---
license: apache-2.0
base_model: google/electra-base-discriminator
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: electra-base-multiple-choice-fp16
  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. -->

# electra-base-multiple-choice-fp16

This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2735
- Accuracy: 0.8977
- Precision: 0.8961
- Recall: 0.8997
- F1: 0.8979

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 269  | 0.2713          | 0.8845   | 0.8729    | 0.9    | 0.8863 |
| 0.3422        | 2.0   | 538  | 0.2594          | 0.8964   | 0.9017    | 0.8898 | 0.8957 |
| 0.3422        | 3.0   | 807  | 0.2735          | 0.8977   | 0.8961    | 0.8997 | 0.8979 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0