update model card README.md

README.md

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+---
+license: mit
+tags:
+- token-classification
+- generated_from_trainer
+model-index:
+- name: roberta-base-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. -->
+
+# roberta-base-finetuned-WikiNeural
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0871
+- Loc: {'precision': 0.9276567437219359, 'recall': 0.9366918555835433, 'f1': 0.9321524064171123, 'number': 5955}
+- Misc: {'precision': 0.8334231805929919, 'recall': 0.916419679905157, 'f1': 0.872953133822699, 'number': 5061}
+- Org: {'precision': 0.9296179258833669, 'recall': 0.9382429689765149, 'f1': 0.9339105339105339, 'number': 3449}
+- Per: {'precision': 0.9688723570869224, 'recall': 0.9499040307101727, 'f1': 0.9592944369063772, 'number': 5210}
+- Overall Precision: 0.9124
+- Overall Recall: 0.9352
+- Overall F1: 0.9237
+- Overall Accuracy: 0.9910
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1086        | 1.0   | 5795  | 0.1001          | {'precision': 0.9148971193415638, 'recall': 0.9333333333333333, 'f1': 0.9240232751454697, 'number': 5955} | {'precision': 0.8157800785433774, 'recall': 0.9029836000790358, 'f1': 0.8571696520678983, 'number': 5061} | {'precision': 0.9133903133903134, 'recall': 0.9295447955929255, 'f1': 0.9213967524069551, 'number': 3449} | {'precision': 0.9642018779342723, 'recall': 0.9460652591170825, 'f1': 0.9550474714202672, 'number': 5210} | 0.8997            | 0.9282         | 0.9137     | 0.9896           |
+| 0.0727        | 2.0   | 11590 | 0.0871          | {'precision': 0.9276567437219359, 'recall': 0.9366918555835433, 'f1': 0.9321524064171123, 'number': 5955} | {'precision': 0.8334231805929919, 'recall': 0.916419679905157, 'f1': 0.872953133822699, 'number': 5061}   | {'precision': 0.9296179258833669, 'recall': 0.9382429689765149, 'f1': 0.9339105339105339, 'number': 3449} | {'precision': 0.9688723570869224, 'recall': 0.9499040307101727, 'f1': 0.9592944369063772, 'number': 5210} | 0.9124            | 0.9352         | 0.9237     | 0.9910           |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.1
+- Datasets 2.13.0
+- Tokenizers 0.13.3