metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
base_model: roberta-base
model-index:
  - name: run-1
    results: []

run-1

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

Loss: 2.3480
Accuracy: 0.73
Precision: 0.6930
Recall: 0.6829
F1: 0.6871

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
1.0042	1.0	50	0.8281	0.665	0.6105	0.6240	0.6016
0.8062	2.0	100	0.9313	0.665	0.6513	0.6069	0.5505
0.627	3.0	150	0.8275	0.72	0.6713	0.6598	0.6638
0.4692	4.0	200	0.8289	0.68	0.6368	0.6447	0.6398
0.2766	5.0	250	1.1263	0.72	0.6893	0.6431	0.6417
0.1868	6.0	300	1.2901	0.725	0.6823	0.6727	0.6764
0.1054	7.0	350	1.6742	0.68	0.6696	0.6427	0.6384
0.0837	8.0	400	1.6199	0.72	0.6826	0.6735	0.6772
0.0451	9.0	450	1.8324	0.735	0.7029	0.6726	0.6727
0.0532	10.0	500	2.1136	0.705	0.6949	0.6725	0.6671
0.0178	11.0	550	2.1136	0.73	0.6931	0.6810	0.6832
0.0111	12.0	600	2.2740	0.69	0.6505	0.6430	0.6461
0.0205	13.0	650	2.3026	0.725	0.6965	0.6685	0.6716
0.0181	14.0	700	2.2901	0.735	0.7045	0.6806	0.6876
0.0074	15.0	750	2.2277	0.74	0.7075	0.6923	0.6978
0.0063	16.0	800	2.2720	0.75	0.7229	0.7051	0.7105
0.0156	17.0	850	2.1237	0.73	0.6908	0.6841	0.6854
0.0027	18.0	900	2.2376	0.73	0.6936	0.6837	0.6874
0.003	19.0	950	2.3359	0.735	0.6992	0.6897	0.6937
0.0012	20.0	1000	2.3480	0.73	0.6930	0.6829	0.6871

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu116
Tokenizers 0.13.2