distilbert_rand_100_v2_stsb

This model is a fine-tuned version of Hartunka/distilbert_rand_100_v2 on the GLUE STSB dataset. It achieves the following results on the evaluation set:

• Loss: 2.3172
• Pearson: 0.2906
• Spearmanr: 0.2841
• Combined Score: 0.2874

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: 256
• eval_batch_size: 256
• seed: 10
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Pearson	Spearmanr	Combined Score
2.7709	1.0	23	2.5323	0.1404	0.1165	0.1284
1.9319	2.0	46	2.3231	0.1868	0.1617	0.1742
1.6901	3.0	69	2.5205	0.2196	0.2097	0.2146
1.4033	4.0	92	2.3172	0.2906	0.2841	0.2874
1.0661	5.0	115	2.4881	0.2764	0.2746	0.2755
0.8233	6.0	138	2.4506	0.3105	0.3055	0.3080
0.6527	7.0	161	2.6287	0.3095	0.3037	0.3066
0.5076	8.0	184	2.6804	0.3066	0.2993	0.3029
0.4436	9.0	207	2.5319	0.3202	0.3142	0.3172

Framework versions

• Transformers 4.50.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.21.1