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fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens

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README.md ADDED
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+ ---
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+ license: cc
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+ base_model: joelniklaus/legal-swiss-roberta-large
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - swiss_judgment_prediction
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: swiss_judgment_prediction
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+ type: swiss_judgment_prediction
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+ config: de
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+ split: test
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+ args: de
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8030848329048843
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # fine_tuned_model_on_SJP_dataset_de_balanced_2048_tokens
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+
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+ This model is a fine-tuned version of [joelniklaus/legal-swiss-roberta-large](https://huggingface.co/joelniklaus/legal-swiss-roberta-large) on the swiss_judgment_prediction dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6456
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+ - Accuracy: 0.8031
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6562 | 1.0 | 8865 | 0.6456 | 0.8031 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.2.0+cu118
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+ - Datasets 2.17.0
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+ - Tokenizers 0.15.1
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+ "layer_norm_eps": 1e-05,
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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