readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: DSPFirst-Finetuning-5
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Important Note:
  I created the `combined` metric (55% F1 score + 45% exact match score) and load the state with the best result at the end. Here is the setting in the `TrainingArguments`:
  ```
    load_best_model_at_end=True,
    metric_for_best_model='combined',
    greater_is_better=True,
  ```
  
  # DSPFirst-Finetuning-5
  
  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: 0.8529
  - Exact: 67.0964
  - F1: 74.4842
  - Combined: 71.1597
  
  ## More accurate metrics:
  
  ### Before fine-tuning:
  
  ```
   'HasAns_exact': 54.71817606079797,
   'HasAns_f1': 61.08672724332754,
   'HasAns_total': 1579,
   'NoAns_exact': 88.78048780487805,
   'NoAns_f1': 88.78048780487805,
   'NoAns_total': 205,
   'best_exact': 58.63228699551569,
   'best_exact_thresh': 0.0,
   'best_f1': 64.26902596256402,
   'best_f1_thresh': 0.0,
   'exact': 58.63228699551569,
   'f1': 64.26902596256404,
   'total': 1784
  ```
  
  ### After fine-tuning:
  
  ```
   'HasAns_exact': 67.57441418619379,
   'HasAns_f1': 75.92137683558988,
   'HasAns_total': 1579,
   'NoAns_exact': 63.41463414634146,
   'NoAns_f1': 63.41463414634146,
   'NoAns_total': 205,
   'best_exact': 67.0964125560538,
   'best_exact_thresh': 0.0,
   'best_f1': 74.48422310728503,
   'best_f1_thresh': 0.0,
   'exact': 67.0964125560538,
   'f1': 74.48422310728503,
   'total': 1784
  ```
  
  # 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
  
  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
  
  - `batch_size` of 6 results in 14.03 GB VRAM
  - Utilizes `gradient_accumulation_steps` to get total batch size to 516 (total 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](https://colab.research.google.com/drive/1dJXNstk2NSenwzdtl9xA8AqjP4LL-Ks_?usp=sharing)
  - Utilizes Tesla P100 16GB, took 6.3 hours to train
  - `load_best_model_at_end` is enabled in TrainingArguments
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 6
  - eval_batch_size: 6
  - seed: 42
  - gradient_accumulation_steps: 86
  - total_train_batch_size: 516
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 7
  
  ### Model hyperparameters
  
  - hidden_dropout_prob: 0.36
  - attention_probs_dropout_prob = 0.36
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Exact   | F1      | Combined |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:--------:|
  | 2.3222        | 0.81  | 20   | 1.0363          | 60.3139 | 68.8586 | 65.0135  |
  | 1.6149        | 1.65  | 40   | 0.9702          | 64.7422 | 72.5555 | 69.0395  |
  | 1.2375        | 2.49  | 60   | 1.0007          | 64.6861 | 72.6306 | 69.0556  |
  | 1.0417        | 3.32  | 80   | 0.9963          | 66.0874 | 73.8634 | 70.3642  |
  | 0.9401        | 4.16  | 100  | 0.8803          | 67.0964 | 74.4842 | 71.1597  |
  | 0.8799        | 4.97  | 120  | 0.8652          | 66.7040 | 74.1267 | 70.7865  |
  | 0.8712        | 5.81  | 140  | 0.8921          | 66.3677 | 73.7213 | 70.4122  |
  | 0.8311        | 6.65  | 160  | 0.8529          | 66.3117 | 73.4039 | 70.2124  |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  