CeLLaTe3.0_with_vague_pubmed_bert_with_gaz

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

Model description

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 3407
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7289	1.0	403	0.2346	0.4923	0.6770	0.5700	0.9374
0.1461	2.0	806	0.1511	0.7226	0.7618	0.7417	0.9591
0.0786	3.0	1209	0.1685	0.7478	0.7896	0.7681	0.9606
0.0444	4.0	1612	0.1856	0.7923	0.8182	0.8050	0.9641
0.028	5.0	2015	0.2203	0.7529	0.7830	0.7677	0.9604
0.017	6.0	2418	0.2399	0.7545	0.7962	0.7748	0.9620
0.0118	7.0	2821	0.2602	0.7611	0.7921	0.7763	0.9617
0.0087	8.0	3224	0.2681	0.7722	0.8182	0.7945	0.9621
0.0062	9.0	3627	0.2789	0.7820	0.8296	0.8051	0.9641
0.0047	10.0	4030	0.2880	0.7632	0.8272	0.7939	0.9622
0.0034	11.0	4433	0.3139	0.7468	0.7639	0.7552	0.9597
0.003	12.0	4836	0.2834	0.7907	0.8418	0.8154	0.9648
0.002	13.0	5239	0.2971	0.7816	0.8213	0.8009	0.9636
0.0017	14.0	5642	0.3275	0.7582	0.7761	0.7670	0.9607
0.0017	15.0	6045	0.3233	0.7761	0.8161	0.7956	0.9622
0.0014	16.0	6448	0.3258	0.7696	0.7955	0.7824	0.9619
0.001	17.0	6851	0.3229	0.7859	0.8310	0.8078	0.9636
0.0007	18.0	7254	0.3228	0.7930	0.8248	0.8086	0.9634
0.0007	19.0	7657	0.3268	0.7887	0.8150	0.8016	0.9630
0.0005	20.0	8060	0.3315	0.7828	0.8157	0.7989	0.9626

Safetensors

Model size

0.1B params

Tensor type

F32

Base model

Finetuned

(147)

this model