Shree Ganeshay Namah, NER-POS Training with seqeval metrics complete

README.md

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: bert-base-cased
+base_model: om-ashish-soni/pos-ner-tagging-v2
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9130091029531177
+      value: 0.9393653920267203
     - name: Recall
       type: recall
-      value: 0.9162395728346879
+      value: 0.9408358887483113
     - name: F1
       type: f1
-      value: 0.9146214853742335
+      value: 0.9401000653531749
     - name: Accuracy
       type: accuracy
-      value: 0.8980543945369989
+      value: 0.9270324365691411
 ---
 
 <!-- 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. -->
 
 # pos-ner-tagging-v2
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
+This model is a fine-tuned version of [om-ashish-soni/pos-ner-tagging-v2](https://huggingface.co/om-ashish-soni/pos-ner-tagging-v2) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5154
-- Precision: 0.9130
-- Recall: 0.9162
-- F1: 0.9146
-- Accuracy: 0.8981
+- Loss: 0.6442
+- Precision: 0.9394
+- Recall: 0.9408
+- F1: 0.9401
+- Accuracy: 0.9270
 
 ## Model description
 
@@ -73,13 +73,28 @@ 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: 1
+- num_epochs: 16
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.4773        | 1.0   | 1756 | 0.5154          | 0.9130    | 0.9162 | 0.9146 | 0.8981   |
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3297        | 1.0   | 1756  | 0.4190          | 0.9189    | 0.9231 | 0.9210 | 0.9051   |
+| 0.2521        | 2.0   | 3512  | 0.3836          | 0.9210    | 0.9300 | 0.9255 | 0.9114   |
+| 0.1932        | 3.0   | 5268  | 0.4155          | 0.9295    | 0.9338 | 0.9316 | 0.9183   |
+| 0.1325        | 4.0   | 7024  | 0.3969          | 0.9328    | 0.9356 | 0.9342 | 0.9211   |
+| 0.0973        | 5.0   | 8780  | 0.4247          | 0.9332    | 0.9367 | 0.9349 | 0.9222   |
+| 0.0799        | 6.0   | 10536 | 0.4606          | 0.9338    | 0.9374 | 0.9356 | 0.9229   |
+| 0.0554        | 7.0   | 12292 | 0.4836          | 0.9333    | 0.9379 | 0.9356 | 0.9239   |
+| 0.0415        | 8.0   | 14048 | 0.5271          | 0.9361    | 0.9391 | 0.9376 | 0.9245   |
+| 0.0285        | 9.0   | 15804 | 0.5363          | 0.9366    | 0.9397 | 0.9381 | 0.9253   |
+| 0.022         | 10.0  | 17560 | 0.5653          | 0.9377    | 0.9396 | 0.9387 | 0.9258   |
+| 0.0146        | 11.0  | 19316 | 0.5962          | 0.9374    | 0.9400 | 0.9387 | 0.9259   |
+| 0.0121        | 12.0  | 21072 | 0.6061          | 0.9385    | 0.9401 | 0.9393 | 0.9266   |
+| 0.0085        | 13.0  | 22828 | 0.6263          | 0.9384    | 0.9403 | 0.9394 | 0.9261   |
+| 0.0062        | 14.0  | 24584 | 0.6365          | 0.9381    | 0.9399 | 0.9390 | 0.9259   |
+| 0.0053        | 15.0  | 26340 | 0.6386          | 0.9384    | 0.9402 | 0.9393 | 0.9264   |
+| 0.0042        | 16.0  | 28096 | 0.6442          | 0.9394    | 0.9408 | 0.9401 | 0.9270   |
 
 
 ### Framework versions