metadata
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
base_model:
- google/mobilebert-uncased
pipeline_tag: text-classification
library_name: transformers
metrics:
- f1
- precision
- recall
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
This checkpoint is the original MobileBert Optimized Uncased English: uncased_L-24_H-128_B-512_A-4_F-4_OPT checkpoint.
This model was fine-tuned on html tags and labels using Fathom.
How to use MobileBERT in transformers
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="vazish/mobile_bert_autofill",
tokenizer="vazish/mobile_bert_autofill"
)
print(
classifier('<input class="cc-number" placeholder="Enter credit card number..." />')
)
Model Training Info
HyperParameters: {
'learning_rate': 0.000082,
'num_train_epochs': 12,
'weight_decay': 0.1,
'per_device_train_batch_size': 32,
}
Model Performance
Test Performance:
Precision: 0.97043
Recall: 0.96966
F1: 0.96921
precision recall f1-score support
CC Expiration 1.000 0.875 0.933 16
CC Expiration Month 1.000 0.972 0.986 36
CC Expiration Year 0.973 0.973 0.973 37
CC Name 1.000 0.968 0.984 31
CC Number 0.942 0.980 0.961 50
CC Payment Type 0.934 0.760 0.838 75
CC Security Code 0.929 0.951 0.940 41
CC Type 0.857 0.857 0.857 14
Confirm Password 1.000 0.860 0.925 57
Email 0.972 0.945 0.958 73
First Name 0.833 1.000 0.909 5
Form 0.950 0.974 0.962 39
Last Name 0.833 1.000 0.909 5
New Password 0.915 1.000 0.956 97
Other 0.981 0.989 0.985 1235
Phone 0.600 1.000 0.750 3
Zip Code 0.939 0.969 0.954 32
accuracy 0.970 1846
macro avg 0.921 0.945 0.928 1846
weighted avg 0.970 0.970 0.969 1846