bert base cased

Introduction

BERT (Bidirectional Encoder Representations from Transformers) is a transformer model pretrained on a large corpus of English data using a masked language modeling (MLM) objective. It is designed to understand the context of words in a sentence by considering both past and future tokens, unlike traditional models that process words sequentially. BERT's pretraining objectives include masked language modeling and next sentence prediction, enabling it to learn bidirectional representations and relationships between sentences.

Architecture

BERT's architecture is based on transformers, which use attention mechanisms to process the entire input sequence simultaneously. It utilizes a large vocabulary size of 30,000 and is capable of handling sequences up to 512 tokens long. The model processes inputs in the form [CLS] Sentence A [SEP] Sentence B [SEP], where sentences A and B are either consecutive sentences or randomly paired.

Training

The BERT model was pretrained using the BookCorpus and English Wikipedia datasets. Preprocessing involved tokenizing the text using WordPiece. The training was conducted on 4 cloud TPUs in Pod configuration, using the Adam optimizer with a learning rate of 1e-4 and a batch size of 256. The sequence length was limited to 128 tokens for 90% of the training steps and 512 for the remaining 10%.

Guide: Running Locally

To run BERT locally, follow these steps:

1. Installation: Install the transformers library from Hugging Face.

   pip install transformers
   

2. Model Loading: Use the following code to load and run the BERT model for masked language modeling:

   from transformers import pipeline
   unmasker = pipeline('fill-mask', model='bert-base-cased')
   result = unmasker("Hello I'm a [MASK] model.")
   print(result)
   

3. Feature Extraction: For feature extraction, use the BertTokenizer and BertModel classes.

   from transformers import BertTokenizer, BertModel
   tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
   model = BertModel.from_pretrained("bert-base-cased")
   text = "Replace me by any text you'd like."
   encoded_input = tokenizer(text, return_tensors='pt')
   output = model(**encoded_input)
   

4. Cloud GPUs: For large-scale or resource-intensive tasks, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The BERT model is released under the Apache 2.0 license, allowing for both personal and commercial use, modification, and distribution.