metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
base_model: roberta-base
model-index:
  - name: run-4
    results: []

run-4

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

Loss: 2.6296
Accuracy: 0.685
Precision: 0.6248
Recall: 0.6164
F1: 0.6188

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: 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.0195	1.0	50	0.8393	0.615	0.4126	0.5619	0.4606
0.7594	2.0	100	0.7077	0.7	0.6896	0.6663	0.6178
0.5515	3.0	150	0.9342	0.68	0.6334	0.5989	0.6016
0.3739	4.0	200	0.7755	0.735	0.7032	0.7164	0.7063
0.2648	5.0	250	0.9200	0.7	0.6584	0.6677	0.6611
0.1726	6.0	300	1.1898	0.71	0.6653	0.6550	0.6570
0.1452	7.0	350	1.5086	0.73	0.6884	0.6768	0.6812
0.0856	8.0	400	2.6159	0.68	0.6754	0.5863	0.5951
0.1329	9.0	450	1.9491	0.71	0.6692	0.6442	0.6463
0.0322	10.0	500	1.7897	0.74	0.6977	0.6939	0.6946
0.0345	11.0	550	1.9100	0.725	0.6827	0.6853	0.6781
0.026	12.0	600	2.5041	0.68	0.6246	0.6115	0.6137
0.0084	13.0	650	2.5343	0.715	0.6708	0.6617	0.6637
0.0145	14.0	700	2.4112	0.715	0.6643	0.6595	0.6614
0.0119	15.0	750	2.5303	0.705	0.6479	0.6359	0.6390
0.0026	16.0	800	2.6299	0.705	0.6552	0.6447	0.6455
0.0077	17.0	850	2.4044	0.715	0.6667	0.6576	0.6596
0.0055	18.0	900	2.8077	0.68	0.6208	0.6065	0.6098
0.0078	19.0	950	2.5608	0.68	0.6200	0.6104	0.6129
0.0018	20.0	1000	2.6296	0.685	0.6248	0.6164	0.6188

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu116
Tokenizers 0.13.2