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