End of training

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: distilbert-base-uncased
+base_model: bert-base-multilingual-cased
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9999768614928964
+      value: 0.9998842940781709
     - name: Recall
       type: recall
-      value: 0.9999305876908838
+      value: 0.9998380192062941
     - name: F1
       type: f1
-      value: 0.9999537240565493
+      value: 0.9998611561068173
     - name: Accuracy
       type: accuracy
-      value: 0.9999695484028137
+      value: 0.999938944347773
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # my_awesome_wnut_model
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
+This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the ner dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0001
-- Precision: 1.0000
-- Recall: 0.9999
-- F1: 1.0000
-- Accuracy: 1.0000
+- Precision: 0.9999
+- Recall: 0.9998
+- F1: 0.9999
+- Accuracy: 0.9999
 
 ## Model description
 
@@ -79,11 +79,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0405        | 1.0   | 688  | 0.0024          | 0.9962    | 0.9969 | 0.9965 | 0.9980   |
-| 0.0078        | 2.0   | 1376 | 0.0017          | 0.9972    | 0.9989 | 0.9981 | 0.9990   |
-| 0.0024        | 3.0   | 2064 | 0.0004          | 0.9995    | 0.9998 | 0.9997 | 0.9998   |
-| 0.0008        | 4.0   | 2752 | 0.0002          | 0.9999    | 0.9999 | 0.9999 | 0.9999   |
-| 0.001         | 5.0   | 3440 | 0.0001          | 1.0000    | 0.9999 | 1.0000 | 1.0000   |
+| 0.0342        | 1.0   | 688  | 0.0063          | 0.9950    | 0.9917 | 0.9934 | 0.9956   |
+| 0.0117        | 2.0   | 1376 | 0.0015          | 0.9979    | 0.9974 | 0.9977 | 0.9988   |
+| 0.0049        | 3.0   | 2064 | 0.0006          | 0.9991    | 0.9994 | 0.9992 | 0.9995   |
+| 0.0017        | 4.0   | 2752 | 0.0001          | 0.9997    | 0.9997 | 0.9997 | 0.9999   |
+| 0.001         | 5.0   | 3440 | 0.0001          | 0.9999    | 0.9998 | 0.9999 | 0.9999   |
 
 
 ### Framework versions