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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: xlm-roberta-base_original_esp-kin-eng_train |
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results: [] |
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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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# xlm-roberta-base_original_esp-kin-eng_train |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0301 |
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- Spearman Corr: 0.7160 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 32 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Spearman Corr | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------:| |
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| No log | 1.63 | 200 | 0.0292 | 0.6299 | |
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| 0.0375 | 3.27 | 400 | 0.0290 | 0.6845 | |
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| 0.0212 | 4.9 | 600 | 0.0259 | 0.6877 | |
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| 0.0164 | 6.53 | 800 | 0.0287 | 0.7094 | |
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| 0.0127 | 8.16 | 1000 | 0.0285 | 0.7219 | |
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| 0.0127 | 9.8 | 1200 | 0.0318 | 0.7103 | |
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| 0.0102 | 11.43 | 1400 | 0.0299 | 0.7125 | |
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| 0.008 | 13.06 | 1600 | 0.0357 | 0.7183 | |
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| 0.0067 | 14.69 | 1800 | 0.0282 | 0.7207 | |
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| 0.0056 | 16.33 | 2000 | 0.0314 | 0.7154 | |
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| 0.0056 | 17.96 | 2200 | 0.0317 | 0.7145 | |
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| 0.0049 | 19.59 | 2400 | 0.0302 | 0.7111 | |
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| 0.0044 | 21.22 | 2600 | 0.0301 | 0.7160 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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