Update README.md

README.md

@@ -65,7 +65,7 @@ configs:
 
 The wa-hls4ml projects dataset, comprized of the Vivado/Vitis projects of neural networks converted into HLS Code via hls4ml. Projects are complete, and include all logs, HLS Code, VHDL Code, Intermediete Representations, and source keras models. 
 
-This is a companion dataset to the [wa-hls4ml](fastmachinelearning/wa-hls4ml) dataset. There is a reference CSV for each model type that contains a reference to each individual model name, the artifacts file for that model, and which batch archive file contains that project archive for that model.
+This is a companion dataset to the [wa-hls4ml](https://huggingface.co/datasets/fastmachinelearning/wa-hls4ml) dataset. There is a reference CSV for each model type that contains a reference to each individual model name, the artifacts file for that model, and which batch archive file contains that project archive for that model.
 
 **PLEASE NOTE:** The dataset is currently incomplete, and is in the process of being cleaned and uploaded. 
 
@@ -160,7 +160,7 @@ The resulting projects, reports, logs, and a JSON file containing the resource/l
 The data, excluding the projects and logs, were then further processed into a collection of JSON files, distributed alongside this paper and described below.
 
 - **Repository:** [fastmachinelearning/wa-hls4ml-paper](https://github.com/fastmachinelearning/wa-hls4ml-paper)
-- **Paper [optional]:** [In Review]
+- **Paper [optional]:** In Review
 
 ---
 
@@ -181,18 +181,7 @@ As this dataset is generated using the hls4ml and Vivado/Vitis tools, it should
 
 ## Dataset Structure
 
-The training, validation, and test sets of the benchmark currently consist of 683,176 synthetic samples, consisting of data about synthesized samples of 608,679 fully-connected neural networks, 31,278 one-dimensional convolutional neural networks, and 43,219 two-dimensional convolutional neural networks.  
-Each sample contains the model architecture, hls4ml conversion parameters, the latency and resource usage numbers for that network post-logic synthesis, and associated metadata.  
-In addition to the training, validation, and test sets, the dataset also includes 887 samples representing the successful logic synthesis of the exemplar models with varying conversion parameters.  
-The dataset as a whole is split, distributed, and intended to be used as follows:  
-
-- **Training set**: The set of 478,220 samples intended to be used for training a given estimator.  
-- **Validation set**: The set of 102,472 samples intended to be used during training for validation purposes.  
-- **Test set**: The set of 102,484 samples intended to be used for testing and generating results for a given estimator.  
-- **Exemplar test set**: The set of 887 samples, comprising the models described in the benchmark, intended to be used for testing and generating results for a given estimator.  
-
-Within each subset, excluding the exemplar test set, the data is further grouped as follows.  
-These categories explain the composition of our dataset but have no bearing on how a given estimator should be trained.  
+Within each subset, excluding the exemplar test set, the data is grouped as follows.  
 
 - **2_20 (rule4ml)**: The updated rule4ml dataset, containing fully-connected neural networks that were randomly generated with layer counts between 2 and 20 layers, using hls4ml resource and latency strategies.  
 - **2_layer**: A subset containing 2-layer deep fully-connected neural networks generated via a grid search using hls4ml resource and io_parallel strategies.  
@@ -253,7 +242,7 @@ We intend to continuously update this dataset, addressing this imbalance over ti
 
 Appropriate measures should be taken to weight the data to account for the dataset imbalance at training time. 
 
-## Citation [optional]
+## Citation
 
 Paper currently in review.