transformers pytorch tensorboard fnet text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: fnet-large-finetuned-sst2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE SST2
      type: glue
      args: sst2
    metrics:
    - type: accuracy
      value: 0.9048165137614679
      name: Accuracy
---

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# fnet-large-finetuned-sst2

This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5240
- Accuracy: 0.9048

## 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: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.394         | 1.0   | 16838 | 0.3896          | 0.8968   |
| 0.2076        | 2.0   | 33676 | 0.5100          | 0.8956   |
| 0.1148        | 3.0   | 50514 | 0.5240          | 0.9048   |


### Framework versions

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