Update README.md

README.md

@@ -3,7 +3,7 @@ language: en
 thumbnail: https://huggingface.co/front/thumbnails/google.png
 license: apache-2.0
 base_model:
-- google/bert_uncased_L-2_H-128_A-2
+- cross-encoder/ms-marco-TinyBERT-L-2-v2
 pipeline_tag: text-classification
 library_name: transformers
 metrics:
@@ -14,17 +14,13 @@ datasets:
 - Mozilla/autofill_dataset
 ---
 
-## BERT Miniatures
+## Cross-Encoder for MS Marco with TinyBert
 
-This is the tiny version of the 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
+This is a fine-tuned version of the model checkpointed at [cross-encoder/ms-marco-TinyBert-L-2](https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L-2).
 
-This checkpoint is the original TinyBert Optimized Uncased English:
-[TinyBert](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2)
-checkpoint.
+It 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 TinyBert in `transformers`
+## How to use this model in `transformers`
 
 ```python
 from transformers import pipeline
@@ -44,7 +40,7 @@ print(
 ```python
 HyperParameters: {
     'learning_rate': 0.000082,
-    'num_train_epochs': 59,
+    'num_train_epochs': 71,
     'weight_decay': 0.1,
     'per_device_train_batch_size': 32,
 }
@@ -55,40 +51,31 @@ More information on how the model was trained can be found here: https://github.
 # Model Performance
 ```
 Test Performance:
-Precision: 0.96778
-Recall: 0.96696
-F1: 0.9668
+Precision: 0.9653
+Recall: 0.9648
+F1: 0.9644
 
                      precision    recall  f1-score   support
 
-      CC Expiration      1.000     0.750     0.857        16
-CC Expiration Month      0.972     0.972     0.972        36
- CC Expiration Year      0.946     0.946     0.946        37
-            CC Name      0.882     0.968     0.923        31
-          CC Number      0.942     0.980     0.961        50
-    CC Payment Type      0.918     0.893     0.905        75
-   CC Security Code      0.950     0.927     0.938        41
+      CC Expiration      1.000     0.625     0.769        16
+CC Expiration Month      0.919     0.944     0.932        36
+ CC Expiration Year      0.897     0.946     0.921        37
+            CC Name      0.938     0.968     0.952        31
+          CC Number      0.926     1.000     0.962        50
+    CC Payment Type      0.903     0.867     0.884        75
+   CC Security Code      0.975     0.951     0.963        41
             CC Type      0.917     0.786     0.846        14
-   Confirm Password      0.961     0.860     0.907        57
-              Email      0.909     0.959     0.933        73
-         First Name      0.800     0.800     0.800         5
+   Confirm Password      0.911     0.895     0.903        57
+              Email      0.933     0.959     0.946        73
+         First Name      0.833     1.000     0.909         5
                Form      0.974     0.974     0.974        39
-          Last Name      0.714     1.000     0.833         5
-       New Password      0.913     0.979     0.945        97
-              Other      0.986     0.983     0.985      1235
+          Last Name      0.667     0.800     0.727         5
+       New Password      0.929     0.938     0.933        97
+              Other      0.985     0.985     0.985      1235
               Phone      1.000     0.667     0.800         3
-           Zip Code      0.912     0.969     0.939        32
-
-           accuracy                          0.967      1846
-          macro avg      0.923     0.907     0.910      1846
-       weighted avg      0.968     0.967     0.967      1846
-```
+           Zip Code      0.909     0.938     0.923        32
 
-```
-@article{turc2019,
-  title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
-  author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
-  journal={arXiv preprint arXiv:1908.08962v2 },
-  year={2019}
-}
+           accuracy                          0.965      1846
+          macro avg      0.919     0.897     0.902      1846
+       weighted avg      0.965     0.965     0.964      1846
 ```