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--- |
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license: mit |
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base_model: nielsr/lilt-xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xfun |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: checkpoints |
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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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# checkpoints |
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This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Precision: 0.3911 |
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- Recall: 0.6703 |
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- F1: 0.4940 |
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- Loss: 0.1352 |
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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: 1e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 2 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:------:|:-----:|:------:|:---------------:|:---------:|:------:| |
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| 0.1469 | 20.83 | 500 | 0 | 0.1467 | 0 | 0 | |
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| 0.0896 | 41.67 | 1000 | 0.0837 | 0.1454 | 0.2946 | 0.0487 | |
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| 0.1027 | 62.5 | 1500 | 0.1225 | 0.1353 | 0.3333 | 0.0750 | |
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| 0.0485 | 83.33 | 2000 | 0.3536 | 0.1571 | 0.3364 | 0.3727 | |
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| 0.0597 | 104.17 | 2500 | 0.4448 | 0.1546 | 0.3535 | 0.5997 | |
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| 0.0367 | 125.0 | 3000 | 0.4940 | 0.1352 | 0.3911 | 0.6703 | |
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| 0.033 | 145.83 | 3500 | 0.4977 | 0.1749 | 0.3902 | 0.6870 | |
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| 0.0176 | 166.67 | 4000 | 0.5087 | 0.2262 | 0.4034 | 0.6883 | |
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| 0.0123 | 187.5 | 4500 | 0.5050 | 0.2358 | 0.3978 | 0.6915 | |
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| 0.0194 | 208.33 | 5000 | 0.5173 | 0.2976 | 0.4090 | 0.7037 | |
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| 0.0118 | 171.88 | 5500 | 0.4159 | 0.6863 | 0.5179 | 0.2836 | |
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| 0.0054 | 187.5 | 6000 | 0.4356 | 0.6703 | 0.5280 | 0.3100 | |
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| 0.01 | 203.12 | 6500 | 0.4229 | 0.6979 | 0.5266 | 0.3430 | |
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| 0.0062 | 218.75 | 7000 | 0.4272 | 0.7062 | 0.5324 | 0.3652 | |
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| 0.0051 | 234.38 | 7500 | 0.4306 | 0.6947 | 0.5317 | 0.3496 | |
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| 0.0048 | 250.0 | 8000 | 0.4400 | 0.6940 | 0.5386 | 0.3943 | |
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| 0.0087 | 265.62 | 8500 | 0.4290 | 0.6992 | 0.5317 | 0.3782 | |
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| 0.0077 | 281.25 | 9000 | 0.4394 | 0.7049 | 0.5414 | 0.3855 | |
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| 0.0014 | 296.88 | 9500 | 0.4363 | 0.7004 | 0.5377 | 0.3933 | |
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| 0.0035 | 312.5 | 10000 | 0.4350 | 0.6992 | 0.5363 | 0.4045 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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