Bert Large Cased Whole Word Masking
The BERT Large Cased Whole Word Masking model is a powerful tool for natural language processing tasks. It was trained on a large corpus of English data using a unique technique called Whole Word Masking, where all tokens corresponding to a word are masked at once. This model is particularly good at tasks that require understanding the context of a sentence, such as sequence classification, token classification, or question answering. With 24 layers, 1024 hidden dimensions, and 16 attention heads, it has 336 million parameters. While it can be used for masked language modeling or next sentence prediction, it's primarily intended to be fine-tuned on a specific task. Keep in mind that this model may have biased predictions, especially when it comes to gender. If you're looking for a model for text generation, you might want to consider alternatives like GPT2.
Table of Contents
Model Overview
The BERT Large Model (Cased) Whole Word Masking is a powerful language model that can help you with a variety of natural language processing tasks. It’s a type of transformer model that’s been trained on a massive dataset of English text.
What makes it special?
- It’s trained using a technique called Whole Word Masking, which means it masks entire words at once, rather than individual tokens.
- It’s a cased model, which means it can tell the difference between “english” and “English”.
- It’s been trained on a huge dataset of English text, including books and Wikipedia articles.
Capabilities
The BERT Large Model (Cased) Whole Word Masking is a powerful language model that can perform a variety of tasks. Here are some of its key capabilities:
Primary Tasks
- Masked Language Modeling: The model can predict missing words in a sentence, even if the words are not next to each other.
- Next Sentence Prediction: The model can determine if two sentences are related or not.
Strengths
- Bidirectional Representation: The model can learn to represent a sentence in both directions, allowing it to capture more context and nuances.
- Whole Word Masking: The model can mask entire words at once, which helps it to learn more about the relationships between words.
Unique Features
- Cased Model: The model is case-sensitive, which means it can distinguish between “english” and “English”.
- Pretrained on Large Corpus: The model was trained on a large corpus of English data, including books and Wikipedia articles.
Performance
The BERT Large Model (Cased) Whole Word Masking is a powerful language model that has shown remarkable performance in various tasks. But how fast is it? How accurate is it? And how efficient is it?
Speed
The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. This means it can process a large amount of data quickly. But what about its inference speed? Well, it can process 128 tokens per step, which is relatively fast.
Accuracy
The model has achieved impressive results on downstream tasks, such as:
| Task | Score |
|---|---|
| SQUAD 1.1 F1/EM | 92.9/86.7 |
| Multi NLI Accuracy | 86.46 |
These scores are high, indicating that the model is accurate in understanding and generating text.
Efficiency
The model has 24 layers, 1024 hidden dimension, and 16 attention heads, which makes it a relatively large model. However, it only has 336M parameters, which is relatively small compared to other models like GPT2. This means it can be trained and fine-tuned on smaller datasets, making it more efficient.
Limitations
The BERT Large Model (Cased) Whole Word Masking is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Biased Predictions
You might have noticed that the model can make biased predictions. For example, when asked to fill in the blank for “The man worked as a [MASK].”, it’s more likely to suggest traditional male-dominated professions like “carpenter” or “mechanic”. On the other hand, when asked to fill in the blank for “The woman worked as a [MASK].”, it’s more likely to suggest traditional female-dominated professions like “maid” or “nurse”.
This bias can affect all fine-tuned versions of the model. So, it’s essential to keep this in mind when using the model for your tasks.
Limited Training Data
The model was trained on a large corpus of English data, including BookCorpus and English Wikipedia. However, this training data might not be representative of all languages, cultures, or domains. This means that the model might not perform well on tasks that require a deeper understanding of specific languages, cultures, or domains.
Masked Language Modeling Limitations
The model uses masked language modeling to predict missing words in a sentence. However, this approach has its limitations. For example, the model might struggle to predict missing words in sentences with complex grammar or syntax.
Sequence Length Limitations
The model has a maximum sequence length of 512 tokens. This means that it might not be able to handle very long texts or documents.
Fine-Tuning Requirements
The model is primarily aimed at being fine-tuned on downstream tasks. This means that you’ll need to fine-tune it on your specific task to get the best results.
Format
The model utilizes a transformer architecture and accepts input in the form of tokenized text sequences, requiring a specific pre-processing step for sentence pairs.
Architecture
- The model consists of
24layers, with a hidden dimension of1024and16attention heads. - It has a total of
336Mparameters.
Data Formats
- The model supports input in the form of tokenized text sequences.
- The model expects input to be pre-processed into a specific format, with sentence pairs separated by
[SEP]tokens.
Special Requirements
- The model requires input to be lowercased and tokenized using WordPiece with a vocabulary size of
30,000. - The input sequence length should be limited to
512tokens.
Example Usage
You can use the model directly with a pipeline for masked language modeling:
from transformers import pipeline
unmasker = pipeline('fill-mask', model='bert-large-cased-whole-word-masking')
unmasker("Hello I'm a [MASK] model.")
Alternatively, you can use the model to get the features of a given text in PyTorch:
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-large-cased-whole-word-masking')
model = BertModel.from_pretrained("bert-large-cased-whole-word-masking")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
Or in TensorFlow:
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-large-cased-whole-word-masking')
model = TFBertModel.from_pretrained("bert-large-cased-whole-word-masking")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
Note that the model can have biased predictions, and this bias will also affect all fine-tuned versions of the model.
