Vandita/EmoCentricSarcBERT27FebRstate1kAvgPadding

EmoCentricSarcBERT27FebRstate1000AvgPadding

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7978
• Accuracy: 0.8800
• Precision: 0.8571
• Recall: 0.8306
• F1: 0.8436
• Mcc: 0.7466
• Roc Auc: 0.9506

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: 32
• eval_batch_size: 32
• seed: 42
• distributed_type: tpu
• optimizer: Use OptimizerNames.ADAMW_TORCH_XLA with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Mcc	Roc Auc
0.3887	1.0	735	0.2875	0.8690	0.8458	0.8118	0.8284	0.7229	0.9441
0.2685	2.0	1470	0.2775	0.8749	0.8385	0.8410	0.8398	0.7372	0.9534
0.1384	3.0	2205	0.3958	0.8816	0.8683	0.8205	0.8437	0.7493	0.9546
0.0982	4.0	2940	0.5692	0.8799	0.8670	0.8170	0.8413	0.7456	0.9496
0.0487	5.0	3675	0.7054	0.8758	0.8858	0.7821	0.8307	0.7367	0.9465
0.0427	6.0	4410	0.7538	0.8787	0.8448	0.8437	0.8442	0.7448	0.9487
0.0275	7.0	5145	0.7978	0.8800	0.8571	0.8306	0.8436	0.7466	0.9506

Framework versions

• Transformers 5.0.0
• Pytorch 2.9.0+cpu
• Datasets 4.5.0
• Tokenizers 0.22.2