add model

README.md

@@ -2,24 +2,26 @@
 tags:
 - generated_from_trainer
 datasets:
-- null
+- wikiann
 metrics:
 - precision
 - recall
 - f1
 - accuracy
-language:
-- sr
 model_index:
 - name: bert-srb-ner
   results:
   - task:
       name: Token Classification
       type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      args: sr
     metric:
       name: Accuracy
       type: accuracy
-      value: 0.9641060273510046
+      value: 0.9542715764169646
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -27,33 +29,21 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-srb-ner
 
-This model was finetuned from Aleksandar/bert-srb-cased-oscar on the setimes.SR dataset.
+This model was trained from scratch on the wikiann dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1647
-- Precision: 0.8247
-- Recall: 0.8454
-- F1: 0.8349
-- Accuracy: 0.9641
+- Loss: 0.3045
+- Precision: 0.8922
+- Recall: 0.9050
+- F1: 0.8986
+- Accuracy: 0.9543
 
 ## Model description
 
-Default settings for BERT model, finetuned with batch size of 16. 
+More information needed
 
 ## Intended uses & limitations
-| Tag (IOB)   | Numerical representation | Meaning   (Beginning = B.,  Inside = I.) |
-|-------------|--------------------------|------------------------------------------|
-| O           | 0                        | Other                                    |
-| B-per       | 1                        | B.Person                                 |
-| I-per       | 2                        | I. Person                                |
-| B-org       | 3                        | B. organization                          |
-| I-org       | 4                        | I. organization                          |
-| B-loc       | 5                        | B. location                              |
-| I-loc       | 6                        | I. location                              |
-| B-misc      | 7                        | B. Miscellaneous                         |
-| I-misc      | 8                        | I. Miscellaneous                         |
-| B-deriv-per | 9                        | B. Derived Person                        |
-
-MIT license
+
+More information needed
 
 ## Training and evaluation data
 
@@ -74,18 +64,18 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 207  | 0.2040          | 0.7006    | 0.7466 | 0.7228 | 0.9411   |
-| No log        | 2.0   | 414  | 0.1561          | 0.7299    | 0.7868 | 0.7573 | 0.9519   |
-| 0.2313        | 3.0   | 621  | 0.1455          | 0.7693    | 0.7992 | 0.7840 | 0.9567   |
-| 0.2313        | 4.0   | 828  | 0.1628          | 0.7760    | 0.8037 | 0.7896 | 0.9570   |
-| 0.0828        | 5.0   | 1035 | 0.1424          | 0.7997    | 0.8299 | 0.8145 | 0.9604   |
-| 0.0828        | 6.0   | 1242 | 0.1512          | 0.7983    | 0.8361 | 0.8168 | 0.9618   |
-| 0.0828        | 7.0   | 1449 | 0.1587          | 0.8084    | 0.8415 | 0.8246 | 0.9627   |
-| 0.0362        | 8.0   | 1656 | 0.1613          | 0.8154    | 0.8358 | 0.8255 | 0.9632   |
-| 0.0362        | 9.0   | 1863 | 0.1685          | 0.8211    | 0.8429 | 0.8319 | 0.9632   |
-| 0.0174        | 10.0  | 2070 | 0.1647          | 0.8247    | 0.8454 | 0.8349 | 0.9641   |
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.276         | 1.0   | 1250  | 0.2359          | 0.8355    | 0.8334 | 0.8344 | 0.9276   |
+| 0.1722        | 2.0   | 2500  | 0.2016          | 0.8731    | 0.8685 | 0.8708 | 0.9426   |
+| 0.1155        | 3.0   | 3750  | 0.1897          | 0.8707    | 0.8860 | 0.8783 | 0.9463   |
+| 0.0849        | 4.0   | 5000  | 0.2151          | 0.8755    | 0.8980 | 0.8866 | 0.9494   |
+| 0.0554        | 5.0   | 6250  | 0.2373          | 0.8820    | 0.8923 | 0.8871 | 0.9495   |
+| 0.039         | 6.0   | 7500  | 0.2644          | 0.8808    | 0.8953 | 0.8880 | 0.9505   |
+| 0.0286        | 7.0   | 8750  | 0.2737          | 0.8915    | 0.8961 | 0.8938 | 0.9520   |
+| 0.018         | 8.0   | 10000 | 0.2879          | 0.8860    | 0.9039 | 0.8948 | 0.9526   |
+| 0.0116        | 9.0   | 11250 | 0.2973          | 0.8930    | 0.9032 | 0.8981 | 0.9542   |
+| 0.0079        | 10.0  | 12500 | 0.3045          | 0.8922    | 0.9050 | 0.8986 | 0.9543   |
 
 
 ### Framework versions

config.json

@@ -15,10 +15,7 @@
     "3": "LABEL_3",
     "4": "LABEL_4",
     "5": "LABEL_5",
-    "6": "LABEL_6",
-    "7": "LABEL_7",
-    "8": "LABEL_8",
-    "9": "LABEL_9"
+    "6": "LABEL_6"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
@@ -29,10 +26,7 @@
     "LABEL_3": 3,
     "LABEL_4": 4,
     "LABEL_5": 5,
-    "LABEL_6": 6,
-    "LABEL_7": 7,
-    "LABEL_8": 8,
-    "LABEL_9": 9
+    "LABEL_6": 6
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,

pytorch_model.bin

@@ -1,3 +1,3 @@
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+size 430983665

training_args.bin

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