Training complete

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: mHossain/bengali_pos_v1_200000
+base_model: mHossain/bengali_pos_v1_400000
 tags:
 - generated_from_trainer
 datasets:
@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.7824197234815842
+      value: 0.7758382692500753
     - name: Recall
       type: recall
-      value: 0.7854805341357909
+      value: 0.7796834604956177
     - name: F1
       type: f1
-      value: 0.783947141194579
+      value: 0.7777561122887353
     - name: Accuracy
       type: accuracy
-      value: 0.839060352367862
+      value: 0.8338714666872897
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bengali_pos_v1_300000
 
-This model is a fine-tuned version of [mHossain/bengali_pos_v1_200000](https://huggingface.co/mHossain/bengali_pos_v1_200000) on the pos_tag_100k dataset.
+This model is a fine-tuned version of [mHossain/bengali_pos_v1_400000](https://huggingface.co/mHossain/bengali_pos_v1_400000) on the pos_tag_100k dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6558
-- Precision: 0.7824
-- Recall: 0.7855
-- F1: 0.7839
-- Accuracy: 0.8391
+- Loss: 0.5865
+- Precision: 0.7758
+- Recall: 0.7797
+- F1: 0.7778
+- Accuracy: 0.8339
 
 ## Model description
 
@@ -78,9 +78,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.3708        | 1.0   | 22500 | 0.6313          | 0.7688    | 0.7733 | 0.7711 | 0.8304   |
-| 0.3099        | 2.0   | 45000 | 0.6491          | 0.7770    | 0.7789 | 0.7780 | 0.8353   |
-| 0.3127        | 3.0   | 67500 | 0.6558          | 0.7824    | 0.7855 | 0.7839 | 0.8391   |
+| 0.587         | 1.0   | 22500 | 0.5821          | 0.7614    | 0.7643 | 0.7628 | 0.8232   |
+| 0.4808        | 2.0   | 45000 | 0.5726          | 0.7733    | 0.7762 | 0.7747 | 0.8316   |
+| 0.3909        | 3.0   | 67500 | 0.5865          | 0.7758    | 0.7797 | 0.7778 | 0.8339   |
 
 
 ### Framework versions