update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: fine-tuned-NLI-idk-mrc-nli-drop-with-xlm-roberta-large
+  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. -->
+
+# fine-tuned-NLI-idk-mrc-nli-drop-with-xlm-roberta-large
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0842
+- Accuracy: 0.9791
+- F1: 0.9791
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 128
+- 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 1.252         | 0.5   | 39   | 0.6815          | 0.5288   | 0.3962 |
+| 0.727         | 1.0   | 78   | 0.1220          | 0.9647   | 0.9646 |
+| 0.2545        | 1.5   | 117  | 0.0908          | 0.9751   | 0.9751 |
+| 0.1242        | 2.0   | 156  | 0.0785          | 0.9791   | 0.9791 |
+| 0.1242        | 2.5   | 195  | 0.0773          | 0.9699   | 0.9699 |
+| 0.0866        | 3.0   | 234  | 0.0718          | 0.9817   | 0.9817 |
+| 0.0636        | 3.5   | 273  | 0.0827          | 0.9699   | 0.9699 |
+| 0.0467        | 4.0   | 312  | 0.0658          | 0.9777   | 0.9777 |
+| 0.0426        | 4.5   | 351  | 0.0842          | 0.9791   | 0.9791 |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu117
+- Datasets 2.2.0
+- Tokenizers 0.13.2