metadata

license: apache-2.0
base_model: t5-large
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: t5-large_cola_sp0_ar0_one
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: cola
          split: validation
          args: cola
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.87890625

t5-large_cola_sp0_ar0_one

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

Loss: 0.4212
Accuracy: 0.8789

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: 16
eval_batch_size: 32
seed: 1
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20
training_steps: 0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6975	0.05	25	0.6708	0.6913
0.5747	0.11	50	0.5123	0.7210
0.4924	0.16	75	0.5004	0.7939
0.4259	0.21	100	0.4760	0.7987
0.3834	0.27	125	0.5001	0.8111
0.3942	0.32	150	0.4982	0.8092
0.4213	0.37	175	0.5078	0.8150
0.3845	0.42	200	0.4346	0.8092
0.4145	0.48	225	0.4562	0.8150
0.3751	0.53	250	0.4948	0.8169
0.4134	0.58	275	0.4356	0.8236
0.3777	0.64	300	0.4627	0.8188
0.3815	0.69	325	0.4772	0.8226
0.367	0.74	350	0.4117	0.8313
0.342	0.8	375	0.4177	0.8351
0.3136	0.85	400	0.5026	0.8265
0.3222	0.9	425	0.5323	0.8303
0.3863	0.96	450	0.4937	0.8245
0.348	1.01	475	0.4704	0.8188
0.2134	1.06	500	0.6430	0.8207
0.2671	1.11	525	0.5518	0.8226
0.1892	1.17	550	0.5869	0.8370
0.2184	1.22	575	0.5816	0.8332
0.22	1.27	600	0.5451	0.8274
0.1982	1.33	625	0.7300	0.8313
0.2734	1.38	650	0.7040	0.8351
0.2186	1.43	675	0.6650	0.8341
0.2835	1.49	700	0.6628	0.8322
0.2503	1.54	725	0.5194	0.8341
0.2438	1.59	750	0.5362	0.8313
0.2307	1.65	775	0.5405	0.8293
0.2111	1.7	800	0.6129	0.8265
0.1952	1.75	825	0.6411	0.8255
0.2873	1.8	850	0.6279	0.8245
0.295	1.86	875	0.5938	0.8236
0.2967	1.91	900	0.5694	0.8265
0.2128	1.96	925	0.5576	0.8265

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.11.6