metadata

license: apache-2.0
base_model: t5-base
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: t5-base_sst2_dense_epochs-8
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: glue
          type: glue
          config: sst2
          split: validation
          args: sst2
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9231651376146789

t5-base_sst2_dense_epochs-8

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

Loss: 0.2179
Accuracy: 0.9232

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: 32
eval_batch_size: 64
seed: 0
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
0.6384	0.02	50	0.6360	0.7064
0.3416	0.05	100	0.2955	0.8922
0.29	0.07	150	0.2512	0.9094
0.2371	0.1	200	0.2511	0.9106
0.2059	0.12	250	0.2379	0.9174
0.2617	0.14	300	0.2299	0.9174
0.2266	0.17	350	0.2190	0.9243
0.2288	0.19	400	0.2292	0.9255
0.2385	0.21	450	0.2263	0.9232
0.161	0.24	500	0.2368	0.9243
0.158	0.26	550	0.2411	0.9174
0.2469	0.29	600	0.2381	0.9209
0.2417	0.31	650	0.2349	0.9163
0.1614	0.33	700	0.2251	0.9174
0.2764	0.36	750	0.2129	0.9266
0.1499	0.38	800	0.2248	0.9197
0.1376	0.4	850	0.2285	0.9232
0.1875	0.43	900	0.2324	0.9312
0.1819	0.45	950	0.2302	0.9220
0.2373	0.48	1000	0.2179	0.9232
0.0956	0.5	1050	0.2077	0.9278
0.2396	0.52	1100	0.3249	0.9266
0.2543	0.55	1150	0.4440	0.9243
0.0942	0.57	1200	0.1982	0.9312
0.1296	0.59	1250	0.4270	0.9335
0.1618	0.62	1300	0.1893	0.9392
0.1902	0.64	1350	0.1911	0.9381
0.1234	0.67	1400	0.1903	0.9346
0.1369	0.69	1450	0.4157	0.9335
0.1149	0.71	1500	0.4121	0.9323
0.1501	0.74	1550	0.6343	0.9358
0.1679	0.76	1600	0.5294	0.9323
0.1462	0.78	1650	0.4037	0.9392
0.2111	0.81	1700	0.4094	0.9323
0.0902	0.83	1750	0.4094	0.9346
0.1185	0.86	1800	0.4059	0.9323
0.1602	0.88	1850	0.2946	0.9323
0.1212	0.9	1900	0.3037	0.9312

Framework versions

Transformers 4.34.1
Pytorch 2.0.1+cu117
Datasets 2.9.0
Tokenizers 0.14.1