trainind complete

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.6477093206951027
+      value: 0.656957928802589
     - name: Recall
       type: recall
-      value: 0.4904306220095694
+      value: 0.48564593301435405
     - name: F1
       type: f1
-      value: 0.5582028590878148
+      value: 0.5584594222833563
     - name: Accuracy
       type: accuracy
-      value: 0.9344202521095948
+      value: 0.933951453276383
 ---
 
 <!-- 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 [Babelscape/wikineural-multilingual-ner](https://huggingface.co/Babelscape/wikineural-multilingual-ner) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4102
-- Precision: 0.6477
-- Recall: 0.4904
-- F1: 0.5582
-- Accuracy: 0.9344
+- Loss: 0.3577
+- Precision: 0.6570
+- Recall: 0.4856
+- F1: 0.5585
+- Accuracy: 0.9340
 
 ## Model description
 
@@ -79,9 +79,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 425  | 0.3037          | 0.5963    | 0.5072 | 0.5482 | 0.9321   |
-| 0.0672        | 2.0   | 850  | 0.3751          | 0.6604    | 0.4653 | 0.5460 | 0.9316   |
-| 0.0451        | 3.0   | 1275 | 0.4102          | 0.6477    | 0.4904 | 0.5582 | 0.9344   |
+| No log        | 1.0   | 425  | 0.3191          | 0.6465    | 0.4091 | 0.5011 | 0.9273   |
+| 0.1595        | 2.0   | 850  | 0.3320          | 0.6719    | 0.4629 | 0.5482 | 0.9316   |
+| 0.0681        | 3.0   | 1275 | 0.3577          | 0.6570    | 0.4856 | 0.5585 | 0.9340   |
 
 
 ### Framework versions