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tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: DSPFirst-Finetuning-3
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-3
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.
It achieves the following results on the evaluation set:
- Loss: 0.9996
- Exact: 63.9193
- F1: 72.1090
# 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
})
})
```
## Model description
More information needed
## Intended uses & limitations
More information needed
## 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: 3
### Model hyperparameters
- hidden_dropout_prob: 0.35
- attention_probs_dropout_prob = 0.35
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.3511 | 0.99 | 28 | 1.1388 | 62.9941 | 71.4102 |
| 1.0052 | 1.99 | 56 | 1.0255 | 65.0126 | 73.0388 |
| 0.8699 | 2.99 | 84 | 0.9996 | 63.9193 | 72.1090 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|