Update README.md

README.md

@@ -13,25 +13,60 @@ should probably proofread and complete it, then remove this comment. -->
 
 # DSPFirst-Finetuning-4
 
-This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512) on an unknown dataset.
+This model is a fine-tuned version of [ahotrod/electra_large_discriminator_squad2_512](https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512) on a generated Questions and Answers dataset from the DSPFirst textbook based on the SQuAD 2.0 format.<br />
 It achieves the following results on the evaluation set:
 - Loss: 0.9028
 - Exact: 66.9843
 - F1: 74.2286
 
-## Model description
-
-More information needed
+## More accurate metrics:
+
+### Before fine-tuning:
+
+```
+  "exact": 57.006726457399104,
+  "f1": 61.997705120754276
+```
+
+### After fine-tuning:
+
+```
+  "exact": 66.98430493273543,
+  "f1": 74.2285867775556
+```
+
+# Dataset
+A visualization of the dataset can be found [here](https://github.gatech.edu/pages/VIP-ITS/textbook_SQuAD_explore/explore/textbookv1.0/textbook/).<br />
+The split between train and test is 70% and 30% respectively.
+```
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'title', 'context', 'question', 'answers'],
+        num_rows: 4160
+    })
+    test: Dataset({
+        features: ['id', 'title', 'context', 'question', 'answers'],
+        num_rows: 1784
+    })
+})
+```
 
 ## Intended uses & limitations
 
-More information needed
+This model is fine-tuned to answer questions from the DSPFirst textbook. I'm not really sure what I am doing so you should review before using it.<br />
+Also, you should improve the Dataset either by using a **better generated questions and answers model** (currently using https://github.com/patil-suraj/question_generation) or perform **data augmentation** to increase dataset size.
 
 ## Training and evaluation data
 
-More information needed
+- `batch_size` of 6 results in 14.82 GB VRAM
+- Utilizes `gradient_accumulation_steps` to get total batch size to 514 (batch size should be at least 256)
+- 4.52 GB RAM
+- 30% of the total questions is dedicated for evaluating.
 
 ## Training procedure
+- The model was trained from Google Colab
+- Utilizes Tesla P100 16GB, took 6.3 hours to train
+- `load_best_model_at_end` is enabled in TrainingArguments
 
 ### Training hyperparameters
 
@@ -46,6 +81,11 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 10
 
+### Model hyperparameters
+
+- hidden_dropout_prob: 0.36
+- attention_probs_dropout_prob = 0.36
+
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Exact   | F1      |