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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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+ - lener_br
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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-large-finetuned-lener-br
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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: lener_br
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+ type: lener_br
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+ config: lener_br
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+ split: train
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+ args: lener_br
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8545767716535433
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+ - name: Recall
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+ type: recall
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+ value: 0.8976479710519514
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+ - name: F1
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+ type: f1
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+ value: 0.8755830076893987
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.979126510974644
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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-large-finetuned-lener-br
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+
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+ This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lener_br dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: nan
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+ - Precision: 0.8546
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+ - Recall: 0.8976
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+ - F1: 0.8756
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+ - Accuracy: 0.9791
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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: 2
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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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+ - 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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+ | 0.0836 | 1.0 | 3914 | nan | 0.5735 | 0.8348 | 0.6799 | 0.9526 |
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+ | 0.0664 | 2.0 | 7828 | nan | 0.8153 | 0.8315 | 0.8233 | 0.9658 |
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+ | 0.0505 | 3.0 | 11742 | nan | 0.6885 | 0.9147 | 0.7857 | 0.9644 |
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+ | 0.1165 | 4.0 | 15656 | nan | 0.7572 | 0.8067 | 0.7811 | 0.9641 |
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+ | 0.0206 | 5.0 | 19570 | nan | 0.8678 | 0.8770 | 0.8723 | 0.9774 |
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+ | 0.02 | 6.0 | 23484 | nan | 0.7285 | 0.8907 | 0.8015 | 0.9669 |
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+ | 0.0248 | 7.0 | 27398 | nan | 0.8717 | 0.9095 | 0.8902 | 0.9793 |
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+ | 0.0223 | 8.0 | 31312 | nan | 0.8407 | 0.8801 | 0.8600 | 0.9766 |
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+ | 0.0084 | 9.0 | 35226 | nan | 0.8354 | 0.8684 | 0.8516 | 0.9705 |
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+ | 0.0067 | 10.0 | 39140 | nan | 0.8312 | 0.9062 | 0.8671 | 0.9753 |
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+ | 0.006 | 11.0 | 43054 | nan | 0.8866 | 0.8953 | 0.8909 | 0.9784 |
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+ | 0.0058 | 12.0 | 46968 | nan | 0.8961 | 0.8987 | 0.8974 | 0.9807 |
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+ | 0.0062 | 13.0 | 50882 | nan | 0.8360 | 0.8785 | 0.8567 | 0.9783 |
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+ | 0.0053 | 14.0 | 54796 | nan | 0.8327 | 0.8749 | 0.8533 | 0.9782 |
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+ | 0.003 | 15.0 | 58710 | nan | 0.8546 | 0.8976 | 0.8756 | 0.9791 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.23.1
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.1