---
license: apache-2.0
base_model: google/electra-base-generator
tags:
- generated_from_trainer
datasets:
- datasets/all_binary_and_xe_ey_fae_counterfactual
metrics:
- accuracy
model-index:
- name: electra-adapter-finetuned-xe_ey_fae
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: datasets/all_binary_and_xe_ey_fae_counterfactual
      type: datasets/all_binary_and_xe_ey_fae_counterfactual
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6258363412553052
---

<!-- 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. -->

# electra-adapter-finetuned-xe_ey_fae

This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0392
- Accuracy: 0.6258

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.9488        | 0.06  | 500   | 3.1500          | 0.5509   |
| 2.942         | 0.13  | 1000  | 2.5844          | 0.5680   |
| 2.6751        | 0.19  | 1500  | 2.4443          | 0.5790   |
| 2.582         | 0.26  | 2000  | 2.3701          | 0.5869   |
| 2.5267        | 0.32  | 2500  | 2.3097          | 0.5937   |
| 2.4722        | 0.39  | 3000  | 2.2695          | 0.5986   |
| 2.4289        | 0.45  | 3500  | 2.2329          | 0.6024   |
| 2.404         | 0.52  | 4000  | 2.2063          | 0.6055   |
| 2.3826        | 0.58  | 4500  | 2.1840          | 0.6087   |
| 2.3633        | 0.64  | 5000  | 2.1646          | 0.6109   |
| 2.3425        | 0.71  | 5500  | 2.1557          | 0.6121   |
| 2.333         | 0.77  | 6000  | 2.1350          | 0.6141   |
| 2.311         | 0.84  | 6500  | 2.1292          | 0.6152   |
| 2.3014        | 0.9   | 7000  | 2.1182          | 0.6166   |
| 2.2974        | 0.97  | 7500  | 2.1121          | 0.6170   |
| 2.2866        | 1.03  | 8000  | 2.1079          | 0.6173   |
| 2.2675        | 1.1   | 8500  | 2.0940          | 0.6192   |
| 2.2789        | 1.16  | 9000  | 2.0882          | 0.6201   |
| 2.2684        | 1.22  | 9500  | 2.0873          | 0.6200   |
| 2.2608        | 1.29  | 10000 | 2.0796          | 0.6209   |
| 2.2478        | 1.35  | 10500 | 2.0827          | 0.6204   |
| 2.2524        | 1.42  | 11000 | 2.0741          | 0.6215   |
| 2.2502        | 1.48  | 11500 | 2.0685          | 0.6220   |
| 2.243         | 1.55  | 12000 | 2.0665          | 0.6228   |
| 2.2417        | 1.61  | 12500 | 2.0632          | 0.6229   |
| 2.2398        | 1.68  | 13000 | 2.0593          | 0.6232   |
| 2.2233        | 1.74  | 13500 | 2.0600          | 0.6232   |
| 2.2277        | 1.8   | 14000 | 2.0535          | 0.6236   |
| 2.2344        | 1.87  | 14500 | 2.0485          | 0.6248   |
| 2.2274        | 1.93  | 15000 | 2.0507          | 0.6245   |
| 2.2212        | 2.0   | 15500 | 2.0428          | 0.6256   |
| 2.214         | 2.06  | 16000 | 2.0464          | 0.6244   |
| 2.2104        | 2.13  | 16500 | 2.0477          | 0.6250   |
| 2.2185        | 2.19  | 17000 | 2.0397          | 0.6257   |
| 2.2157        | 2.26  | 17500 | 2.0419          | 0.6257   |
| 2.2128        | 2.32  | 18000 | 2.0439          | 0.6255   |
| 2.2154        | 2.38  | 18500 | 2.0372          | 0.6259   |
| 2.2099        | 2.45  | 19000 | 2.0337          | 0.6263   |
| 2.2045        | 2.51  | 19500 | 2.0396          | 0.6259   |
| 2.2138        | 2.58  | 20000 | 2.0390          | 0.6262   |
| 2.2103        | 2.64  | 20500 | 2.0339          | 0.6263   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2