update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- chn_senti_corp
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: kt_punc
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: chn_senti_corp
+      type: chn_senti_corp
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7078651685393258
+    - name: Recall
+      type: recall
+      value: 0.7313662547821116
+    - name: F1
+      type: f1
+      value: 0.7194238380517767
+    - name: Accuracy
+      type: accuracy
+      value: 0.957316742326961
+---
+
+<!-- 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. -->
+
+# kt_punc
+
+This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the chn_senti_corp dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1703
+- Precision: 0.7079
+- Recall: 0.7314
+- F1: 0.7194
+- Accuracy: 0.9573
+
+## 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: 16
+- eval_batch_size: 16
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1661        | 1.0   | 600  | 0.1351          | 0.6566    | 0.6833 | 0.6697 | 0.9498   |
+| 0.1246        | 2.0   | 1200 | 0.1330          | 0.6854    | 0.6665 | 0.6758 | 0.9521   |
+| 0.1121        | 3.0   | 1800 | 0.1303          | 0.6885    | 0.6994 | 0.6939 | 0.9537   |
+| 0.1008        | 4.0   | 2400 | 0.1359          | 0.6836    | 0.7248 | 0.7036 | 0.9543   |
+| 0.0809        | 5.0   | 3000 | 0.1404          | 0.7035    | 0.7082 | 0.7059 | 0.9559   |
+| 0.0696        | 6.0   | 3600 | 0.1449          | 0.6986    | 0.7224 | 0.7103 | 0.9560   |
+| 0.0628        | 7.0   | 4200 | 0.1563          | 0.7063    | 0.7214 | 0.7138 | 0.9567   |
+| 0.0561        | 8.0   | 4800 | 0.1618          | 0.7024    | 0.7333 | 0.7175 | 0.9568   |
+| 0.0525        | 9.0   | 5400 | 0.1669          | 0.7083    | 0.7335 | 0.7207 | 0.9574   |
+| 0.0453        | 10.0  | 6000 | 0.1703          | 0.7079    | 0.7314 | 0.7194 | 0.9573   |
+
+
+### Framework versions
+
+- Transformers 4.19.1
+- Pytorch 1.11.0+cu113
+- Datasets 2.2.1
+- Tokenizers 0.12.1