)

Fixed a link in docs.

docs/source/en/perf_torch_compile.md

@@ -98,7 +98,7 @@ Below you can find the list of the models we benchmarked.
 - [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
 - [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k)
 - [facebook/convnext-large-224](https://huggingface.co/facebook/convnext-large-224)
-- [microsoft/resnet-50](https://huggingface.co/)
+- [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50)
 
 **Image Segmentation** 
 - [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)

docs/source/en/tf_xla.md

@@ -157,7 +157,7 @@ Execution time -- 79.0 ms
 
 Execution time -- 78.9 ms
 ```
-The first call to `xla_generate()` is time-consuming because of tracing, but the successive calls are orders of magnitude faster. Keep in mind that any change in the generation options at any point with trigger re-tracing and thus leading to slow-downs in the generation time. 
+The first call to `xla_generate()` is time-consuming because of tracing, but the successive calls are orders of magnitude faster. Keep in mind that any change in the generation options at any point will trigger re-tracing and thus leading to slow-downs in the generation time. 
 
 We didn’t cover all the text generation options 🤗 Transformers provides in this document. We encourage you to read the documentation for advanced use cases.