End of training

README.md

@@ -1,9 +1,8 @@
 ---
 license: cc-by-sa-4.0
+base_model: klue/bert-base
 tags:
 - generated_from_trainer
-datasets:
-- klue
 metrics:
 - precision
 - recall
@@ -11,29 +10,7 @@ metrics:
 - accuracy
 model-index:
 - name: token_classification
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: klue
-      type: klue
-      config: ner
-      split: validation
-      args: ner
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.5973782771535581
-    - name: Recall
-      type: recall
-      value: 0.6673640167364017
-    - name: F1
-      type: f1
-      value: 0.6304347826086957
-    - name: Accuracy
-      type: accuracy
-      value: 0.9227913554602908
+  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. -->
 
 # token_classification
 
-This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
+This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2382
-- Precision: 0.5974
-- Recall: 0.6674
-- F1: 0.6304
-- Accuracy: 0.9228
+- Loss: 0.2401
+- Precision: 0.5859
+- Recall: 0.6590
+- F1: 0.6203
+- Accuracy: 0.9231
 
 ## Model description
 
@@ -78,13 +55,13 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 313  | 0.2539          | 0.5534    | 0.6395 | 0.5933 | 0.9205   |
-| 0.3052        | 2.0   | 626  | 0.2382          | 0.5974    | 0.6674 | 0.6304 | 0.9228   |
+| No log        | 1.0   | 313  | 0.2617          | 0.5639    | 0.6304 | 0.5953 | 0.9161   |
+| 0.3262        | 2.0   | 626  | 0.2401          | 0.5859    | 0.6590 | 0.6203 | 0.9231   |
 
 
 ### Framework versions
 
-- Transformers 4.29.2
-- Pytorch 2.0.1+cu118
-- Datasets 2.12.0
-- Tokenizers 0.13.3
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.0