Fnet Base Finetuned Cola

🚀 fnet-base-finetuned-cola

This model is a fine - tuned version of google/fnet-base on the GLUE COLA dataset, aiming to compare it with bert-base-cased.

🚀 Quick Start

This model is a fine-tuned version of google/fnet-base on the GLUE COLA dataset. It achieves the following results on the evaluation set:

• Loss: 0.5929
• Matthews Correlation: 0.3594

The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

📦 Installation

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 cola \\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 3 \\n  --output_dir fnet-base-finetuned-cola \\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: 3.0

Training results

Framework versions

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

📄 License

This project is licensed under the Apache-2.0 license.