Update README.md

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.3840122394339262
+      value: 0.65322
     - name: Recall
       type: recall
-      value: 0.5055387713997986
+      value: 0.74169
     - name: F1
       type: f1
-      value: 0.43647429627214435
+      value: 0.69465
     - name: Accuracy
       type: accuracy
-      value: 0.8564247370217519
+      value: 0.90517
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,25 +42,21 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-base-chinese-finetuned-ner
 
-This model is a fine-tuned version of [Danielwei0214/bert-base-chinese-finetuned-ner](https://huggingface.co/Danielwei0214/bert-base-chinese-finetuned-ner) on the gyr66/privacy_detection dataset.
+This model is a fine-tuned version of [Danielwei0214/bert-base-chinese-finetuned-ner](https://huggingface.co/Danielwei0214/bert-base-chinese-finetuned-ner) on the [gyr66/privacy_detection](https://huggingface.co/datasets/gyr66/privacy_detection) dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4895
-- Precision: 0.3840
-- Recall: 0.5055
-- F1: 0.4365
-- Accuracy: 0.8564
+- Loss: 0.7929
+- Precision: 0.6532
+- Recall: 0.7417
+- F1: 0.6947
+- Accuracy: 0.9052
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
+The model is used for competition: "https://www.datafountain.cn/competitions/472"
 
 ## Training and evaluation data
 
-More information needed
+The training and evaluation data is from [gyr66/privacy_detection](https://huggingface.co/datasets/gyr66/privacy_detection) dataset.
 
 ## Training procedure
 
@@ -73,15 +69,6 @@ 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: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 1.3112        | 1.0   | 36   | 0.7305          | 0.2062    | 0.2535 | 0.2274 | 0.8028   |
-| 0.6083        | 2.0   | 72   | 0.5295          | 0.3598    | 0.4668 | 0.4064 | 0.8451   |
-| 0.4782        | 3.0   | 108  | 0.4895          | 0.3840    | 0.5055 | 0.4365 | 0.8564   |
 
 
 ### Framework versions

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ccf9ff545a196f400917a7c721bac2651279f2b8b6d3edd6e19b699bfd552e65
+oid sha256:a750e4818a90b3fd65d4ad00682e03bb2d43465356e4102cb81673e48bb44cfd
 size 3579