update model card README.md

README.md

@@ -1,8 +1,9 @@
 ---
+license: apache-2.0
 tags:
 - generated_from_trainer
 datasets:
-- wikiann
+- xglue
 metrics:
 - precision
 - recall
@@ -15,24 +16,24 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: wikiann
-      type: wikiann
-      config: es
-      split: train
-      args: es
+      name: xglue
+      type: xglue
+      config: ner
+      split: validation.es
+      args: ner
     metrics:
     - name: Precision
       type: precision
-      value: 0.8655875585178132
+      value: 0.5184780231795321
     - name: Recall
       type: recall
-      value: 0.889079054604727
+      value: 0.5445567294441892
     - name: F1
       type: f1
-      value: 0.8771760543561292
+      value: 0.5311974907583735
     - name: Accuracy
       type: accuracy
-      value: 0.9432045651459472
+      value: 0.8905679788803479
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -40,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-This model was trained from scratch on the wikiann dataset.
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the xglue dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2685
-- Precision: 0.8656
-- Recall: 0.8891
-- F1: 0.8772
-- Accuracy: 0.9432
+- Loss: 0.8429
+- Precision: 0.5185
+- Recall: 0.5446
+- F1: 0.5312
+- Accuracy: 0.8906
 
 ## Model description
 
@@ -77,14 +78,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.245         | 1.0   | 2500 | 0.2470          | 0.8224    | 0.8577 | 0.8397 | 0.9303   |
-| 0.1472        | 2.0   | 5000 | 0.2469          | 0.8651    | 0.8876 | 0.8762 | 0.9415   |
-| 0.0965        | 3.0   | 7500 | 0.2685          | 0.8656    | 0.8891 | 0.8772 | 0.9432   |
+| 0.0782        | 1.0   | 1756 | 0.6432          | 0.4982    | 0.5420 | 0.5192 | 0.8972   |
+| 0.039         | 2.0   | 3512 | 0.7474          | 0.4994    | 0.5496 | 0.5233 | 0.8908   |
+| 0.0189        | 3.0   | 5268 | 0.8429          | 0.5185    | 0.5446 | 0.5312 | 0.8906   |
 
 
 ### Framework versions
 
-- Transformers 4.25.1
+- Transformers 4.26.0
 - Pytorch 1.13.1+cu116
 - Datasets 2.8.0
 - Tokenizers 0.13.2