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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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+ - wnut_17
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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-WNUT-ner
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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: wnut_17
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+ type: wnut_17
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+ config: wnut_17
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+ split: test
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+ args: wnut_17
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.7013977128335451
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+ - name: Recall
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+ type: recall
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+ value: 0.5115848007414272
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+ - name: F1
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+ type: f1
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+ value: 0.5916398713826366
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9570402667350603
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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-WNUT-ner
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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 wnut_17 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3570
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+ - Precision: 0.7014
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+ - Recall: 0.5116
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+ - F1: 0.5916
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+ - Accuracy: 0.9570
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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: 16
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+ - eval_batch_size: 16
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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: 10
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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 | 213 | 0.2223 | 0.5588 | 0.4495 | 0.4982 | 0.9504 |
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+ | No log | 2.0 | 426 | 0.2326 | 0.6602 | 0.4430 | 0.5302 | 0.9514 |
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+ | 0.1516 | 3.0 | 639 | 0.2792 | 0.6846 | 0.4124 | 0.5147 | 0.9520 |
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+ | 0.1516 | 4.0 | 852 | 0.2417 | 0.6510 | 0.5134 | 0.5741 | 0.9574 |
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+ | 0.0427 | 5.0 | 1065 | 0.2954 | 0.6850 | 0.4856 | 0.5683 | 0.9544 |
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+ | 0.0427 | 6.0 | 1278 | 0.3033 | 0.6761 | 0.4893 | 0.5677 | 0.9557 |
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+ | 0.0427 | 7.0 | 1491 | 0.3502 | 0.7007 | 0.4838 | 0.5724 | 0.9563 |
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+ | 0.0178 | 8.0 | 1704 | 0.3712 | 0.6995 | 0.4875 | 0.5745 | 0.9563 |
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+ | 0.0178 | 9.0 | 1917 | 0.3541 | 0.6951 | 0.4986 | 0.5807 | 0.9569 |
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+ | 0.0068 | 10.0 | 2130 | 0.3570 | 0.7014 | 0.5116 | 0.5916 | 0.9570 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.0
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2