Update README.md

README.md

@@ -25,12 +25,12 @@ This model was created using the [nn_pruning](https://github.com/huggingface/nn_
 
 The model contains **50.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
 
-With a simple resizing of the linear matrices it ran **1.84x as fast as /home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune** on the evaluation.
+With a simple resizing of the linear matrices it ran **1.84x as fast as the dense model** on the evaluation.
 This is possible because the pruning method lead to structured matrices: to visualize them, hover below on the plot to see the non-zero/zero parts of each matrix.
 
 <div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/density_info.js" id="3aca15eb-8def-482c-800a-d9f8a6e8cea5"></script></div>
 
-In terms of accuracy, its **F1 is 88.72**, compared with 88.5 for /home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune, a **F1 gain of 0.22**.
+In terms of accuracy, its **F1 is 88.72**, compared with 88.5 for the dense version, a **F1 gain of 0.22**.
 
 ## Fine-Pruning details
 This model was fine-tuned from the HuggingFace [model](https://huggingface.co//home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune) checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1)