Update README.md

README.md

@@ -63,15 +63,8 @@ scheduler.remove_pruning()
  ```
 For more code examples, refer to the [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).
 
-| Factors | Description | 
-| ----------- | ----------- | 
-| Groups | Many Wikipedia articles with question and answer labels are contained in the training data | 
-| Instrumentation | - |
-| Environment | - |
-| Card Prompts | - |
-
 
-## Metrics (Model Performance):
+### Metrics (Model Performance):
 | Model                         | Model Size | SQuADv1.1 (EM/F1) | MNLI-m (Acc) | MNLI-mm (Acc) | QQP (Acc/F1) | QNLI (Acc) | SST-2 (Acc) |
 |-------------------------------|:----------:|:-----------------:|:------------:|:-------------:|:------------:|:----------:|:-----------:|
 | [80% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa)  |   -   |    81.29/88.47    |     -    |     -     | - |    -   |    -    |
@@ -87,7 +80,7 @@ All the results are the mean of two seperate experiments with the same hyper-par
 | ----------- | ----------- | 
 | Datasets | SQuAD1.1: "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable." (https://huggingface.co/datasets/squad)|
 | Motivation | To build an efficient and accurate model for the question answering task. |
-| Preprocessing | "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." | 
+| Preprocessing | "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." Following the pre-training on Wikipedia, fine-tuning is completed on the SQuAD1.1 dataset. | 
 
 | Ethical Considerations | Description | 
 | ----------- | ----------- |