update model card README.md

README.md

@@ -1,3 +1,92 @@
 ---
 license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: bert-base-sst-2
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: glue
+      type: glue
+      config: sst2
+      split: validation
+      args: sst2
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9185779816513762
+    - name: F1
+      type: f1
+      value: 0.9184998453245223
+    - name: Precision
+      type: precision
+      value: 0.9189738319234722
+    - name: Recall
+      type: recall
+      value: 0.9183190199545339
 ---
+
+<!-- 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-sst-2
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2150
+- Accuracy: 0.9186
+- F1: 0.9185
+- Precision: 0.9190
+- Recall: 0.9183
+
+## 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: 0.0001
+- train_batch_size: 160
+- eval_batch_size: 160
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 640
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.2397        | 1.0   | 105  | 0.2150          | 0.9186   | 0.9185 | 0.9190    | 0.9183 |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3