update model card README.md

README.md

@@ -9,15 +9,6 @@ metrics:
 model-index:
 - name: KoELECTRA-small-v3-modu-ner
   results: []
-language:
-- ko
-pipeline_tag: token-classification
-examples:
-widget:
-- text: "서울역으로 안내해줘"
-  example_title: "Sentence_1"
-- text: "에어컨 온도를 3도 올려줘"
-  example_title: "Sentence_2"
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -27,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1354
-- Precision: 0.8084
-- Recall: 0.8311
-- F1: 0.8196
-- Accuracy: 0.9599
+- Loss: 0.1350
+- Precision: 0.8068
+- Recall: 0.8308
+- F1: 0.8186
+- Accuracy: 0.9598
 
 ## Model description
 
@@ -64,21 +55,21 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 3788  | 0.2991          | 0.6481    | 0.6373 | 0.6426 | 0.9229   |
-| No log        | 2.0   | 7576  | 0.1904          | 0.7479    | 0.7418 | 0.7448 | 0.9438   |
-| No log        | 3.0   | 11364 | 0.1620          | 0.7577    | 0.7940 | 0.7754 | 0.9502   |
-| No log        | 4.0   | 15152 | 0.1505          | 0.7890    | 0.7982 | 0.7936 | 0.9544   |
-| No log        | 5.0   | 18940 | 0.1417          | 0.7905    | 0.8163 | 0.8032 | 0.9563   |
-| No log        | 6.0   | 22728 | 0.1392          | 0.7914    | 0.8250 | 0.8079 | 0.9572   |
-| No log        | 7.0   | 26516 | 0.1363          | 0.8060    | 0.8231 | 0.8144 | 0.9589   |
-| No log        | 8.0   | 30304 | 0.1367          | 0.8035    | 0.8294 | 0.8162 | 0.9592   |
-| No log        | 9.0   | 34092 | 0.1349          | 0.8085    | 0.8296 | 0.8189 | 0.9597   |
-| 0.2299        | 10.0  | 37880 | 0.1354          | 0.8084    | 0.8311 | 0.8196 | 0.9599   |
+| No log        | 1.0   | 3788  | 0.3021          | 0.6356    | 0.6380 | 0.6368 | 0.9223   |
+| No log        | 2.0   | 7576  | 0.1905          | 0.7397    | 0.7441 | 0.7419 | 0.9431   |
+| No log        | 3.0   | 11364 | 0.1612          | 0.7611    | 0.7897 | 0.7751 | 0.9505   |
+| No log        | 4.0   | 15152 | 0.1494          | 0.7855    | 0.7998 | 0.7926 | 0.9544   |
+| No log        | 5.0   | 18940 | 0.1427          | 0.7833    | 0.8194 | 0.8009 | 0.9559   |
+| No log        | 6.0   | 22728 | 0.1398          | 0.7912    | 0.8223 | 0.8064 | 0.9572   |
+| No log        | 7.0   | 26516 | 0.1361          | 0.8035    | 0.8240 | 0.8136 | 0.9587   |
+| No log        | 8.0   | 30304 | 0.1360          | 0.8047    | 0.8280 | 0.8162 | 0.9592   |
+| No log        | 9.0   | 34092 | 0.1346          | 0.8058    | 0.8299 | 0.8177 | 0.9596   |
+| 0.2256        | 10.0  | 37880 | 0.1350          | 0.8068    | 0.8308 | 0.8186 | 0.9598   |
 
 
 ### Framework versions
 
-- Transformers 4.27.3
+- Transformers 4.27.4
 - Pytorch 1.13.1+cu116
-- Datasets 2.10.1
-- Tokenizers 0.13.2
+- Datasets 2.11.0
+- Tokenizers 0.13.2