tiny-bert-finetuned-cuad

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the portion of cuad dataset. It achieves the following results on the evaluation set:

• Loss: 0.4606

Note

The model was not trained on the whole dataset but, the first 10% of train + the first 10% of test.

raw_datasets_train, raw_datasets_test = load_dataset("cuad", split=['train[:10%]', 'test[:10%]']) 
datasets = DatasetDict({'train': raw_datasets_train, 'validation': raw_datasets_test})

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: 1024
• eval_batch_size: 1024
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.21.0
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1