update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- boolq
+metrics:
+- accuracy
+model-index:
+- name: MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: boolq
+      type: boolq
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7379204892966361
+---
+
+<!-- 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. -->
+
+# MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq
+
+This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large) on the boolq dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5417
+- Accuracy: 0.7379
+
+## 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: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 0.85  | 250  | 0.6579          | 0.6190   |
+| 0.6352        | 1.69  | 500  | 0.5907          | 0.6841   |
+| 0.6352        | 2.54  | 750  | 0.5613          | 0.7196   |
+| 0.535         | 3.39  | 1000 | 0.5444          | 0.7373   |
+| 0.535         | 4.24  | 1250 | 0.5417          | 0.7379   |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.14.4
+- Tokenizers 0.13.3