muhtasham/bert-tiny-finetuned-cuad

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

Training Loss	Epoch	Step	Validation Loss
No log	1.0	136	2.9644
No log	2.0	272	1.9337
No log	3.0	408	1.4375
2.7124	4.0	544	1.0978
2.7124	5.0	680	0.8571
2.7124	6.0	816	0.6907
2.7124	7.0	952	0.5799
0.9512	8.0	1088	0.5105
0.9512	9.0	1224	0.4726
0.9512	10.0	1360	0.4606

Framework versions

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