metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- fnet-bert-base-comparison
datasets:
- glue
metrics:
- accuracy
model-index:
- name: fnet-base-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5492957746478874
fnet-base-finetuned-wnli
This model is a fine-tuned version of google/fnet-base on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6887
- Accuracy: 0.5493
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
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 script. The following command was used:
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name wnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-wnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7052 | 1.0 | 40 | 0.6902 | 0.5634 |
| 0.6957 | 2.0 | 80 | 0.7013 | 0.4366 |
| 0.6898 | 3.0 | 120 | 0.6898 | 0.5352 |
| 0.6958 | 4.0 | 160 | 0.6874 | 0.5634 |
| 0.6982 | 5.0 | 200 | 0.6887 | 0.5493 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3