Bert Base Cased Finetuned Cola

🚀 bert-base-cased-finetuned-cola

This model is a fine - tuned variant of [bert - base - cased](https://huggingface.co/bert - base - cased) on the GLUE COLA dataset. It offers a comparison between [google/fnet - base](https://huggingface.co/google/fnet - base) and [bert - base - cased](https://huggingface.co/bert - base - cased), aiming to evaluate their performance on text classification tasks. The model achieves notable results on the evaluation set, such as a Matthews Correlation of 0.5957.

🚀 Quick Start

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 for training:

#!/usr/bin/bash

python ../run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name cola \
  --do_train \
  --do_eval \
  --max_seq_length 512 \
  --per_device_train_batch_size 16 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir bert-base-cased-finetuned-cola \
  --push_to_hub \
  --hub_strategy all_checkpoints \
  --logging_strategy epoch \
  --save_strategy epoch \
  --evaluation_strategy epoch

✨ Features

• This model is a fine - tuned version of [bert - base - cased](https://huggingface.co/bert - base - cased) on the GLUE COLA dataset.
• It achieves specific results on the evaluation set, including a Loss of 0.6747 and a Matthews Correlation of 0.5957.
• The model was fine - tuned to compare [google/fnet - base](https://huggingface.co/google/fnet - base) against [bert - base - cased](https://huggingface.co/bert - base - cased).

📦 Installation

No specific installation steps other than the training script are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Model Information

Evaluation Results

This model achieves the following results on the evaluation set:

• Loss: 0.6747
• Matthews Correlation: 0.5957

Training Procedure

Training Command

#!/usr/bin/bash

python ../run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name cola \
  --do_train \
  --do_eval \
  --max_seq_length 512 \
  --per_device_train_batch_size 16 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir bert-base-cased-finetuned-cola \
  --push_to_hub \
  --hub_strategy all_checkpoints \
  --logging_strategy epoch \
  --save_strategy epoch \
  --evaluation_strategy epoch

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

🔧 Technical Details

The model is fine - tuned on the GLUE COLA dataset to compare the performance of [google/fnet - base](https://huggingface.co/google/fnet - base) and [bert - base - cased](https://huggingface.co/bert - base - cased). It uses the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text - classification/run_glue.py) script for training with specific hyperparameters and achieves certain evaluation results.

📄 License

This model is licensed under the Apache - 2.0 license.