End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9790805727433154
+      value: 0.9860607282009942
     - name: Recall
       type: recall
-      value: 0.9648238440626479
+      value: 0.9693364297742606
     - name: F1
       type: f1
-      value: 0.9718999284886718
+      value: 0.9776270584382788
     - name: Accuracy
       type: accuracy
-      value: 0.985535111315454
+      value: 0.9882459717748076
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0457
-- Precision: 0.9791
-- Recall: 0.9648
-- F1: 0.9719
-- Accuracy: 0.9855
+- Loss: 0.0372
+- Precision: 0.9861
+- Recall: 0.9693
+- F1: 0.9776
+- Accuracy: 0.9882
 
 ## Model description
 
@@ -79,11 +79,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1213        | 0.58  | 500  | 0.0608          | 0.9658    | 0.9626 | 0.9642 | 0.9815   |
-| 0.0562        | 1.17  | 1000 | 0.0513          | 0.9746    | 0.9638 | 0.9692 | 0.9841   |
-| 0.0514        | 1.75  | 1500 | 0.0484          | 0.9778    | 0.9643 | 0.9710 | 0.9851   |
-| 0.0468        | 2.33  | 2000 | 0.0471          | 0.9776    | 0.9653 | 0.9715 | 0.9853   |
-| 0.0461        | 2.91  | 2500 | 0.0457          | 0.9791    | 0.9648 | 0.9719 | 0.9855   |
+| 0.0461        | 1.0   | 858  | 0.0450          | 0.9853    | 0.9602 | 0.9725 | 0.9859   |
+| 0.0408        | 2.0   | 1716 | 0.0400          | 0.9836    | 0.9679 | 0.9757 | 0.9873   |
+| 0.0391        | 3.0   | 2574 | 0.0372          | 0.9861    | 0.9693 | 0.9776 | 0.9882   |
 
 
 ### Framework versions