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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_latin_kin-amh-eng_train_loss |
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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_latin_kin-amh-eng_train_loss |
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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: 0.0326 |
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- Spearman Corr: 0.7395 |
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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 | 0.59 | 200 | 0.0303 | 0.6125 | |
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| No log | 1.17 | 400 | 0.0269 | 0.6780 | |
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| No log | 1.76 | 600 | 0.0393 | 0.6855 | |
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| 0.036 | 2.35 | 800 | 0.0338 | 0.7111 | |
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| 0.036 | 2.93 | 1000 | 0.0303 | 0.6886 | |
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| 0.036 | 3.52 | 1200 | 0.0327 | 0.7025 | |
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| 0.0243 | 4.11 | 1400 | 0.0269 | 0.7220 | |
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| 0.0243 | 4.69 | 1600 | 0.0287 | 0.7246 | |
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| 0.0243 | 5.28 | 1800 | 0.0260 | 0.7336 | |
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| 0.0243 | 5.87 | 2000 | 0.0266 | 0.7234 | |
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| 0.0185 | 6.45 | 2200 | 0.0252 | 0.7347 | |
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| 0.0185 | 7.04 | 2400 | 0.0281 | 0.7276 | |
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| 0.0185 | 7.62 | 2600 | 0.0294 | 0.7298 | |
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| 0.0141 | 8.21 | 2800 | 0.0274 | 0.7219 | |
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| 0.0141 | 8.8 | 3000 | 0.0285 | 0.7260 | |
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| 0.0141 | 9.38 | 3200 | 0.0276 | 0.7315 | |
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| 0.0141 | 9.97 | 3400 | 0.0291 | 0.7329 | |
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| 0.0109 | 10.56 | 3600 | 0.0310 | 0.7339 | |
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| 0.0109 | 11.14 | 3800 | 0.0326 | 0.7395 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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