Update README.md

README.md

@@ -6,6 +6,10 @@ 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
@@ -17,7 +21,7 @@ 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)
+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`
 
@@ -34,4 +38,47 @@ 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,
+}
+```
+
+# 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
 ```