bert-base-uncased-mrpc

This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.

It is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between "english" and "English". 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 = "according to the theory of aerodynamics and wind tunnel experiments the bumble bee is unable to fly. This is bcause the size, weight, and shape of his body in relation to total wingspread makes flying impossible. But, the bumble bee being ignorant of these pround scientific truths goes ahead and flies anyway, and manages to make a little honey everyday."
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
# 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=), 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,

# 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])