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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_lr0.0001_seed42_basic_original_amh-hau-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_lr0.0001_seed42_basic_original_amh-hau-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: 0.0468 |
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- Spearman Corr: 0.0355 |
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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: 0.0001 |
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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 | 1.55 | 200 | 0.0533 | -0.1261 | |
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| 0.0648 | 3.1 | 400 | 0.0450 | 0.0883 | |
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| 0.0593 | 4.65 | 600 | 0.0551 | nan | |
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| 0.0584 | 6.2 | 800 | 0.0453 | 0.0647 | |
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| 0.0584 | 7.75 | 1000 | 0.0535 | 0.1302 | |
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| 0.0584 | 9.3 | 1200 | 0.0447 | 0.1247 | |
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| 0.0564 | 10.85 | 1400 | 0.0568 | 0.0145 | |
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| 0.0564 | 12.4 | 1600 | 0.0447 | nan | |
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| 0.056 | 13.95 | 1800 | 0.0491 | 0.0365 | |
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| 0.056 | 15.5 | 2000 | 0.0472 | nan | |
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| 0.0564 | 17.05 | 2200 | 0.0473 | nan | |
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| 0.0555 | 18.6 | 2400 | 0.0457 | nan | |
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| 0.0555 | 20.16 | 2600 | 0.0472 | -0.0083 | |
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| 0.0555 | 21.71 | 2800 | 0.0473 | nan | |
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| 0.055 | 23.26 | 3000 | 0.0447 | 0.0283 | |
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| 0.0549 | 24.81 | 3200 | 0.0468 | 0.0355 | |
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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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