BERT_BIOMAT_NER3600_ST_DA – AI Model by judithrosell | AlphaNeural AI

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judithrosell/
BERT_BIOMAT_NER3600_ST_DA

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google-bert/bert-base-uncased

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BERT_BIOMAT_NER3600_ST_DA

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

Loss: 0.3385
Precision: 0.9673
Recall: 0.9631
F1: 0.9652
Accuracy: 0.9636

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
0.2217	1.0	601	0.2356	0.9543	0.9508	0.9525	0.9472
0.0692	2.0	1202	0.2555	0.9571	0.9529	0.9550	0.9525
0.0406	3.0	1803	0.2416	0.9626	0.9584	0.9605	0.9584
0.0257	4.0	2404	0.2984	0.9644	0.9594	0.9619	0.9598
0.011	5.0	3005	0.2851	0.9663	0.9622	0.9643	0.9627
0.0079	6.0	3606	0.3022	0.9665	0.9622	0.9644	0.9627
0.0056	7.0	4207	0.3214	0.9668	0.9619	0.9644	0.9625
0.0043	8.0	4808	0.3227	0.9673	0.9631	0.9652	0.9636
0.0036	9.0	5409	0.3405	0.9672	0.9629	0.9650	0.9634
0.0025	10.0	6010	0.3385	0.9673	0.9631	0.9652	0.9636

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1