BERT
This model is a pre-trained version of BERT on the WikiText language modeling dataset for educational purposes (see the [Training BERT from Scratch series on Medium(https://medium.com/p/b048682c795f)]). You cannot use it for any production purposes whatsoever.
It achieves the following results on the evaluation set:
- Loss: 7.9307
- Masked Language Modeling (Masked LM) Accuracy: 0.1485
- Next Sentence Prediction (NSP) Accuracy: 0.7891
Model description
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a revolutionary Natural Language Processing (NLP) model developed by Google in 2018. Its introduction marked a significant advancement in the field, setting new state-of-the-art benchmarks across various NLP tasks. For many, this is regarded as the ImageNet moment for the field.
BERT is pre-trained on a massive amount of data, with one goal: to understand what language is and what’s the meaning of context in a document. As a result, this pre-trained model can be fine-tuned for specific tasks such as question-answering or sentiment analysis.
Intended uses & limitations
This repository contains the model trained for 20 epochs on the WikiText dataset. Please note that the model is not suitable for production use and will not provide accurate predictions for Masked Language Modeling tasks.
Training and evaluation data
The model was trained for 20 epochs on the WikiText language modeling dataset using the
wikitext-2-raw-v1 subset.
Training procedure
We usually divide the training of BERT into two distinct phases. The first phase, known as "pre-training," aims to familiarize the model with language structure and the contextual significance of words. The second phase, termed "fine-tuning," focuses on adapting the model for specific, useful tasks.
The model available in this repository has only undergone the pre-training phase.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
The table below illustrates the model's training progress across the 20 epochs.
| Training Loss | Epoch | Step | Validation Loss | Masked LM Accuracy | NSP Accuracy |
|---|---|---|---|---|---|
| 7.9726 | 1.0 | 564 | 7.5680 | 0.1142 | 0.5 |
| 7.5085 | 2.0 | 1128 | 7.4155 | 0.1329 | 0.5557 |
| 7.4112 | 3.0 | 1692 | 7.3729 | 0.1380 | 0.5675 |
| 7.3352 | 4.0 | 2256 | 7.2816 | 0.1398 | 0.6060 |
| 7.2823 | 5.0 | 2820 | 7.1709 | 0.1414 | 0.6884 |
| 7.1828 | 6.0 | 3384 | 7.1503 | 0.1417 | 0.7109 |
| 7.0796 | 7.0 | 3948 | 7.0909 | 0.1431 | 0.7430 |
| 6.8699 | 8.0 | 4512 | 7.1666 | 0.1422 | 0.7238 |
| 6.7819 | 9.0 | 5076 | 7.2507 | 0.1467 | 0.7345 |
| 6.7269 | 10.0 | 5640 | 7.2654 | 0.1447 | 0.7484 |
| 6.6701 | 11.0 | 6204 | 7.3642 | 0.1439 | 0.7784 |
| 6.613 | 12.0 | 6768 | 7.5089 | 0.1447 | 0.7677 |
| 6.5577 | 13.0 | 7332 | 7.7611 | 0.1469 | 0.7655 |
| 6.5197 | 14.0 | 7896 | 7.5984 | 0.1465 | 0.7827 |
| 6.4626 | 15.0 | 8460 | 7.6738 | 0.1449 | 0.8030 |
| 6.4026 | 16.0 | 9024 | 7.7009 | 0.1457 | 0.7869 |
| 6.3861 | 17.0 | 9588 | 7.7586 | 0.1503 | 0.7955 |
| 6.3779 | 18.0 | 10152 | 7.7792 | 0.1494 | 0.8019 |
| 6.357 | 19.0 | 10716 | 7.8532 | 0.1479 | 0.7966 |
| 6.3354 | 20.0 | 11280 | 7.9307 | 0.1485 | 0.7891 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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