---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: fnet-base-finetuned-qqp
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE QQP
      type: glue
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8847390551570616
    - name: F1
      type: f1
      value: 0.8466197090382463
---

<!-- 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. -->

# fnet-base-finetuned-qqp

This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3686
- Accuracy: 0.8847
- F1: 0.8466
- Combined Score: 0.8657

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:

```bash
#!/usr/bin/bash


#!/usr/bin/bash


python ../run_glue.py \
  --model_name_or_path google/fnet-base \
  --task_name qqp \
  --do_train \
  --do_eval \
  --max_seq_length 512 \
  --per_device_train_batch_size 16 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir fnet-base-finetuned-qqp \
  --push_to_hub \
  --hub_strategy all_checkpoints \
  --logging_strategy epoch \
  --save_strategy epoch \
  --evaluation_strategy epoch \
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3484        | 1.0   | 22741 | 0.3014          | 0.8676   | 0.8297 | 0.8487         |
| 0.2387        | 2.0   | 45482 | 0.3011          | 0.8801   | 0.8429 | 0.8615         |
| 0.1739        | 3.0   | 68223 | 0.3686          | 0.8847   | 0.8466 | 0.8657         |


### Framework versions

- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3