bert-base-uncased-mrpc
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset. The GLUE MRPC dataset, from The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
It is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. This model, bert-base-uncased-mrpc, is uncased: it does not make a difference between "english" and "English". Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This means the model has full access to the tokens on the left and right. Masked language modeling is great for tasks that require a good contextual understanding of an entire sequence. BERT is an example of a masked language model. For this model, you don’t need labels (also known as an unsupervised task) because the next word (MLM) is the label BERT base model (uncased)
It provides:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.
Results
It achieves the following results on the evaluation set:
- Loss: 0.6978
- Accuracy: 0.8603
- F1: 0.9042
- Combined Score: 0.8822
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu102
- Datasets 1.14.0
- Tokenizers 0.11.6
To use:
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('Intel/bert-base-uncased-mrpc')
model = BertModel.from_pretrained("Intel/bert-base-uncased-mrpc")
text = "The inspector analyzed the soundness in the building."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
# print BaseModelOutputWithPoolingAndCrossAttentions and pooler_output
# Print tokens * ids in of inmput string below
print('Tokenized Text: ', tokenizer.tokenize(text), '\n')
print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)))
#Print tokens in text
encoded_input['input_ids'][0]
tokenizer.convert_ids_to_tokens(encoded_input['input_ids'][0])
Output similar to:
BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[ 0.0219, 0.1258, -0.8529, ..., 0.6416, 0.6275, 0.5583],
[ 0.3125, -0.1921, -0.9895, ..., 0.6069, 1.8431, -0.5939],
[ 0.6147, -0.6098, -0.3517, ..., -0.1145, 1.1748, -0.7104],
...,
[ 0.8959, -0.2324, -0.6311, ..., 0.2424, 0.1025, 0.2101],
[ 0.2484, -0.3004, -0.9474, ..., 1.0401, 0.5493, -0.4170],
[ 0.8206, 0.2023, -0.7929, ..., 0.7073, 0.0779, -0.2781]]],
grad_fn=<NativeLayerNormBackward0>), pooler_output=tensor([[-0.7867, 0.1878, -0.8186, 0.8494, 0.4263, 0.5157, 0.9564, 0.1514,
-0.9176, -0.9994, 0.2962, 0.2891, -0.3301, 0.8786, 0.9234, -0.7643,
0.2487, -0.5245, -0.0649, -0.6722, 0.8550, 1.0000, -0.7785, 0.5322,
0.6056, 0.4622, 0.2838, 0.5501, 0.6981, 0.2597, -0.7896, -0.1189,
Related work on QuantizationAwareTraining
An Int8 Quantized version of this model can be found link
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
Ethical Considerations and Limitations
bert-base-uncased-mrpc can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of bert-base-uncased-mrpc, developers should perform safety testing.
Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
BibTeX entry and citation info
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}