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-indonli_mnli_squadid-nli-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-indonli_mnli_squadid-nli-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: 0.2874
+- Accuracy: 0.9148
+- F1: 0.9152
+
+## 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     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
+| 0.3454        | 0.5   | 2499  | 0.2659          | 0.8987   | 0.8988 |
+| 0.3177        | 1.0   | 4998  | 0.2420          | 0.9081   | 0.9087 |
+| 0.2821        | 1.5   | 7497  | 0.2407          | 0.9111   | 0.9114 |
+| 0.249         | 2.0   | 9996  | 0.2258          | 0.9159   | 0.9158 |
+| 0.2246        | 2.5   | 12495 | 0.2454          | 0.9143   | 0.9146 |
+| 0.2308        | 3.0   | 14994 | 0.2370          | 0.9155   | 0.9159 |
+| 0.1869        | 3.5   | 17493 | 0.2691          | 0.9147   | 0.9149 |
+| 0.18          | 4.0   | 19992 | 0.2616          | 0.9143   | 0.9151 |
+| 0.1329        | 4.5   | 22491 | 0.2874          | 0.9148   | 0.9152 |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu117
+- Datasets 2.2.0
+- Tokenizers 0.13.2