README.md

---
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
datasets:
- vazish/autofill_dataset
---

## 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](https://storage.googleapis.com/cloud-tpu-checkpoints/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT.tar.gz)
checkpoint.

This model was fine-tuned on html tags and labels using [Fathom](https://mozilla.github.io/fathom/commands/label.html).

## How to use MobileBERT in `transformers`

```python
from transformers import pipeline

classifier = pipeline(
 "text-classification",
 model="vazish/mobile_bert_autofill"
)

print(
 classifier('<input class="cc-number" placeholder="Enter credit card number..." />')
)

```

## Model Training Info
```python
HyperParameters: {
'learning_rate': 0.000082,
'num_train_epochs': 12,
'weight_decay': 0.1,
'per_device_train_batch_size': 32,
}
```

More information on how the model was trained can be found here: https://github.com/mozilla/smart_autofill

# 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
```