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fnet-large-finetuned-mrpc
gchhablani/fnet-large-finetuned-mrpc
fnet-large-finetuned-mrpc is a text classification model from gchhablani. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- fnet
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Sep 22, 2021
- SHA
- bc05e37c
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Datasets
Model card
From the Hugging Face model README
<!-- 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-large-finetuned-mrpc
This model is a fine-tuned version of google/fnet-large on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 1.0872
- Accuracy: 0.8260
- F1: 0.8799
- Combined Score: 0.8529
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: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.5656 | 1.0 | 917 | 0.6999 | 0.7843 | 0.8581 | 0.8212 |
| 0.3874 | 2.0 | 1834 | 0.7280 | 0.8088 | 0.8691 | 0.8390 |
| 0.1627 | 3.0 | 2751 | 1.1274 | 0.8162 | 0.8780 | 0.8471 |
| 0.0751 | 4.0 | 3668 | 1.0289 | 0.8333 | 0.8870 | 0.8602 |
| 0.0339 | 5.0 | 4585 | 1.0872 | 0.8260 | 0.8799 | 0.8529 |
Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
- Task
- Text Classification
- Library
- transformers
- Type
- fnet
- License
- apache-2.0
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Sep 22, 2021