---
tags:
- generated_from_trainer
model-index:
- name: DSPFirst-Finetuning-1
  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. -->

# DSPFirst-Finetuning-1

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.

# Dataset
A visualization of the dataset can be found [here](https://github.gatech.edu/pages/VIP-ITS/textbook_SQuAD_explore/explore/textbookv1.0/textbook/). The split between train and test is 80% and 20% respectively.
```
DatasetDict({
    train: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers'],
        num_rows: 4755
    })
    test: Dataset({
        features: ['id', 'title', 'context', 'question', 'answers'],
        num_rows: 1189
    })
})
```

It achieves the following results on the evaluation set:
- Loss: 0.9236

## Model description

More information needed

## Intended uses & limitations

Since the dataset is generated from the DSPFirst textbook, its quality is not guaranteed.

## Training and evaluation data

More information needed

## Training procedure

### 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: 4

### Model hyperparameters
- hidden_dropout_prob: 0.5
- attention_probs_dropout_prob = 0.5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.0131        | 0.7   | 20   | 0.9549          |
| 6.1542        | 1.42  | 40   | 0.9302          |
| 6.1472        | 2.14  | 60   | 0.9249          |
| 5.9662        | 2.84  | 80   | 0.9248          |
| 6.1467        | 3.56  | 100  | 0.9236          |


### Framework versions

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