update model card README.md

README.md

@@ -1,6 +1,5 @@
 ---
 license: apache-2.0
-base_model: BSC-LT/roberta-base-bne-capitel-ner
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8712310133756518
+      value: 0.8637694213015087
     - name: Recall
       type: recall
-      value: 0.8830422794117647
+      value: 0.8814338235294118
     - name: F1
       type: f1
-      value: 0.8770968846285518
+      value: 0.8725122256340272
     - name: Accuracy
       type: accuracy
-      value: 0.978961189654646
+      value: 0.9780298635072827
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 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: 0.1255
-- Precision: 0.8712
-- Recall: 0.8830
-- F1: 0.8771
-- Accuracy: 0.9790
+- Loss: 0.1137
+- Precision: 0.8638
+- Recall: 0.8814
+- F1: 0.8725
+- Accuracy: 0.9780
 
 ## Model description
 
@@ -73,21 +72,22 @@ 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: 4
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0135        | 1.0   | 1041 | 0.1233          | 0.8615    | 0.8803 | 0.8708 | 0.9783   |
-| 0.0111        | 2.0   | 2082 | 0.1099          | 0.8709    | 0.8853 | 0.8781 | 0.9799   |
-| 0.0061        | 3.0   | 3123 | 0.1203          | 0.8569    | 0.8739 | 0.8653 | 0.9781   |
-| 0.0035        | 4.0   | 4164 | 0.1255          | 0.8712    | 0.8830 | 0.8771 | 0.9790   |
+| 0.0041        | 1.0   | 1041 | 0.1137          | 0.8638    | 0.8814 | 0.8725 | 0.9780   |
+| 0.004         | 2.0   | 2082 | 0.1137          | 0.8638    | 0.8814 | 0.8725 | 0.9780   |
+| 0.0039        | 3.0   | 3123 | 0.1137          | 0.8638    | 0.8814 | 0.8725 | 0.9780   |
+| 0.003         | 4.0   | 4164 | 0.1137          | 0.8638    | 0.8814 | 0.8725 | 0.9780   |
+| 0.0032        | 5.0   | 5205 | 0.1137          | 0.8638    | 0.8814 | 0.8725 | 0.9780   |
 
 
 ### Framework versions
 
-- Transformers 4.35.0
+- Transformers 4.30.0
 - Pytorch 2.0.1+cu117
 - Datasets 2.14.4
-- Tokenizers 0.14.1
+- Tokenizers 0.13.3