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README.md

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+---
+language:
+- zh
+datasets:
+- gyr66/privacy_detection
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: RoBERTa-ext-large-chinese-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+        name: gyr66/privacy_detection
+        type: gyr66/privacy_detection
+        config: privacy_detection
+        split: train
+        args: privacy_detection
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.67106
+    - name: Recall
+      type: recall
+      value: 0.75554
+    - name: F1
+      type: f1
+      value: 0.7108
+    - name: Accuracy
+      type: accuracy
+      value: 0.91212
+---
+
+# bert-base-chinese-finetuned-ner
+
+This model is a fine-tuned version of [chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on the [gyr66/privacy_detection](https://huggingface.co/datasets/gyr66/privacy_detection) dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7408
+- Precision: 0.6711
+- Recall: 0.7555
+- F1: 0.7108
+- Accuracy: 0.9121
+
+## Model description
+
+The model is used for competition: "https://www.datafountain.cn/competitions/472"
+
+## Training and evaluation data
+
+The training and evaluation data is from [gyr66/privacy_detection](https://huggingface.co/datasets/gyr66/privacy_detection) dataset.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 28
+- eval_batch_size: 28
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+
+
+### Framework versions
+
+- Transformers 4.27.3
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.5
+- Tokenizers 0.13.2