Update README.md

README.md

@@ -178,8 +178,6 @@ This repository provides the three cached feature subsets listed below. Raw imag
 | TokBench | Original images/annotations and matching tokenizer reconstructions | [TokBench](https://huggingface.co/datasets/Junfeng5/TokBench) |
 | MLLM training / evaluation | Training: LCS-558K and filtered mix665k; evaluation: the 11 benchmarks below | [Model Zoo](https://huggingface.co/336labs/VisionEncoder-to-MLLM-ModelZoo) and the linked dataset sources |
 
-ImageNet kNN features have class labels but no paired captions, so they cannot supply an alignment probe's image–text inputs on their own. A full linear-probe benchmark also needs independent validation data.
-
 ### Downstream MLLM benchmarks used in this paper
 
 | Benchmark / required TSV | Source |
@@ -196,7 +194,8 @@ ImageNet kNN features have class labels but no paired captions, so they cannot s
 | `MSCOCO_KARPATHY_TEST.tsv` | [Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [COCO images](https://cocodataset.org/#download) |
 | `FLICKR30K_KARPATHY_TEST.tsv` | [Karpathy splits](https://cs.stanford.edu/people/karpathy/deepimagesent/) and [Flickr30k images](https://shannon.cs.illinois.edu/DenotationGraph/) |
 
-Use the [VLMEvalKit dataset loaders](https://github.com/open-compass/VLMEvalKit/tree/main/vlmeval/dataset) for benchmark preparation. Original dataset downloads must be converted to the required TSV format; changing a filename alone is insufficient. Configuration and experiment commands are in the code repository's [Setup](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#setup) and [Running](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#running) sections.
+Use the [VLMEvalKit dataset loaders](https://github.com/open-compass/VLMEvalKit/tree/main/vlmeval/dataset) for benchmark preparation. 
+Configuration used in our paper are in the code repository's [Setup](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#setup) and [Running](https://github.com/JuntaoTang/MLLM-VisionEncoder-Eval#running) sections.
 
 ## Metadata and Integrity