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prompt_fine_tuned_rte_XLMroberta

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+ ---
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+ license: mit
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+ library_name: peft
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+ tags:
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+ - generated_from_trainer
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+ base_model: xlm-roberta-base
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: prompt_fine_tuned_rte_XLMroberta
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+ results: []
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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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+ # prompt_fine_tuned_rte_XLMroberta
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7052
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+ - Accuracy: 0.3448
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+ - F1: 0.3399
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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: 8
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+ - eval_batch_size: 8
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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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+ - training_steps: 400
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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 | F1 |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
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+ | 0.6899 | 1.7241 | 50 | 0.7067 | 0.4138 | 0.3957 |
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+ | 0.7066 | 3.4483 | 100 | 0.7054 | 0.4483 | 0.4221 |
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+ | 0.6996 | 5.1724 | 150 | 0.7054 | 0.4483 | 0.4221 |
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+ | 0.6876 | 6.8966 | 200 | 0.7056 | 0.4138 | 0.3957 |
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+ | 0.699 | 8.6207 | 250 | 0.7051 | 0.4138 | 0.3957 |
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+ | 0.6936 | 10.3448 | 300 | 0.7055 | 0.3448 | 0.3399 |
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+ | 0.6909 | 12.0690 | 350 | 0.7052 | 0.3448 | 0.3399 |
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+ | 0.69 | 13.7931 | 400 | 0.7052 | 0.3448 | 0.3399 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.11.1
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+ - Transformers 4.41.0
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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+ "num_transformer_submodules": 1,
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+ "peft_type": "PROMPT_TUNING",
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+ "prompt_tuning_init": "RANDOM",
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