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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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datasets: |
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- glue |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: fnet-base-finetuned-mrpc |
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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 MRPC |
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type: glue |
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args: mrpc |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7720588235294118 |
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- name: F1 |
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type: f1 |
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value: 0.8502415458937198 |
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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-mrpc |
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This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MRPC dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9653 |
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- Accuracy: 0.7721 |
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- F1: 0.8502 |
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- Combined Score: 0.8112 |
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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 \ |
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--model_name_or_path google/fnet-base \ |
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--task_name mrpc \ |
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--do_train \ |
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--do_eval \ |
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--max_seq_length 512 \ |
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--per_device_train_batch_size 16 \ |
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--learning_rate 2e-5 \ |
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--num_train_epochs 5 \ |
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--output_dir fnet-base-finetuned-mrpc \ |
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--push_to_hub \ |
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--hub_strategy all_checkpoints \ |
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--logging_strategy epoch \ |
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--save_strategy epoch \ |
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--evaluation_strategy epoch \ |
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``` |
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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 | F1 | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| |
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| 0.544 | 1.0 | 230 | 0.5272 | 0.7328 | 0.8300 | 0.7814 | |
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| 0.4034 | 2.0 | 460 | 0.6211 | 0.7255 | 0.8298 | 0.7776 | |
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| 0.2602 | 3.0 | 690 | 0.9110 | 0.7230 | 0.8306 | 0.7768 | |
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| 0.1688 | 4.0 | 920 | 0.8640 | 0.7696 | 0.8489 | 0.8092 | |
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| 0.0913 | 5.0 | 1150 | 0.9653 | 0.7721 | 0.8502 | 0.8112 | |
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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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