update model card README.md

README.md

@@ -13,60 +13,25 @@ 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 a generated Questions and Answers dataset from the DSPFirst textbook based on the SQuAD 2.0 format.<br />
+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.
 It achieves the following results on the evaluation set:
-- Loss: 1.1113
-- Exact: 63.9013
-- F1: 72.1497
-
-## More accurate metrics:
-
-### Before fine-tuning:
-
-```
-  "exact": 56.2219730941704,
-  "f1": 61.903777053610895
-```
-
-### After fine-tuning:
-
-```
-  "exact": 64.01345291479821,
-  "f1": 72.2551864039602
-```
-
-# 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
-    })
-})
-```
+- Loss: 0.9028
+- Exact: 66.9843
+- F1: 74.2286
+
+## Model description
+
+More information needed
 
 ## Intended uses & limitations
 
-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.
+More information needed
 
 ## Training and evaluation data
 
-- `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.
+More information needed
 
 ## Training procedure
-- The model was trained from Google Colab
-- Utilizes Tesla P100 16GB, took 3.8 hours to train
-- `load_best_model_at_end` is enabled in TrainingArguments
 
 ### Training hyperparameters
 
@@ -79,24 +44,24 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 516
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 6
-
-### Model hyperparameters
-
-- hidden_dropout_prob: 0.37
-- attention_probs_dropout_prob = 0.37
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Exact   | F1      |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
-| 2.4306        | 0.81  | 20   | 1.1873          | 58.8004 | 67.0961 |
-| 1.8178        | 1.64  | 40   | 1.1572          | 62.8924 | 71.2319 |
-| 1.7696        | 2.48  | 60   | 1.0879          | 63.6771 | 71.6848 |
-| 1.8313        | 3.32  | 80   | 1.1332          | 63.2848 | 71.8749 |
-| 1.5811        | 4.16  | 100  | 1.0473          | 63.4529 | 71.6780 |
-| 1.477         | 4.97  | 120  | 1.0720          | 64.1816 | 72.2297 |
-| 1.5882        | 5.81  | 140  | 1.1113          | 63.9013 | 72.1497 |
+| 2.4411        | 0.81  | 20   | 1.4556          | 62.0516 | 71.1082 |
+| 2.2027        | 1.64  | 40   | 1.1508          | 65.0224 | 73.8669 |
+| 1.2827        | 2.48  | 60   | 1.0030          | 65.8632 | 74.3959 |
+| 1.0925        | 3.32  | 80   | 1.0155          | 66.8722 | 75.2204 |
+| 1.03          | 4.16  | 100  | 0.8863          | 66.1996 | 73.8166 |
+| 0.9085        | 4.97  | 120  | 0.9675          | 67.9372 | 75.7764 |
+| 0.8968        | 5.81  | 140  | 0.8635          | 67.2085 | 74.3725 |
+| 0.8867        | 6.64  | 160  | 0.9035          | 65.9753 | 73.4569 |
+| 0.8456        | 7.48  | 180  | 0.9098          | 67.2085 | 74.6798 |
+| 0.8506        | 8.32  | 200  | 0.8807          | 66.6480 | 74.2903 |
+| 0.7972        | 9.16  | 220  | 0.8711          | 66.6480 | 73.5801 |
+| 0.7795        | 9.97  | 240  | 0.9028          | 66.9843 | 74.2286 |
 
 
 ### Framework versions