update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- privacy_detection
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: RoBERTa-finetuned-privacy-detection
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: privacy_detection
+      type: privacy_detection
+      config: privacy_detection
+      split: train
+      args: privacy_detection
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6038216560509554
+    - name: Recall
+      type: recall
+      value: 0.716012084592145
+    - name: F1
+      type: f1
+      value: 0.655148583275743
+    - name: Accuracy
+      type: accuracy
+      value: 0.9050795916467558
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# RoBERTa-finetuned-privacy-detection
+
+This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on the privacy_detection dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3400
+- Precision: 0.6038
+- Recall: 0.7160
+- F1: 0.6551
+- Accuracy: 0.9051
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 56
+- eval_batch_size: 56
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 36   | 0.8623          | 0.1797    | 0.1979 | 0.1884 | 0.7756   |
+| No log        | 2.0   | 72   | 0.4946          | 0.4109    | 0.5317 | 0.4636 | 0.8557   |
+| No log        | 3.0   | 108  | 0.3918          | 0.4847    | 0.6073 | 0.5391 | 0.8824   |
+| No log        | 4.0   | 144  | 0.3618          | 0.5390    | 0.6631 | 0.5946 | 0.8915   |
+| No log        | 5.0   | 180  | 0.3495          | 0.5604    | 0.6883 | 0.6178 | 0.8981   |
+| No log        | 6.0   | 216  | 0.3396          | 0.5836    | 0.6966 | 0.6351 | 0.9020   |
+| No log        | 7.0   | 252  | 0.3458          | 0.5900    | 0.7047 | 0.6423 | 0.9018   |
+| No log        | 8.0   | 288  | 0.3412          | 0.5989    | 0.7090 | 0.6493 | 0.9047   |
+| No log        | 9.0   | 324  | 0.3425          | 0.5955    | 0.7145 | 0.6496 | 0.9042   |
+| No log        | 10.0  | 360  | 0.3400          | 0.6038    | 0.7160 | 0.6551 | 0.9051   |
+
+
+### Framework versions
+
+- Transformers 4.27.3
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.5
+- Tokenizers 0.13.2