| | --- |
| | 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 |
| |
|
