ST_CEMS – AI Model by judithrosell | AlphaNeural AI

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judithrosell/
ST_CEMS

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token-classification

generated_from_trainer

m3rg-iitd/matscibert

autotrain_compatible

endpoints_compatible

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ST_CEMS

This model is a fine-tuned version of m3rg-iitd/matscibert on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0598
Precision: 0.9368
Recall: 0.9226
F1: 0.9296
Accuracy: 0.9898

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0492	1.0	569	0.0347	0.9181	0.9091	0.9136	0.9881
0.0177	2.0	1138	0.0331	0.9406	0.9177	0.9290	0.9905
0.0109	3.0	1707	0.0454	0.9116	0.9122	0.9119	0.9876
0.0066	4.0	2276	0.0454	0.9596	0.8970	0.9272	0.9896
0.0042	5.0	2845	0.0477	0.9352	0.9061	0.9204	0.9889
0.0027	6.0	3414	0.0525	0.9352	0.9146	0.9248	0.9896
0.0018	7.0	3983	0.0498	0.9405	0.9159	0.9280	0.9899
0.0008	8.0	4552	0.0555	0.9312	0.9238	0.9275	0.9896
0.0007	9.0	5121	0.0602	0.9406	0.9165	0.9284	0.9897
0.0006	10.0	5690	0.0598	0.9368	0.9226	0.9296	0.9898

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1