transformers tensorboard safetensors hybridbert fill-mask generated_from_trainer dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset base_model:gokuls/HBERTv1_L12_H768_A12_ffn_1 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: gokuls/HBERTv1_L12_H768_A12_ffn_1
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_complete_processed_bert_dataset
metrics:
- accuracy
model-index:
- name: HBERTv1_48_L12_H768_A12_ffn_1_KD
  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: 0.4616582527830643
---

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# HBERTv1_48_L12_H768_A12_ffn_1_KD

This model is a fine-tuned version of [gokuls/HBERTv1_L12_H768_A12_ffn_1](https://huggingface.co/gokuls/HBERTv1_L12_H768_A12_ffn_1) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 132.3779
- Accuracy: 0.4617

## 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: 32
- eval_batch_size: 32
- 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: 100

### Training results



### Framework versions

- Transformers 4.35.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.15.0
- Tokenizers 0.15.0
