relevant_profession – AI Model by ledigajobb | AlphaNeural AI

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relevant_profession

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relevant_profession

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

Loss: 0.8640
Acc At K: 0.9666
Acc: 0.7152

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Acc At K	Acc
3.6523	0.5	18304	2.1025	0.8154	0.5818
1.7365	1.0	36608	1.4014	0.9097	0.6590
1.2636	1.5	54912	1.1711	0.9350	0.6845
1.092	2.0	73216	1.0605	0.9473	0.6931
0.9662	2.5	91520	1.0046	0.9533	0.7012
0.9233	3.0	109824	0.9643	0.9573	0.7025
0.8521	3.5	128128	0.9436	0.9594	0.7060
0.8334	4.0	146432	0.9189	0.9616	0.7078
0.7845	4.5	164736	0.9082	0.9631	0.7091
0.7754	5.0	183040	0.8953	0.9639	0.7105
0.7355	5.5	201344	0.8907	0.9646	0.7108
0.7334	6.0	219648	0.8795	0.9649	0.7124
0.6991	6.5	237952	0.8772	0.9657	0.7132
0.7001	7.0	256256	0.8670	0.9662	0.7129
0.674	7.5	274560	0.8667	0.9664	0.7149
0.672	8.0	292864	0.8640	0.9666	0.7152

Framework versions

Transformers 4.26.1
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3