End of training

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: bert-base-multilingual-cased
+base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9950245302230862
+      value: 0.999514136319467
     - name: Recall
       type: recall
-      value: 0.9949324324324325
+      value: 0.999560388708931
     - name: F1
       type: f1
-      value: 0.9949784791965567
+      value: 0.9995372619791305
     - name: Accuracy
       type: accuracy
-      value: 0.9976269686240996
+      value: 0.9997259356253235
 ---
 
 <!-- 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 [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the ner dataset.
+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: 0.0019
-- Precision: 0.9950
-- Recall: 0.9949
-- F1: 0.9950
-- Accuracy: 0.9976
+- Loss: 0.0004
+- Precision: 0.9995
+- Recall: 0.9996
+- F1: 0.9995
+- Accuracy: 0.9997
 
 ## Model description
 
@@ -73,17 +73,15 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 3
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0679        | 1.0   | 688  | 0.0131          | 0.9998    | 0.9826 | 0.9911 | 0.9920   |
-| 0.0331        | 2.0   | 1376 | 0.0085          | 0.9998    | 0.9826 | 0.9911 | 0.9921   |
-| 0.0248        | 3.0   | 2064 | 0.0057          | 0.9999    | 0.9826 | 0.9912 | 0.9921   |
-| 0.0155        | 4.0   | 2752 | 0.0026          | 0.9948    | 0.9936 | 0.9942 | 0.9969   |
-| 0.0103        | 5.0   | 3440 | 0.0019          | 0.9950    | 0.9949 | 0.9950 | 0.9976   |
+| 0.0416        | 1.0   | 688  | 0.0029          | 0.9957    | 0.9972 | 0.9965 | 0.9980   |
+| 0.008         | 2.0   | 1376 | 0.0010          | 0.9985    | 0.9990 | 0.9987 | 0.9993   |
+| 0.0023        | 3.0   | 2064 | 0.0004          | 0.9995    | 0.9996 | 0.9995 | 0.9997   |
 
 
 ### Framework versions