mistral_sparse_80_percent_boolq_1000

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

• Loss: 0.3381
• Accuracy: 0.8664

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: 1e-05
• train_batch_size: 2
• eval_batch_size: 4
• seed: 2
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• total_eval_batch_size: 8
• 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
0.4991	0.05	50	0.5522	0.7216
0.3812	0.1	100	0.4342	0.8141
0.369	0.15	150	0.4112	0.8170
0.4132	0.2	200	0.4139	0.8382
0.4219	0.25	250	0.3940	0.8339
0.4144	0.3	300	0.3803	0.8481
0.1534	0.35	350	0.3786	0.8516
0.4855	0.4	400	0.3821	0.8502
0.2109	0.45	450	0.3583	0.8516
0.3026	0.5	500	0.3675	0.8558
0.2903	0.55	550	0.3744	0.8537
0.2988	0.6	600	0.3573	0.8587
0.3432	0.65	650	0.3396	0.8657
0.3156	0.7	700	0.3299	0.8671
0.4978	0.75	750	0.3623	0.8657
0.4523	0.8	800	0.3240	0.8700
0.2367	0.85	850	0.3393	0.8678
0.3334	0.9	900	0.3252	0.8834
0.3286	0.95	950	0.3605	0.8742
0.1659	1.0	1000	0.3269	0.8742
0.2373	1.05	1050	0.3256	0.8792
0.5102	1.1	1100	0.3633	0.8749

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0