metadata

license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
  - generated_from_trainer
datasets:
  - generator
metrics:
  - accuracy
model-index:
  - name: salt_language_ID
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: generator
          type: generator
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9734543010752689

salt_language_ID

This model is a fine-tuned version of google/t5-efficient-tiny on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.0158
Accuracy: 0.9735

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: 0.001
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 20000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5256	0.025	500	0.1505	0.7698
0.0708	0.05	1000	0.0447	0.9237
0.0547	0.075	1500	0.0540	0.9093
0.0476	0.1	2000	0.0428	0.9264
0.0413	0.125	2500	0.0334	0.9399
0.0404	0.15	3000	0.0293	0.9479
0.0374	0.175	3500	0.0324	0.9459
0.0359	0.2	4000	0.0257	0.9493
0.0353	0.225	4500	0.0267	0.9513
0.0336	0.25	5000	0.0234	0.9587
0.0337	0.275	5500	0.0253	0.9560
0.0324	0.3	6000	0.0186	0.9684
0.0307	0.325	6500	0.0208	0.9634
0.028	0.35	7000	0.0253	0.9573
0.0297	0.375	7500	0.0224	0.9617
0.0264	0.4	8000	0.0224	0.9607
0.027	0.425	8500	0.0185	0.9667
0.0266	0.45	9000	0.0222	0.9634
0.0259	0.475	9500	0.0221	0.9617
0.0244	0.5	10000	0.0187	0.9688
0.0243	0.525	10500	0.0164	0.9694
0.0248	0.55	11000	0.0184	0.9674
0.024	0.575	11500	0.0155	0.9704
0.0228	0.6	12000	0.0176	0.9671
0.0241	0.625	12500	0.0146	0.9755
0.0234	0.65	13000	0.0181	0.9681
0.0226	0.675	13500	0.0142	0.9758
0.0225	0.7	14000	0.0169	0.9718
0.0218	0.725	14500	0.0151	0.9711
0.0212	0.75	15000	0.0176	0.9735
0.0199	0.775	15500	0.0131	0.9741
0.0208	0.8	16000	0.0131	0.9775
0.0217	0.825	16500	0.0123	0.9788
0.0208	0.85	17000	0.0145	0.9758
0.0217	0.875	17500	0.0154	0.9694
0.0197	0.9	18000	0.0138	0.9765
0.0205	0.925	18500	0.0138	0.9748
0.0203	0.95	19000	0.0146	0.9748
0.0198	0.975	19500	0.0131	0.9755
0.0204	1.0	20000	0.0158	0.9735

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1