---
license: apache-2.0
base_model: google/fnet-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NLPGroupProject-Finetune-FNet
  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. -->

# NLPGroupProject-Finetune-FNet

This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1397
- Accuracy: 0.661

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2489        | 0.25  | 500  | 1.1166          | 0.601    |
| 1.0966        | 0.5   | 1000 | 0.9805          | 0.6      |
| 1.0379        | 0.75  | 1500 | 1.0110          | 0.624    |
| 0.9708        | 1.0   | 2000 | 0.9348          | 0.633    |
| 0.891         | 1.25  | 2500 | 1.1107          | 0.622    |
| 0.948         | 1.5   | 3000 | 1.0165          | 0.656    |
| 0.9148        | 1.75  | 3500 | 1.0472          | 0.655    |
| 0.969         | 2.0   | 4000 | 1.0291          | 0.65     |
| 0.855         | 2.25  | 4500 | 1.2743          | 0.635    |
| 0.8445        | 2.5   | 5000 | 1.1520          | 0.655    |
| 0.8057        | 2.75  | 5500 | 1.1107          | 0.662    |
| 0.7467        | 3.0   | 6000 | 1.1397          | 0.661    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1