update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
  metrics:
  - name: Precision
  type: precision
- value: 0.4158878504672897
+ value: 0.29015544041450775
  - name: Recall
  type: recall
- value: 0.5028248587570622
+ value: 0.27722772277227725
  - name: F1
  type: f1
- value: 0.45524296675191817
+ value: 0.2835443037974684
  - name: Accuracy
  type: accuracy
- value: 0.8060836501901141
+ value: 0.7297843665768194
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lg-ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7681
-- Precision: 0.4159
-- Recall: 0.5028
-- F1: 0.4552
-- Accuracy: 0.8061
+- Loss: 1.0530
+- Precision: 0.2902
+- Recall: 0.2772
+- F1: 0.2835
+- Accuracy: 0.7298
 
 ## Model description
 
@@ -78,16 +78,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log | 1.0 | 25 | 0.9702 | 0.2686 | 0.3672 | 0.3103 | 0.7240 |
-| No log | 2.0 | 50 | 0.8977 | 0.2702 | 0.3785 | 0.3153 | 0.7468 |
-| No log | 3.0 | 75 | 0.8785 | 0.2517 | 0.4124 | 0.3126 | 0.7551 |
-| No log | 4.0 | 100 | 0.8608 | 0.2927 | 0.4746 | 0.3621 | 0.7567 |
-| No log | 5.0 | 125 | 0.7859 | 0.4053 | 0.4350 | 0.4196 | 0.7909 |
-| No log | 6.0 | 150 | 0.7728 | 0.4010 | 0.4350 | 0.4173 | 0.7901 |
-| No log | 7.0 | 175 | 0.7647 | 0.4118 | 0.4746 | 0.4409 | 0.7932 |
-| No log | 8.0 | 200 | 0.7800 | 0.3929 | 0.4972 | 0.4389 | 0.7985 |
-| No log | 9.0 | 225 | 0.7706 | 0.4211 | 0.4972 | 0.4560 | 0.8053 |
-| No log | 10.0 | 250 | 0.7681 | 0.4159 | 0.5028 | 0.4552 | 0.8061 |
+| No log | 1.0 | 25 | 1.2878 | 0.0 | 0.0 | 0.0 | 0.7271 |
+| No log | 2.0 | 50 | 1.2373 | 0.0 | 0.0 | 0.0 | 0.7271 |
+| No log | 3.0 | 75 | 1.2309 | 0.3542 | 0.1683 | 0.2282 | 0.7244 |
+| No log | 4.0 | 100 | 1.1505 | 0.2712 | 0.2376 | 0.2533 | 0.7183 |
+| No log | 5.0 | 125 | 1.1360 | 0.2579 | 0.2426 | 0.25 | 0.7170 |
+| No log | 6.0 | 150 | 1.0932 | 0.3108 | 0.2277 | 0.2629 | 0.7338 |
+| No log | 7.0 | 175 | 1.0761 | 0.2989 | 0.2574 | 0.2766 | 0.7298 |
+| No log | 8.0 | 200 | 1.0645 | 0.2805 | 0.3069 | 0.2931 | 0.7244 |
+| No log | 9.0 | 225 | 1.0577 | 0.3022 | 0.2723 | 0.2865 | 0.7325 |
+| No log | 10.0 | 250 | 1.0530 | 0.2902 | 0.2772 | 0.2835 | 0.7298 |
 
 
 ### Framework versions