update model card README.md

README.md

@@ -1,10 +1,9 @@
 ---
 license: apache-2.0
-base_model: bert-base-cased
 tags:
 - generated_from_trainer
 datasets:
-- conll2003
+- conll2002
 metrics:
 - precision
 - recall
@@ -17,24 +16,24 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: conll2003
-      type: conll2003
-      config: conll2003
+      name: conll2002
+      type: conll2002
+      config: es
       split: validation
-      args: conll2003
+      args: es
     metrics:
     - name: Precision
       type: precision
-      value: 0.9276174773289365
+      value: 0.8596766951055231
     - name: Recall
       type: recall
-      value: 0.9468192527768429
+      value: 0.8798253676470589
     - name: F1
       type: f1
-      value: 0.9371200133255602
+      value: 0.8696343402225755
     - name: Accuracy
       type: accuracy
-      value: 0.9857979631482898
+      value: 0.9784573574765641
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-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 [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0566
-- Precision: 0.9276
-- Recall: 0.9468
-- F1: 0.9371
-- Accuracy: 0.9858
+- Loss: 0.0936
+- Precision: 0.8597
+- Recall: 0.8798
+- F1: 0.8696
+- Accuracy: 0.9785
 
 ## Model description
 
@@ -79,14 +78,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2173        | 1.0   | 878  | 0.0741          | 0.8992    | 0.9281 | 0.9135 | 0.9803   |
-| 0.0449        | 2.0   | 1756 | 0.0580          | 0.9153    | 0.9398 | 0.9273 | 0.9844   |
-| 0.0268        | 3.0   | 2634 | 0.0566          | 0.9276    | 0.9468 | 0.9371 | 0.9858   |
+| 0.1004        | 1.0   | 521  | 0.0850          | 0.8579    | 0.8821 | 0.8698 | 0.9782   |
+| 0.0336        | 2.0   | 1042 | 0.0849          | 0.8584    | 0.8775 | 0.8679 | 0.9783   |
+| 0.0197        | 3.0   | 1563 | 0.0936          | 0.8597    | 0.8798 | 0.8696 | 0.9785   |
 
 
 ### Framework versions
 
-- Transformers 4.32.0.dev0
-- Pytorch 2.0.1+cu117
-- Datasets 2.13.1
+- Transformers 4.30.2
+- Pytorch 2.0.0
+- Datasets 2.1.0
 - Tokenizers 0.13.3