transformers tensorboard safetensors bert fill-mask generated_from_trainer dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset base_model:bert-large-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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
---

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# BERT_pretraining_h_100

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/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
