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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- fnet-bert-base-comparison |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: fnet-base-finetuned-wnli |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE WNLI |
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type: glue |
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args: wnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5492957746478874 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# fnet-base-finetuned-wnli |
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This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE WNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6887 |
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- Accuracy: 0.5493 |
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The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: |
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```bash |
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#!/usr/bin/bash |
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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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.7052 | 1.0 | 40 | 0.6902 | 0.5634 | |
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| 0.6957 | 2.0 | 80 | 0.7013 | 0.4366 | |
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| 0.6898 | 3.0 | 120 | 0.6898 | 0.5352 | |
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| 0.6958 | 4.0 | 160 | 0.6874 | 0.5634 | |
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| 0.6982 | 5.0 | 200 | 0.6887 | 0.5493 | |
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### Framework versions |
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- Transformers 4.11.0.dev0 |
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- Pytorch 1.9.0 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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