metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - stereoset
metrics:
  - accuracy
model-index:
  - name: t5-small_stereoset_finetuned_HBRPOI
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: stereoset
          type: stereoset
          config: intersentence
          split: validation
          args: intersentence
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6028257456828885

t5-small_stereoset_finetuned_HBRPOI

This model is a fine-tuned version of t5-small on the stereoset dataset. It achieves the following results on the evaluation set:

Loss: 0.4383
Accuracy: 0.6028
Tp: 0.4890
Tn: 0.1138
Fp: 0.3854
Fn: 0.0118

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: 0.0005
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Tp	Tn	Fp	Fn
0.4447	0.43	20	0.3978	0.5008	0.5008	0.0	0.4992	0.0
0.3776	0.85	40	0.3448	0.6232	0.5008	0.1224	0.3768	0.0
0.3649	1.28	60	0.3269	0.5612	0.5	0.0612	0.4380	0.0008
0.3275	1.7	80	0.3218	0.5330	0.4992	0.0338	0.4655	0.0016
0.2969	2.13	100	0.3104	0.6256	0.4961	0.1295	0.3697	0.0047
0.3283	2.55	120	0.3111	0.5730	0.4992	0.0738	0.4254	0.0016
0.3046	2.98	140	0.3040	0.5416	0.4992	0.0424	0.4568	0.0016
0.2603	3.4	160	0.3057	0.5447	0.4992	0.0455	0.4537	0.0016
0.2828	3.83	180	0.3186	0.5479	0.4984	0.0495	0.4498	0.0024
0.2326	4.26	200	0.3036	0.6193	0.4937	0.1256	0.3736	0.0071
0.2289	4.68	220	0.3328	0.5479	0.4976	0.0502	0.4490	0.0031
0.2234	5.11	240	0.3140	0.5777	0.4976	0.0801	0.4192	0.0031
0.2225	5.53	260	0.3245	0.5691	0.4976	0.0714	0.4278	0.0031
0.187	5.96	280	0.3300	0.5785	0.4961	0.0824	0.4168	0.0047
0.179	6.38	300	0.3344	0.5848	0.4961	0.0887	0.4105	0.0047
0.1523	6.81	320	0.3528	0.5895	0.4969	0.0926	0.4066	0.0039
0.1499	7.23	340	0.3788	0.6232	0.4906	0.1327	0.3666	0.0102
0.1292	7.66	360	0.3889	0.5942	0.4914	0.1028	0.3964	0.0094
0.13	8.09	380	0.3959	0.5903	0.4937	0.0965	0.4027	0.0071
0.1216	8.51	400	0.4169	0.5856	0.4922	0.0934	0.4058	0.0086
0.1306	8.94	420	0.4227	0.6005	0.4898	0.1107	0.3885	0.0110
0.0968	9.36	440	0.4334	0.5965	0.4914	0.1052	0.3940	0.0094
0.1044	9.79	460	0.4383	0.6028	0.4890	0.1138	0.3854	0.0118

Framework versions

Transformers 4.26.1
Pytorch 1.13.1
Datasets 2.10.1
Tokenizers 0.13.2