update model card README.md

README.md

@@ -0,0 +1,66 @@
+---
+license: apache-2.0
+tags:
+- token-classification
+- generated_from_trainer
+model-index:
+- name: bert-base-cased-finetuned-WikiNeural
+  results: []
+---
+
+<!-- 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. -->
+
+# bert-base-cased-finetuned-WikiNeural
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0881
+- Loc: {'precision': 0.9282034236330398, 'recall': 0.9378673383711167, 'f1': 0.9330103575008353, 'number': 5955}
+- Misc: {'precision': 0.8336608897623727, 'recall': 0.9219521833629718, 'f1': 0.8755864139613436, 'number': 5061}
+- Org: {'precision': 0.9351851851851852, 'recall': 0.9370832125253696, 'f1': 0.9361332367849385, 'number': 3449}
+- Per: {'precision': 0.9728037566034045, 'recall': 0.9543186180422265, 'f1': 0.9634725317314214, 'number': 5210}
+- Overall Precision: 0.9145
+- Overall Recall: 0.9380
+- Overall F1: 0.9261
+- Overall Accuracy: 0.9912
+
+## 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: 2
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Loc                                                                                                       | Misc                                                                                                      | Org                                                                                                       | Per                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1           | 1.0   | 5795  | 0.0943          | {'precision': 0.9075480846937126, 'recall': 0.9429051217464316, 'f1': 0.9248888156811068, 'number': 5955} | {'precision': 0.8320190720704199, 'recall': 0.8964631495751828, 'f1': 0.8630397565151225, 'number': 5061} | {'precision': 0.9151428571428571, 'recall': 0.9286749782545666, 'f1': 0.9218592603252267, 'number': 3449} | {'precision': 0.9683036587751908, 'recall': 0.9499040307101727, 'f1': 0.9590155992636372, 'number': 5210} | 0.9039            | 0.9303         | 0.9169     | 0.9901           |
+| 0.0578        | 2.0   | 11590 | 0.0881          | {'precision': 0.9282034236330398, 'recall': 0.9378673383711167, 'f1': 0.9330103575008353, 'number': 5955} | {'precision': 0.8336608897623727, 'recall': 0.9219521833629718, 'f1': 0.8755864139613436, 'number': 5061} | {'precision': 0.9351851851851852, 'recall': 0.9370832125253696, 'f1': 0.9361332367849385, 'number': 3449} | {'precision': 0.9728037566034045, 'recall': 0.9543186180422265, 'f1': 0.9634725317314214, 'number': 5210} | 0.9145            | 0.9380         | 0.9261     | 0.9912           |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0
+- Datasets 2.11.0
+- Tokenizers 0.13.3