metadata

tags:
  - generated_from_trainer
datasets:
  - gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
  - accuracy
model-index:
  - name: HBERTv1_emb_compress_48_L10_H256_A4
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: gokuls/wiki_book_corpus_complete_processed_bert_dataset
          type: gokuls/wiki_book_corpus_complete_processed_bert_dataset
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.15093352306316574

HBERTv1_emb_compress_48_L10_H256_A4

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

Loss: 6.0495
Accuracy: 0.1509

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: 64
eval_batch_size: 64
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10000
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
7.1164	0.11	10000	7.0967	0.0830
6.694	0.22	20000	6.6867	0.1065
6.545	0.33	30000	6.5445	0.1171
6.4556	0.44	40000	6.4527	0.1250
6.3891	0.55	50000	6.3831	0.1305
6.3404	0.66	60000	6.3334	0.1350
6.2962	0.76	70000	6.2940	0.1377
6.2669	0.87	80000	6.2629	0.1398
6.2352	0.98	90000	6.2361	0.1412
6.2179	1.09	100000	6.2150	0.1429
6.191	1.2	110000	6.1970	0.1443
6.1809	1.31	120000	6.1829	0.1441
6.1699	1.42	130000	6.1692	0.1455
6.1623	1.53	140000	6.1562	0.1453
6.1422	1.64	150000	6.1480	0.1468
6.1397	1.75	160000	6.1367	0.1468
6.1342	1.86	170000	6.1284	0.1475
6.1291	1.97	180000	6.1214	0.1478
6.1157	2.08	190000	6.1132	0.1483
6.1146	2.18	200000	6.1094	0.1484
6.1018	2.29	210000	6.1013	0.1488
6.1014	2.4	220000	6.0979	0.1488
6.0935	2.51	230000	6.0936	0.1489
6.0899	2.62	240000	6.0881	0.1491
6.0858	2.73	250000	6.0851	0.1498
6.0872	2.84	260000	6.0819	0.1497
6.0858	2.95	270000	6.0784	0.1500
6.0775	3.06	280000	6.0745	0.1501
6.0715	3.17	290000	6.0720	0.1502
6.0704	3.28	300000	6.0699	0.1502
6.0678	3.39	310000	6.0668	0.1503
6.0662	3.5	320000	6.0649	0.1503
6.0569	3.6	330000	6.0622	0.1505
6.0604	3.71	340000	6.0612	0.1506
6.0525	3.82	350000	6.0586	0.1507
6.0553	3.93	360000	6.0582	0.1506
6.053	4.04	370000	6.0544	0.1508
6.0594	4.15	380000	6.0553	0.1507
6.0488	4.26	390000	6.0527	0.1509
6.051	4.37	400000	6.0516	0.1509
6.0553	4.48	410000	6.0518	0.1509
6.0507	4.59	420000	6.0520	0.1509
6.0514	4.7	430000	6.0501	0.1509
6.0511	4.81	440000	6.0496	0.1511
6.0527	4.92	450000	6.0493	0.1509

Framework versions

Transformers 4.33.2
Pytorch 1.14.0a0+410ce96
Datasets 2.14.5
Tokenizers 0.13.3