Update README.md

README.md

@@ -12,15 +12,40 @@ language: en
 ## BUDDI Table Factory: A toolbox for generating synthetic documents with annotated tables and cells
 
 
-[BUDDI Table Factory]() XX
+**About**
 
-We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
+In Cell detection, we initialize the weights with a pre-trained CDeCNet model using COCO dataset. We re-train the model for five epochs using a stochastic gradient descent optimizer with a learning rate of 0.00125, the momentum of 0.9, and weight decay of 0.0001.
+
+***Hardware Used***
+
+We perform all the experiments on NVIDIA GeForce RTX 2080 Ti GPU with 12 GB GPU memory, Intel(R) Xeon(R) CPU E5-2640 v2 @ 2.00GHz, and 128 GB of RAM.
+
+**Table Detection Model & Training Parameter**
+***Optimizer*** 
+| Parameter |Value  |
+|--|--|
+| Type | SGD |
+| Learning Rate |0.00125  |
+| Momentum | 0.8 |
+| Weight Decay |0.001  |
+
+
+*** Learning Policy ***
+| Parameter |Value  |
+|--|--|
+| Policy | Step |
+|Warmup | Linear  |
+| Warmup Iteration | 100 |
+| Warmup Ratio |0.001  |
+| Step  | 4,16,32  |
+
+***General Parameter***
+| Parameter |Value  |
+|--|--|
+| Epoch | 10 |
+| Step Interval |50  |
+        
 
-| Model             | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
-|-------------------|-----------|-----------|--------|
-| RoBERTa-base      | 91.5/84.6 | 83.7/80.5 | 87.6   |
-| XLNet-Large       | -/-       | -/80.2    | 86.8   |
-| **DeBERTa-base**  | 93.1/87.2 | 86.2/83.1 | 88.8   |
 
 ### Citation