update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- recall
+- precision
+- f1
+model-index:
+- name: bert-base-uncased_title_fine_tuned
+  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-uncased_title_fine_tuned
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3368
+- Accuracy: {'accuracy': 0.8810840405146455}
+- Recall: {'recall': 0.8611674554879423}
+- Precision: {'precision': 0.890468422279189}
+- F1: {'f1': 0.8755728689275893}
+
+## 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: 5e-05
+- train_batch_size: 24
+- eval_batch_size: 24
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy                         | Recall                         | Precision                         | F1                         |
+|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:------------------------------:|:---------------------------------:|:--------------------------:|
+| 0.3224        | 1.0   | 3045  | 0.3079          | {'accuracy': 0.8730358609362168} | {'recall': 0.8139508677034032} | {'precision': 0.915346597389431}  | {'f1': 0.861676110945422}  |
+| 0.2818        | 2.0   | 6090  | 0.3153          | {'accuracy': 0.8814672871612373} | {'recall': 0.8299526707234618} | {'precision': 0.9182146864480738} | {'f1': 0.8718555785735426} |
+| 0.2394        | 3.0   | 9135  | 0.3104          | {'accuracy': 0.8830002737476047} | {'recall': 0.8548568852828488} | {'precision': 0.8993479549496147} | {'f1': 0.8765382171124848} |
+| 0.204         | 4.0   | 12180 | 0.3368          | {'accuracy': 0.8810840405146455} | {'recall': 0.8611674554879423} | {'precision': 0.890468422279189}  | {'f1': 0.8755728689275893} |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.0+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1