t5-base_cola_dense_mare_mlp_einsum

lukeleeai/t5-base_cola_dense_mare_mlp_einsum

t5-base_cola_dense_mare_mlp_einsum is a text classification model from lukeleeai. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Oct 18, 2023
Updated: Oct 18, 2023
SHA: eb886c32

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Base models

lukeleeai/t5-base_cola_densedense_baseline

Datasets

glue

Model card

From the Hugging Face model README

t5-base_cola_dense_mare_mlp_einsum

This model is a fine-tuned version of lukeleeai/t5-base_cola_densedense_baseline on the glue dataset. It achieves the following results on the evaluation set:

Loss: 0.7682
Accuracy: 0.7517

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: 8
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 2
total_train_batch_size: 32
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5856	0.19	50	0.6260	0.6913
0.5836	0.37	100	0.6029	0.6913
0.5724	0.56	150	0.6055	0.6932
0.6635	0.75	200	0.6171	0.6922
0.5634	0.93	250	0.6162	0.6999
0.5361	1.12	300	0.6142	0.6932
0.5426	1.31	350	0.5920	0.7057
0.6255	1.5	400	0.5884	0.7095
0.6312	1.68	450	0.5723	0.7095
0.5686	1.87	500	0.5894	0.7057
0.5486	2.06	550	0.5590	0.7124
0.4436	2.24	600	0.5838	0.7220
0.4405	2.43	650	0.6176	0.7315
0.4785	2.62	700	0.6236	0.7296
0.5759	2.8	750	0.6233	0.7191
0.6156	2.99	800	0.6807	0.7392
0.4843	3.18	850	0.6337	0.7373
0.5408	3.36	900	0.7107	0.7392
0.4327	3.55	950	0.6256	0.7239
0.4318	3.74	1000	0.6951	0.7478
0.4047	3.93	1050	0.6566	0.7430
0.423	4.11	1100	0.6731	0.7440
0.3919	4.3	1150	0.6750	0.7392
0.4041	4.49	1200	0.6464	0.7421
0.3941	4.67	1250	0.6580	0.7517
0.3834	4.86	1300	0.6257	0.7459
0.2678	5.05	1350	0.6464	0.7555
0.3202	5.23	1400	0.7048	0.7507
0.2869	5.42	1450	0.7405	0.7565
0.3359	5.61	1500	0.6393	0.7593
0.3528	5.79	1550	0.6249	0.7555
0.3304	5.98	1600	0.6349	0.7565
0.2862	6.17	1650	0.7497	0.7670
0.2315	6.36	1700	0.7787	0.7622
0.3251	6.54	1750	0.7038	0.7555
0.3584	6.73	1800	0.7732	0.7603
0.1804	6.92	1850	0.8226	0.7584
0.2264	7.1	1900	0.7420	0.7613
0.2374	7.29	1950	0.7825	0.7507
0.203	7.48	2000	0.7575	0.7641
0.238	7.66	2050	1.9945	0.7603
0.2328	7.85	2100	0.7682	0.7517

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.9.0
Tokenizers 0.11.6

Task: Text Classification
Library: transformers
Type: t5
License: apache-2.0
Created: Oct 18, 2023
Updated: Oct 18, 2023