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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: 0.2809 |
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- Recall: 0.5051 |
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- F1: 0.3610 |
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- Loss: 1.6168 |
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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: 8 |
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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: 8000 |
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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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| 0.1546 | 41.67 | 500 | 0 | 0.2482 | 0 | 0 | |
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| 0.1674 | 83.33 | 1000 | 0 | 0.2477 | 0 | 0 | |
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| 0.1368 | 125.0 | 1500 | 0.1502 | 0.2256 | 0.1975 | 0.1212 | |
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| 0.0727 | 166.67 | 2000 | 0.2732 | 0.3218 | 0.2091 | 0.3939 | |
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| 0.0718 | 208.33 | 2500 | 0.3385 | 0.3518 | 0.2579 | 0.4924 | |
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| 0.0612 | 250.0 | 3000 | 0.3371 | 0.5235 | 0.2555 | 0.4949 | |
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| 0.0504 | 291.67 | 3500 | 0.3353 | 0.5280 | 0.2536 | 0.4949 | |
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| 0.0418 | 333.33 | 4000 | 0.3476 | 0.6919 | 0.2657 | 0.5025 | |
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| 0.0308 | 375.0 | 4500 | 0.3490 | 0.7819 | 0.2613 | 0.5253 | |
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| 0.039 | 416.67 | 5000 | 0.3463 | 1.0291 | 0.2627 | 0.5076 | |
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| 0.0301 | 458.33 | 5500 | 0.3443 | 1.1661 | 0.2626 | 0.5 | |
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| 0.0245 | 500.0 | 6000 | 0.3414 | 1.2341 | 0.2642 | 0.4823 | |
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| 0.0347 | 541.67 | 6500 | 0.3389 | 1.4114 | 0.2605 | 0.4848 | |
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| 0.0327 | 583.33 | 7000 | 0.3422 | 1.4326 | 0.2683 | 0.4722 | |
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| 0.0117 | 625.0 | 7500 | 0.3670 | 1.6092 | 0.2899 | 0.5 | |
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| 0.0255 | 666.67 | 8000 | 0.3607 | 1.6141 | 0.2805 | 0.5051 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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