update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: bert-large-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bert-large-uncased-Fake_Reviews_Classifier
+  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-large-uncased-Fake_Reviews_Classifier
+
+This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5336
+- Accuracy: 0.8381
+- Weighted f1: 0.8142
+- Micro f1: 0.8381
+- Macro f1: 0.6308
+- Weighted recall: 0.8381
+- Micro recall: 0.8381
+- Macro recall: 0.6090
+- Weighted precision: 0.8101
+- Micro precision: 0.8381
+- Macro precision: 0.7029
+
+## 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: 0.001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
+| 0.633         | 1.0   | 10438 | 0.5608          | 0.8261   | 0.7914      | 0.8261   | 0.5745   | 0.8261          | 0.8261       | 0.5643       | 0.7844             | 0.8261          | 0.6542          |
+| 0.6029        | 2.0   | 20876 | 0.6490          | 0.8331   | 0.7724      | 0.8331   | 0.5060   | 0.8331          | 0.8331       | 0.5239       | 0.7892             | 0.8331          | 0.6929          |
+| 0.5478        | 3.0   | 31314 | 0.5508          | 0.8305   | 0.8071      | 0.8305   | 0.6189   | 0.8305          | 0.8305       | 0.6003       | 0.8002             | 0.8305          | 0.6784          |
+| 0.513         | 4.0   | 41752 | 0.5459          | 0.8347   | 0.8101      | 0.8347   | 0.6224   | 0.8347          | 0.8347       | 0.6023       | 0.8049             | 0.8347          | 0.6916          |
+| 0.5288        | 5.0   | 52190 | 0.5336          | 0.8381   | 0.8142      | 0.8381   | 0.6308   | 0.8381          | 0.8381       | 0.6090       | 0.8101             | 0.8381          | 0.7029          |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1
+- Datasets 2.13.1
+- Tokenizers 0.13.3