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

Loss: 0.1994
Precision: 0.4726
Recall: 0.5278
F1: 0.4987
Accuracy: 0.9007

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 18

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	219	0.2462	0.3017	0.3623	0.3292	0.8584
No log	2.0	438	0.2436	0.3176	0.3485	0.3323	0.8656
0.2463	3.0	657	0.2434	0.3333	0.4792	0.3932	0.8622
0.2463	4.0	876	0.2402	0.3398	0.3567	0.3480	0.8675
0.2453	5.0	1095	0.2388	0.3299	0.3708	0.3491	0.8686
0.2453	6.0	1314	0.2381	0.3230	0.3740	0.3467	0.8689
0.2421	7.0	1533	0.2384	0.3448	0.3508	0.3477	0.8691
0.2421	8.0	1752	0.2343	0.3427	0.3711	0.3563	0.8705
0.2421	9.0	1971	0.2334	0.3448	0.3433	0.3440	0.8713
0.2388	10.0	2190	0.2314	0.3696	0.4533	0.4072	0.8768
0.2388	11.0	2409	0.2238	0.3846	0.4643	0.4207	0.8812
0.2337	12.0	2628	0.2216	0.3968	0.4703	0.4305	0.8832
0.2337	13.0	2847	0.2135	0.4169	0.4939	0.4521	0.8898
0.2268	14.0	3066	0.2117	0.4387	0.5200	0.4759	0.8919
0.2268	15.0	3285	0.2059	0.4565	0.5146	0.4838	0.8963
0.2197	16.0	3504	0.2043	0.4669	0.5359	0.4990	0.8977
0.2197	17.0	3723	0.2005	0.4701	0.5356	0.5007	0.8997
0.2197	18.0	3942	0.1994	0.4726	0.5278	0.4987	0.9007

Framework versions

Transformers 4.40.2
Pytorch 2.0.1+cu117
Datasets 2.19.1
Tokenizers 0.19.1

casual/nlp_til

nlp_til

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

casual/
nlp_til