<!-- 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:

load_best_model_at_end is not working properly (I specified metric_for_best_model on another training but it still does not work), but the training results still show a valid trend.

DSPFirst-Finetuning-4

This model is a fine-tuned version of 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

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.<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 14.82 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

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: 10

Model hyperparameters

• hidden_dropout_prob: 0.36
• attention_probs_dropout_prob = 0.36

Training results

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.1.0
• Tokenizers 0.12.1