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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_lr5e-06_seed42_basic_original_kin-amh-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_lr5e-06_seed42_basic_original_kin-amh-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: 0.0312 |
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- Spearman Corr: 0.7479 |
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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: 5e-06 |
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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 | 1.75 | 200 | 0.0278 | 0.6150 | |
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| 0.0637 | 3.51 | 400 | 0.0254 | 0.6786 | |
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| 0.0312 | 5.26 | 600 | 0.0284 | 0.7074 | |
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| 0.0249 | 7.02 | 800 | 0.0320 | 0.7195 | |
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| 0.0199 | 8.77 | 1000 | 0.0344 | 0.7285 | |
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| 0.0189 | 10.53 | 1200 | 0.0303 | 0.7318 | |
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| 0.0167 | 12.28 | 1400 | 0.0299 | 0.7356 | |
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| 0.0154 | 14.04 | 1600 | 0.0319 | 0.7411 | |
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| 0.0154 | 15.79 | 1800 | 0.0304 | 0.7382 | |
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| 0.0141 | 17.54 | 2000 | 0.0327 | 0.7460 | |
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| 0.0134 | 19.3 | 2200 | 0.0305 | 0.7507 | |
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| 0.0123 | 21.05 | 2400 | 0.0312 | 0.7479 | |
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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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