language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-mrpc
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- type: accuracy
value: 0.8602941176470589
name: Accuracy
- type: f1
value: 0.9042016806722689
name: F1
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: glue
type: glue
config: mrpc
split: validation
metrics:
- type: accuracy
value: 0.8602941176470589
name: Accuracy
verified: true
verifyToken: >-
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- type: precision
value: 0.8512658227848101
name: Precision
verified: true
verifyToken: >-
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- type: recall
value: 0.96415770609319
name: Recall
verified: true
verifyToken: >-
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- type: auc
value: 0.8985718651885194
name: AUC
verified: true
verifyToken: >-
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- type: f1
value: 0.9042016806722689
name: F1
verified: true
verifyToken: >-
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- type: loss
value: 0.6978028416633606
name: loss
verified: true
verifyToken: >-
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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
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])