update model card README.md

README.md

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+---
+license: mit
+tags:
+- token-classification
+- generated_from_trainer
+model-index:
+- name: xlnet-base-cased-finetuned-WikiCorpus-PoS
+  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. -->
+
+# xlnet-base-cased-finetuned-WikiCorpus-PoS
+
+This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0949
+- Loc: {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955}
+- Misc: {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061}
+- Org: {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449}
+- Per: {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210}
+- Overall Precision: 0.9068
+- Overall Recall: 0.9324
+- Overall F1: 0.9194
+- Overall Accuracy: 0.9904
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.1119        | 1.0   | 5795  | 0.1067          | {'precision': 0.9053637984119267, 'recall': 0.9382031905961377, 'f1': 0.9214910110506349, 'number': 5955} | {'precision': 0.7967393230551125, 'recall': 0.8883619837976684, 'f1': 0.8400597907324365, 'number': 5061} | {'precision': 0.911225658648339, 'recall': 0.9225862568860539, 'f1': 0.9168707679008787, 'number': 3449}  | {'precision': 0.958470156461271, 'recall': 0.9523992322456813, 'f1': 0.9554250505439492, 'number': 5210}  | 0.8899            | 0.9264         | 0.9078     | 0.9887           |
+| 0.0724        | 2.0   | 11590 | 0.0949          | {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955}  | {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061} | {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449} | {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210} | 0.9068            | 0.9324         | 0.9194     | 0.9904           |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0
+- Datasets 2.11.0
+- Tokenizers 0.13.3