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15epoch_test_march19

Token Classification
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lilt
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+ ---
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+ library_name: transformers
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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: 15epoch_test_march19
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+ results: []
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+ ---
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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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+
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+ # 15epoch_test_march19
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+
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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: 0.1852
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+ - Precision: 0.9341
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+ - Recall: 0.9304
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+ - F1: 0.9323
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+ - Accuracy: 0.9727
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.7937 | 100 | 0.2436 | 0.8360 | 0.8644 | 0.8500 | 0.9391 |
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+ | No log | 1.5873 | 200 | 0.1391 | 0.9038 | 0.9044 | 0.9041 | 0.9629 |
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+ | No log | 2.3810 | 300 | 0.1335 | 0.9178 | 0.9386 | 0.9281 | 0.9706 |
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+ | No log | 3.1746 | 400 | 0.1400 | 0.9369 | 0.9210 | 0.9289 | 0.9706 |
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+ | 0.246 | 3.9683 | 500 | 0.1348 | 0.9377 | 0.9184 | 0.9279 | 0.9705 |
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+ | 0.246 | 4.7619 | 600 | 0.1324 | 0.9338 | 0.9262 | 0.9300 | 0.9717 |
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+ | 0.246 | 5.5556 | 700 | 0.1422 | 0.9257 | 0.9359 | 0.9308 | 0.9718 |
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+ | 0.246 | 6.3492 | 800 | 0.1484 | 0.9334 | 0.9228 | 0.9281 | 0.9713 |
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+ | 0.246 | 7.1429 | 900 | 0.1558 | 0.9346 | 0.9237 | 0.9291 | 0.9714 |
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+ | 0.0481 | 7.9365 | 1000 | 0.1509 | 0.9296 | 0.9392 | 0.9343 | 0.9731 |
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+ | 0.0481 | 8.7302 | 1100 | 0.1540 | 0.9366 | 0.9376 | 0.9371 | 0.9743 |
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+ | 0.0481 | 9.5238 | 1200 | 0.1642 | 0.9347 | 0.9332 | 0.9340 | 0.9729 |
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+ | 0.0481 | 10.3175 | 1300 | 0.1665 | 0.9369 | 0.9325 | 0.9347 | 0.9734 |
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+ | 0.0481 | 11.1111 | 1400 | 0.1720 | 0.9341 | 0.9334 | 0.9338 | 0.9732 |
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+ | 0.0218 | 11.9048 | 1500 | 0.1673 | 0.9366 | 0.9319 | 0.9342 | 0.9733 |
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+ | 0.0218 | 12.6984 | 1600 | 0.1867 | 0.9355 | 0.9302 | 0.9329 | 0.9730 |
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+ | 0.0218 | 13.4921 | 1700 | 0.1872 | 0.9358 | 0.9372 | 0.9365 | 0.9736 |
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+ | 0.0218 | 14.2857 | 1800 | 0.1852 | 0.9341 | 0.9304 | 0.9323 | 0.9727 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.3
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.4.1
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+ - Tokenizers 0.21.1