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# DSPFirst-Finetuning-2

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: 0.8057
- Exact: 65.9378
- F1: 72.3603

# 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: 0.3
- attention_probs_dropout_prob = 0.3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact   | F1      |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.8393        | 0.98  | 28   | 0.8157          | 66.1060 | 73.0203 |
| 0.7504        | 1.98  | 56   | 0.7918          | 66.3583 | 72.4657 |
| 0.691         | 2.98  | 84   | 0.8057          | 65.9378 | 72.3603 |


### Framework versions

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

{"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "DSPFirst-Finetuning-2", "results": []}]}
transformers pytorch electra question-answering generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us 

DSPFirst-Finetuning-2
=====================


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.
It achieves the following results on the evaluation set:


* Loss: 0.8057
* Exact: 65.9378
* F1: 72.3603


Dataset
=======


A visualization of the dataset can be found here. The split between train and test is 80% and 20% respectively.


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: 0.3
* attention\_probs\_dropout\_prob = 0.3


### Training results



### Framework versions


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



### 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: 0.3
* attention\_probs\_dropout\_prob = 0.3 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us 
 ### 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: 0.3
* attention\_probs\_dropout\_prob = 0.3 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us 
### 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: 0.3
* attention\_probs\_dropout\_prob = 0.3### Training results### Framework versions


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