nucleotide-transformer-500m-human-ref_ft_BioS2_1kbpHG19_DHSs_H3K27AC

tanoManzo/nucleotide-transformer-500m-human-ref_ft_BioS2_1kbpHG19_DHSs_H3K27AC

nucleotide-transformer-500m-human-ref_ft_BioS2_1kbpHG19_DHSs_H3K27AC is a machine learning model from tanoManzo. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-sa-4.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

License: cc-by-nc-sa-4.0
Model type: esm
Access: Public
Created: Sep 24, 2024
Updated: Sep 24, 2024
SHA: 5967171a

Base models

InstaDeepAI/nucleotide-transformer-500m-human-ref

Model card

From the Hugging Face model README

nucleotide-transformer-500m-human-ref_ft_BioS2_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of InstaDeepAI/nucleotide-transformer-500m-human-ref on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4074
F1 Score: 0.8357
Precision: 0.8177
Recall: 0.8545
Accuracy: 0.8268
Auc: 0.8999
Prc: 0.8854

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Score	Precision	Recall	Accuracy	Auc	Prc
0.5531	0.0841	500	0.4955	0.7552	0.8200	0.6999	0.7661	0.8646	0.8453
0.4826	0.1681	1000	0.4657	0.7900	0.8043	0.7763	0.7873	0.8686	0.8463
0.469	0.2522	1500	0.4324	0.8114	0.8090	0.8138	0.8050	0.8829	0.8696
0.4508	0.3362	2000	0.4388	0.8072	0.8311	0.7847	0.8068	0.8906	0.8743
0.4562	0.4203	2500	0.4340	0.8304	0.7769	0.8917	0.8122	0.8902	0.8798
0.4393	0.5044	3000	0.4291	0.8330	0.7695	0.9080	0.8124	0.8946	0.8823
0.4368	0.5884	3500	0.4122	0.8382	0.7903	0.8924	0.8225	0.8971	0.8839
0.4354	0.6725	4000	0.4191	0.8359	0.7751	0.9070	0.8164	0.8953	0.8821
0.4346	0.7566	4500	0.4110	0.8326	0.8113	0.8552	0.8228	0.8991	0.8874
0.4298	0.8406	5000	0.4074	0.8357	0.8177	0.8545	0.8268	0.8999	0.8854

Framework versions

Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.18.0
Tokenizers 0.19.0

Type: esm
License: cc-by-nc-sa-4.0
Created: Sep 24, 2024
Updated: Sep 24, 2024