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xlm-roberta-base-conll2003-en

Token Classification
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PyTorch
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xlm-roberta
Generated from Trainer
Eval Results
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - conll2003
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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: xlm-roberta-base-conll2003-en
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: conll2003
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+ type: conll2003
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+ config: conll2003
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+ split: validation
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+ args: conll2003
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.9478680879413725
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+ - name: Recall
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+ type: recall
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+ value: 0.9588879528222409
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+ - name: F1
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+ type: f1
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+ value: 0.9533461763966831
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9917972098823162
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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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+ # xlm-roberta-base-conll2003-en
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+
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0534
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+ - Precision: 0.9479
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+ - Recall: 0.9589
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+ - F1: 0.9533
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+ - Accuracy: 0.9918
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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: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+ - 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 | 1.0 | 439 | 0.0535 | 0.9131 | 0.9238 | 0.9184 | 0.9865 |
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+ | 0.1663 | 2.0 | 878 | 0.0461 | 0.9305 | 0.9390 | 0.9348 | 0.9887 |
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+ | 0.0404 | 3.0 | 1317 | 0.0366 | 0.9431 | 0.9501 | 0.9466 | 0.9910 |
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+ | 0.0252 | 4.0 | 1756 | 0.0381 | 0.9395 | 0.9516 | 0.9455 | 0.9908 |
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+ | 0.0172 | 5.0 | 2195 | 0.0398 | 0.9409 | 0.9523 | 0.9466 | 0.9911 |
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+ | 0.0119 | 6.0 | 2634 | 0.0429 | 0.9389 | 0.9560 | 0.9474 | 0.9910 |
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+ | 0.0091 | 7.0 | 3073 | 0.0463 | 0.9451 | 0.9548 | 0.9500 | 0.9913 |
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+ | 0.0063 | 8.0 | 3512 | 0.0446 | 0.9478 | 0.9575 | 0.9526 | 0.9919 |
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+ | 0.0063 | 9.0 | 3951 | 0.0513 | 0.9424 | 0.9569 | 0.9496 | 0.9911 |
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+ | 0.0049 | 10.0 | 4390 | 0.0494 | 0.9470 | 0.9545 | 0.9507 | 0.9915 |
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+ | 0.0036 | 11.0 | 4829 | 0.0506 | 0.9477 | 0.9553 | 0.9515 | 0.9917 |
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+ | 0.0029 | 12.0 | 5268 | 0.0518 | 0.9472 | 0.9586 | 0.9529 | 0.9919 |
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+ | 0.0026 | 13.0 | 5707 | 0.0530 | 0.9451 | 0.9567 | 0.9508 | 0.9916 |
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+ | 0.0021 | 14.0 | 6146 | 0.0526 | 0.9468 | 0.9567 | 0.9517 | 0.9917 |
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+ | 0.0016 | 15.0 | 6585 | 0.0534 | 0.9479 | 0.9589 | 0.9533 | 0.9918 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2