update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- swiss_judgment_prediction
+metrics:
+- accuracy
+model-index:
+- name: xlm-roberta-large-xnli-finetuned-mnli-SJP
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: swiss_judgment_prediction
+      type: swiss_judgment_prediction
+      args: all_languages
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7957142857142857
+---
+
+<!-- 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. -->
+
+# xlm-roberta-large-xnli-finetuned-mnli-SJP
+
+This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3456
+- Accuracy: 0.7957
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 5    | 1.8460          | 0.7956   |
+| No log        | 2.0   | 10   | 1.3456          | 0.7957   |
+| No log        | 3.0   | 15   | 1.2799          | 0.7957   |
+| No log        | 4.0   | 20   | 1.2866          | 0.7957   |
+| No log        | 5.0   | 25   | 1.3162          | 0.7956   |
+
+
+### Framework versions
+
+- Transformers 4.20.0
+- Pytorch 1.11.0+cu113
+- Datasets 2.3.2
+- Tokenizers 0.12.1