bert-biocause-trainer-oversample – AI Model by alenatz | AlphaNeural AI

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alenatz/
bert-biocause-trainer-oversample

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text-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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bert-biocause-trainer-oversample

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

Loss: 0.4503
Accuracy: 0.8199
F1: 0.6028
Recall: 0.5346
Precision: 0.6911

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: 5e-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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.5982	0.07	25	0.5728	0.7637	0.1503	0.0818	0.9286
0.6258	0.14	50	0.6959	0.5482	0.5027	0.8931	0.3498
0.5442	0.22	75	0.5258	0.7749	0.5270	0.4906	0.5693
0.5752	0.29	100	0.4511	0.7878	0.4590	0.3522	0.6588
0.5428	0.36	125	0.4674	0.8071	0.5238	0.4151	0.7097
0.531	0.43	150	0.5982	0.6511	0.5562	0.8553	0.4121
0.4607	0.5	175	0.4654	0.8151	0.5344	0.4151	0.75
0.4932	0.58	200	0.4532	0.8135	0.5167	0.3899	0.7654
0.393	0.65	225	0.4812	0.7797	0.6226	0.7107	0.5539
0.427	0.72	250	0.4590	0.8151	0.6440	0.6541	0.6341
0.4661	0.79	275	0.4516	0.8312	0.6688	0.6667	0.6709
0.3976	0.86	300	0.4505	0.8232	0.6207	0.5660	0.6870
0.4464	0.94	325	0.4450	0.8199	0.6028	0.5346	0.6911

Framework versions

Transformers 4.37.2
Pytorch 2.3.1
Datasets 2.19.1
Tokenizers 0.15.1