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_wo_deepspeed
  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: 0.15387755648267093
---

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# BERT_pretraining_h_100_wo_deepspeed

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: 5.7778
- Accuracy: 0.1539

## 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: 208
- eval_batch_size: 208
- seed: 10
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 6.8769        | 0.36  | 10000  | 6.7582          | 0.1101   |
| 6.4647        | 0.71  | 20000  | 6.4764          | 0.1314   |
| 6.3679        | 1.07  | 30000  | 6.3218          | 0.1407   |
| 6.252         | 1.42  | 40000  | 6.2139          | 0.1454   |
| 6.2132        | 1.78  | 50000  | 6.1398          | 0.1478   |
| 6.0407        | 2.13  | 60000  | 6.0774          | 0.1502   |
| 6.0694        | 2.49  | 70000  | 6.0303          | 0.1516   |
| 5.9996        | 2.84  | 80000  | 5.9893          | 0.1521   |
| 5.9166        | 3.2   | 90000  | 5.9553          | 0.1526   |
| 5.8915        | 3.55  | 100000 | 5.9261          | 0.1530   |
| 5.8924        | 3.91  | 110000 | 5.8996          | 0.1534   |
| 5.8972        | 4.26  | 120000 | 5.8814          | 0.1533   |
| 5.8454        | 4.62  | 130000 | 5.8626          | 0.1532   |
| 5.8104        | 4.97  | 140000 | 5.8494          | 0.1534   |
| 5.8461        | 5.33  | 150000 | 5.8378          | 0.1534   |
| 5.8476        | 5.68  | 160000 | 5.8246          | 0.1536   |
| 5.7255        | 6.04  | 170000 | 5.8155          | 0.1532   |
| 5.8431        | 6.39  | 180000 | 5.8068          | 0.1537   |
| 5.7526        | 6.75  | 190000 | 5.7981          | 0.1537   |
| 5.7826        | 7.1   | 200000 | 5.7886          | 0.1537   |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
