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Mardiyyah/CeLLaTe-tapt-pubmedbert-tokenizer-adapted_v2_lr-3e5

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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CeLLaTe-tapt-pubmedbert-tokenizer-adaptedv2lr-3e5

This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on the Mardiyyah/TAPT_CeLLaTe2.0 dataset. It achieves the following results on the evaluation set:

—Loss: 1.1493
—Accuracy: 0.7653
—Perplexity: 3.1561

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: 3e-05
—trainbatchsize: 32
—evalbatchsize: 32
—seed: 3407
—gradientaccumulationsteps: 2
—totaltrainbatch_size: 64
—optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizerargs=No additional optimizer arguments
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.06
—num_epochs: 100
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Perplexity
1.3584	1.0	14	1.2016	0.7595	3.3253
1.3699	2.0	28	1.1943	0.7615	3.3012
1.3374	3.0	42	1.1951	0.7587	3.3038
1.3226	4.0	56	1.2118	0.7565	3.3596
1.2976	5.0	70	1.2074	0.7574	3.3449
1.283	6.0	84	1.1959	0.7552	3.3066
1.2645	7.0	98	1.1472	0.7638	3.1494
1.2597	8.0	112	1.2038	0.7514	3.3326
1.2291	9.0	126	1.1464	0.7597	3.1469
1.2186	10.0	140	1.1744	0.7571	3.2362
1.2329	11.0	154	1.1545	0.7565	3.1724
1.1898	12.0	168	1.1718	0.7589	3.2278
1.1865	13.0	182	1.2333	0.7520	3.4325
1.1772	14.0	196	1.1856	0.7567	3.2727

Framework versions

—Transformers 4.48.2
—Pytorch 2.4.1+cu121
—Datasets 3.0.2
—Tokenizers 0.21.0