metadata

license: apache-2.0
base_model: bert-large-uncased
tags:
  - generated_from_trainer
datasets:
  - gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
  - accuracy
model-index:
  - name: BERT_pretraining_h_100
    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: 0.046532742314357264

BERT_pretraining_h_100

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

Loss: 7.2715
Accuracy: 0.0465

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: 0.0001
train_batch_size: 36
eval_batch_size: 36
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: 100000
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
6.5136	0.06	10000	6.4841	0.1332
6.3225	0.12	20000	6.2865	0.1452
6.0806	0.18	30000	6.1945	0.1482
6.1449	0.25	40000	6.1284	0.1497
5.7325	0.31	50000	5.8403	0.1609
4.0177	0.37	60000	3.7789	0.3887
3.3942	0.43	70000	3.1742	0.4638
3.2801	0.49	80000	3.0618	0.4775
7.2562	0.55	90000	7.2798	0.0432
7.226	0.61	100000	7.2771	0.0465
7.2174	0.68	110000	7.2764	0.0465
7.232	0.74	120000	7.2745	0.0465
7.2003	0.8	130000	7.2730	0.0465
7.0964	0.86	140000	7.2725	0.0466
7.5174	0.92	150000	7.2729	0.0465
7.2674	0.98	160000	7.2729	0.0465
7.2044	1.04	170000	7.2729	0.0466

Framework versions

Transformers 4.37.1
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.1