frame_classification_bigbird

This model is a fine-tuned version of google/bigbird-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.8803
• Accuracy: 0.8991
• F1: 0.9396
• Precision: 0.9353
• Recall: 0.9440

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: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
0.6673	1.0	1288	0.9270	0.9570	0.4955	0.9390	0.9757
0.5913	2.0	2576	0.9099	0.9477	0.6212	0.9178	0.9795
0.5858	3.0	3864	0.9270	0.9572	0.4327	0.9343	0.9813
0.5384	4.0	5152	0.9317	0.9599	0.4998	0.9377	0.9832
0.6131	5.0	6440	0.9255	0.9561	0.5642	0.9373	0.9757
0.5834	6.0	7728	0.9239	0.9553	0.6238	0.9340	0.9776
0.5023	7.0	9016	0.9208	0.9533	0.7194	0.9354	0.9720
0.5271	8.0	10304	0.9177	0.9516	0.7188	0.9320	0.9720
0.4755	9.0	11592	0.8618	0.9177	0.9514	0.9351	0.9683
0.4173	10.0	12880	0.8803	0.8991	0.9396	0.9353	0.9440

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2