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BERT-Tamil

Token Classification

Inference Endpoints

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Ambareeshkumar commited on Dec 5, 2022

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@@ -35,13 +35,14 @@ Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER o
 <h1>Results</h1>
-Step	Training Loss	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Loc F1	  Org F1	 Per F1
-1000	0.386900	        0.300006	   0.833469	           0.824748	     0.829086	   0.912857	       0.835343	 0.781625	0.867752
-2000	0.210200	        0.251389	   0.845455	           0.842052	     0.843750	   0.924861	       0.851711	 0.790198	0.886515
-3000	0.140000	        0.264964	   0.866952	           0.856137	     0.861510	   0.930141	       0.874872	 0.818150	0.885203
-4000	0.095400	        0.298542	   0.860871	           0.882696   	 0.871647	   0.935692	       0.881348	 0.829285	0.899245
-5000	0.062200	        0.296011	   0.871805	           0.878471	     0.875125	   0.938806	       0.875434	 0.850889	0.898148
-6000	0.042200            0.320418	   0.868416	           0.879074	     0.873713	   0.937497	       0.877588	 0.833611	0.907737
 Example
 ```py

 <h1>Results</h1>
+| Step | Training Loss|	Validation Loss|Overall Precision|Overall Recall|Overall F1|Overall Accuracy| Loc F1  | Org F1  | Per F1  |
+|----- |--------------|----------------|-----------------|--------------|----------|----------------|---------|---------|---------|
+| 1000 |    0.386900  |	    0.300006   |   0.833469	     |   0.824748	| 0.829086 |   0.912857	    | 0.835343|	0.781625| 0.867752|
+| 2000 |	0.210200  |	    0.251389   |   0.845455	     |   0.842052	| 0.843750 |   0.924861	    | 0.851711|	0.790198| 0.886515|
+| 3000 |	0.140000  |	    0.264964   |   0.866952	     |   0.856137   | 0.861510 |   0.930141	    | 0.874872|	0.818150| 0.885203|
+| 4000 |	0.095400  | 	0.298542   |   0.860871	     |   0.882696   | 0.871647 |   0.935692	    | 0.881348|	0.829285| 0.899245|
+| 5000 |	0.062200  |	    0.296011   |   0.871805	     |   0.878471   | 0.875125 |   0.938806	    | 0.875434|	0.850889| 0.898148|
+| 6000 |	0.042200  |     0.320418   |   0.868416	     |   0.879074   | 0.873713 |   0.937497	    | 0.877588|	0.833611| 0.907737|
 Example
 ```py