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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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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: LiLT-SER-EN |
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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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# LiLT-SER-EN |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3960 |
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- Precision: 0.7248 |
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- Recall: 0.7458 |
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- F1: 0.7351 |
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- Accuracy: 0.7438 |
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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-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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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0832 | 6.67 | 500 | 1.0009 | 0.6741 | 0.6923 | 0.6831 | 0.7292 | |
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| 0.052 | 13.33 | 1000 | 1.4186 | 0.7225 | 0.7320 | 0.7272 | 0.7441 | |
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| 0.0027 | 20.0 | 1500 | 1.5508 | 0.7218 | 0.7376 | 0.7297 | 0.7464 | |
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| 0.0034 | 26.67 | 2000 | 1.7198 | 0.7051 | 0.7382 | 0.7213 | 0.7422 | |
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| 0.002 | 33.33 | 2500 | 1.8116 | 0.7106 | 0.7392 | 0.7246 | 0.7424 | |
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| 0.0002 | 40.0 | 3000 | 1.8843 | 0.6769 | 0.7514 | 0.7122 | 0.7435 | |
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| 0.0009 | 46.67 | 3500 | 1.9528 | 0.7401 | 0.7514 | 0.7457 | 0.7518 | |
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| 0.0224 | 53.33 | 4000 | 2.0602 | 0.7178 | 0.7529 | 0.7350 | 0.7476 | |
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| 0.0002 | 60.0 | 4500 | 2.2901 | 0.7283 | 0.7509 | 0.7394 | 0.7287 | |
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| 0.0001 | 66.67 | 5000 | 2.1746 | 0.7198 | 0.7433 | 0.7313 | 0.7371 | |
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| 0.0001 | 73.33 | 5500 | 1.9452 | 0.7214 | 0.7387 | 0.7299 | 0.7641 | |
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| 0.0 | 80.0 | 6000 | 2.0976 | 0.7350 | 0.7560 | 0.7454 | 0.7442 | |
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| 0.0021 | 86.67 | 6500 | 2.3034 | 0.7200 | 0.7387 | 0.7292 | 0.7365 | |
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| 0.0 | 93.33 | 7000 | 2.2409 | 0.7348 | 0.7636 | 0.7489 | 0.7499 | |
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| 0.0 | 100.0 | 7500 | 2.2742 | 0.7362 | 0.7193 | 0.7276 | 0.7472 | |
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| 0.0 | 106.67 | 8000 | 2.4953 | 0.7312 | 0.7509 | 0.7409 | 0.7363 | |
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| 0.0 | 113.33 | 8500 | 2.4936 | 0.7340 | 0.7438 | 0.7389 | 0.7396 | |
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| 0.0 | 120.0 | 9000 | 2.3976 | 0.7239 | 0.7453 | 0.7344 | 0.7440 | |
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| 0.0001 | 126.67 | 9500 | 2.3723 | 0.7282 | 0.7478 | 0.7379 | 0.7441 | |
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| 0.0 | 133.33 | 10000 | 2.3960 | 0.7248 | 0.7458 | 0.7351 | 0.7438 | |
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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 2.18.0 |
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- Tokenizers 0.15.1 |
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