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has-abi/
distilBERT-finetuned-resumes-sections

Name: distilBERT-finetuned-resumes-sections
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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distilBERT-finetuned-resumes-sections

This model is a fine-tuned version of Geotrend/distilbert-base-en-fr-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0369
F1: 0.9652
Roc Auc: 0.9808
Accuracy: 0.9621

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: 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

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.0509	1.0	1173	0.0331	0.9439	0.9659	0.9356
0.024	2.0	2346	0.0274	0.9550	0.9750	0.9493
0.0148	3.0	3519	0.0290	0.9493	0.9712	0.9446
0.0089	4.0	4692	0.0324	0.9492	0.9714	0.9442
0.0071	5.0	5865	0.0317	0.9540	0.9732	0.9476
0.0064	6.0	7038	0.0324	0.9527	0.9742	0.9484
0.0036	7.0	8211	0.0320	0.9574	0.9766	0.9540
0.0042	8.0	9384	0.0367	0.9528	0.9732	0.9493
0.0052	9.0	10557	0.0342	0.9563	0.9757	0.9531
0.0027	10.0	11730	0.0294	0.9629	0.9800	0.9595
0.0017	11.0	12903	0.0355	0.9605	0.9778	0.9582
0.0022	12.0	14076	0.0338	0.9627	0.9792	0.9591
0.0012	13.0	15249	0.0358	0.9609	0.9780	0.9591
0.0011	14.0	16422	0.0360	0.9618	0.9791	0.9604
0.0009	15.0	17595	0.0358	0.9648	0.9807	0.9625
0.0007	16.0	18768	0.0373	0.9627	0.9794	0.9595
0.0006	17.0	19941	0.0397	0.9597	0.9774	0.9574
0.0008	18.0	21114	0.0369	0.9652	0.9808	0.9621
0.0007	19.0	22287	0.0377	0.9646	0.9801	0.9621
0.0005	20.0	23460	0.0381	0.9639	0.9797	0.9616

Framework versions

Transformers 4.21.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1

has-abi/distilBERT-finetuned-resumes-sections

distilBERT-finetuned-resumes-sections

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

has-abi/
distilBERT-finetuned-resumes-sections