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---
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
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