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tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: DSPFirst-Finetuning-4
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:
`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](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: 0.9028
- 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](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 14.82 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](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: 10
### Model hyperparameters
- hidden_dropout_prob: 0.36
- attention_probs_dropout_prob = 0.36
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 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
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|