update model card README.md

README.md

@@ -1,9 +1,8 @@
 ---
 license: apache-2.0
+base_model: bert-base-cased
 tags:
 - generated_from_trainer
-datasets:
-- conll2002
 metrics:
 - precision
 - recall
@@ -11,29 +10,7 @@ metrics:
 - accuracy
 model-index:
 - name: bert-finetuned-ner
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: conll2002
-      type: conll2002
-      config: es
-      split: validation
-      args: es
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.8734693877551021
-    - name: Recall
-      type: recall
-      value: 0.8851102941176471
-    - name: F1
-      type: f1
-      value: 0.8792513124857338
-    - name: Accuracy
-      type: accuracy
-      value: 0.979541360041528
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner
 
-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.
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0911
-- Precision: 0.8735
-- Recall: 0.8851
-- F1: 0.8793
-- Accuracy: 0.9795
+- Loss: 0.0602
+- Precision: 0.9370
+- Recall: 0.9509
+- F1: 0.9439
+- Accuracy: 0.9864
 
 ## Model description
 
@@ -67,8 +44,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 8
+- eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -78,14 +55,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0981        | 1.0   | 521  | 0.0849          | 0.8612    | 0.8814 | 0.8712 | 0.9789   |
-| 0.0327        | 2.0   | 1042 | 0.0833          | 0.8634    | 0.8814 | 0.8723 | 0.9796   |
-| 0.0193        | 3.0   | 1563 | 0.0911          | 0.8735    | 0.8851 | 0.8793 | 0.9795   |
+| 0.0803        | 1.0   | 1756 | 0.0857          | 0.9059    | 0.9302 | 0.9179 | 0.9802   |
+| 0.0413        | 2.0   | 3512 | 0.0578          | 0.9248    | 0.9483 | 0.9364 | 0.9858   |
+| 0.0259        | 3.0   | 5268 | 0.0602          | 0.9370    | 0.9509 | 0.9439 | 0.9864   |
 
 
 ### Framework versions
 
-- Transformers 4.30.2
-- Pytorch 2.0.0
-- Datasets 2.1.0
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.18.0
 - Tokenizers 0.13.3