update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- azaheadhealth
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: bert-azahead-v1.0
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: azaheadhealth
+      type: azaheadhealth
+      config: small
+      split: test
+      args: small
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7083333333333334
+    - name: F1
+      type: f1
+      value: 0.46153846153846156
+    - name: Precision
+      type: precision
+      value: 0.5
+    - name: Recall
+      type: recall
+      value: 0.42857142857142855
+---
+
+<!-- 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-azahead-v1.0
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the azaheadhealth dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7204
+- Accuracy: 0.7083
+- F1: 0.4615
+- Precision: 0.5
+- Recall: 0.4286
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.5889        | 1.0   | 10   | 0.5438          | 0.625    | 0.0    | 0.0       | 0.0    |
+| 0.4926        | 2.0   | 20   | 0.4309          | 0.75     | 0.5714 | 0.5714    | 0.5714 |
+| 0.3613        | 3.0   | 30   | 0.4260          | 0.75     | 0.5714 | 0.5714    | 0.5714 |
+| 0.2628        | 4.0   | 40   | 0.4989          | 0.75     | 0.5714 | 0.5714    | 0.5714 |
+| 0.1658        | 5.0   | 50   | 0.5883          | 0.7083   | 0.4615 | 0.5       | 0.4286 |
+| 0.1153        | 6.0   | 60   | 0.6374          | 0.6667   | 0.3333 | 0.4       | 0.2857 |
+| 0.074         | 7.0   | 70   | 0.6709          | 0.6667   | 0.3333 | 0.4       | 0.2857 |
+| 0.0548        | 8.0   | 80   | 0.6848          | 0.7083   | 0.4615 | 0.5       | 0.4286 |
+| 0.0456        | 9.0   | 90   | 0.7322          | 0.7083   | 0.4615 | 0.5       | 0.4286 |
+| 0.0439        | 10.0  | 100  | 0.7204          | 0.7083   | 0.4615 | 0.5       | 0.4286 |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.2.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.13.2